International Journal of Population Data Science (2022) 7:1:04
International Journal of
Population Data Science
Journal Website: www.ijpds.org
A population level study into health vulnerabilities of mothers and fathers
involved in public law care proceedings in Wales, UK between 2011 and 2019
∗
Rhodri D. Johnson1, , Laura North1 , Bachar Alrouh2 , Ann John1 , Kerina Jones1 , Ashley Akbari1 , Jon Smart1 , Simon Thompson1 ,
Claire Hargreaves2 , Stefanie Doebler2 , Linda Cusworth2 , Karen Broadhurst2 , David V. Ford1 , and Lucy J. Griffiths1
Abstract
Submission History
Submitted:
Accepted:
Published:
6/12/2021
3/3/2022
05/04/2022
1
Population
Data
Science,
Swansea
University
Medical
School, Swansea, SA2 8PP
2
Centre for Child and & Family
Justice
Research,
Lancaster
University, Lancaster, LA1 4YW
Introduction
Under section 31 of the Children Act 1989, public law care proceedings can be issued if there is
concern a child is subject to, or at risk of significant harm, which can lead to removal of a child
from parents. Appropriate and effective health and social support are required to potentially prevent
some of the need for these proceedings. More comprehensive evidence of the health needs and
vulnerabilities of parents will enable enhanced response from family courts and integrated other
services.
Objective
To examine health vulnerabilities of parents involved in care proceedings in the two-year period prior
to involvement.
Methods
Family court data provided by Cafcass Cymru were linked to population-based health records held
within the Secure Anonymised Information Linkage Databank. Linked data were available for 8,821
parents of children involved in care proceedings between 2011 and 2019. Findings were benchmarked
with reference to a comparison group of parents matched on sex, age, and deprivation (n = 32,006),
not subject to care proceedings. Demographic characteristics, overall health service use, and health
profiles of parents were examined. Descriptive and statistical tests of independence were used.
Results
Nearly half of cohort parents (47.6%) resided in the most deprived quintile. They had higher levels of
healthcare use compared to the comparison group across multiple healthcare settings, with the most
pronounced differences for emergency department attendances (59.3% vs 37.0%). Health conditions
with the largest variation between groups were related to mental health (43.6% vs 16.0%), substance
use (19.4% vs 1.6%) and injuries (41.5% vs 23.6%).
Conclusion
This study highlights the heightened socioeconomic and health vulnerabilities of parents who
experience care proceedings concerning a child. Better understanding of the needs and vulnerabilities
of this population may provide opportunities to improve a range of support and preventative
interventions that respond to crises in the community.
Keywords
family justice; data linkage; vulnerable populations; mental health; substance-related disorders;
wounds and injuries
∗ Corresponding
Author:
Email Address: r.d.johnson@swansea.ac.uk (Rhodri D. Johnson)
https://doi.org/10.23889/ijpds.v7i1.1723
April 6, 2022 © The Authors. Open Access under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.en)
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Introduction
Under section 31 (s.31) of the Children Act 1989, (public
law) care proceedings can be issued if there is concern a
child is subject to, or at risk of significant harm, which
can lead to removal of a child from their parents. Previous
analysis of infants and newborn babies subject to care
proceedings in Wales revealed the scale and rising number of
families involved and recommended the need for preventative
action [1]. Characteristics of mothers of infants involved in
such proceedings have also been examined, including mental
health needs [2–4]. We aim to extend this work by examining a
broader range of parental vulnerabilities for both mothers and
fathers of children of any age involved in care proceedings.
Appropriate and effective health and social support are
required to potentially prevent some of the need for care
proceedings [5, 6]. However, a joined-up health and children’s
social care response to parents requires far greater knowledge
about parents’ healthcare needs and their interaction with
health, and social care services. This study aims to advance
the evidence base regarding interaction with health services
by focusing on parents in care proceedings and providing
completely new evidence, which will enable services to be more
effectively tailored.
Combinations of domestic violence, parental mental health
issues and/or learning disability, and parental alcohol and/or
drug misuse have received considerable attention in relation to
risk of child abuse and neglect [7, 8]. Skinner and colleagues [9]
have recently called for a better understanding of wider
factors impacting on families involved with child protection
services. More comprehensive evidence of health needs and
vulnerabilities, including more in-depth exploration of specific
health conditions of parents entering care proceedings and
their use of different types of healthcare provision (routine;
emergency), will also enable enhanced response from the
family courts and other services.
This study sought to address such evidence gaps with
a view to aiding assessment of current policy and its
future development. Population-level data collected routinely
by Cafcass Cymru (a Welsh Government organisation that
represents children’s best interests in family justice proceedings
in Wales) for mothers and fathers was linked to electronic
health records, to examine demographic characteristics of
parents, overall health service use, and health profiles.
