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Child Care in Practice
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Hospital Service Use Among Children With Obesity
in Ireland: A Micro-costing Study
Louise Tully, Jan Sorensen & Grace O'Malley
To cite this article: Louise Tully, Jan Sorensen & Grace O'Malley (2022) Hospital Service Use
Among Children With Obesity in Ireland: A Micro-costing Study, Child Care in Practice, 28:4,
593-609, DOI: 10.1080/13575279.2022.2035682
To link to this article: https://doi.org/10.1080/13575279.2022.2035682
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 04 Mar 2022.
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Hospital Service Use Among Children With Obesity in Ireland:
A Micro-costing Study
Louise Tully
a
, Jan Sorensen
b
and Grace O’Malley
a,c
a
Obesity Research and Care Group, School of Physiotherapy, Royal College of Surgeons in Ireland, Dublin 2,
Ireland;
b
Healthcare Outcomes Research Centre, Royal College of Surgeons in Ireland, Dublin 2, Ireland;
c
W82GO Child and Adolescent Weight Management Service, Children’s Health Ireland at Temple Street,
Dublin 1, Ireland
ABSTRACT
Background: Childhood obesity affects around 7–8% of children in
Ireland and is associated with increased risks of health
complications. Data on healthcare resource use and the related
costs for children with obesity are important for research, future
service-planning, efforts to reduce the burden on families, and
care pathways. However, there is little or no data available to
describe these in Ireland.
Methods: We undertook a retrospective chart review for 322
children attending a national paediatric weight management
service to assess their hospital service utilisation, and the
associated costs, over a four-year period. We used a micro-costing
approach and estimated unit costs for different types of hospital
services. Multivariable negative binomial regression analyses and
Cragg hurdle models were used to assess characteristics
associated with type, frequency and costs of hospital care.
Results: Eighty-two percent of children had severe obesity, and
thirty-eight percent had a co-morbid condition. Over the four-
year period, children had a mean of 27 (median 24, IQR 16–33)
episodes of care at a mean cost of €2590 per child (median
€1659, IQR 1026–3103). The presence of a co-morbid condition
was associated with more frequent visits. Neither severity of
obesity nor socioeconomic status were associated with overall
service utilisation. The Cragg hurdle model did not identify
statistically significant differences in hospital costs according to
participant characteristics.
Conclusion: Children with obesity frequently visit a variety of
paediatric services and children with co-morbid conditions have
greater levels of hospital utilisation. Further research is needed
with larger sample sizes to explore variation in healthcare
utilisation in this population, and the relationship between
common co-morbidities and weight status. This would facilitate
assessment of the implications for care pathways and
examination of associations between patient outcomes and
related healthcare costs and cost-effectiveness.
KEYWORDS
Paediatric obesity;
healthcare utilisation;
comorbidity; healthcare cost
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License
(http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Louise Tully louisetully@rcsi.com Obesity Research and Care Group, School of Physiotherapy, Royal
College of Surgeons in Ireland, Dublin 2, Ireland
CHILD CARE IN PRACTICE
2022, VOL. 28, NO. 4, 593–609
https://doi.org/10.1080/13575279.2022.2035682
Introduction
The estimated prevalence of childhood obesity is between 7–8% in the Republic of
Ireland (ROI) (Economic and Social Research Institute, 2019; Mitchell et al., 2020).
The increasing prevalence over the past four decades (Abarca-Gómez et al., 2017) has
resulted in a concurrent increase in associated health complications (Reilly & Kelly,
2011), thus affecting health service utilisation. There is consistent evidence from high-
income countries demonstrating higher healthcare utilisation among children with
obesity compared with those considered to have a “normal”or “healthy”weight
(Hasan et al., 2020).
Obesity is defined in simple terms as “abnormal or excessive fat accumulation that
may impair health”(World Health Organization, 2021). Scientific consensus allows for
recognition of obesity as a chronic, relapsing and progressive disease, influenced by
complex genetic, biological, environmental and socioeconomic factors (Farpour-
Lambert et al., 2015). Excess adiposity in childhood is associated with conditions
affecting almost every system in the body, including physical function, pain, and muscu-
loskeletal issues (de Lima et al., 2020), liver disease (Weiss & Kaufman, 2008) and associ-
ated complications (Zhao et al., 2019), anxiety/depression (Quek et al., 2017) and
respiratory conditions (Dooley & Pillai, 2020), in addition to well-documented endocrine
and cardiovascular diseases such as dyslipidaemia, reduced insulin sensitivity and early
type II diabetes mellitus. Despite the recent emergence of nuanced staging systems
which go beyond size and capture metabolic, mechanical, mental health and social
milieu (Hadjiyannakis et al., 2016), obesity is defined in this study using age- and sex-
adjusted body mass index (BMI) centiles (Cole, 1997). A child/adolescent whose
growth lies ≥98th centile is considered to have clinical obesity, while ≥99.6th centile is
considered severe obesity.
