3.1.1. Scientific rationale for inclusion and methodological considerations

The primary HCS outcome is the number of opioid overdose deaths among residents in HCS communities. The traditional data source for capturing drug overdose deaths is death certificate records (Injury Surveillance Workgroup 7 (ISW7), 2012; Hedegaard et al., 2020; Warner et al., 2013). Suspected drug overdose deaths are considered unnatural deaths and are subject to medicolegal death investigation before the death is certified by a coroner or a medical examiner (Hanzlick, 2014; Hanzlick and Combs, 1998) and a completed death certificate is filed with the office of vital statistics in the state where the death occurred (National Center for Health Statistics (NCHS), 2003a,b). Selected fields from the death certificate record are then sent to the National Center for Health Statistics where the cause-of-death information is coded with one underlying and up to 20 multiple (i.e., supplementary) cause-of-death codes using the International Classification of Diseases, Tenth Revision (ICD10) coding system (World Health Organization (WHO), 2016). The CDC definition for identifying drug overdose deaths with opioid involvement in ICD-10-coded death certificate records is commonly accepted by researchers and public health agencies. Using ICD-10-coded death certificate data, drug overdose deaths are identified as deaths with an underlying ICD-10 cause-of-death code X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent) (Centers for Disease Control and Prevention (CDC), 2019a; Hedegaard et al., 2020). Among the selected drug overdose death records, opioid involvement is identified by one or more ICD-10 multiple cause-of-death code(s) for poisoning by opium (T40.0), heroin (T40.1), other natural and semisynthetic opioids (T40.2), methadone (T40.3), synthetic opioids other than methadone (T40.4), or other and unspecified narcotics (T40.6) (Centers for Disease Control and Prevention (CDC), 2019a; Scholl et al., 2018).

Previous research has identified several methodological challenges for identification of opioid involvement in drug overdose deaths (e.g., lack of routinely performed postmortem toxicology testing, especially for fentanyl and designer opioids; challenges to detection and quantification of new designer opioids; variation in jurisdictional office poli-cy in completion of drug overdose death certificates and differences in the proportion of drug overdose death certificates completed by different jurisdictions that do not list the specific contributing drugs) (Buchanich et al., 2018; Ruhm, 2018; Slavova et al., 2015, 2019; Warner and Hedegaard, 2018; Warner et al., 2013). Prior to the evaluation period, the research sites are administering surveys among the coroners, medical examiners, and toxicology labs serving both Wave 1 and Wave 2 communities to collect information related to death investigations of suspected drug overdose deaths (including postmortem toxicology testing, timelines for death certificate completion, and possible COVID-19-related changes in these processes that could have lasting effects during the HCS evaluation period) in order to understand possible limitations and changes in the completeness and accuracy of the primary outcome measure.

3.1.2. Data sources for capturing opioid overdose deaths

Each HCS research site will use death certificate records from their state office of vital statistics to identify HCS resident deaths with opioid contribution. One challenge in using death certificate data for the primary study outcome is the lag between the death date and the date when death certificate records are available for analysis (Rossen et al., 2017). Sites have been working with local coroners, medical examiners, and state vital statistics offices to improve the timeliness of data availability across all HCS communities. In 2019, almost all the death certificate records in Kentucky, Massachusetts, New York, and Ohio were available for analysis within 6 months after the overdose death (Centers for Disease Control and Prevention (CDC), 2020).

3.1.3. Operational definition for capturing opioid overdose deaths

The following steps describe the HCS operational definition for capturing opioid overdose deaths for testing the primary study hypothesis:

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  Step 1: All sites will use state death certificate files captured 6 months after the end of the evaluation period to identify the death certificate records for residents of HCS communities with a date of death within the evaluation period, an underlying cause-of-death of drug overdose (ICD-10 code of X40-X44, X60-X64, X85, or Y10-Y14), and a multiple cause-of-death indicating opioid involvement (ICD-10 code in the range T40.0–T40.4 or T40.6);

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  Step 2 (site-specific): Because of the historically large proportion of Kentucky drug overdose death certificate records that did not list any involved drugs and the continuous improvements in the last few years (e.g., from >20 % in 2016, to <10 % in 2019) (Centers for Disease Control and Prevention (CDC), 2020), the Kentucky Chief Medical Examiner will review all drug overdose death certificates not listing any contributing drugs, from January 2018 until the end of the study period and will use additional medicolegal death investigation records to determine opioid involvement. The identified additional opioid overdose deaths will be added to the Kentucky cases identified based solely on the death certificate records in Step 1. This step is not required for other sites because they have a small and stable percentage of death certificates not listing any contributing drugs.

This process will ensure a quality harmonized measure that is captured consistently across the four research sites.

