Systematic review of the costs of adverse childhood experiences (ACEs): presenting the evidence on odds ratios and costs.

doi:10.21203/rs.3.rs-1532626/v1

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Systematic review of the costs of adverse childhood experiences (ACEs): presenting the evidence on odds ratios and costs.

https://doi.org/10.21203/rs.3.rs-1532626/v1

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Background

Adverse childhood experiences (ACEs) are known to cause an array of diseases later in the life course [1]. However, there have been to date no studies that explicitly look at the costs of ACEs to society. Insofar as ACEs have an association with such diseases as cancer it is considered a worthwhile exercise to attempt to put a financial cost on these diseases and to calculate how much of this cost is attributable to ACEs. This is to make the case for investing in early years and linking services across sectors e.g. health, social care, crime and justice etc. so that the balance of spending is shifted towards prevention. Such information is useful for evidence-based policy making.

Methods

This systematic review follows the design, methods and processes of the Campbell and Cochrane Economics Method Group (CCEMG) [2]. The JSTOR, PubMed, Web of Science, Medline, Embase and PsycInfo electronic databases were extensively searched with relevant search terms.

Results

The review is divided into two parts, the first part looking at odds ratios of the five disease types that link ACEs with these diseases. We then calculate the likelihood of how much of a particular disease is caused by certain ACEs and present these in tables. The second part of the review looks at cost of illness studies for the five highest expenditure disease types in the UK. These are Mental Disorders, Circulatory disorders, cancer, muscoskeletal disorders and genitourinary disorders. The results are presented in a tabular format by taking costs of illness from different studies and presenting them all, converted to GBP and 2020 figures, in an accessible format. The most striking figure in the odds ratios part is the relationship between sexual abuse and psychosis at 15.47 signifying a strong relationship and a suggestion that interventions to reduce psychosis might well benefit from concentrating on tackling sexual abuse. In terms of cost, we see that the lowest costs are those that do not consider indirect costs and only report direct medical expenditure.

Conclusion

The information gathered here will serve as the basis to look at the relationship between cost of illness and adverse childhood experiences. The results of this paper will be useful to those who wish to use the odds ratios (after converting into relative risk ratios) to calculate population attributable fractions associated with each ACE so that they are able to calculate the percentage of each disease that is caused by certain ACEs. This percentage can then be applied to the costs gleaned from part 2 of this review to arrive at a cost that can be attributed to certain ACEs.

Almost 1 in 2 children in Wales are affected by adverse childhood experiences (ACEs). ACEs are defined as 1 of 10 possible experiences that affect children as they grow up, ranging from physical abuse to having a parent with mental illness. Much is known about ACEs and how they affect the lifetime health of individuals.

Fang et al [3] have argued that calculating the economic burden of ACEs is important for several reasons. First it increases the awareness of the severity of ACEs, assists policy makers in funding decisions about developing preventive services to tackle ACEs and provides data for the economic evaluations of interventions aimed at reducing or preventing child maltreatment [3].

This paper presents the findings of two PRISMA (2009) informed systematic reviews to explore the link between ACEs and the top five life-course diseases in terms of expenditure: Mental illness, Circulatory disease, Cancer, Muscoskeletal disease and Genitourinary disease and to identify the costs of these diseases and the extent to which these diseases are associated with ACEs.

This systematic review aims to gather the necessary information to help us put a lifetime cost on ACEs. It is divided into two parts of investigation. The first part of the review investigates odds and relative risk ratios in the way ACEs affect the diseases identified later in this review. The second part aims to gather the evidence base for the cost of illness of the five main diseases in the UK that can be said to be partly caused by ACEs. This will allow the calculation of lifetime costs attributable to different denominations of ACEs. The question of attribution is key here.