Previous studies in the ROI have sought to estimate health service use among children
with overweight and obesity, but limitations included a lack of detailed, patient-level
clinical data. One study using nationally representative longitudinal data (Doherty
et al., 2017) demonstrated that BMI in adolescents was positively associated with
general practice (GP) visits and hospital stays. However, this was assessed using
parent-reported recall of engagement with a GP service over the previous year or any
hospital stay within the child’s lifetime, and did not report frequency of visits.
Another study using international data (Perry et al., 2017) sought to estimate the lifetime
cost of childhood obesity with a model-based approach, and the authors recommended
more detailed studies to provide greater accuracy of local data. A recent systematic review
highlighted that, despite the consistent evidence for increased healthcare utilisation
among children with obesity, there is an evidence gap for the impact of obesity-related
complications on healthcare use (Hasan et al., 2020).
There are currently no available cost data for specialist paediatric healthcare resources
in the ROI to facilitate estimation of expenditure associated with secondary or tertiary
paediatric care. There is no universally available unit cost schedule for health and
social care activities in the ROI (Jabakhanji et al., 2021) equivalent to those available
from the Personal Social Services Research Unit in the UK, for example. As a result,
costs are often estimated using crude, high-level data from hospital sites, or estimated
based on adult care, which does not account for the complex considerations associated
594 L. TULLY ET AL.
with paediatrics. Estimating such costs in the absence of any national reference data is
challenging (Asaria et al., 2016), and without detailed clinical information from which
to assess unit costs (Frick, 2009), researchers are unable to undertake accurate economic
analyses of paediatric conditions or interventions.
Within the public healthcare system in the ROI, specialist paediatric hospitals are all
located in the greater Dublin area. These receive tertiary referrals from regional and
general hospitals across the country, while also functioning as secondary care facilities
for the local population of Dublin (Staines et al., 2016). They, therefore, account for a
high percentage of service provision in this population.
There is one specialist clinical multidisciplinary obesity service available to children
and adolescents in the ROI public healthcare system, located within one of the specialist
paediatric hospitals (Children’s Health Ireland at Temple Street, 2020). This service is
available to children and young people who are referred for assessment or treatment
of clinical obesity. Children are eligible for the service if they are aged under 16 years,
have a BMI ≥98th centile (Cole, 1997) at the time of referral and have been referred
by a medical or surgical consultant based in a specialist paediatric hospital (Children’s
Health Ireland).
This study aimed to: (i) describe the frequency and types of hospital service utilis-
ation among a consecutive sample of children referred to the obesity service; (ii)
identify whether participant characteristics (severity of obesity on referral, socioeco-
nomic status (SES), age, or known co-morbidities) were associated with differences
in hospital service utilisation, and (iii) estimate unit costs of hospital visits and thus
estimate the total cost of hospital service utilisation in this population over a four-
year period.
Materials and methods
Design
We undertook a retrospective chart review of children and adolescents referred for clini-
cal obesity management, to assess their hospital service utilisation over four years. This
included inpatient stays, emergency department (ED) visits, and also outpatient visits to
specialty consultants or health and social care professionals (HSCPs). We used a micro-
costing approach (Frick, 2009) from a healthcare system perspective to assess the costs.
This study was approved by the department of research at Children’s Health Ireland at
Temple Street (19.011).
Data collection
Inclusion criteria
We included hospital utilisation data for children referred to the obesity service, from a
consecutive sample recorded in the digital hospital record management system between
2008 and 2016. We excluded children who had been injured in road traffic accidents, as
they accounted for a small number of children (n= 6) with high resource use and costs,
unrelated to their obesity, which may represent cost outliers and potentially bias the cost
assessment.