Methods
within the SAIL Databank, individuals are assigned unique
identifier fields – Anonymous Linking Field (ALF) and
Residential Anonymous Linking Field (RALF) [13, 14] – to
link data at individual and residential levels respectively.
The primary source of family justice data was electronic
case management data routinely produced and maintained by
Cafcass Cymru. All instances of s.31 care proceedings initiated
between January 2011 and December 2019 were included.
Further detail on Cafcass data are available elsewhere [15, 16].
Demographic information for parents was obtained using
the Welsh Demographic Service Dataset (WDSD), which
provides demographic characteristics of people registered with
a general practice (GP).
Health records from the Patient Episode Database for
Wales (PEDW), Emergency Department Data Set (EDDS),
Outpatient Data Set Wales (OPDW) and Welsh Longitudinal
General Practice (WLGP) were analysed for two years pre care
proceedings. These records contain attendance and diagnosis
data on inpatient activity, emergency admissions, outpatient
appointments, and GP appointments respectively.
Study population
Parents of children involved in s.31 care proceedings in
Wales between January 2011 and December 2019 were
included in the study (n = 11,349). Of these, 9,269 were
successfully matched and assigned an ALF. Only parents with
valid demographic information of sex, age and deprivation
were included. The final cohort consisted of 8,821 parents
(Figure 1).
An existing method was used [17] to create a list of all
parents with children in Wales at a fixed date of 1st July
2015 (study period mid-point) and who were not involved in
care proceedings. A comparison group of parents was selected
from this list using frequency matching (matched on area-level
income domain deprivation quintiles, sex, and parent age band
(<=25, 26-35, and >=36) at index date). The final matched
comparison group consisted of 32,006 parents.
Index dates for the cohort were set at the earliest court
date, and study mid-point for the comparisons. The baseline
period for health data coverage was set as two years proceeding
an individual’s index date.
Measures
Demographic characteristics
A population-level cohort study with a matched comparison
group, with the group of interest being parents involved in
public law care proceedings.
Demographic characteristics of sex, parental age and youngest
child age were derived from Cafcass Cymru and WDSD at
index date. The income domain from the 2014 version of the
Welsh Index of Multiple Deprivation (WIMD) was used as area
level deprivation, taken at or within two years of index date
and grouped into quintiles.
Data sources and linkage
Overall healthcare use
Data were accessed via the SAIL (Secure Anonymised
Information Linkage) Databank [10–12], a trusted research
environment (TRE) that hosts extensive individual-level
anonymised health and administrative data for the population
of Wales. During the anonymisation process of data sources
Healthcare interactions within the baseline period were
analysed for any hospital admission, new emergency
department attendances (excluding follow-up), new outpatient
appointment attendance and any GP record. Hospital
admissions were categorised into emergency, elective or
Study design and data sources
2
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Figure 1: Flow diagram of study participants
maternity; and emergency department attendances were
classified as urgent and non-urgent based on triage
classifications (urgent included ‘1-immediate’, ‘2-very urgent’,
or ‘3-urgent’).
General health conditions
We provide a broad categorisation of health conditions
grouped according to the chapter level of the International
Statistical Classification of Diseases (ICD-10) [18]. For
emergency hospital admissions, all diagnostic codes in primary
or secondary diagnostic code positions were included. As a
high proportion of mothers would have had routine pregnancy
and birth related admissions we excluded ICD-10 chapter 15
(pregnancy related). Any primary care diagnoses codes within
the GP Read classification system were included with codes
mapped to approximations of ICD-10 chapters (excluding
pregnancy related chapters) (Supplementary Table 1).
Mental health and substance use conditions
Parents’ primary care (GP) and hospital records were examined
for the presence of clinical codes indicating mental health
contacts or admissions. If an individual had one or more mental
health-related contact or admission code recorded during the
baseline period, they were categorised as having a mental
health outcome. Code lists developed and provided by the
Adolescent Mental Health Data Platform [19] were used and
included common mental disorders e.g. depression and anxiety;
severe mental illness; eating disorders; neurodevelopmental
disorders e.g. attention deficit hyperactivity disorder, autistic
spectrum disorder; and conduct disorders [20].
3
Health records were also analysed for clinical codes
indicating substance use indicative of problem, harmful or
hazardous use of alcohol and/or illicit drugs [20]. If an
individual had any such code recorded during the baseline
period, they were classified as having a substance use contact
or admission.
Injuries
Emergency department attendances were analysed for the
presence of injury-related clinical codes during the baseline
period, using the attendance group variable for accidents,
assault, and self-harm [21].
