HEALing Communities Study: Data measures for supporting a community-based intervention to reduce opioid overdose deaths.

HEALing Communities Study: Data Measures for Supporting a Community-based Intervention to Reduce Opioid Overdose Deaths - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Drug Alcohol Depend . Author manuscript; available in PMC: 2026 Jun 2. Published in final edited form as: Drug Alcohol Depend. 2025 Jun 2;274:112738. doi: 10.1016/j.drugalcdep.2025.112738 HEALing Communities Study: Data Measures for Supporting a Community-based Intervention to Reduce Opioid Overdose Deaths Svetla Slavova a, * , Jennifer Villani b , Daniel J Feaster c , Austin Booth d , JaNae L Holloway e , Peter J Rock f , Lindsey R Hammerslag g , Aimee Mack h , Charles E Knott i , John V McCarthy j , Jeffery Talbert k , Marc R LaRochelle l , Bridget Freisthler m , Brent J Gibbons n , Gregory Patts o , Matthew J Bullard p , Sharon L Walsh r Author information Article notes Copyright and License information a Department of Biostatistics, University of Kentucky, Healthy Kentucky Research Building, Suite 260, 760 Press Avenue, Lexington, KY 40536, USA b National Institutes of Health, National Institute on Drug Abuse, 3WFN, MSC 6025, 301 North Stonestreet Avenue, Bethesda, MD 20892, USA c Department of Public Health Sciences, University of Miami Miller School of Medicine, 1425 NW 10 th Avenue, Room 1059, Miami, FL 33136, USA d Social, Statistical and Environment Sciences, Health Practice Area, RTI International, 6110 Executive Blvd, Suite 900, Rockville, MD 20852, USA e Center for Clinical Research, Analytics Practice Area, RTI International, 3040 E Cornwallis Rd, Research Triangle Park, NC, 27709, USA f Institute for Biomedical Informatics, University of Kentucky, Healthy Kentucky Research Building, Suite 260, 760 Press Avenue, Lexington, KY 40536, USA g Division for Biomedical Informatics, College of Medicine, University of Kentucky, Healthy Kentucky Research Building, Suite 260, 760 Press Avenue, Lexington, KY 40536, USA h The Ohio Colleges of Medicine Government Resource Center, The Ohio State University Wexner Medical Center, 150 Pressey Hall, 1070 Carmack Road, Columbus, OH, 43210, USA i Social, Statistical and Environment Sciences, Data Practice Area, RTI International, 3040 E. Cornwallis Road, Research Triangle Park, NC 27709, USA j Social, Statistical and Environment Sciences, Analytics Practice Area, RTI International, 3040 E. Cornwallis Road, Research Triangle Park, NC 27709, USA k Division of Biomedical Informatics, University of Kentucky College of Medicine, 267 Healthy, Kentucky Research Building, 760 Press Avenue, Lexington, KY 40536, USA l Clinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine and Boston Medical Center, 801 Massachusetts Avenue, 2nd Floor, Boston, MA 02118, USA m Ohio State University College of Social Work, 1947 N. College Road, Columbus, OH 43210, USA n Social, Statistical and Environment Sciences, Health Practice Area, RTI International, 3040 E. Cornwallis Road, Research Triangle Park, NC 27709, USA o Center for Health Data Science (CHDS), Boston University School of Public Health, 715 Albany St, Boston, MA 02118, USA p Center for Health Data Science (CHDS), Boston University School of Public Health, 715 Albany St, Boston, MA 02118, USA r Department of Behavioral Science, University of Kentucky; and Center on Drug and Alcohol Research, University of Kentucky, College of Medicine, 845 Angliana Avenue, Lexington, KY 40508, USA Contributors All authors contributed to the development of the HCS measure definitions and specifications, the review and interpretation of the statistical results, the development and the editing of the manuscript. SS, JV, CK, and SW drafted the introduction; SS, JM, and JH drafted the methods; SS, DF, AM, PR, LH, GP, MB, CK, JT, SW, JV, and BG drafted the results; SS, JV, and SW drafted the discussion; JM provided the common data model; JH calculated the crude rates; all authors reviewed and edited the manuscript. * Contact author information: Svetla Slavova, PhD Professor, Department of Biostatistics, University of Kentucky Healthy Kentucky Research Building, Office 261 760 Press Ave, Lexington, KY 40536, ssslav2@email.uky.edu Issue date 2025 Sep 1. PMC Copyright notice PMCID: PMC12238825  NIHMSID: NIHMS2088313  PMID: 40482517 The publisher's version of this article is available at Drug Alcohol Depend Abstract Introduction : The HEALing Communities Study (HCS) tested a community-based intervention in 67 communities across Kentucky, Massachusetts, New York, and Ohio to reduce opioid overdose deaths. This paper introduces the HCS measures for monitoring the intervention uptake, reports crude rates for benchmarking, and highlights the importance of interpreting jurisdictional trends in the context of state policies. Methods : We present technical specifications for the HCS measures and the common data model. Crude rates for the evaluation period (July 2021- June 2022) are reported by state and study arm (intervention/Wave 1 or wait-listed/Wave 2 communities), along with longitudinal trends from 2017 to 2023. Year 2023 serves as a