TY - JOUR
AU - R. Piroddi
AU - A. Astbury
AU - W. Baker
AU - K. Daras
AU - J. Rafferty
AU - I. Buchan
AU - B. Barr
A1 - 
AB - BACKGROUND: Adversity in childhood is increasing in the United Kingdom. Complex health and social problems affecting children cluster in families where adults also have high need, but services are rarely aligned to support the whole family. Household level segmentation can help identify households most needing integrated support. Thus, the aim was to develop a segmentation model to identify those households with children who have high levels of complex cross-sectoral needs, to use as a case-finding tool for health and social care services, and to describe characteristics of identified households, to inform service integration. METHOD: Working with stakeholders-in an English region of 2.7m population- we agreed a definition of families having complex needs which would benefit from service integration - including households with high intensity use, who had both physical and mental health problems amongst both adults and children and wider social risks. We then used individual and household linked data across multiple health and social care services to identify these households, providing an algorithm to be used in a case finding interface. Finally, to understand the needs of this segment, and to identify unmet need, to tailor support, we used descriptive statistics and Poisson regression to profile the segment and compare them with the rest of the population. RESULTS: Twenty one thousand and five hundreds twenty seven households (8% of the population of the region) were identified with complex needs, including 89,631 people (41,382 children), accounting for 34% of health and social care costs for families with children, £362 million in total, of which 42% was on children in care of local authorities. The households had contact with 3-4 different services, had high prevalence of mental health problems, most frequently co-morbid with respiratory problems, with high mental health emergency service use particularly amongst teenage girls many of whom had no prior elective treatment for conditions. CONCLUSION: Our model provides a potentially useful tool for identifying households that could benefit from better integration of services and targeted family support that can help break intergenerational transfer of adversity.
AD - Department of Public Health Policy and Systems, University of Liverpool, Brownlow Street, Liverpool, L69 3GF, UK. rpiroddi@liverpool.ac.uk.; NHS Cheshire and Merseyside Integrated Care Board, 920 Centre Park Square, Warrington, WA1 1QY, UK.; NHS Mersey Care Foundation Trust, Kings Business Park, Prescot, L34 1PJ, UK.; Department of Public Health Policy and Systems, University of Liverpool, Brownlow Street, Liverpool, L69 3GF, UK.
AN - 39871232
BT - BMC Health Serv Res
C5 - Healthcare Disparities
CP - 1
DA - Jan 27
DO - 10.1186/s12913-024-12100-x
DP - NLM
ET - 20250127
IS - 1
JF - BMC Health Serv Res
LA - eng
N2 - BACKGROUND: Adversity in childhood is increasing in the United Kingdom. Complex health and social problems affecting children cluster in families where adults also have high need, but services are rarely aligned to support the whole family. Household level segmentation can help identify households most needing integrated support. Thus, the aim was to develop a segmentation model to identify those households with children who have high levels of complex cross-sectoral needs, to use as a case-finding tool for health and social care services, and to describe characteristics of identified households, to inform service integration. METHOD: Working with stakeholders-in an English region of 2.7m population- we agreed a definition of families having complex needs which would benefit from service integration - including households with high intensity use, who had both physical and mental health problems amongst both adults and children and wider social risks. We then used individual and household linked data across multiple health and social care services to identify these households, providing an algorithm to be used in a case finding interface. Finally, to understand the needs of this segment, and to identify unmet need, to tailor support, we used descriptive statistics and Poisson regression to profile the segment and compare them with the rest of the population. RESULTS: Twenty one thousand and five hundreds twenty seven households (8% of the population of the region) were identified with complex needs, including 89,631 people (41,382 children), accounting for 34% of health and social care costs for families with children, £362 million in total, of which 42% was on children in care of local authorities. The households had contact with 3-4 different services, had high prevalence of mental health problems, most frequently co-morbid with respiratory problems, with high mental health emergency service use particularly amongst teenage girls many of whom had no prior elective treatment for conditions. CONCLUSION: Our model provides a potentially useful tool for identifying households that could benefit from better integration of services and targeted family support that can help break intergenerational transfer of adversity.
PY - 2025
SN - 1472-6963
SP - 152
ST - Identifying households with children who have complex needs: a segmentation model for integrated care systems
T1 - Identifying households with children who have complex needs: a segmentation model for integrated care systems
T2 - BMC Health Serv Res
TI - Identifying households with children who have complex needs: a segmentation model for integrated care systems
U1 - Healthcare Disparities
U3 - 10.1186/s12913-024-12100-x
VL - 25
VO - 1472-6963
Y1 - 2025
ER -