Use of a forecast SROI analysis to evaluate a health bus intervention to help people at risk of substance use harms in Northeast England
- 1Department of Applied Health Sciences, University of Birmingham
- 2Global Health Economics Centre and Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine
- 3Population Health Sciences Institute, Newcastle University
Abstract
Introduction: A local authority public health team in the Northeast of England worked in partnership with a group of primary care practices and a lived experience recovery organisation to deliver a Health Bus intervention to address unmet health needs of the alcohol and substance-using population. The economic evaluation of community-based interventions can be difficult to measure as there is a very broad range of impacts which goes beyond the healthcare sector. In this study, we provide an example of how Social Return on Investment (SROI) analysis enabled us to estimate the monetary value of selected outcomes through healthcare costs potentially averted.
Methods: We used the forecast SROI approach. A logic model was developed by consulting local stakeholders including NGOs, charity providers and people with lived experiences. The primary outcome of interest was the referrals made through the Health Bus for the most common conditions among the target population. The time horizon was 1 year, and the perspective of the analysis was the NHS system and programme beneficiaries. The SROI was calculated based on the cost averted because of the early referrals. Additional sensitivity and scenario analysis were conducted around the parameters such as deadweight and attribution rates, productivity gain, population coverage and opportunity costs.
Findings: Under the base-case with 164 participants, the estimated SROI ratio was 1.10:1, equivalent to an estimated £1.10 of social value per £1 invested. In the 4% population-coverage scenario, the estimated SROI ratio increased to 2.02:1. Results were most sensitive to changes in deadweight and attribution as well as referral rates. Relying on secondary data sources was challenging as we couldn't obtain the parameters that we needed for broader analysis, and we made certain assumptions by relying on literature.
Conclusion: The findings indicate that estimated SROI ratio was highly sensitive to population coverage and attribution assumptions. The reliance on secondary data sources and assumptions, limited the analytical scope focused only on unmet health needs. Future studies can focus on broadening the analysis to incorporate non-health outcomes by primary data collection and tailored surveys in a longer implementation period, which then allows for projections beyond the pilot duration.
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BibTeX
@article{jebeli2026forecast,
title = {{Use of a forecast SROI analysis to evaluate a health bus intervention to help people at risk of substance use harms in Northeast England}},
author = {Sedighe Hosseini Jebeli and Luke Vale and Emma-Joy Holland and Amy O'Donnell and Elizabeth Titchener and Katherine Jackson},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1959188},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1959188/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Use of a forecast SROI analysis to evaluate a health bus intervention to help people at risk of substance use harms in Northeast England
AU - Sedighe Hosseini Jebeli
AU - Luke Vale
AU - Emma-Joy Holland
AU - Amy O'Donnell
AU - Elizabeth Titchener
AU - Katherine Jackson
PY - 2026
DA - 2026/10/20
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1959188
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1959188/full
AB - Introduction: A local authority public health team in the Northeast of England worked in partnership with a group of primary care practices and a lived experience recovery organisation to deliver a Health Bus intervention to address unmet health needs of the alcohol and substance-using population. The economic evaluation of community-based interventions can be difficult to measure as there is a very broad range of impacts which goes beyond the healthcare sector. In this study, we provide an example of how Social Return on Investment (SROI) analysis enabled us to estimate the monetary value of selected outcomes through healthcare costs potentially averted. Methods: We used the forecast SROI approach. A logic model was developed by consulting local stakeholders including NGOs, charity providers and people with lived experiences. The primary outcome of interest was the referrals made through the Health Bus for the most common conditions among the target population. The time horizon was 1 year, and the perspective of the analysis was the NHS system and programme beneficiaries. The SROI was calculated based on the cost averted because of the early referrals. Additional sensitivity and scenario analysis were conducted around the parameters such as deadweight and attribution rates, productivity gain, population coverage and opportunity costs. Findings: Under the base-case with 164 participants, the estimated SROI ratio was 1.10:1, equivalent to an estimated £1.10 of social value per £1 invested. In the 4% population-coverage scenario, the estimated SROI ratio increased to 2.02:1. Results were most sensitive to changes in deadweight and attribution as well as referral rates. Relying on secondary data sources was challenging as we couldn't obtain the parameters that we needed for broader analysis, and we made certain assumptions by relying on literature. Conclusion: The findings indicate that estimated SROI ratio was highly sensitive to population coverage and attribution assumptions. The reliance on secondary data sources and assumptions, limited the analytical scope focused only on unmet health needs. Future studies can focus on broadening the analysis to incorporate non-health outcomes by primary data collection and tailored surveys in a longer implementation period, which then allows for projections beyond the pilot duration.
SN - 2296-2565
ER -