post-intervention period for Wave 1 communities and an intervention year for Wave 2 communities. Results: After unprecedented increases in 2020–2021, the HCS crude opioid overdose death rates declined in 2023, but remained higher than the 2019 pre-pandemic rates. Opioid overdose death rates exceeded 100/100,000 adults among Non-Hispanic Black individuals in several states. In response to the rapid increase in opioid overdose deaths in Kentucky, the HCS team expanded the naloxone distribution in Kentucky intervention communities, reaching a 10-fold increase in Quarter 3 of 2021 (1,498.2 units/100,000 residents). The methadone medication for opioid use disorder (MOUD) treatment rate for Medicaid enrollees with opioid use disorder during the evaluation period was highest in Massachusetts intervention communities (274/1,000), while the buprenorphine MOUD treatment rate was highest in Kentucky (441/1,000). Conclusions: The HCS measures support comprehensive planning and evaluation of population-level opioid overdose prevention interventions and policies. Keywords: HEALing Communities Study (HCS), opioid overdose, medication for opioid use disorder (MOUD), naloxone, methadone, buprenorphine 1. Introduction In 2019, the National Institute on Drug Abuse and the Substance Abuse and Mental Health Services Administration launched the HEALing Communities Study (HCS) ( Walsh et al., 2020 ) to test the effectiveness of an intervention to reduce opioid overdose deaths. The HCS intervention incorporated: Community engagement with local coalitions to drive the selection and implementation of evidence-based practices (EBPs) and communication strategies ( Sprague Martinez et al., 2020 ); Implementation of EBPs and strategies to expand opioid overdose education and naloxone distribution, the delivery of medications for opioid use disorder (MOUD), and safer opioid prescribing - all deployed across health care, behavioral health, criminal legal involvement, and other community-based settings ( Winhusen et al., 2020 ); Locally tailored health communication campaigns to reduce stigma and drive demand for EBPs ( Lefebvre et al., 2020 ). The study included 67 communities highly affected by the opioid crisis in four states (Kentucky [KY], Massachusetts [MA], New York [NY], and Ohio [OH]). The target population was adult residents 18 years of age or older. The study design was a multi-site, 2-arm, parallel, community-level, cluster randomized, open, wait-list controlled comparison trial designed to assess the effectiveness of the HCS intervention in reducing opioid overdose deaths (primary hypothesis) in the communities that received the intervention first (i.e., Wave 1 [W1] communities, n=34) compared to the wait-listed control communities (Wave 2 [W2] communities, n=33). It was further hypothesized (secondary hypotheses) that the W1 communities, compared to W2 communities, would 1) increase naloxone availability; 2) expand the utilization of MOUD; and 3) reduce high-risk opioid prescribing. The intervention implementation in W1 communities started in January 2020; the COVID-19 pandemic started 3 months after the beginning of the intervention. The HCS evaluation period was July 2021 to June 2022 ( Walters et al., 2021 ). The wait-listed control communities received the intervention after the end of the evaluation period, from July 2022 to December 2023. The process for developing and harmonizing the HCS measures has been previously published ( Slavova et al., 2020a ). The comprehensive set of study measures supported the HCS communities in selecting EBPs and monitoring the uptake of their implementation via web-based community dashboards ( Wu et al., 2020 ). The complex community-based HCS intervention required a nuanced approach and an array of population-level study measures, as there could be considerable differences among study communities regarding baseline characteristics (e.g., demographics, prevalence of opioid use disorder (OUD)), and structural factors (e.g., treatment availability, harm reduction services). The current paper introduces the HCS measures with their technical specifications, and common data model. We present various measures monitored by the HCS study team to inform intervention uptake decisions. We report crude rates for several HCS measures during the 12-month evaluation period, providing benchmark data for other jurisdictions. Additionally, this is the first HCS publication to include data from the post-intervention period for W1 communities and data from the intervention year 2023 for the wait-listed W2 communities. 