CHILD CARE IN PRACTICE 595
Patient data
We used electronic hospital records to assess the total number of inpatient admissions
and bed days, ED and outpatient visits to hospital services for each child within the
two years prior to commencing obesity management and the two years immediately fol-
lowing that. This time period was chosen in order to capture the type of care received by
children prior to their referral for obesity management or prior to developing obesity,
whilst also capturing their care throughout their obesity management and beyond. We
also recorded patient demographics and characteristics including their BMI centile on
referral to the service. We assessed deprivation using a national deprivation index
(Pobal, 2019) based on geographical and socioeconomic data.
Cost assessments
In order to assess the cost of medical, surgical and HSCP outpatient visits in addition to
ED visits, we used a micro-costing approach (Kaplan & Anderson, 2003; Keel et al., 2017;
Yangyang et al., 2016) to specify a base case (Figure 1). This involved staffinterviews and
observations to assess the staff-related workflow costs associated with each instance of
clinical care. This is a detailed, bottom-up approach that assesses resource use, in this
case primarily stafftime, by mapping the process of clinical care to get a realistic
picture of the costs involved. We interviewed various administrative staff, medical con-
sultants, nurses, and HSCPs (dietitians, physiotherapists and psychologists) to assess the
workflow for outpatient appointment time within their departments. We interviewed an
emergency medicine consultant to map the typical workflow processes for three common
emergency presentations and those most common among children with obesity in our
cohort; a suspected fracture, abdominal pain, and respiratory distress. The researcher
(LT) took field notes and used these to compile a systematic base case for time associated
with activities. Costs for inpatient stays were obtained from publicly available infor-
mation on payment levies for private patients (Citizen’s Information, 2020). We did
not assess investigations such as laboratory tests or complex diagnostic procedures.
Figure 1. Micro-costing methods used to develop base case estimates.
596 L. TULLY ET AL.
The unit costs according to stafftime were calculated using a formula from local gui-
dance (Health Information Quality Authority, 2019) that incorporates gross salaries
including pay-related social insurance (PRSI) and pension contributions, overheads
and nominal working time with patient-related activities. We calculated hourly rates
according to these, using midpoints from the Health Service Executive (HSE, the
public health system in the ROI) salary scales published in September 2019 (Health
Service Executive, 2019b). Government documents were used to calculate PRSI (Depart-
ment of Employment Affairs and Social Protection, 2020). The most recently available
HSE circular relating to staffholidays was used to adjust for annual leave (Health
Service Executive, 2019a). We carried out a sensitivity analysis to assess the effect of
extended time parameters and additional examinations for appointments and ED
visits on overall costs and factors associated with increased costs.
Data analysis
We used descriptive statistics to describe the sample and their patterns of hospital service
use. We used multivariable negative binomial regression models (expressed using inci-
dence rate ratio, IRR) to assess the effect of age, severity of obesity, SES, presence of a
co-morbid condition, or intellectual disability/learning disorder on total or specific
types of hospital utilisation and costs over the four years by adjusting for these variables.
We also specified a seemingly unrelated regression (SUR) model to assess variations
when accounting for inter-dependence of variables. A Cragg hurdle model was
specified to assess whether the presence of excess zeros affected the total costs or costs
per type of visit. Wilcoxon signed-rank test were used to assess differences between
costs by year or in the periods pre- and post-obesity referral by children with and
without known co-morbid conditions. We undertook sensitivity analyses to assess
how variations in cost assumptions affected the estimated mean costs. All data analyses
were carried out using Stata 15 (Stata Corp, College Station, TX, USA).
Missing data
Due to a changeover in the digital hospital record management system, some outpatient
data were missing from 2006–2008, resulting in a slightly shorter observation time (mean
2.1 months omitted) for children whose referral was before 2010 (n= 93, 29%). Where ≥
six months of outpatient visit data were unavailable for a given year, the number of out-
patient visits for that year was considered missing. For frequently used outpatient ser-
vices including dietetics and physiotherapy, missing data were replaced with
predictions from negative binomial regression models with personal characteristics as
independent variables (Hernández-Herrera et al., 2020).
Results
Patient characteristics
Resource use data were compiled for a consecutive sample of children (n= 322) who had
been referred to the obesity service. Demographic information and characteristics col-
lected on referral are shown in Table 1. The mean age was 11.6 years (SD 3.3, range
1.3–17.5). The mean child body mass index (BMI) centile was 99.7 (SD 0.6, range
CHILD CARE IN PRACTICE 597
92.6–100), and mean BMI standardised deviation score was 3.2 (SD 0.6, range 1.5–6.3).