Data analysis
Descriptive analyses were conducted to characterise the cohort
and comparison groups. Proportions of parents with the
measures of interest were calculated during the two-year period
prior to the index date; outcomes were not required to be
mutually exclusive. One-way analysis of variance tests were
computed to compare means between cohort and comparison
groups for continuous variables. Chi-squared analyses was
used to investigate differences between groups for categorical
variables. Data processing and analyses were carried out using
SQL and R [22].
Results
Demographic characteristics
Mothers accounted for 57.4% of the cohort. Nearly three
quarters (73.0%) of cohort parents lived within the two most
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Table 1: Demographic characteristics for the cohort (n = 8,821) and comparison group (n = 32,006)
Variable
(Chi-squared)
Sex
Deprivation at index date
Parental age at index date
(One-way ANOVA)
Mother’s age at index date (mean (SD))
Father’s age at index date (mean (SD))
Age of youngest child at index date (mean
(SD))
Age of youngest child at index date (median
(IQR))
Cohort n (%)
Female
Male
Quintile 1: Most deprived
Quintile 2
Quintile 3
Quintile 4
Quintile 5: Least deprived
15-19
20-24
25-29
30-34
35-39
40-44
>45
5062
3759
4199
2240
1311
701
370
744
1771
1873
1768
1248
763
654
(57.4)
(42.6)
(47.6)
(25.4)
(14.9)
(7.9)
(4.2)
(8.4)
(20.1)
(21.2)
(20.0)
(14.1)
(8.6)
(7.4)
Comparison n (%)
p-value
18369 (57.4)
13637 (42.6)
15241 (47.6)
8129 (25.4)
4756 (14.9)
2541 (7.9)
1339 (4.2)
413 (1.3)
7116 (22.2)
7019 (21.9)
7211 (22.5)
3730 (11.7)
2779 (8.7)
3738 (11.7)
1.000
1.000
<0.001
Age in years
Age in years
Age in years
29.2 (8.2)
32.3 (9.1)
3.2 (4.3)
30.6 (8.2)
33.8 (9.4)
4.7 (4.7)
<0.001
<0.001
<0.001
Age in years
1 (5)
3 (6)
<0.001
Given the matched comparison design there were no significant differences between cohort and comparison for sex and deprivation.
For all other variables shown in this table p-values were >0.001.
∗
deprived quintiles (Table 1). The mean age of cohort mothers
(29.2 years) was around three years younger than fathers (32.3
years). There was a notable difference in the proportions of
younger parents (<20 years) between the groups, with 8.4%
of cohort parents aged 15-19 years compared to 1.3% of the
comparison group. The mean age of the youngest child was
1.5 years younger in the cohort, compared to the comparison
group.
Health measures
Overall healthcare use
Both cohort mothers and fathers experienced higher healthcare
use across all measured healthcare settings apart from elective
hospital admissions in the two years prior to care proceedings
(Figure 2, Supplementary Table 2). Differences between the
groups were generally more pronounced for mothers than
fathers.
The largest differences between groups were ‘reactive’
type health services, such as emergency admissions and
attendances. Within the cohort, a third of mothers (33.6%)
and nearly a fifth (18.5%) of fathers had at least one
emergency hospital admission compared to 15.3% and 7.8%
in the comparison respectively. Cohort mothers (62.7%) and
fathers (54.9%) had higher emergency attendances than
comparisons (37.2% for mothers and 36.8% for fathers).
Cohort parents were also more likely to have higher severity
emergency attendances (27.7% compared to 12.3% for
comparisons), based on attendances triaged as ‘immediate’,
‘very urgent’, or ‘urgent’.
4
Since a greater proportion of cohort mothers had infants
at the index date compared to the comparisons, this may have
influenced the maternity hospital admissions.
Health conditions
There were also higher levels of emergency admissions
in the cohort than comparison group for both parents
(Figure 2), with reasons for these admissions shown in
Figure 3 and Supplementary Table 3. The most common
conditions in the cohort also had the largest variation
compared to the comparison group, which included mental
and behavioural disorders (13.4% mothers and 8.5%
fathers), external causes of morbidity and mortality (7.9%
mothers and 5.9% fathers), and injury, poisoning and other
consequences of external causes (6.9% mothers and 5.7%
fathers).
Significant differences were found between groups for both
mothers and fathers (p<0.001) except for neoplasms, eye
diseases, ear diseases, congenital conditions, and genitourinary
system diseases for fathers.
The conditions with large relative differences between the
study groups in primary care diagnosis records were mental
disorders, ‘injury and poisoning’, causes of injury and poisoning
(for example, accidents, assault, and self-harm) and causes of
morbidity and mortality (Figure 4, Supplementary Table 4).
These were in common with emergency hospital admission
conditions. The conditions with the largest relative differences
in emergency hospital admissions and GP records are assessed
in more detail in Supplementary Tables 3, 4.