2. Methods 2.1. HCS measures The HCS team developed a technical specification guide for HCS measures based on statewide secondary administrative data sources, including death certificates, inpatient and emergency department (ED) discharge records, emergency medical services records, prescription drug monitoring program data, Medicaid claims, syndromic surveillance data, community naloxone distribution and other sources ( Appendix A ). Each measure is described with a conceptual definition, data source(s), background, measurement period(s), stratifications, an operational definition describing the methodology for the calculation, and a suggested population estimate to use as a denominator for a crude rate calculation or an offset in the negative binomial regression models utilized by the HCS study in hypothesis testing ( Samet et al., 2024 ; Westgate et al., 2022 ). HCS calculated stratified measures for the 12-month evaluation period (July 2021-June 2022) and for calendar years because mandatory suppression of small counts ( Appendix B ) made monthly and quarterly stratified measures less useful for communities. To reduce small count suppression issues, the following groupings for the stratification variables were used: age (18–34, 35–54, 55–64 for the Medicaid population; 18–34, 35–54, 55 or older, for general adult population), race/ethnicity (Hispanic Any Race, Non-Hispanic Black, Non-Hispanic White, Non-Hispanic Other Race), and biological sex (male, female). The HCS measures are catalogued into 4 sections, reflected in the first digit of the measure numbering ( Appendix A ). Section 1: Primary outcome measure on opioid overdose mortality. Section 2: Secondary measures on outcomes that are part of the linked pathway to reducing opioid overdose mortality. Section 3: Measures on structural aims that can impact the secondary outcomes and reduce opioid overdose mortality. Section 4: Measures supporting health economics studies on incremental cost, cost effectiveness, economic modeling, and sustainability. In this paper we focused on the following measures (definitions in Appendix A ): 1 Number of opioid overdose deaths 2.3 Number of nonfatal opioid overdose hospitalizations and ED visits 2.5.2 Number of individuals who received methadone for treatment of OUD (Medicaid) 2.5.3 Number of individuals who received naltrexone (combined injectable and oral) for treatment of OUD (Medicaid) 2.5.5 Number of individuals who received buprenorphine for treatment of OUD (Medicaid) 2.13 Number of individuals with a new high-risk opioid prescribing episode 2.14.1 Number of naloxone units distributed through community-based programs 2.18 Number of individuals with new opioid prescriptions limited to a 7-day supply 3.4 Number of providers who actively prescribed buprenorphine for treatment of OUD 4.3.1 Number of nights at a residential treatment center NOT involving detoxification (Medicaid) 2.2. Analysis We provided descriptive statistics and visualizations of longitudinal (quarterly or annual) rates, by state (KY, MA, NY, OH) and by study arm (W1, W2) within each state. The rates reported in this paper are for illustrative purposes and are crude rates calculated as the total number of events within a group of communities, divided by the total population residing in the group of communities (typically reported per 100,000). Crude rates were chosen for illustration as these are the rates that the HCS team monitored (without hypothesis testing) during the intervention implementation. These crude rates are typically the rates that states monitor as part of ongoing surveillance efforts. In other HCS publications focused on HCS hypothesis testing, unadjusted W1 and W2 rates were reported, calculated as the average of the community-specific crude rates (e.g., unadjusted rates of opioid-related overdose deaths reported by Samet and colleagues ( Samet et al., 2024 )). While we presented W1 and W2 group-level trend data, there was no attempt to assess the statistical significance in differences in levels, trends, or associated changes over time with HCS intervention. The year 2019 was considered a baseline year. Changes in trends in wait-listed W2 communities during 2019 to 2021 should not be affected by the HCS intervention. Year 2023 is a post-evaluation year for the intervention communities and an intervention year for the wait-listed communities. For simplicity, for the rest of the paper we will use “ state ” with the understanding that we refer to only the HCS communities within this state. Advarra provided the Institutional Review Board approval. 2.3. Common Data Model The HCS used a Common Data Model (CDM; Appendix C ) as a standardized reporting format for submission of the study measures by the research sites (KY/University of Kentucky, MA/Boston Medical Center, NY/Columbia University, and OH/Ohio State University) to the HCS Data Coordinating Center at RTI International. The CDM allowed for the submission of measures for pre-specified time points (i.e., month, quarter, year) at community-level granularity. Many state agencies that provided data to the HCS research sites had data reporting policies requiring suppression of small counts ( Appendix B ). Most agencies allowed unsuppressed small counts to be shared with the Data Coordinating Center for statistical analyses. The CDM reflects these structured properties of the transmitted data. The CDM also supports reporting of stratified measures and notes alongside data observations to aid in data quality control and querying. Field level details of the CDM and an example of a mocked data submission are presented in Appendix C . 