TY - JOUR
AU - J. Wills
AU - J. T. Oha
AU - G. Bridge
AU - P. Callaghan
AU - C. Flood
AU - C. Jenkins
AU - P. Reavey
AU - S. Sykes
A1 - 
AB - Population Health Management uses available data to tailor services to identified and latent needs. It is advocated by the integrated care system in England, yet challenges remain regarding data availability, linkage, and application. This paper reports on the adoption of a population health management approach to design a complex programme aimed at improving young people's mental health. In-depth qualitative interviews were conducted with local government public health professionals (PH) (n = 5), intervention leads (n = 3), and one focus group of young contributors to intervention design (n = 5) to explore how population health management informed programme design and was perceived by stakeholders. Data were analysed using Delve.io. Key learning for public health included: (i) Data analysis for the PHM approach was strengthened by a dedicated data scientist, though some regarded the PHM approach as not new; (ii) Routine data had limited capacity to fully identify need, and linking datasets across health, social care, and education remained difficult; (iii) Local insights and co-production with young people were critical in identifying target groups not visible in routine datasets. Routine health data capture only part of the picture, often reflecting those already in contact with services. PHM approaches in public health need to integrate qualitative insights and local intelligence alongside quantitative analysis to address inequalities effectively.
AD - College of Health and Life Sciences, London South Bank University, London, United Kingdom.; The Academy, Central London Community Health Care Trust, London, United Kingdom.
AN - 41190674
BT - Eur J Public Health
C5 - Healthcare Disparities
CP - 6
DA - Dec 1
DO - 10.1093/eurpub/ckaf200
DP - NLM
IS - 6
JF - Eur J Public Health
LA - eng
N2 - Population Health Management uses available data to tailor services to identified and latent needs. It is advocated by the integrated care system in England, yet challenges remain regarding data availability, linkage, and application. This paper reports on the adoption of a population health management approach to design a complex programme aimed at improving young people's mental health. In-depth qualitative interviews were conducted with local government public health professionals (PH) (n = 5), intervention leads (n = 3), and one focus group of young contributors to intervention design (n = 5) to explore how population health management informed programme design and was perceived by stakeholders. Data were analysed using Delve.io. Key learning for public health included: (i) Data analysis for the PHM approach was strengthened by a dedicated data scientist, though some regarded the PHM approach as not new; (ii) Routine data had limited capacity to fully identify need, and linking datasets across health, social care, and education remained difficult; (iii) Local insights and co-production with young people were critical in identifying target groups not visible in routine datasets. Routine health data capture only part of the picture, often reflecting those already in contact with services. PHM approaches in public health need to integrate qualitative insights and local intelligence alongside quantitative analysis to address inequalities effectively.
PY - 2025
SN - 1101-1262 (Print); 1101-1262
SP - 1178
EP - 1183+
ST - Opportunities and challenges of a population health management approach for improving young people's mental health
T1 - Opportunities and challenges of a population health management approach for improving young people's mental health
T2 - Eur J Public Health
TI - Opportunities and challenges of a population health management approach for improving young people's mental health
U1 - Healthcare Disparities
U3 - 10.1093/eurpub/ckaf200
VL - 35
VO - 1101-1262 (Print); 1101-1262
Y1 - 2025
ER -