Adversity and patterns of health visiting in England: Evidence from linked administrative data and expert-by-experience workshops
Mc Grath-Lone, L.; Harron, K.; Barlow, J.; Bennett, S.; Kendall, S.; Kirman, J.; Lamont, A.; Liu, M.; Saloniki, E.-C.; Woodman, J.
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Background: Health visiting in England is a universal service designed to identify family need and offer more support where needed (proportionate universalism). We know little about how local areas deliver services for families facing adversity or their associated outcomes. Methods: Using linked Community Services Data Set and Hospital Episode Statistics data (2018-20) for 57/152 areas, we (a) described health visiting service delivery for children whose mother had a previous hospital admission with mental health issues, alcohol/substance misuse or violence/abuse recorded (study definition of adversity), (b) identified common service delivery patterns using latent class analysis; and (c) tested whether patterns were associated with child and maternal outcomes, overall and in terms of inequalities for families facing adversity. We conducted four experts-by-experience workshops to aid interpretation. Results: Three service delivery patterns for families facing adversity emerged, distinguished primarily by the intensity and age-distribution of contacts. In areas where health visiting was delivered with a high intensity across the whole 0-5 years age range (rather than targeted primarily at babies), children were more likely to meet expected levels of development at age 2-2.5 years (ORadj: 0.79, 95% CI:0.63-0.99) and child inequalities in injury-related hospital admissions and some developmental domains were reduced. Conclusions: We found that a pattern of health visiting aligned with proportionate universalism was associated with mitigating disadvantage for families facing adversity. With limited outcomes and forms of adversity captured in our study, we cannot say which service delivery pattern is best, but our findings evidence the potential of health visiting to reduce inequalities.
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