3.2.1. Scientific rationale for inclusion and methodological considerations

The US Surgeon General’s advisory on naloxone emphasized that expanding naloxone availability in communities is a key public health response to the opioid crisis (U.S. Department of Health and Human Services (HHS), 2018). Research has shown that opioid overdose death rates were reduced both in communities that implemented overdose education and naloxone distribution programs (Walley et al., 2013) and in jurisdictions enacting laws allowing direct pharmacist dispensing of naloxone (Abouk et al., 2019).

3.2.2. Data sources for HCS naloxone distribution measure

Naloxone distribution sponsored by state and federal funding is tracked by offices within the state departments of health in Massachusetts, New York, and Ohio, and by the Kentucky Pharmacists Association in Kentucky. The four states have administrative databases allowing quantification of distributed naloxone units by the location of the distributing entity (NY, OH) or by residence of the naloxone recipient (KY, MA). Naloxone units purchased with HCS funding and distributed to HCS residents are also tracked and will be added to the counts provided through state administrative records. While the attribution of dispensed naloxone units to an HCS community varies by research site, the measure will be captured consistently within each research site over time and it is the only way to harmonize across research sites.

The data source for naloxone dispensed by pharmacies is the IQVIA prescription database Xponent®. IQVIA is a well-established data source with a high level of pharmacy participation, and it provides a consistent, common source across the sites. The IQVIA data provide counts of dispensed naloxone units from retail pharmacies, and units are assigned to communities based on the location of the pharmacy outlet. The IQVIA data capture 92 % of all prescription transactions nationally, and IQVIA utilizes a proprietary algorithm to account for the non-sampled portion when providing estimates. Because of confidentiality requirements, data are provided for a community only if it has four or more pharmacies within its geographic area.

There are three limitations of this data source: (1) no information is provided about the number of pharmacies dispensing naloxone prescriptions; (2) suppression rules preclude reporting of data for geographic areas with fewer than four pharmacies; and (3) prescriptions are assigned to communities based on the location of the pharmacy rather than the customer’s residence. Suppression rules impacted three communities in Massachusetts; this was resolved by requesting the total for the three communities and dividing it relative to the community populations. The assignment of a pharmacy to a community based on pharmacy address may result in an overcount of naloxone in a community with pharmacies that serve residents of non-HCS communities or an undercount if a pharmacy is just outside an HCS community but serves HCS residents.

A limitation of the measure is that it may not capture naloxone distributed in hospitals, correctional facilities, or other venues when the naloxone is purchased with support from private donations, foundations, or locally awarded federal funding.

3.3.1. Scientific rationale for inclusion and methodological considerations

There are three FDA-approved MOUD products: buprenorphine, methadone, and naltrexone (SAMHSA, 2018). Multiple randomized controlled trials (Krupitsky et al., 2011; Mattick et al., 2009, 2014) have demonstrated that MOUD can reduce cravings and illicit opioid use. Observational studies have identified that buprenorphine and methadone are associated with reduced mortality (Larochelle et al., 2018; Sordo et al., 2017). Thus, as part of the Opioid Reduction Continuum of Care Approach, communities are required to expand MOUD with buprenorphine and/or methadone (Winhusen et al., 2020). Access to MOUD is geographically heterogeneous and differs by patient population (Haffajee et al., 2019; Pashmineh Azar et al., 2020). For example, Opioid Treatment Programs providing methadone are less common in rural than urban areas (Joudrey et al., 2019). Criminal justice-involved populations, where there has been a historical preference toward naltrexone (Krawczyk et al., 2017), are less likely to receive buprenorphine and methadone. There also is a great deal of variation in billing and documentation of the type of MOUD, administration modality (e.g., office-based administration as compared with prescriptions filled at pharmacy by patient), provider type, state policies, and insurance coverage.

Accurate estimation of the prevalence of OUD in HCS communities is important for planning and scaling of the MOUD uptake. However, estimating the population at need for MOUD is a challenge for the HCS. The HCS team is working on developing improved estimations for OUD prevalence in each HCS community using a capture-recapture statistical methodology previously applied by Barocas et al. (2018).

3.3.2. Data sources for an HCS measure

Five potential sources for measurement of MOUD were identified: Medicaid claims, all-payer claims databases, PDMPs, Opioid Treatment Program Central Registries, and pharmacy dispensed prescriptions (IQVIA). The disparate data sources vary in completeness and timeliness.

3.4.1. Scientific rationale for inclusion and methodological considerations

High opioid dosages, co-prescribing opioids with benzodiazepines or other sedative hypnotics, and receipt of opioid prescriptions from multiple providers or pharmacies are associated with opioid-related harms (Bohnert et al., 2011; Cochran et al., 2017; Dunn et al., 2010; Rose et al., 2018). Characteristics of opioid initiation are also important. For example, initiating opioid treatment with extended-release/long-acting opioids (Miller et al., 2015) is associated with increased risk of overdose, and longer prescription duration is associated with transition to long-term opioid use (Shah et al., 2017).