3. Results 3.1. Measures for opioid overdose mortality and morbidity a). Opioid overdose deaths (Measure 1) The beginning of the COVID-19 pandemic coincided with the beginning of the HCS intervention in W1 communities. Figure 1A shows notable increases in opioid overdose death rates from 2019 to 2020, especially in KY and NY (both W1 and W2). The potential differential effect of the COVID-19 pandemic in the HCS states is explored by comparing changes in the rates for the wait-listed W2 groups that didn’t receive HCS intervention activities until July 2022 ( Figure 1 ). The relative increase in opioid overdose mortality rate per 100,000 adult residents varied from 8.0% in MA/W2 (56.4 in 2019; 60.9 in 2020) to 67.0% in KY/W2 (44.3 in 2019; 73.9 in 2020). Figure 1A shows growing separation and steeper increases in W2 compared to W1 in all HCS states in 2020 (the first year of the intervention for W1 communities) and in later years. After unprecedented increases in 2020–2021 (reaching 76.6 in KY/W2 in 2021), the HCS crude opioid overdose death rates declined by 2023 (ranging from 42.2 in NY/W1 to 64.0 in KY/W2 in 2023) but remained higher than the 2019 pre-pandemic rates. During the 12-month HCS evaluation period, the crude opioid overdose death rates ranged from 42.9 in NY/W1 to 75.5 in KY/W2 ( Appendix D , Table D.1 ). Figure 1: Open in a new tab Annual rates of opioid overdose deaths per 100,000 adult residents in HEALing Communities Study [HCS] communities, 2017–2023. (A) by HCS state (Kentucky [KY], Massachusetts [MA], New York [NY], Ohio [OH]) and intervention arm (Wave 1 [W1] received intervention from Jan 2020 to Jun 2022; Wave 2 [W2] received intervention from Jul 2022 to Dec 2023); (B) by HCS state, intervention arm, and age group (18–34, 35–54, 55+); (C) by HCS state, intervention arm, and sex group (Female, Male); (D) by HCS state, intervention arm, and race/ethnicity group (Non-Hispanic Black, Non-Hispanic White). Note: Details on the measure definition are provided in Appendix A , M 1: Number of opioid overdose deaths . The stratified measures exhibited known secular trends across HCS states and waves: 1) highest rates for the middle aged group of 35–54 year old, followed by 18–34 year old, and then 55 years of age or older ( Figure 1B ); 2) higher rates for males vs females ( Figure 1C ); 3) lower rates for the Non-Hispanic Other Race groups and increased rates for Hispanic groups in some states; rates for the Black Non-Hispanic group increased steeply and surpassed the rates for the White Non-Hispanic group by the end of the study period ( Figure 1D ). Rates for Non-Hispanic Black individuals surpassed 100/100,000 in KY/W2, and OH/W2 by the end of the study period. The rate for Non-Hispanic Black individuals in NY/W2 reached 146.7 in 2023, more than 3 times the rate for NY/W2 Non-Hispanic White individuals (45.8). b) Nonfatal opioid overdose hospitalizations and ED visits (Measure 2.3) The trends in non-fatal opioid overdose hospitalizations and ED visits were not consistent across the HCS states ( Appendix D , Figure D.2.A ). The W2 group had a 29.1% increase in KY (from 285.0 per 100,000 adults at baseline in 2019 to 367.9 in 2020) and a 13.3% increase in NY (from 152.1 to 172.4); OH/W2 rates were relatively stable with a 1.1% increase (from 232.1 to 234.6), while MA/W2 experienced a decline of 2.5% (from 354.0 to 345.3). KY and MA showed growing separation between W1 and W2 groups. Overall, during the 12-month evaluation period, the lowest rate was observed in NY/W1 (140.5), more than two times lower than the highest rate of 347.5 in MA/W2 ( Appendix D , Table D.1 ). The stratified measures showed a general annual decline over the study period for most subgroups (data not shown). There was an increase over time in non-fatal opioid overdoses for the Non-Hispanic Black population ( Appendix D , Figure D.2.B ), with W2 showing more pronounced increases than W1 after 2019. 3.2. Measures supporting opioid overdose prevention education and naloxone distribution Naloxone units distributed through community-based programs (Measure 2.14.1) Figure 2 illustrates the timeline for quarterly community-based distribution of naloxone per 100,000 residents from Q1–2019 to Q4–2023. During the early intervention period, KY/W1 had a steep, 3-fold increase in community naloxone distribution rate, from 156.0 naloxone units (per 100,000 population) in Q1–2020 to 475.0 in Q3–2020. By the first quarter of the evaluation period (Q3–2021), KY/W1 naloxone distribution reached 1,498.2/100,000, almost a 10-fold increase from the first intervention quarter, that was not observed in W1 groups in other states. MA data revealed a later start, with W1 exceeding W2 rates by Q2–2021. The intervention in the wait-listed W2 communities started in July 2022 and the naloxone distribution in W2 communities in 2023 outpaced the distribution in W1. In MA, W2 communities received 2,427.6 units (per 100,000) in Q4–2023. Figure 2: Open in a new tab Quarterly rates of naloxone units distributed via community-based programs, per 100,000 residents of all ages, by HEALing Communities Study [HCS] state (Kentucky [KY], Massachusetts [MA], New York [NY], Ohio [OH]), and intervention arm (Wave 1 [W1] vs Wave 2 [W2]), Q1–2019 to Q4–2023. Note: W1 HCS communities received intervention from Jan 2020 to Jun 2022; W2 HCS communities received intervention from Jul 2022 to Dec 2023. Note: Details on the measure definition are provided in Appendix A , M 2.14.1: Number of naloxone units distributed through community-based programs 3.3. Measures for safer opioid prescribing a). Individuals with new high-risk opioid prescribing episodes (Measure 2.13) The HCS data showed a strong secular trend of declining rates of individuals experiencing new episodes of high-risk opioid prescribing during 2018–2023 ( Figure 3A ). In 2019, the new high-risk opioid prescribing episode rates ranged from 1.1 per 100 adult residents in MA/W2 to 1.8 in KY/W2 ( Figure 3A ). The 2023 incident high-risk opioid prescribing remained higher in KY communities (1.1 in KY/W1, 1.3 in KY/W2, vs rates between 0.8 and 1.0 in the remaining groups; data not shown). The incidence of high-risk opioid prescribing was consistently higher in older age groups and in women across HCS groups and years (data not shown). During the 12-month evaluation period, rates ranged from 0.9 to 1.4 ( Appendix D , Table D.1 ). Figure 3: Open in a new tab Annual rates of individuals with (A) New episodes of high-risk opioid prescribing, (B) New opioid prescriptions limited to 7-day supply, 2018 – 2023. Figure 3 : (A) Annual rates of individuals with new episodes of high-risk opioid prescribing per 100 adult residents, by HEALing Communities Study [HCS] state (Kentucky [KY], Massachusetts [MA], New York [NY], Ohio [OH]) and intervention arm (Wave 1[W1] vs Wave 2 [W2]), 2018–2023. (B) Annual rates of individuals with new opioid prescriptions limited to 7-day supply, per 100 adult residents with a new opioid analgesic prescribing episode, by HCS state and intervention arm, 2018–2023. Note: W1 HCS communities received HCS intervention from Jan 2020 to Jun 2022; W2 HCS communities received HCS intervention from Jul 2022 to Dec 2023. “New” high-risk opioid prescribing episodes and “new” opioid analgesic prescriptions are identified as episodes/prescriptions after at least 45-day washout period. Note: Details on the measure definitions are provided in Appendix A , M 2.13: Number of individuals with a new high-risk opioid prescribing episode ; M 2.18: Number of individuals with new opioid prescriptions limited to a 7-day supply b). Individuals with new opioid prescriptions limited to 7-day supply (Measure 2.18) A new opioid analgesic prescribing episode was defined as filling an opioid analgesic prescription after a 45-day period without an active opioid prescription. Figure 3B shows a notable increase from 2018 to 2019 in the rates of individuals with new opioid prescriptions with ≤7-day supply. After this initial increase, the annual rates appeared stable, with no more than ~1% annual change within each group and remaining within 5 percentage points of each other after the start of the HCS intervention in 2020. In 2023, >90 of every 100 adults with new opioid analgesic prescribing episodes in MA and OH received opioid prescriptions no longer than 7 days. 3.4. Measures for MOUD We discuss three measures for MOUD treatment among individuals who were Medicaid enrollees diagnosed with OUD. The rates of Medicaid enrollees in MOUD treatment (per 1,000 Medicaid enrollees ages 18–64 with OUD diagnoses) varied notably among HCS states, with no such notable variation between W1 and W2 groups within each state ( Figure 4 ). Figure 4: Open in a new tab Annual rates of individuals with opioid use disorder [OUD] who received medications for treatment of OUD via Medicaid, per 1,000 Medicaid recipients with OUD diagnosis, 2018–2022, by HEALing Communities Study [HCS] state (Kentucky [KY], Massachusetts [MA], New York [NY], Ohio [OH]), intervention arm (Wave 1 [W1], Wave 2 [W2]), and OUD medication (buprenorphine, methadone, naltrexone). Note: W1 HCS communities received HCS intervention from Jan 2020 to Jun 2022; W2 HCS communities received HCS intervention from Jul 2022 to Dec 2023. Note: Details on the measure definitions are provided in Appendix A : M 2.5.2: Number of individuals who received methadone for treatment of OUD (Medicaid) ; M 2.5.3: Number of individuals who received naltrexone (combined injectable and oral) for treatment of OUD (Medicaid) ; M 2.5.5: Number of individuals who received buprenorphine for treatment of OUD (Medicaid) a). Individuals receiving naltrexone MOUD via Medicaid (Measure 2.5.3) Overall, naltrexone MOUD was less frequently used than buprenorphine or methadone ( Figure 4 ). During the 12-month evaluation period, the W1 rates ranged from 45.3 (per 1,000) in NY/W1 to 71.7 in OH/W1 ( Appendix D , Table D.1 ). b). Individuals receiving methadone MOUD via Medicaid (Measure 2.5.2) The KY rates for methadone MOUD were 0 in 2018 and <50.0 in 2019 ( Figure 4 ) because methadone MOUD became a covered Medicaid benefit in KY in July 2019. In 2021, the methadone MOUD treatment rates in KY surpassed the levels in OH (e.g., 133.2 in KY/W1, 118.2 in OH/W1), but were still two times lower than the 275.1 rate in MA/W1. c). Individuals receiving buprenorphine MOUD via