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Figure 2: Proportion of individuals within study groups by type of healthcare use for two years prior to care proceedings
P-values: ∗ p < 0.05;
∗∗
p < 0.01;
∗∗∗
p < 0.001.
Figure 3: Proportion of individuals within study groups by health condition (ICD-10 chapter grouping for emergency hospital
admissions) for two years prior to care proceedings
P-values: ∗ p < 0.05;
∗∗
p < 0.01;
∗∗∗
p < 0.001.
Mental health and substance use conditions
Mental disorders were by far the most common recorded health
condition for individuals in the cohort (53.2% for mothers
5
and 30.6% for fathers), over 2.5 and 3 times higher than for
comparison mothers and fathers respectively (Table 2). The
most common type of mental health condition was depression.
The relative differences of severe mental illnesses (including
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Figure 4: Proportion of individuals within study groups by health condition (ICD-10 chapter grouping for GP diagnosis records) for
two years prior to care proceedings
P-values: ∗ p < 0.05;
∗∗
p < 0.01;
∗∗∗
p < 0.001.
schizophrenia and bipolar disorders) were 11 times higher for
mothers in the cohort group and 7 times higher for fathers.
Conditions such as developmental disorders, attention deficit
hyperactivity disorders, eating disorders and autism were all
also considerably more prevalent in the cohort group.
Substance use was recorded for around one in five parents.
Parents in the cohort were around 14 and 10 times more likely
to have drug and alcohol related substance use conditions
recorded respectively.
Injuries
The cohort group had increased levels of accident and
emergency attendances for overall injury and all injury subcategories (Table 3). Accident-related injuries were 1.5 times
more likely in the cohort. Cohort mothers were nearly 10
times more likely to have an assault related attendance; cohort
fathers were 5 times as likely. Cohort mothers and fathers were
14 and 10 times more likely respectively to have an attendance
for self-harm than the comparison group.
Discussion
Summary of main findings
The most pronounced difference between the cohort parents
and the comparison group was found in emergency type
6
health services. Differences between the study groups were
particularly pronounced with regards the use of services for
mental health need, substance use and injuries/injury and
poisoning. Although overall healthcare use across healthcare
settings was higher for mothers in the study cohort than
fathers, the differences between the cohort parents and the
comparison groups were similar. Common mental health
conditions were around three times more likely in cohort
parents. Although overall only a small proportion of parents
had severe mental illness diagnoses (under 5%), the levels
were far greater for cohort parents, than comparison parents
(9 times higher). It is evident that for a proportion of
parents, vulnerabilities include both mental health need and
problems of substance use. The elevated level of assault or
self-harm for the cohort parents is also notable, with a stark
difference between parents involved in care proceedings and
our comparison group.
Study strengths and limitations
To our knowledge, this is the first study to examine parental
vulnerabilities of both mothers and fathers involved in care
proceedings in the UK, and as such allows comparisons
between parent type within the cohort, but also against a
comparison group of parents matched on age and deprivation.
This paper builds on previous work by the same lead authors
of the Family Justice Data Partnership [23] first published as
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Table 2: Numbers and proportions of individuals with hospital admissions or GP records indicating mental health conditions or
substance use disorders for two years prior to court proceedings
Mothers, n (%)
Variable
Fathers, n (%)
Cohort
Comparison
p-value
Cohort
Comparison
p-value
Mental Health
Any mental health condition
Depression
Anxiety
Severe mental illness
Developmental disorder
Attention deficit hyperactivity disorder
Eating disorder
Autism spectrum disorder
Conduct disorder
2693 (53.2)
2214 (43.7)
1215 (24.0)
216 (4.3)
119 (2.4)
61 (1.2)
45 (0.9)
18 (0.4)
8 (0.2)
3752 (20.4)
2804 (15.3)
1764 (9.6)
79 (0.4)
16 (0.1)
15 (0.1)
45 (0.2)
7 (0.0)
<5 (0.0)
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
1151 (30.6)
887 (23.6)
573 (15.2)
78 (2.1)
12 (0.3)
39 (1.0)
6 (0.2)
9 (0.2)
6 (0.2)
1359 (10.0)
956 (7.0)
667 (4.9)
40 (0.3)
6 (0.0)
18 (0.1)
<5 (0.0)
7 (0.1)
<5 (0.0)
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.010
0.002
0.001
Substance use
Any substance use
Substance use: drugs
Substance use: alcohol
1042 (20.6)
801 (15.8)
434 (8.6)
214 (1.2)
134 (0.7)
101 (0.5)
<0.001
<0.001
<0.001
665 (17.7)
489 (13.0)
309 (8.2)
313 (2.3)
186 (1.4)
160 (1.2)
<0.001
<0.001
<0.001
∗
p-values indicate differences between cohort and comparison groups by parent type.