Medical histories showed respiratory conditions to be the most common type of co-
morbid condition (Table 1), most commonly asthma.
Frequency and type of hospital utilisation
The annual mean and median episodes of care by type of visit for the whole sample is
shown in Table 2. 117 (36%) children had an inpatient stay at some point during the
four-year period, and 213 children (66%) had visited the ED. Physiotherapy, general pae-
diatrics, obesity service, dietetics and psychology were the outpatient services utilised by
the highest number of children and with the highest numbers of total visits.
Relationship between hospital utilisation patterns and participant
characteristics
The negative binomial regression analyses (Table 3) demonstrated that having one or
more comorbid condition was associated with greater numbers of total episodes of
care (IRR 1.24; 95% confidence interval [CI] 1.11, 1.39; p< 0.001) while being of
average SES was associated with fewer total episodes (IRR 0.88; 95% CI 0.79, 0.99; p=
0.029).
Table 1. Demographic characteristics of the sample (n= 322) at initiation
of weight management.
Characteristic N%
Weight status
a
Overweight/obesity (91st–99.5th centile) 58 18.0
Severe obesity (≥99.8th centile) 263 81.7
Sex
Male 144 44.7
Female 178 55.3
Age group
0-6 years 26 8.1
6-12 years 122 37.9
12-18 years 174 54.0
Socioeconomic status
b
Affluent or very affluent 42 13.0
Marginally above average 93 28.9
Marginally below average 83 25.8
Disadvantaged 73 22.7
Very disadvantaged 31 9.6
Intellectual disability/learning disorder 44 13.7
Co-morbid condition(s)
No diagnosed conditions 200 62.1
One co-morbid condition 100 31.1
Multiple co-morbidities 22 6.8
Condition type
Respiratory 64 19.9
Neurological 23 7.1
Musculoskeletal 22 6.8
Other
c
32 9.9
a
According to cut-offs from the Royal College of Paediatrics and Child Health (UK90);
measurement data missing for n=1.
b
Estimated using Pobal small area deprivation index using child’s home address.
c
Endocrine, Renal, Otolaryngological, Gastrointestinal or Cardiovascular.
598 L. TULLY ET AL.
Table 2. Average annual hospital utilisation by type among all children in the sample (n= 322).
Type of utilisation
Year 1
(24–13 months before WM)
Year 2
(12–0 months before WM)
Year 3
(1–12 months after
commencing WM)
Year 4
(13–24 months after
commencing WM)
Total
(total over four years)
Mean
(SD) Median (range)
Mean
(SD) Median (range)
Mean
(SD) Median (range)
Mean
(SD) Median (range)
Mean
(SD) Median (range)
Inpatient days 0.12
(0.52)
0
(0–5)
0.63
(2.22)
0
(0–26)
0.19
(0.72)
0
(0–7)
0.30
(1.50)
0
(0–17)
1.24
(2.88)
0
(0–26)
ED attendances 0.68
(1.08)
0
(0–7)
0.82
(1.64)
0
(0–17)
0.51
(1.11)
0
(0–10)
0.35
(0.79)
0
(0–6)
2.25
(3.09)
1
(0–18)
Outpatient visits 1.51
(2.88)
0.4
(0–23)
4.92
(4.34)
4
(0–39)
8.32
(4.98)
8
(0–24)
4.85
(3.74)
4
(0–20)
19.60
(10.9)
17
(1–77)
Missed appointments 0.23
(0.64)
0
(0–6)
0.69
(1.23)
0
(0–9)
1.71
(1.99)
1
(0–15)
1.23
(1.51)
1
(0–8)
3.86
(3.37)
3
(0–24)
Total episodes of care 2.43
(3.79)
1
(0–29)
7.06
(6.40)
5
(0–47)
10.74
(6.75)
10
(0–51)
6.72
(5.28)
5
(0–35)
26.95
(15.06)
24
(1–110)
Note: WM weight management, SD standard deviation, ED Emergency Department.
Table 3. Results of negative binomial regression for types of hospital utilisation by patient characteristics.
