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P22 Evaluation of a novel population-health system for proactively identifying children’s biopsychosocial needs and improving equity of access to care
  1. Julia Forman1,
  2. Elizabeth Cecil1,
  3. Nan Hu2,
  4. James Newham3,
  5. Rose-Marie Satherley4,
  6. Raghu Lingam2,
  7. Ingrid Wolfe1
  1. 1Women and Children’s Health, King’s College London, London, UK
  2. 2School of Women’s and Children’s Health, University of New South Wales, Sydney, Australia
  3. 3Department of Psychology, Northumbria University, Newcastle, UK
  4. 4Department of Psychological Interventions, University of Surrey, Guildford, UK


Background In the UK and internationally, healthcare is usually siloed, reactive, inequitable, and individual-focused, resulting in poor health outcomes and increasingly unmanageable demand for urgent services. The Children and Young People’s Health Partnership (CYPHP) developed an integrated care model that aims to provide a proactive and equitable service, at population scale. The CYPHP model was implemented and evaluated for all children registered with a GP (n=~120,000) in two diverse London boroughs with high socioeconomic deprivation (Southwark and Lambeth).

In the CYPHP model, primary care data are used to identify children with ongoing conditions and regularly invite them to complete an online biopsychosocial pre-assessment health check with validated measures of condition status, emotional well-being, and social factors. Nurse-led early intervention biopsychosocial care is offered when indicated, and self-management advice and signposting are provided for all children.

The system has three aims, each with associated outcomes:

1. Deliver a biopsychosocial assessment at population-scale. Outcome: Response rates to online pre-assessment health check invitations.

2. Proactively identify unmet needs. Outcome: Needs identified in health checks.

3. Equitably reach the population. Outcome: Associations between health check completion rates and characteristics of (non)responders.

Methods We report descriptive statistics regarding the response rate to health check invitations (Aim 1), and the levels of unmet need (Aim 2). To investigate equity (Aim 3), we use logistic regression analyses to determine factors associated with health check completion.

Results Aim 1. Population reach: 15,945 children met criteria for a health check, and 7,779 health checks were completed (48.8%).

Aim 2. Unmet need: 56.2% of children with a complete health check had at least one uncontrolled condition (4,371/7,779).

Aim 3. Equity of responses: Controlling for other demographic factors, children from the most deprived neighbourhoods had 31% lower odds of completing a health check compared with the least deprived (OR 0.69, 95% CI 0.58 to 0.82). Investigating ethnicity, with the White group as the reference, the odds of completing a health check were lower in Asian (OR 0.80, 95% CI 0.68 to 0.94), Black (OR 0.83, 95% CI 0.74 to 0.92) and other ethnic groups (OR 0.77, 95% CI 0.63 to 0.93).

Conclusion As nearly 50% of patients took up this model of care, this population health management approach is an effective way of reaching families, at scale. This approach uncovers important unmet needs, with most responders experiencing uncontrolled symptoms, despite their conditions being known to primary care providers. Further work is underway to improve the equity of reach.

  • health services redesign research
  • population-level
  • health service equity evaluation

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