Table 3: Numbers and proportions of individuals with injury-related emergency department attendances for two years prior to court
proceedings
Variable
Any injury
Accident
Assault
Self-harm
∗
Mothers, n (%)
Cohort
2124
1391
439
313
(42.0)
(27.5)
(8.7)
(6.2)
Fathers, n (%)
Comparison
p-value
3969 (21.6)
3206 (17.5)
163 (0.9)
83 (0.5)
<0.001
<0.001
<0.001
<0.001
Cohort
1538
1141
254
191
(40.9)
(30.4)
(6.8)
(5.1)
Comparison
p-value
3585 (26.3)
2987 (21.9)
176 (1.3)
66 (0.5)
<0.001
<0.001
<0.001
<0.001
p-values indicate differences between cohort and comparison groups by parent type.
a descriptive funder report [24]. For this publication we have
added further academic rigour through addition of statistical
testing for all health care use outcomes.
Better understanding of the needs and vulnerabilities of
these parents may provide opportunities to improve a range
of support and preventative intervention for these families.
This work covers wide range of measures, providing a broad
picture of health service use and underlying conditions, and by
linking health and family justice data at population level for
fathers, builds on the evidence base [25, 26] for this group as
well - a group often excluded from such research and policy
work [27, 28].
Studies based on administrative data are necessarily limited
by the scope and quality of available data and are collected
primarily for non-research purposes. Specific strengths and
limitations of Cafcass Cymru data are reported elsewhere [15,
16]. Cohort parents had more children aged under 1 year at
the index date compared to comparisons, which may have
influenced levels of healthcare use for mothers – for example,
for pre- and post-natal appointments. The earliest application
date within the study period for each parent was also used, as
a proxy measure to represent the first occurrence within care
proceedings. We recommend future work aims to account for
any bias resulting from recurrent care proceedings [29].
7
The SAIL Databank contains data from around 80% of
general practitioner (GP) practices in Wales; as such, data for
GP-based measures was available for the majority, but not all
individuals; GP measures were not adjusted for the reduced
coverage, which we recognise is a study limitation.
We compared cohort findings against a matched
comparison group (using age, deprivation, sex, and parenttype); this study design choice was made to allow more
meaningful comparisons to be drawn between study groups.
Factors such as deprivation are known to adversely affect
health outcomes [30, 31] and as we matched on deprivation
readers should be aware that results are likely to underestimate
effects in comparison to the general population. As our
comparison group selection method used category matching
using age bands this resulted in imbalances in ages between
groups as noted in Table 1; this choice was made to increase
the size of the comparison group, however, further studies
should consider matching on more closely aligned ages.
It should be noted that match rates in the cohort were
82% (Figure 1), whilst this is in line with previous work [16]
we have no further information to understand if there are
differences between the matched and non-matched parents.
It could be hypothesised that the non-matched parents are
more vulnerable and as such could result in under reporting
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
of heath care use. It would be worthwhile for agencies such
as Cafcass Cymru to aim to improve data quality to improve
future match rates and research design.
Comparison of research findings with previous
literature
Uniquely, this study examined both mothers and fathers
interaction with healthcare services prior to court proceedings.
However, there is an important body of related international
research on the mental health needs and co-morbid substance
use, for parents involved with child welfare services [32–
37], and children in care [38–40]. Although the research we
report is specific to parents who are involved in formal family
court proceedings, our findings are consistent with the broader
published research in reporting elevated rates of mental health
need often co-occurring with substance use. Notable in the
published literature, is the work of Wall-Wiehler and colleagues
in Canada (2017) who reported elevated rates of mental health
diagnoses, treatment use and social factors for mothers, both
2 years prior to and 2 years after children were taken into
care. In adding to the extant knowledge, the findings we report
draw fathers clearly into view, a group whose needs are often
marginal to discussions about the family justice system [41].
By differentiating health service utilisation, we have also
uncovered the higher use of accident and emergency health
services among parents with problems of mental health and
substance use in this particular population [42, 43]. A key
finding in the international literature is that parents with
problems of mental health and substance use are more likely
to require emergency healthcare on account of accidents,
injury, or self-harm, or because they have not sought help
with health conditions at a timely point from primary care
providers. However, this is the first-time emergency health
care use has been evidenced for mothers and fathers in
care proceedings. Looking ahead it will be important to
understand causal factors implicated in elevated use of highcost emergency health care. Drawing on the broader literature,
a range of explanations have been proffered, which include that
same-day GP appointments can be difficult to obtain [44],
that there are significant waiting lists for mental health and
drug and alcohol services, and that these gaps in provision
result in parents’ turning to emergency healthcare [45]. The
same can be said, where community-based crisis services are
unavailable [46]. Further research to probe reasons behind high
rates of emergency care use are important, given problems of
access to health care have been exacerbated by the COVID-19
pandemic [47].