Medicaid (Measure 2.5.5) Buprenorphine MOUD had the highest prevalence across all HCS states. The highest rate of buprenorphine MOUD during the 12-month evaluation period was 440.8 in KY/W1; the lowest, 274.6 in MA/W2 ( Appendix D , Table D.1 ). There was a general trend for increases in buprenorphine treatment rates, except for MA/W2 group (a decline from 306.4 in 2018 to 280.9 in 2021; data not shown). The rates for buprenorphine MOUD were higher in W2 vs W1 in NY and OH ( Figure 4 ). d). Providers who actively prescribed buprenorphine MOUD (Measure 3.4) During the 12-month evaluation period, the rate of providers actively prescribing buprenorphine MOUD (per 100,000 adult community residents) was highest in MA (691.6 MA/W1; 653.8 MA/W2; Appendix D , Table D.1 ). The rates in NY and OH were about 3 times lower than the MA rates. 3.5. Measures supporting health economics studies There are seventeen health economics measures using Medicaid claims to count total service utilization across a range of service types at the community level. Below we highlighted the measure on the annual number of nights at residential treatment centers not involving detoxification for Medicaid enrollees with OUD (per 1,000 Medicaid-enrolled individuals with OUD; measure 4.3.1 in Appendix A ). Figure 5 shows comparable rate trends for groups W1 and W2 within each state. The initial rates of utilization in the 12-month period from July 1, 2018 to June 30, 2019 (year 2018–2019) were similar for KY, NY, and OH, but about two times lower than the rates in MA. The MA rates increased in 2019–2020 and then dropped in 2020–2021, but still remained above the 2018–2019 baseline. For KY, however, there was a notable drop in 2020–2021 that was sustained into 2021–2022. In comparison, NY and OH remained stable throughout the 4-year period. Figure 5: Open in a new tab Nights at a residential treatment center not involving detoxification, per 1,000 individuals with opioid use disorder (OUD) in the measurement period or in the 12 months prior, among Medicaid enrollees aged 18–64 not dual eligible (for Medicaid and Medicare). The measurement periods were 12-month periods from July 1 to June 30 of the next year (e.g., the first measurement period, from July 1, 2018 to June 30, 2019, is denoted as “18/19” on the X-axis). The HEALing Communities Study [HCS] involved 67 communities in Kentucky [KY], Massachusetts [MA], New York [NY], Ohio [OH], randomized to an intervention wave (Wave 1 [W1], Wave 2 [W2]). W1 HCS communities received HCS intervention from Jan 2020 to Jun 2022; Wave 2 HCS communities received HCS intervention from Jul 2022 to Dec 2023. Note: Details on the measure definition are provided in the Appendix A , M 4.3.1. Number of nights at a residential treatment center NOT involving detoxification (Medicaid) 4. Discussion The HCS intervention was designed to reduce opioid overdose deaths in communities highly affected by the opioid overdose epidemic. Through a community-engaged and data-driven process, the intervention communities selected and implemented EBPs ( Chandler et al., 2023 ). Access to timely, accurate, and locally relevant data was critically important to the deployment of the HCS intervention and monitoring local changes. For example, while opioid overdose mortality was the primary outcome of the study, the lag (4 to 10 months) in full adjudication and reporting of this measure across the 4 participating sites rendered this measure unhelpful for informing time-sensitive or localized changes to the intervention activities. Medicaid data were critically important to assessing the breadth of HCS impact on different health utilization measures but also were not sufficiently timely (9-month lag). In contrast, emergency medical services data allowed monthly updates and were leveraged to detect timely increases or decreases in opioid overdose events. The distribution of naloxone, an opioid antagonist capable of reversing opioid-induced respiratory depression, coupled with education about overdose recognition and response was an implemented EBP for reducing opioid overdose deaths ( Winhusen et al., 2020 ). While naloxone had historically been available only by prescription, policy changes enabled community distribution (without a prescription) through physician standing orders and pharmacy protocols ( Bohler et al., 2023 ; Freeman et al., 2018 ). The FDA revised naloxone’s status to over-the-counter in March 2023 ( FDA, 2023 ). Regular evaluation of the naloxone distribution data and comparison to opioid-overdose mortality data enabled research sites to evaluate whether their selected strategies for expansion of naloxone access were effective and adjust the implementation as needed. Naloxone distribution started earlier than the other EBPs (protocol revision approved March 25, 2020) because, when the U.S. government declared COVID-19 a national public health emergency (March 13, 2020), detention centers and prisons began releasing people with low-level offenses to reduce COVID-19 spread. As many of these individuals would be affected by OUD, they would be at higher risk of fatal overdose after release ( Hartung