Cost of start-up activities to implement a community-level opioid overdose reduction intervention in the HEALing Communities Study.

Community engagement Cost analysis Intervention implementation Opioid use disorder Start-up cost

Journal

Addiction science & clinical practice
ISSN: 1940-0640
Titre abrégé: Addict Sci Clin Pract
Pays: England
ID NLM: 101316917

Informations de publication

Date de publication:
02 Apr 2024
Historique:
received: 02 04 2023
accepted: 18 03 2024
medline: 3 4 2024
pubmed: 3 4 2024
entrez: 2 4 2024
Statut: epublish

Résumé

Communities That HEAL (CTH) is a novel, data-driven community-engaged intervention designed to reduce opioid overdose deaths by increasing community engagement, adoption of an integrated set of evidence-based practices, and delivering a communications campaign across healthcare, behavioral-health, criminal-legal, and other community-based settings. The implementation of such a complex initiative requires up-front investments of time and other expenditures (i.e., start-up costs). Despite the importance of these start-up costs in investment decisions to stakeholders, they are typically excluded from cost-effectiveness analyses. The objective of this study is to report a detailed analysis of CTH start-up costs pre-intervention implementation and to describe the relevance of these data for stakeholders to determine implementation feasibility. This study is guided by the community perspective, reflecting the investments that a real-world community would need to incur to implement the CTH intervention. We adopted an activity-based costing approach, in which resources related to hiring, training, purchasing, and community dashboard creation were identified through macro- and micro-costing techniques from 34 communities with high rates of fatal opioid overdoses, across four states-Kentucky, Massachusetts, New York, and Ohio. Resources were identified and assigned a unit cost using administrative and semi-structured-interview data. All cost estimates were reported in 2019 dollars. State-level average and median start-up cost (representing 8-10 communities per state) were $268,657 and $175,683, respectively. Hiring and training represented 40%, equipment and infrastructure costs represented 24%, and dashboard creation represented 36% of the total average start-up cost. Comparatively, hiring and training represented 49%, purchasing costs represented 18%, and dashboard creation represented 34% of the total median start-up cost. We identified three distinct CTH hiring models that affected start-up costs: hospital-academic (Massachusetts), university-academic (Kentucky and Ohio), and community-leveraged (New York). Hiring, training, and purchasing start-up costs were lowest in New York due to existing local infrastructure. Community-based implementation similar to the New York model may have lower start-up costs due to leveraging of existing infrastructure, relationships, and support from local health departments.

Sections du résumé

BACKGROUND BACKGROUND
Communities That HEAL (CTH) is a novel, data-driven community-engaged intervention designed to reduce opioid overdose deaths by increasing community engagement, adoption of an integrated set of evidence-based practices, and delivering a communications campaign across healthcare, behavioral-health, criminal-legal, and other community-based settings. The implementation of such a complex initiative requires up-front investments of time and other expenditures (i.e., start-up costs). Despite the importance of these start-up costs in investment decisions to stakeholders, they are typically excluded from cost-effectiveness analyses. The objective of this study is to report a detailed analysis of CTH start-up costs pre-intervention implementation and to describe the relevance of these data for stakeholders to determine implementation feasibility.
METHODS METHODS
This study is guided by the community perspective, reflecting the investments that a real-world community would need to incur to implement the CTH intervention. We adopted an activity-based costing approach, in which resources related to hiring, training, purchasing, and community dashboard creation were identified through macro- and micro-costing techniques from 34 communities with high rates of fatal opioid overdoses, across four states-Kentucky, Massachusetts, New York, and Ohio. Resources were identified and assigned a unit cost using administrative and semi-structured-interview data. All cost estimates were reported in 2019 dollars.
RESULTS RESULTS
State-level average and median start-up cost (representing 8-10 communities per state) were $268,657 and $175,683, respectively. Hiring and training represented 40%, equipment and infrastructure costs represented 24%, and dashboard creation represented 36% of the total average start-up cost. Comparatively, hiring and training represented 49%, purchasing costs represented 18%, and dashboard creation represented 34% of the total median start-up cost.
CONCLUSION CONCLUSIONS
We identified three distinct CTH hiring models that affected start-up costs: hospital-academic (Massachusetts), university-academic (Kentucky and Ohio), and community-leveraged (New York). Hiring, training, and purchasing start-up costs were lowest in New York due to existing local infrastructure. Community-based implementation similar to the New York model may have lower start-up costs due to leveraging of existing infrastructure, relationships, and support from local health departments.

Identifiants

pubmed: 38566249
doi: 10.1186/s13722-024-00454-w
pii: 10.1186/s13722-024-00454-w
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

23

Subventions

Organisme : SAMHSA HHS
ID : UM1DA049394
Pays : United States
Organisme : SAMHSA HHS
ID : UM1DA049406
Pays : United States
Organisme : SAMHSA HHS
ID : UM1DA049412
Pays : United States
Organisme : SAMHSA HHS
ID : UM1DA049415
Pays : United States
Organisme : SAMHSA HHS
ID : UM1DA049417
Pays : United States

Informations de copyright

© 2024. The Author(s).

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Auteurs

Iván D Montoya (ID)

Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA.

Colleen Watson (C)

RTI International, Research Triangle Park, NC, USA.

Arnie Aldridge (A)

RTI International, Research Triangle Park, NC, USA.

Danielle Ryan (D)

Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA.

Sean M Murphy (SM)

Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA.

Brenda Amuchi (B)

Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA.

Kathryn E McCollister (KE)

Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA.

Bruce R Schackman (BR)

Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA.

Joshua L Bush (JL)

College of Public Health, University of Kentucky, Lexington, KY, USA.

Drew Speer (D)

College of Public Health, University of Kentucky, Lexington, KY, USA.

Kristin Harlow (K)

College of Public Health, The Ohio State University, Columbus, OH, USA.

Stephen Orme (S)

RTI International, Research Triangle Park, NC, USA.

Gary A Zarkin (GA)

RTI International, Research Triangle Park, NC, USA.

Mathieu Castry (M)

Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA.

Eric E Seiber (EE)

College of Public Health, The Ohio State University, Columbus, OH, USA.

Joshua A Barocas (JA)

Sections of General Internal Medicine and Infectious Diseases, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Benjamin P Linas (BP)

Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA.

Laura E Starbird (LE)

Department of Family and Community Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA. starbird@nursing.upenn.edu.

Classifications MeSH