Recommendations for policy and practice
The findings presented highlight the elevated health needs
of both mothers and fathers prior to involvement in care
proceedings in Wales. Higher levels of mental health needs,
substance use and injury related conditions, compared to
a comparison group are particularly noteworthy. The study
suggests considerable healthcare costs for parents involved
in public law care proceedings, however, this would require
further substantiation through separate analysis of health
and social care utilisation over a longer period. High use of
emergency healthcare services strongly suggests the potential
8
failure in provision of—or access to—support services at an
earlier point to prevent or manage crisis. Elevated rates of
self-harm are very concerning, for example. Given pressures
on emergency healthcare provision, the evidence is that
emergency departments are unable to offer treatment over and
above attending to immediate physical healthcare needs [48].
However, this work indicates that proactively connecting
parents with relevant support services, such as for mental
health, is an important factor for those providing emergency
healthcare services, which may help reduce demand in the
longer term. This point is not new, and there is substantial
literature that calls for better management of patient journeys
through healthcare services, and far greater integration of
health and social care provision (both within child and adult
social services). This conclusion, which calls for improved and
more tailored mental health care provision [49] is particularly
relevant for parents in care proceedings, where services need to
be attuned to parents histories of adversity and trauma [50].
Further work
International literature suggests such vulnerable populations
experience higher rates of repeat emergency hospital use; a
hypothesis that warrants testing through further research, as
a particular service response is required in relation to frequent
users of emergency services [51, 52].
Further work is required to provide more detailed findings
to understand how healthcare use varies depending on a
multitude of factors including: protected characteristics (for
example, race, age, sex) and heritage; household-based factors
(for example, age and number of children, family structure,
parental relationship and presence of domestic violence);
and factors related to family court (for example, type of
court order). In the context of established awareness of the
relationship between inequality and health need [30] it is
critical that parents in care proceedings are not simply treated
as a simple homogenous group. For example, future research
should consider the intersectionality of characteristics such as
gender or race with healthcare needs [53, 54].
In this work we concentrated on the period preceding care
proceedings. Significant life events, such as having a child
removed, can lead to immediate psychosocial crisis prompting
a deterioration in health conditions, especially mental healthrelated issues including suicidal ideation, along with worsened
socioeconomic conditions [55]. It is therefore important to also
consider further work to understand health conditions, and
patterns of healthcare use over the lifetime of involvement in
care proceedings and beyond. This may indicate periods of
highest health service demand and highlight when services are
most required to support parents and families.
Such work should also consider other significant life events,
such as incarceration. Linkage of datasets from across the
justice system via the Data First programme [56] will provide
future ability to investigate levels of incarceration for this
population using SAIL.
Finally, the potential multiple and long-term effects of
such experiences for the children and young people involved
in the family justice system should be examined. This may
further enforce the need for increased advocacy services within
health and social care to support vulnerable children and
families as laid out in the Well-being of Future Generations
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
(Wales) Act (2015), and the Welsh Government Programme
for Government [57].
Conclusion
Both mothers and fathers in care proceedings in Wales
experienced greater levels of health vulnerabilities during
the two-year period prior to court proceedings compared
to a comparison group of parents matched on deprivation,
sex, and age. The higher use of emergency healthcare is
particularly noteworthy and indicates considerable crisis health
need among parents. Elevated mental health, substance use,
and injury-related conditions are coupled with higher use
of emergency services. Better understanding of the needs
and vulnerabilities of this population, including the reasons
why parents are making greater use of emergency healthcare
may provide opportunities to improve a range of support
and preventative interventions that respond to crises in the
community.
Acknowledgements
Authors are part of the Family Justice Data Partnership
(FJDP) - a collaboration between Swansea University and
Lancaster University, and Professor Ann John leads the
Adolescent Mental Health Data Platform.
The authors would like to acknowledge all the data
providers who make data available for research; as well as
the following for their support with this project: Lisa Harker,
Director, Nuffield Family Justice Observatory; Matthew
Pinnell, Deputy Chief Executive, Cafcass Cymru; Saif Ullah,
Senior Research and Evaluation Manager, Cafcass; ADR
Wales (Administrative Data Research Wales); and Welsh
Government.
Statement on conflicts of interest
None to declare.
Funding
Nuffield Family Justice Observatory (Nuffield FJO) aims to
support the best possible decisions for children by improving
the use of data and research evidence in the family justice
system in England and Wales. Covering both public and private
law, Nuffield FJO provides accessible analysis and research for
professionals working in the family courts.
