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Health service use in families where children enter public care: a nested case control study using the General
Douglas E Simkiss1, Nicholas J Spencer, Nigel Stallard
1Health Sciences Research Institute, Warwick Medical School, University of Warwick, Coventry, UK. d.e.simkiss@warwick.ac.uk
Insights
Routine primary care data can identify risk factors for children entering public care. Key indicators include maternal mental illness, drug use, and non-attendance at appointments, highlighting potential for early intervention.
Area of Science:
- Public health
- Child welfare
- Health services research
Background:
- Children in public care experience poorer outcomes in education, employment, and health.
- Previous research identified risk factors using social care or government datasets, but clinical utility is unproven.
- This study explores the use of routine primary health care data to identify these risk factors.
Purpose of the Study:
- To determine if risk factors for children entering public care can be identified using routine primary health care data.
- To assess the clinical utility of identified risk factors in primary care settings.
Main Methods:
- Nested case-control study utilizing United Kingdom primary care data.
- Compared health service use in the 12 months prior to public care entry for case children versus matched controls.
- Risk factors were derived from a systematic literature review.
Main Results:
- Maternal mental illness, drug use, lower socio-economic status, and non-attendance at appointments were significantly associated with children entering public care.
- Child mental illness and hospital admissions also increased the likelihood of public care entry.
- Maternal use of contraception services showed a protective association.
Conclusions:
- Routine primary care data can reveal health service use differences in mother-child pairs with children entering public care, even after adjusting for maternal age and socio-economic status.
- Further research using longitudinal datasets is needed to test cumulative risk models and the interaction between risk factors.
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.
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