The methodology we will be using, in a further study, to calculate the attributable cost of ACEs is population attributable fractions (PAF). This was first proposed by Levin [4] and developed by Miettinen [5] and others into what is known today as population attributable fractions. The idea is that there is seen to be a causal relationship between risk factors (ACEs) and adverse outcomes (disease). Then we can try to find out how much of the disease burden in any given population is reduced if we eliminate the causal factors. That is, how much of the disease would be eliminated if we got rid of ACEs entirely. Put another way it can be thought of as how much of a given disease is caused by ACEs.

“PAF is defined as the fraction of all cases of a particular disease or other adverse condition in a population that is attributable to a specific exposure”

(Mansournia et al, [6] p 1)

It is then possible to take the costs of a certain disease and calculate how much of that cost is attributable to ACEs by multiplying the PAF with the cost. The formula for PAF is as follows:

PAF= ((P(RR-1)))/((1+P(RR-1)))

Where P = percentage of ACE endorsed in the sample and RR= relative risk ratio.

Usually in epidemiologic studies the relative risk or odds ratio is used to measure the strength of association between risk factors and outcomes. PAF takes this further and considers the prevalence of the risk factor into thus allowing for consideration of the importance of the risk factor. To populate this equation and to get estimates of PAF for different ACE counts we need information on the relative risk of diseases that can be partly attributed to ACEs. Further, we need information on the cost of diseases to calculate the cost that is attributable to ACEs. This is what this review seeks to achieve.

The main objective of this review was to provide evidence of the association between ACEs and the top five diseases in the UK in terms of expenditure. This was done by performing a meta-analysis of odds ratios that linked ACEs with these diseases. Another priority was to determine the lifetime costs associated with the diseases that were partly caused by ACEs.

According to the Nuffield Trust [7] as can be seen in figure 1.1 the main diseases, in terms of expenditure, in the UK are as follows: Mental Disorders, Circulatory disorders, cancer, muscoskeletal disorders and genitourinary disorders.

The questions asked in this systematic review are as follows:

What are the odds ratios of the five disease types that link ACEs with these diseases?
What are the lifetime costs associated with diseases that are partly caused by ACEs?
Where do these costs fall (i.e., NHS, society etc)?
What is the proportion of costs that are direct (i.e., healthcare costs) and indirect (i.e., productivity losses)?
Is it possible to take costs from international sources and extrapolate them to the UK?
Is it possible to take costs from one type of disease/condition e.g., cancer and extrapolate them to other types of diseases/conditions to get the total cost of a particular disease/condition?

To address the systematic review questions the Campbell and Cochrane Economics Method Group (CCEMG) [2] design, methods and processes have been followed. The systematic review is in two parts. The first part of the systematic review describes odds ratios for diseases partly caused by ACEs by searching for meta-analyses in this area; and the second part describes cost of illness studies for the five main diseases in terms of expenditure.

2.1 Inclusion and exclusion criteria – Part A of systematic review

Part A of the systematic review includes any study that discusses the odds ratios of how ACEs can partly cause those diseases. It does not include any RCTs or other trials of interventions to reduce the prevalence of these diseases. Systematic reviews are included as they give an idea of other studies that may have been overlooked in the systematic search. The time frame for the studies considered is not limited as the relationship between ACEs and the diseases is not considered to have changed much over time. Studies from areas outside Europe and the US are included in this review as the likelihood of ACEs causing these diseases is not considered to be vastly different based on geographical area.

2.2 Inclusion and exclusion criteria – Part B of systematic review

Part B of the systematic review includes any study that discusses the costs of those diseases identified in this chapter. It does not include any RCTs or other trial interventions to reduce the prevalence of these diseases but merely the economic costing of these diseases. Systematic reviews are excluded due to the volume of information included in them. The time frame for the studies considered is not limited as cost figures are inflated to reflect current (2020) prices. Studies from areas outside Europe and the US are excluded as are those that are not in the English or Welsh languages. Grey literature was also searched including official costing documentation, local authority and charity reports, intervention evaluations not subjected to peer review etc. This was to reduce publication bias in the results.