Nuffield FJO was established by the Nuffield Foundation,
an independent charitable trust with a mission to advance
social well-being. The Foundation funds research that informs
social policy, primarily in education, welfare, and justice. It
also funds student programmes for young people to develop
skills and confidence in quantitative and scientific methods.
The Nuffield Foundation is the founder and co-funder of the
Ada Lovelace Institute and the Nuffield Council on Bioethics.
Nuffield FJO has funded this project (FJO/43766), but the
views expressed are those of the authors and not necessarily
those of Nuffield FJO or the Foundation.
9
Authors’ contributions
RDJ and LJG contributed to the conception. RDJ designed
and performed the analysis, with RDJ and LJG interpreting
the results. RDJ, LJG, LN and KB drafted the first iteration of
the manuscript. All authors critically reviewed the manuscript,
provided important intellectual input, approved the final
version and agreed to be accountable for their contributions.
KB and DF acquired the study funding.
Ethics statement
The project proposal was reviewed by an independent
Information Governance Review Panel (IGRP) at Swansea
University. This panel ensures that work complies with
information governance principles and represents an appropriate
use of data in the public interest. The IGRP includes
representatives of professional and regulatory bodies, data
providers and the general public. Approval for the project was
granted by the IGRP under SAIL project 0990. Cafcass Cymru
(the data owner of the family courts data) also approved use
of the data for this project. The agency considered the public
interest value of the study, benefits to the agency itself, as well
as general standards for safe use of administrative data.
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Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Abbreviations
GP:
ICD:
SAIL:
TRE:
12
General practice
International Classification of Diseases
Secure Anonymised Information Linkage
Trusted Research Environment
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Supplementary Table 1: ICD-10 chapter to Read Diagnosis code chapter descriptions and mapping
ICD-10
Chapter
ICD-10
description
1
A00–B99
Infectious and parasitic diseases
A
C00-D48
D50-D89
Neoplasms
Diseases of the blood and
blood-forming organs
B
D
E00-E90
7
Diseases of the eye and adnexa
H00-H59
8
Diseases of the ear and mastoid
process
Diseases of the circulatory system
Diseases of the respiratory system
Diseases of the digestive system
Diseases of the skin and
subcutaneous tissue
Diseases of the musculoskeletal
system and connective tissue
Diseases of the genitourinary
system
Congenital malformations,
deformations and chromosomal
abnormalities
Injury, poisoning and certain other
consequences of external causes
External causes of morbidity and
mortality
H60-H95
N00-N99
Endocrine, nutritional and
metabolic diseases
Mental disorders
Nervous system and sense organ
diseases
Nervous system and sense organ
diseases
Nervous system and sense organ
diseases
Circulatory system diseases
Respiratory system diseases
Digestive system diseases
Skin and subcutaneous tissue
diseases
Musculoskeletal and connective
tissue diseases
Genitourinary system diseases
C
5
6
Certain infectious and parasitic
diseases
Neoplasms
Diseases of the blood and
blood-forming organs and certain
disorders involving the immune
mechanism
Endocrine, nutritional and
metabolic diseases
Mental and behavioural disorders
Diseases of the nervous system
Q00-Q99
Congenital anomalies
P
S00-T98
Injury and poisoning
S
V01-Y98
External causes of morbidity and
mortality
U
2
3
4
9
10
11
12
13
14
17
19
20
ICD codes
(inclusive)
F00-F99
G00-G99
I00-I99
J00-J99
K00-K93
L00-L99
M00-M99
Read diagnosis
chapter description
Read diagnosis chapter
code (first character)
E
F
F
F
G
H
J
M
N
K
Supplementary Table 2: Type of healthcare interaction two years prior to care proceedings by study group and parental type
Variable
Emergency hospital admission
Elective hospital admission
Maternity hospital admission
Emergency department attendance
Emergency department urgent attendance
Outpatient appointment
GP records
Mothers, n (%)
Fathers, n (%)
Cohort
Comparison
p-value
Cohort
Comparison
p-value
1699 (33.6)
575 (11.4)
2757 (54.5)
3172 (62.7)
1586 (31.3)
3825 (75.6)
4694 (92.7)
2817 (15.3)
2314 (12.6)
5758 (31.3)
6836 (37.2)
2505 (13.6)
10385 (56.5)
16123 (87.8)
<0.001
0.019
<0.001
<0.001
<0.001
<0.001
<0.001
696 (18.5)
273 (7.3)
7 (0.2)
2062 (54.9)
855 (22.7)
1321 (35.1)
3246 (86.4)
1070 (7.8)
1259 (9.2)
0 (0.0)
5016 (36.8)
1429 (10.5)
3794 (27.8)
11233 (82.4)
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
*p-values indicate differences between cohort and comparison groups by parent type.