ED utilisation
IRR (95% CI)
Outpatient utilisation
IRR (95% CI)
Inpatient days
IRR (95% CI)
Missed appointments
IRR (95% CI)
Total episodes of care
IRR (95% CI)
Intercept 2.57** (1.41, 4.68) 14.96** (11.63, 19.25) 1.14 (0.39, 3.33) 3.51** (2.33, 5.29) 22.39 (17.47, 28.68)
Female 1.05 (0.78, 1.40) 1.05 (0.94, 1.18) 0.91 (0.56, 1.46) 0.89 (0.74, 1.06) 1.02 (0.91, 1.14)
Average SES 0.76 (0.57, 1.01) 0.91 (0.81, 1.01) 0.77 (0.48, 1.24) 0.86 (0.72, 1.02) 0.88* (0.79, 0.99)
Age 6–12 years 0.59 (0.35, 1.02) 1.14 (0.92, 1.42) 1.10 (0.43, 2.83) 1.14 (0.80, 1.61) 1.07 (0.86, 1.32)
Age 13–18 years 0.62 (0.37, 1.04) 1.19 (0.97, 1.48) 1.47 (0.59, 3.64) 1.10 (0.78, 1.55) 1.12 (0.91, 0.38)
Intellectual disability/ learning disorder 1.28 (0.85, 1.93) 1.07 (0.91, 1.25) 1.38 (0.70, 2.74) 1.14 (0.88, 1.48) 1.10 (0.93 1.29)
Co-morbid condition 1.31 (0.98, 1.76) 1.24** (1.11, 1.39) 1.70* (1.04, 2.78) 1.10 (0.91, 1.32) 1.24** (1.11, 1.39)
Severe obesity 1.39 (0.95, 2.05) 1.06 (0.92, 1.23) 0.72 (0.39, 1.35) 0.10 (−0.14, 0.34) 1.07 (0.93, 1.24)
*p<0.05
**p<0.01
Note: IRR: Incidence Risk Ratio, CI: Confidence Interval, SES: Socioeconomic status.
CHILD CARE IN PRACTICE 599
Presence of comorbidity was positively associated with visits for the years before com-
mencing obesity treatment (year one: IRR 1.99; 95% CI 1.43, 2.79; p< 0.001; year two:
IRR 1.28; 95% CI 1.07, 1.52; p= 0.006). This was also associated with slightly more
visits in the year commencing 12 months after obesity treatment (year four) (IRR 1.20;
95% CI 1.02, 1.41; p= 0.029) but not the year immediately after commencing treatment
(year three: IRR 1.14; 95% CI 1.0, 1.31; p= 0.058). Older age (12–18 years) was positively
associated with visits in year three (IRR 1.40; 95% CI 1.08, 1.82; p= 0.011), while in year
four, average SES was negatively associated with episodes of care (IRR 0.83; 95% CI 0.71,
0.98; p= 0.024). A SUR analysis confirmed the associations between participant charac-
teristics and hospital utilisation.
Cost implications
High and low unit costs for each type of visit were estimated based on assumptions
around time, salary and activities included in each model. Variation between types of
visits was predominantly due to possible length of appointments (quick review versus
full assessment), differences in salary scales and additional time for clinical diagnostic
tests. The cost of a missed appointment was estimated and these were included in
total costs per patient.
Table 4 outlines the cost of hospital utilisation for each child by year and type of visit.
There was a mean cost per child of €2590 for all four years (€1373 of which came from
outpatient service utilisation). The year immediately prior to commencing weight man-
agement was the most costly year in terms of care.
Despite significantly higher numbers of total visits in the two years before commen-
cing obesity treatment, there was no significant difference in episodes or costs among
children with severe obesity compared to those with overweight or obesity. Children
with a co-morbid condition had a significantly higher number of total hospital visits
(mean 11.5, 95% CI 9.9, 13.1; p< 0.001) and higher costs (mean €1591, 95% CI 1115,
2067; p= 0.009) pre- obesity treatment compared to those without a diagnosed condition
(mean episodes 8.3; mean costs €981).
In the two years after commencing obesity treatment, having a co-morbid condition
was positively associated with visits (IRR 1.16; 95% CI 1.02, 1.32; p= 0.020). Being of
average SES was negatively associated with visits (IRR 0.87; 95% CI 0.77, 0.98; p=
0.021). Multivariable regression analysis showed these associations to be consistent for
costs in the same time period. Figure 2 shows the average annual costs per child by
type of visit. The cost of ED visits can be seen to remain broadly similar over the four
years (Figure 2). Figure 2 also conveys high inpatient cost in the year before admission
to the obesity service and higher outpatient cost in the year just after admission, which
suggests that suitability for the obesity service may have been identified for some patients
during their hospital stay.