2.3 Types of outcome measures

Any evidence on the relative risk of those diseases due to the three categories of ACEs and the costs of diseases. The following are the outcomes we will consider under each part:

Part A: Relative risk evidence: risk ratios, odds ratios; diseases/conditions: mental illness, circulatory, cancer, muscoskeletal, genitourinary; ACEs - emotional, physical, and sexual abuse; neglect; household dysfunction.

Part B: Cost evidence: direct and indirect cost of illness, mental illness, circulatory, cancer, muscoskeletal, genitourinary; year of publication; cost year; methodology; time period; perspective

Figure 1.2 shows the systematic review flow chart. Both parts of the systematic review are guided by the CCEMG [2]. The CCEMG framework has been used to create the search process and terms directly from the objectives. The databases, chosen for their relevance, are JSTOR, PubMed, Embase, Medline, Web of Science and Psycinfo.

A Bangor University health sciences librarian was consulted to define the search terms, in terms of Medical Subject Heading (MeSH) keywords and to help identify relevant databases. These keywords were grouped, and groups of keywords were linked using Boolean operators (and, not, or). The search terms for part A of the systematic review identified were:

Adverse childhood experience AND disease type (e.g., cancer)

Search terms for part B were as follows:

Cost of illness AND disease type (e.g., cancer)

Burden of illness AND disease type (e.g., cancer)

Cost* of disease AND disease type (e.g., cancer)

Economic burden of disease AND disease type (e.g., cancer)

Ancestral or hand searching was performed by searching the reference list of the chosen manuscripts. A search log was created to keep track of how the searching was conducted – this lists the search terms used and in which database they were used. This can enable the search to be replicated.

2.4 Selection of studies

Two researchers independently screened and identified paper title and abstracts for their relevance. After the initial screening those articles considered relevant were obtained. These remaining studies were further scrutinised according to the inclusion/exclusion criteria by the two reviewers so that they were finally included/excluded.

Data extraction forms designed for this review were used to extract data for inclusion in the results section of the review.

For the first part odds ratios, relative risk ratios, hazard ratios and prevalence ratios are extracted from meta-analyses and displayed in tables. Where papers only report odds ratios these will be converted to relative risk using the following formula [8]:

RR = OR / (1 – p + (p x OR))

Where p is equal to the risk in the control group. The costs gleaned from part B will then be applied to the population attributable fraction calculated with information from part A to arrive at the costs of these diseases that are attributable to ACEs.

Cost of illness were extracted for part B from relevant studies and put into tables for comparison. The aim was to glean information from different countries and extrapolate to give an idea of what the spending would be in the UK for each disease.

“Extrapolation from one country where data is available to another where no data is available gives an indication of the burden of a disorder in the latter country.”

(Gustavsson et al [9], p.725)

To this end any international data has been converted into GBP and inflated to 2020 figures.

4.1 Part A of the systematic review

From the initial systematic search for papers, 5,114 papers were found after removing duplicates. After screening titles and abstracts 76 papers were agreed upon by both reviewers to be included in the full text screening. Following the screening of full texts 51 papers were included for data extraction. This process is shown in a PRISMA flow chart in Figure 1.3 below.

4.2 Cancer

Fourteen studies were found that reported odds/relative risk/hazard/prevalence ratios of cancer [10,11,12,13,14,15,16,17,18,19,20,21,22,23]. Odds ratios are shown for different types of ACEs and for different ACE counts. These range from 0.93 for physical abuse and colorectal cancer [10], which means that suffering from physical abuse makes it less likely that this will lead to colorectal cancer to 2.4 for sexual abuse and cervical cancer [17] meaning that sufferers of sexual abuse are more likely to develop cervical cancer.