13
Johnson, RD et al. International Journal of Population Data Science (2022) 7:1:04
Supplementary Table 3: Emergency hospital admissions health conditions by study group and parental type
Mothers, n (%)
Variable
Infectious diseases
Neoplasms
Blood diseases
Endocrine diseases
Mental disorders
Nervous system diseases
Eye diseases
Ear diseases
Circulatory system diseases
Respiratory system diseases
Digestive system diseases
Skin system diseases
Musculoskeletal diseases
Genitourinary system diseases
Congenital conditions
Injury and poisoning
Causes of morbidity and mortality
Fathers, n (%)
Cohort
Comparison
p-value
Cohort
Comparison
p-value
194 (3.8)
13 (0.3)
59 (1.2)
143 (2.8)
880 (17.4)
114 (2.3)
23 (0.5)
11 (0.2)
107 (2.1)
339 (6.7)
225 (4.4)
81 (1.6)
144 (2.8)
283 (5.6)
16 (0.3)
418 (8.3)
479 (9.5)
279 (1.5)
37 (0.2)
78 (0.4)
248 (1.4)
739 (4.0)
161 (0.9)
55 (0.3)
27 (0.1)
161 (0.9)
478 (2.6)
433 (2.4)
84 (0.5)
241 (1.3)
488 (2.7)
36 (0.2)
266 (1.4)
303 (1.6)
<0.001
0.559
<0.001
<0.001
<0.001
<0.001
0.120
0.366
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.150
<0.001
<0.001
62 (1.6)
<5 (0.1)
21 (0.6)
62 (1.6)
425 (11.3)
48 (1.3)
11 (0.3)
<5 (0.1)
75 (2.0)
136 (3.6)
127 (3.4)
51 (1.4)
70 (1.9)
40 (1.1)
6 (0.2)
297 (7.9)
304 (8.1)
87 (0.6)
7 (0.1)
15 (0.1)
109 (0.8)
380 (2.8)
71 (0.5)
28 (0.2)
12 (0.1)
152 (1.1)
183 (1.3)
208 (1.5)
92 (0.7)
105 (0.8)
88 (0.6)
5 (0.0)
294 (2.2)
306 (2.2)
<0.001
0.794
<0.001
<0.001
<0.001
<0.001
0.419
0.979
<0.001
<0.001
<0.001
<0.001
<0.001
0.011
0.022
<0.001
<0.001
*p-values indicate differences between cohort and comparison groups by parent type.
Supplementary Table 4: GP diagnosis health conditions by study group and parental type
Variable
Infectious diseases
Neoplasms
Blood diseases
Endocrine diseases
Mental disorders
Nervous system diseases
Circulatory system diseases
Respiratory system diseases
Digestive system diseases
Skin system diseases
Musculoskeletal diseases
Genitourinary system diseases
Congenital conditions
Injury and poisoning
Causes of injury and poisoning
Causes of morbidity and mortality
Mothers, n (%)
Fathers, n (%)
Cohort
Comparison
p-value
Cohort
Comparison
p-value
802 (15.8)
101 (2.0)
131 (2.6)
198 (3.9)
1932 (38.2)
799 (15.8)
250 (4.9)
1430 (28.2)
672 (13.3)
1016 (20.1)
978 (19.3)
1160 (22.9)
26 (0.5)
985 (19.5)
400 (7.9)
322 (6.4)
2541 (13.8)
603 (3.3)
325 (1.8)
645 (3.5)
2503 (13.6)
2989 (16.3)
825 (4.5)
4883 (26.6)
1940 (10.6)
4019 (21.9)
3527 (19.2)
3814 (20.8)
57 (0.3)
1649 (9.0)
627 (3.4)
165 (0.9)
<0.001
<0.001
<0.001
0.190
<0.001
0.416
0.190
0.019
<0.001
0.006
0.864
0.001
0.043
<0.001
<0.001
<0.001
286 (7.6)
38 (1.0)
7 (0.2)
77 (2.0)
998 (26.5)
405 (10.8)
144 (3.8)
570 (15.2)
364 (9.7)
538 (14.3)
660 (17.6)
147 (3.9)
13 (0.3)
641 (17.1)
182 (4.8)
142 (3.8)
991 (7.3)
295 (2.2)
23 (0.2)
344 (2.5)
1136 (8.3)
1470 (10.8)
459 (3.4)
2299 (16.9)
1094 (8.0)
1967 (14.4)
2284 (16.7)
575 (4.2)
37 (0.3)
1464 (10.7)
329 (2.4)
117 (0.9)
0.499
<0.001
0.994
0.106
<0.001
1.000
0.184
0.014
0.001
0.884
0.251
0.432
0.560
<0.001
<0.001
<0.001
*p-values indicate differences between cohort and comparison groups by parent type.
14