et al., 2023 ). Furthermore, the HCS teams noticed signals for increased opioid overdoses ( Slavova et al., 2020b ), as well as drops in the dispensed prescriptions for buprenorphine MOUD during the initial weeks after the public health emergency declaration as reported later for Kentucky ( Lei et al., 2022 ) and the U.S. ( Stein et al., 2023 ). The HCS teams were successful overall in quickly increasing naloxone distribution to their communities across the course of the intervention. Individual states varied considerably in the intensity of the HCS naloxone as reflected in Figure 2 . A rapid escalation of naloxone distribution was needed to mitigate the unprecedented 67% escalation in KY/W1 opioid overdose deaths from 2019 to 2020 ( Figure 2 ). There were likely Medicaid enrollees receiving methadone MOUD before methadone became a Medicaid-covered benefit in Kentucky in 2019 explaining why the increase in Kentucky methadone MOUD rates in 2019 were not related with notable reciprocal declines in the buprenorphine MOUD utilization rates ( Figure 4 ). Further analysis is needed to understand the policy, access to, and uptake of methadone in Massachusetts, as the methadone treatment rates in Massachusetts were two times higher than the rates in the other HCS states. Buprenorphine was the most prevalent MOUD across all HCS states, reflecting its widespread acceptance, accessibility, and effectiveness. The relatively lower prevalence of naltrexone MOUD may be related to the requirement of a full detoxification period before starting naltrexone treatment, which can be a challenge and a risk for individuals actively using opioids ( Sullivan et al., 2017 ). Policy plays an important role in improving access to and the effectiveness of MOUD for Medicaid beneficiaries. Understanding the barriers to MOUD uptake and retention can inform ongoing public health strategies for education and intervention. The health economics measure for number of non-detoxification residential nights (per 1,000 Medicaid beneficiaries with OUD diagnosis) showed variability among HCS states that is likely rooted in differences in state Medicaid policy. An investigation of the drop in the rates in Kentucky during 2020–2021 uncovered a concurrent marked increase in residential detoxification claims starting in April 2020 (communication with KY Department for Medicaid Services). Effective April 1, 2020, Kentucky allowed residential providers to receive provisional certification based on self-attested American Society of Addiction Medicine levels of care for residential services. Similarly, Massachusetts changed the Medicaid policy for residential detoxification in 2020, expanding the coverage for residential rehabilitation services. The HCS health economics measures are intended to capture service utilization in a way that the resources can ultimately be translated into costs. The measure doesn’t provide information on the number of individuals impacted, only the number of nights. Future research should examine the impacts of policies affecting non-detoxification of residential care on the number of individuals impacted and changes in length of stay. The COVID-19 pandemic also may have had impacts on both the changes in the numerator and the denominator of the displayed rate. The experience of the HCS teams showed that successful response to dynamic changes in opioid overdose mortality and morbidity in the intervention communities required timely monitoring of actionable measures that support EBPs, but such measures are not typically integrated in traditional drug overdose surveillance systems. It is worth noting that the specification and calculation of such indicators required extensive effort and expertise to ensure accuracy, completeness, timeliness, application of appropriate suppression rules for data sharing, and appropriate selection of population denominators to calculate rates comparably across sites. This compendium of measures (and their explicit definitions) is now available to researchers ( Appendix A ) for use in future work and hopefully others will leverage this foundational work. Building on the HCS lessons learned, OH expanded the HCS dashboards to all 88 counties in the state to provide the Ohio Integrated Behavioral Health Dashboard ( Ohio, 2024 ) to state and local officials, and stakeholders. Building on developed HCS data and analytical capacity, with new support from the National Institute on Drug Abuse, the KY team started the development of a new system, Rapid Actionable Data For Opioid Response in KY (RADOR-KY) that also expanded the HCS measures to all KY counties, added new data sources and measures and implemented automation and machine learning for forecasting to inform opioid overdose prevention and control in the state ( RADOR-KY, 2024 ). The HCS measures rely on secondary data sources, which have limitations in timeliness, coverage, and completeness. Data availability varies (e.g., about 1-month lag for prescription monitoring, 6-month lag for death certificates, 9-month lag for Medicaid claims), affecting their use for timely intervention monitoring. Prescription monitoring data captures