For total costs over four years, multivariable regression analyses showed presence of a
co-morbid condition was a statistically significant characteristic, positively associated
with costs (coefficient 964; 95% CI 322, 1606; p= 0.003), and this was also significant
for outpatient costs (coefficient 332; 95% CI 118, 547; p= 0.003). In a sensitivity analysis
to assess costs using both the minimum and maximum cost estimates for total costs and
outpatient costs over four years, this finding remained consistent.
600 L. TULLY ET AL.
Table 4. Annual costs associated with hospital visits per child, by type of visit according to various cost parameters.
Year 1
Mean (SD)
€
Year 2
Mean (SD)
€
Year 3
Mean (SD)
€
Year 4
Mean (SD)
€
Total
Mean (SD)
€
Low
cost
Base
case
High
cost
Low
cost
Base
case
High
cost
Low
cost
Base
case
High
cost
Low
cost
Base
case
High
cost
Low
cost
Base
case
High
cost
Emergency
costs
31
(59)
54
(101)
75
(141)
45
(90)
77
(153)
108 (214) 28
(61)
47
(104)
68 (146) 19
(43)
32
(73)
46 (103) 125
(169)
210
(288)
296 (402)
Outpatient
costs
61
(137)
114
(235)
184
(376)
212
(275)
362
(447)
557 (739) 384
(272)
592
(398)
738 (548) 180
(208)
305
(313)
400 (469) 836
(589)
1373
(938)
1878 (1484)
Inpatient
costs
a
93
(413)
513
(1800)
157
(588)
245
(1217)
1007
(2340)
Total costs 184
(476)
262
(547)
351
(652)
865
(2430)
951
(1977)
1272 (2632) 548
(638)
796
(852)
942 (907) 447
(1300)
582
(1381)
694 (1480) 2047
(2958)
2590
(2801)
3259 (3532)
a
Inpatient costs were not estimated using micro-costing and instead were taken from private insurance levy fee.
Note: SD: Standard deviation.
CHILD CARE IN PRACTICE 601
A Cragg hurdle model did not however reveal any variation in total visits or costs
according to patient characteristics for inpatient stays, outpatient visits, or total episodes
of care. Presence of a comorbid condition was associated with ED related visits (p= 0.04
95% CI 0.02, 0.61), but there was no statistically significant variation among those who
used the ED based on characteristics.
Discussion
We analysed patient-level hospital service utilisation at an urban paediatric hospital
among a consecutive cohort of children with clinical obesity, who are by nature a vulner-
able population, with the aim of describing their patterns of care, differences by charac-
teristics, and assessing the associated costs.
A greater number of children within the sample were from areas considered to be dis-
advantaged (32%) than were from affluent areas (13%), which is consistent with data
from studies of obesity prevalence by deprivation level (Bel-Serrat et al., 2017).
However, we did not find evidence that the hospital costs were related to SES, though
children from “average”SES neighbourhoods had fewer total visits compared to the
entire sample when divided into affluent, average and deprived categories.
Within the sample, 38% of children had a co-morbid condition which could be under-
lying, or a complication of their obesity, or unrelated. Co-morbid conditions were the
only characteristic consistently associated with higher utilisation (inpatient, outpatient and
total episodes). This has not been previously shown in the ROI due to a lack of longitudinal
studies (Perry et al., 2017). It highlights the need to ensure that care for children with multi-
morbidities is delivered in a way that is integrated between paediatric specialties (Schalkwijk
et al., 2016). Clinical paediatric services are often fragmented and exist within separate
departments, and a lack of electronic health records further exacerbates the challenge of coor-
dinating care between healthcare practitionersandacrosspaediatricsites(Stainesetal.,
Figure 2. Mean annual costs per child by visit type.
602 L. TULLY ET AL.
2016); issues that have been highlighted as a barrier to caring for children with obesity in
various other regions also (Bel-Serrat et al., 2018;Dickeyetal.,2017; Houses of the Oireachtas
Committee on the Future of Healthcare, 2017; Johnson et al., 2018; Phelan et al., 2015).