4.3 Circulatory disease

Seven studies were found for circulatory disease, which included heart disease, stroke and myocardial infarction [24,25,26,27,28,29,30]. Two studies were found for stroke, which reported the odds ratio of emotional abuse affecting the likelihood of stroke at 1.097 [24] and an odds ratio of 2.56 for 4 ACEs or more in Campbell et al [25] although this result was not statistically significant. Four studies discussed the odds ratios of ACEs affecting myocardial infarction ranging from 1.77 for the ACE of family member incarcerated in White et al [30] to an odds ratio of 4.67 for four or more ACEs in Campbell et al [25]. Two studies gave information on heart disease which included Dong et al [26] with an odds ratio of 3.6 for 7 to 8 ACEs and Fuller-Thompson [28] which reported an odds ratio of 1.57 for the effect of physical abuse on heart disease.

4.4 Mental health

Twenty-seven studies were included that discussed the odds/relative risk/hazard/prevalence ratios for mental health in terms of the likelihood that ACEs can partly cause certain mental illnesses [28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56]. The odds ratios reported range from 0.92 for the relationship between domestic violence and mental distress [40] to 15.47 for the relationship between sexual abuse and psychosis [36].

4.5 Muscoskeletal disease

Two studies investigated the relationship between muscoskeletal disease, here defined by arthritis, and ACEs. The first (Baiden et al [57]) calculated the odds ratio for the relationship between physical abuse and arthritis to be 1.36 and sexual abuse to be 1.74 while Luiz et al [58] reported the odds ratio of having four or more ACEs as 1.43.

4.6 Genitourinary disease

There were no studies identified in the review that discussed the odds ratios of the relationship between ACEs and genitourinary disease.

