Background: At least 3% of children spend some of their childhood in public care and, as a group, have poor outcomes across a range of education, employment, health and social care outcomes. Research, using social care or government datasets, has identified a number of risk factors associated with children entering public care but the utility of risk factors in clinical practice is not established. This paper uses routine primary health care data to see if risk factors for children entering public care can be identified in clinical practice.
Methods: A nested case control methodology using routine primary care data from the United Kingdom. Health service use data were extracted for the 12 months before the case child entered public care and compared with 12 months of data for four control mother child pairs per case pair, matched on the age and sex of the child and the general practice. Exposures of interest were developed from a systematic review of the literature on risk factors associated with children entering public care.
Results: Conditional logistic regression was used to investigate the combined effect of more than one exposure of interest. Maternal mental illness (OR 2.51, 95% CI 1.55-4.05), maternal age at birth of the child, socio-economic status (5(th) quintile vs. 1(st) quintile OR 7.14, 95% CI 2.92-17.4), maternal drug use (OR 28.8, 95% CI 2.29-363), non attendance at appointments (OR 2.42, 95% CI 1.42-4.14), child mental illness (OR 2.65, 95% CI 1.42-4.96) and child admission to hospital (OR 3.31, 95% CI 1.21-9.02) were all significantly associated with children entering public care. Maternal use of primary care contraception services was negatively associated with children entering public care (OR 0.52, 95% CI 0.31-0.87).
Conclusions: Differences in health service use can be identified from routine primary care data in mother child pairs where children enter public care after controlling for maternal age and socio-economic status. The interaction between different risk factors needs testing in a cumulative risk model using longitudinal datasets.