Our study examined the costs associated with caring for children with obesity in a tertiary
hospital outpatient service using an exploratory, bottom-up method. Over the four-year
observationperiod,themeannumberofcareepisodeswas27perchild,withanestimated
cost of €2590 in total. This is broadly in line with findings from the Netherlands (Wijga et al.,
2018) showing an annual cost of hospital care to be €498 (SD 654) for children who are over-
weight, although not directly comparable. These figures are difficult to compare with existing
local data, as it is the first in the ROI to assess direct costs and patterns using patient-level data
for tertiary paediatric care. The parameters for our cost estimates have been validated by local
clinicians as acceptable estimations for direct care. The lack of information system in the ROI
for surveillance of paediatric healthcare utilisation nationally means that we cannot compare
service use for this population to that of the general paediatric population (Staines et al.,
2016). While many international studies have examined hospital utilisation among similar
populations, they did so in different healthcare systems and often using self- or parent-
reported utilisation data (Wenig, 2012) and differing methods of assessing cost such as
use of insurance (Trasande & Chatterjee, 2012). While co-morbid conditions were consist-
ently associated with higher hospital utilisation in our findings, this association was less clear
forcostsonceadjustmentsweremadeforthosewithverylowcostsusingthehurdlemodel.
Larger, prospectively-designed studies assessing healthcare costs in this population are war-
ranted. While previous studies have compared healthcare utilisation between children of
“healthy”weight and children with overweight/obesity (Hampl et al., 2007;Heringetal.,
2009; Turer et al., 2013), there is a dearth of literature examining whether severity of
obesity affects this. We did not identify any variation in hospital utilisation and severity of
obesity, defined using age- and sex-adjusted BMI cut-offs. This is contrary to what might
be expected, given that overweight and obesity are each associated with increased levels of
healthcare utilisation. However, few studies have explored differences among children
with obesity based on severity of obesity, and this may be an area that warrants further
research. The impact of stigma in healthcare (Phelan et al., 2015), the association with SES
(Bel-Serratetal.,2018) and related complexity, increased numbers of transient families
(O’Brien et al., 2021), or an array of other factors may lead to a delayed or lowered engage-
ment with healthcare services among children with more severe obesity.
Further, although classifying obesity severity based on measures of body size alone
(BMI centiles) is useful in epidemiological studies, this is likely not the case at the
level of the individual child. Recent findings suggest that children present with impaired
metabolic and mental health even at less severe levels of obesity (when obesity is classified
based on body size alone) (Hadjiyannakis et al., 2019). Further research that uses more
nuanced obesity staging classifications (Hadjiyannakis et al., 2016) for obesity, rather
than focusing on shape and size are needed to explore whether a relationship exists
between healthcare costs and obesity severity.
Implications of findings
Our findings demonstrate that this population attend a variety of paediatric specialties,
many of which are clinical services addressing the common comorbidities of childhood
CHILD CARE IN PRACTICE 603
obesity. An integrated and joined-up care approach is recommended for efficiency, econ-
omic benefits, and improving child health outcomes but barriers to such models of care
include lack of information sharing between professionals and a myriad of specialties
working in professional silos (Montgomery-Taylor et al., 2015; Rocks et al., 2020). We
provide formative retrospective data which, despite its limitations, is the first to demon-
strate tertiary hospital care for this population in the ROI. Further prospectively col-
lected, large datasets are needed to explore the relationship between obesity in
paediatric populations and common co-morbidities, to facilitate recommendations for
practice.
Research is also needed to explore the long-term effect of paediatric obesity manage-
ment on healthcare utilisation, while a national integrated system for collecting paedia-
tric healthcare utilisation data is essential. An examination of 12 years of the obesity
management service within CHI Temple Street has demonstrated the positive impact
of a multi-disciplinary, family-orientated lifestyle intervention in reducing BMI standar-
dised deviation score (O’Malley et al., 2012; Wyse et al., 2021), which is associated with
reduced risk of complications. Investigation of whether this translates to reduced health-
care utilisation and costs is warranted. Obesity remains a highly stigmatised condition
which in many countries, is not recognised as a chronic disease despite widely held
expert consensus and advocacy (Farpour-Lambert et al., 2015; Phelan et al., 2015), and
there is a need for continued provision of up-to-date evidence highlighting the case
for supportive healthcare systems for this vulnerable population with complex needs.
Our data supports the need for integrated pathways of care for childhood obesity. By
the time children in our study were referred for obesity treatment, significant healthcare
resources were already being used in the preceding 24 months, possibly for investigation
of obesity-related complications or treatment of co-morbid conditions. As such, children
are likely already engaging with health services for management of obesity-related com-
plications and co-morbidities prior to a decision to address management of obesity itself.