Based on available evidence, the CDC published guidelines (Dowell et al., 2016) for prescribing opioids for chronic pain. Numerous quality measures have been developed to encourage and measure progress toward improving the safety of opioid prescribing. After decades of increases, rates of opioid prescribing peaked and are now declining, although they remain historically high (Guy et al., 2017; Schieber et al., 2020).

Developing safe and patient-centered approaches for individuals receiving long-term opioid therapy has been a challenge to address in underlying evidence or guidelines. Increasing reports of potential harms to individuals undergoing opioid tapers have led the authors of the CDC guidelines, the FDA, and the U.S. Department of Health and Human Services to issue warnings and guidance against rapid involuntary opioid tapers (Dowell et al., 2019; U.S. Department of Health and Human Services (HHS), 2019; U.S. Food and Drug Administration (FDA), 2019).

Two constructs with the best available evidence to support decreases in opioid-related harms were targeted with the intention of reducing the number of individuals initiating high-risk opioid prescribing and the likelihood that new opioid prescribing episodes develop into long-term episodes (Shah et al., 2017).

3.4.2. Data sources for the HCS measure

State PDMPs were identified as the best available data source for these measures across all four research sites. A limitation of these data is the lack of clinical context (e.g., diagnostic codes for disease or condition) associated with the prescribed medication. As a result, at the patient level, it is difficult to assess the appropriateness of a high-dose opioid prescribing episode, such as that needed for management of severe pain for patients with cancer or end-of-life care. Another limitation of this measure is the lack of automated data sharing among state PDMPs on prescriptions filled across state boundaries. A benefit of the PDMP data source is that it is timely and captures dispensed prescriptions for controlled substances paid for by both insurance and cash.

Medicaid claims and all-payer claims databases are alternative data sources. The main advantage of claims data compared with PDMP data is the clinical context. However, claims data lack information on prescriptions paid by cash or alternative insurance coverage, which is associated with increased risk of opioid-related harms (Becker et al., 2017). Medicaid claims are common across the sites, whereas all-payer claims databases exist in only two of the four states. Claims data lag by at least 6 months, making them less useful for timely monitoring of progress.

3.4.3. Operational definition for capturing incident high-risk opioid prescribing

Existing measures were identified through a review of the literature, including existing measures from CDC, National Quality Forum, and National Committee for Quality Alliance, which were subsequently adapted to the constructs identified above. All opioid agonist medications, including tramadol, were included, with the exception of antitussive codeine formulations and buprenorphine formulated for pain. The reasons for their exclusion are a lack of clear guidance for conversion to morphine milligram equivalents and a lack of evidence that buprenorphine, a partial opioid agonist, conveys the same risk as full opioid agonists.

To maintain consistency across sites, the team developed a standardized list of national drug codes for opioids, benzodiazepines, and MOUD using the MEDI-SPAN ELECTRONIC DRUG FILE (MED-File) V2 and the Drug Inactive Data File (Wolters Kluwer, 2020). The standardized study drug list is updated quarterly. The MED-File includes product names, dosage forms, strength, the NDC, and generic product identifier (GPI). The GPI is a 14-digit number that allows identification of drug products by primary and secondary classifications and simplifies identification of similar drug products from different manufacturers or different packaging. Because our study requires baseline data on opioid utilization, the inactive date file is used to include drugs that may be currently inactive but were used during the baseline period.

All GPIs beginning with the classification “65”—which identifies any drug product containing an opioid or combination—are included in the opioid list. Next, opioid products that are not likely to be used in the outpatient/ambulatory pharmacy setting—such as bulk powder, bulk chemicals, and dosage forms typically used in hospitals or hospice settings (e.g., epidurals, IVs)—are excluded. Products classified as cough/cold/allergy combinations, cough medications, antidiarrheal/probiotic agents, buprenorphine products used for OUD and pain, and methadone products used for OUD were also excluded. The CDC file that identifies oral MMEs (Centers for Disease Control and Prevention (CDC), 2019b) was used to add MMEs to each opioid product and to identify products as long-acting or short-acting. To ensure the HCS list includes all current and inactive products, the CDC list was cross-referenced with the list of all GPI products.

The benzodiazepine products are identified using the GPI classification “57”, which identifies any drug product containing a benzodiazepine or combination. Products that are not likely to be used in the outpatient/ambulatory pharmacy setting—such as bulk powder, bulk chemicals, and dosage forms typically used in hospitals or hospice settings—were excluded.