How is health visiting delivered to families in England and at what cost? An analysis of administrative and publicly available data from 2018/19 to 2019/20.

Abstract

Objective
In England, the health visiting (HV) service provides universal support to children aged 0-5 years to improve child health and wellbeing. As HV is known to vary between local authorities (LAs), we aimed to explore whether there are common models of service delivery and to estimate their associated costs.

Methods
The Community Services Dataset (CSDS) contains individual-level information on HV contacts, including duration, location and medium of contact. We applied latent class analysis to CSDS from 2018-2020 to develop a typology that classified HV services based on aspects of delivery, such as population coverage of mandated contacts and relative volume of non-mandated contacts. Using publicly available expenditure data on children’s services and child population estimates, we calculated the spend per child for the service delivery models and tested for statistically significant differences in mean spend using ANOVA.

Results
Our analysis included 57 local authorities (LAs) with CSDS data meeting completeness requirements. We identified three HV service delivery models: 1) ‘high non-mandated to mandated contact ratio, low mandated contact population coverage’, (46% of services), 2) ‘high ratio, high coverage’ (27%) and 3) ‘low ratio, low coverage’ (27%). How non-mandated contacts were delivered varied across models. For example, non-mandated contacts were more likely to last <30 minutes in Model 1 services (69% vs 43% in Model 3 services) and less likely to be at home (40% vs 67%). The mean spend per capita on 0-5 years public health services (which includes HV) was significantly higher in Model 2 (‘high ratio, high coverage’) services (£287) compared to Models 1 and 3 (£226 and £231, respectively).

Conclusion
The identified models indicate distinct approaches to HV service delivery with varying costs. The extent to which different models represent strategic responses to local population needs or funding constraints needs further exploration. Understanding the drivers and associated outcomes of different service models could help identify best practice, foster innovation and highlight potentially underfunded services.