This suggestion is supported by data from the UK (Jones Nielsen et al., 2013) where a
four-fold increase in rates of admission associated with obesity to paediatric hospitals
was reported between 2000 and 2009. The most common reasons for admission where
obesity was a co-morbidity were related to respiratory complications including sleep
apnoea and asthma.
As childhood obesity registries continue to emerge internationally (Kirk et al., 2017),
there may be an opportunity to take advantage of the implementation of a new local
model of care for obesity management (Health Service Executive, 2021) to embed acces-
sible data collection within services or consider development of a local registry, to over-
come data needs and reduce research waste and burden. Finally, alternative means of
healthcare delivery, such as through digital health, and its ability to reduce the burden
of hospital attendance on families with high attendances, and/or prevent missed appoint-
ments, should be considered for future research (Tully et al., 2021).
Strengths and limitations
Due to its retrospective nature, we could only include services recorded in the hospital
records. Our analyses were limited by the availability of only one growth measure per
participant (BMI centile on referral), and we were, therefore, unable to examine the
604 L. TULLY ET AL.
effects of changes in weight status on hospital utilisation throughout the time period
assessed. Further, severity of obesity was established using cut-offs that are based on BMI
centiles for age and gender, which were used in the obesity management service at the
time, but diagnosing obesity on body size alone has well-documented limitations (Blundell
et al., 2014). Our results found there to be little variation in hospital use according to patient
characteristics, but further analyses on larger and more heterogeneous samples are war-
ranted. Our data are observational and we cannot base any causality or direction of associ-
ations on the costs of resource-use based on what is presented and can only place the
findings in the context of previous evidence of significantly greater hospital utilisation
among children with obesity compared to those without. In addition, we assessed hospital
service utilisation among a clinicalpopulation with obesity that had been referred to a Tier 3
hospital-based obesity service and cannot apply the findings to the wider population of chil-
dren with obesity who are not being seen for obesity management. This study did, however,
benefit from a longitudinal design with objective and detailed patient-level data from a
representative cohort of children attending obesity services in Ireland. Our micro-costing
approach allowed for detailed and accurate costs that would have been otherwise
unavailable.
Conclusion
In summary, children attending a hospital-based obesity service had a high-level of hos-
pital specialist service use, in addition to emergency visits and inpatient stays. Almost
40% of children referred for obesity treatment were living with at least one other comor-
bid condition, which significantly increased their hospital utilisation. It is vital that health
professionals and health managers consider the broad impact of obesity on child health
and development and recognise that though children with obesity may not be accessing
specific obesity treatment services, there is a strong likelihood that they are already acces-
sing multiple healthcare teams for investigation of obesity-related comorbidities. Fur-
thermore, an integrated approach to ensuring holistic care for this vulnerable
population is required so that both paediatric care and healthcare policy ensure adequate
provision of obesity treatment, in line with the United Nations Convention on the Rights
of the Child’s with regards access to healthcare (UN General Assembly, 1989). This study
provides formative data which may be hypothesis-generating and may inform future
research questions to help describe the healthcare needs of children with obesity.
There is a need for consistent and standardised national data collection on healthcare
utilisation for children with obesity, to inform robust cost-of-illness studies and econ-
omic evaluations of treatment from a societal perspective.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was supported by Health Research Board: [Grant Number SPHeRE/2013/1]; Royal
College of Surgeons in Ireland: [Grant Number StAR/2151].
CHILD CARE IN PRACTICE 605
Notes on contributors
Louise Tully is a Postdoctoral Researcher in childhood obesity management and health services
research with the Obesity Research and Care Group at RCSI University of Medicine and Health
Sciences.
Jan Sorensen is a Professor of Health Economics and the Director of the Healthcare Outcome
Research Centre at RCSI University of Medicine and Health Sciences.
Grace O’Malley is a Clinician-Scientist in childhood obesity management, and Lecturer in phy-
siotherapy. She leads the Obesity Research and Care group at RCSI University of Medicine and
Health Sciences.
ORCID
Louise Tully http://orcid.org/0000-0002-0257-0408
Jan Sorensen https://orcid.org/0000-0003-0857-9267
Grace O’Malley https://orcid.org/0000-0002-2421-3866
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