buprenorphine MOUD, regardless of insurance coverage, but does not cover methadone MOUD (dispensed in Opioid Treatment Programs, not pharmacies) or naltrexone MOUD (not a controlled substance reported to prescription monitoring programs). Medicaid claims track all three MOUDs but only for Medicaid beneficiaries. Drug involvement in overdose deaths may be underreported if not all contributing drugs are listed on death certificates. The technical specifications of the HCS study measures will serve as a valuable resource for researchers using the HCS data repository at the University of Michigan’s Inter-university Consortium for Political and Social Research. These specifications will enable researchers to explore various aspects of HCS intervention. Additionally, the HCS measures, integrated into routine statewide opioid overdose surveillance, can assist local communities and stakeholders in identifying priorities for funding applications (e.g., opioid settlement funding), making informed recommendations for program spending, and provide agencies with empirical evidence for program planning and evaluation. 5. Conclusions As states embrace community-level interventions to reduce opioid overdose mortality and improve health outcomes associated with OUD, the shared measures, tools, and lessons learned from HCS will streamline implementation for future interventions and research and optimize federal investments in the HCS. Supplementary Material 1 NIHMS2088313-supplement-1.pdf (2.5MB, pdf) 2 NIHMS2088313-supplement-2.pdf (172.7KB, pdf) 3 NIHMS2088313-supplement-3.pdf (92.9KB, pdf) 4 NIHMS2088313-supplement-4.pdf (533.5KB, pdf) Highlights. The HEALing Communities Study (HCS) aimed to reduce opioid overdose deaths. HCS used timely comprehensive data to monitor the community intervention uptake. This paper provides definitions and technical specifications for all HCS measures. We reported crude rates for many HCS measures that can be used for benchmarking. Acknowledgements The authors acknowledge support for this work from the following agencies: 1) Kentucky (KY): KY Cabinet for Health and Family Services, incl. the Office of Inspector General, Office of Health Analytics, Office of Vital Statistics, Department for Medicaid Services, Department for Public Health, Department for Behavioral Health, Developmental and Intellectual Disabilities; KY Board of Emergency Medical Services; Kentucky Pharmacists Association; 2) New York State (NYS): NYS Department of Health, NYS Office of Health Insurance Programs, NYS Bureau of Vital Records, NYS Bureau of Narcotic Enforcement, Office of Primary Care and Health Systems Management, NYS Statewide Planning and Research Cooperative System, NYS Bureau of All Payer Systems and Informatics, Office of Quality and Patient Safety, NYS Bureau of Emergency Medical Services and Trauma Systems, NYS Office of Alcoholism and Substance Abuse Services, NYS Office of Drug User Health; 3) Ohio (OH): RecoveryOhio, the Ohio Department of Medicaid, the Ohio Department of Health, and the Ohio Department of Mental Health and Addiction Services, Ohio Department of Public Safety, Ohio Department of Rehabilitation and Corrections, Ohio Department of Administrative Services (InnovateOhio Platform), Ohio Board of Pharmacy; and 4) Massachusetts (MA): Department of Public Health, MassHealth (Massachusetts Medicaid), and the Biostatistics and Epidemiology Data Analytics Center (BEDAC) at Boston University. The state partners’ data owners reviewed the manuscript and provided input. The authors thank Victoria Vessels from the University of Kentucky for producing the figures for this paper. Role of the funding source This research was supported by the National Institutes of Health (NIH) and the Substance Abuse and Mental Health Services Administration through the NIH HEAL (Helping to End Addiction Long-term ® ) Initiative under award numbers UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, UM1DA049417 ( NClinicalTrials.gov Identifier: NCT04111939 ). Dr. Jennifer Villani was substantially involved in UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, and UM1DA049417 consistent with her role as Science Officer. This study protocol (Pro00038088) was approved by Advarra Inc., the HEALing Communities Study Single Institutional Review Board. We wish to acknowledge the participation of the HEALing Communities Study communities, community coalitions, community partner organizations and agencies, and Community Advisory Boards and state government officials who partnered with us on this study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Substance Abuse and Mental Health Services Administration or the NIH HEAL Initiative ® . Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 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Supplementary Materials 1 NIHMS2088313-supplement-1.pdf (2.5MB, pdf) 2 NIHMS2088313-supplement-2.pdf (172.7KB, pdf) 3 NIHMS2088313-supplement-3.pdf (92.9KB, pdf) 4 NIHMS2088313-supplement-4.pdf (533.5KB, pdf) ACTIONS View on publisher site PDF (1.0 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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