Table 1.1

Odds ratios from systematic review

	Author	Year	Disease	Country	Theme	ACE	Measure	Main outcome
Cancer	Alcala et al	2017	Colorectal cancer	US	Relationship between ACEs and CRC screening	Physical abuse	Odds Ratio	0.93
						Sexual abuse	Odds Ratio	1.18
						Emotional abuse	Odds Ratio	1.09
						Mentally ill	Odds Ratio	1.04
						Problem drinker	Odds Ratio	1.06
						Substance misuse	Odds Ratio	0.95
						Jailed	Odds Ratio	0.99
						Divorced	Odds Ratio	0.99
						Domestic violence	Odds Ratio	0.92
	Alcala et al	2017	Cancer	US	Relationship between ACEs and cancer	Physical abuse	Odds Ratio	1.31
						Sexual abuse	Odds Ratio	1.63
						Emotional abuse	Odds Ratio	1.34
						Mentally ill	Odds Ratio	1.36
						Problem drinker	Odds Ratio	1.22
						Substance misuse	Odds Ratio	1.52
						Divorced	Odds Ratio	1.08
						Domestic violence	Odds Ratio	1.19
	Amemiya et al	2019	Cancer	Japan	Relationship between ACEs and cancer	Any ACEs	Odds Ratio	1.26
						Number of ACEs	Odds Ratio	1.16
	Bellis et al	2014	Cancer	UK	Relationship between ACEs and 6 diseases inc cancer	All ACEs	Hazard ratio	2.38
	Brown et al	2010	Lung cancer	US	Relationship between ACEs and lung cancer	0	Risk ratio	1.00 (REFERENT)
						1	Risk ratio	0.73
						2	Risk ratio	1.48
						3	Risk ratio	3.10
						4 or 5	Risk ratio	2.55
						6,7,8	Risk ratio	3.18
	Felliti et al	1998	Cancer	US	Relationship between ACEs and heart attack, cancer, stroke, COPD and diabetes	0	Odds Ratio	1.00
						1	Odds Ratio	1.20
						2	Odds Ratio	1.20
						3	Odds Ratio	1.00
						4 or more	Odds Ratio	1.90
	Brown et al	2013	Cancer	US	Relationship between ACEs and cancer	Component 1	Odds Ratio	1.21
	Coker et al	2009	Cervical cancer	US	Relationship between ACEs and cervical cancer	Sexual abuse	Odds Ratio	2.40
	Fuller-Thompson et al	2005	Cancer	Canada	Relationship between ACEs and cancer	Physical abuse	Odds Ratio	1.45
						Parental unemployment	Odds Ratio	1.58
	Hyland et al	2013	Cancer	Saudi Arabia	Relationship between ACEs and cancer	Physical abuse once a year	Risk ratio	3.60
						Physical abuse every 2/3 days	Risk ratio	3.66
						Psychological abuse once a year	Risk ratio	1.38
						Psychological abuse once every 2/3 days	Risk ratio	4.05
	Hu et al	2021	Cancer	China	Relationship between ACEs and cancer	2 or 3 ACEs	Odds Ratio	1.35
						4 or more ACEs	Odds Ratio	2.17
	Hughes et al	2017	Cancer	UK	Relationship between ACEs and vaious conditions including cancer	4 or more ACEs	Odds Ratio	2.31
	Morton et al	2012	Cancer	US	Relationship between ACEs and cancer	Frequent psychological and physical abuse	Odds Ratio	Men - 3.558
	Pettrucelli et al	2019	Cancer	Various	Relationship between ACEs and various conditions including cancer	1	Odds Ratio	1.16
						2	Odds Ratio	1.09
						3	Odds Ratio	1.13
						4 or more ACEs	Odds Ratio	0.99
						Frequent psychological and physical abuse	Odds Ratio	Women - 2.184
Mental Health	Almeida et al	2011	Depression	Australia	Relationship between ACEs and depression	Physical abuse	Odds Ratio	3.25
						Sexual abuse	Odds Ratio	2.53
	Almuneef et al	2016	Depression	Kingdom of Saudi Arabia	Relationship between ACEs illnesses including depression	1	Odds Ratio	1.32
						2	Odds Ratio	2.11
						3	Odds Ratio	3.34
						4 or more	Odds Ratio	4.85
	Almuneef et al	2017	Depression	Kingdom of Saudi Arabia	Relationship between ACEs illnesses including depression	4 or more	Odds Ratio	7.00
	Anda et al	2007	Psychotropic medication	US	Relationship between ACE Score and prescriptions for psychotropic medications	0	Risk ratio	1.00 (referent)
						1	Risk ratio	1.3
							Risk ratio	1.6
						3	Risk ratio	1.6
	Al Shawi et al	2019	Depression	Iraq	Relationship between ACEs and depression	Emotional abuse	Odds Ratio	2.29
						Emotional neglect	Odds Ratio	2.78
						Physical abuse	Odds Ratio	1.71
						Physical neglect	Odds Ratio	2.13
						4	Risk ratio	2.3
						5+	Risk ratio	2.9
	Bebbington et al	2004	Psychosis	UK	Relationship between ACEs and psychosis	Sexual abuse	Odds Ratio	15.47
						Violence in the home	Odds Ratio	8.97
	Bielas et al	2016	Anxiety	Switzerland	Relationship between ACEs and anxiety in a cohort of young offenders	Total ACE score	Odds Ratio	1.68
			Depression		Relationship between ACEs and depression in a cohort of young offenders	Total ACE score	Odds Ratio	2.33
	Cambron et al	2014	Anxiety	US	Relationship between ACEs and anxiety	0	Odds Ratio	1.00 (referent)
						1	Odds Ratio	2
						2	Odds Ratio	2.3
						3	Odds Ratio	3
			Bipolar		Relationship between ACEs and bipolar	0	Odds Ratio	1.00 (referent)
						1	Odds Ratio	2.4
						2	Odds Ratio	3.7
						3	Odds Ratio	7.1
	Choi et al	2017	Depression	UK	Relationship between ACEs and depression	Psychological abuse	Odds Ratio	1.23
						Physical abuse	Odds Ratio	1.12
						Sexual abuse	Odds Ratio	1.12
						Emotional neglect	Odds Ratio	0.87
						Physical neglect	Odds Ratio	1.27
						Parental substance abuse	Odds Ratio	1
	Crouch et al	2017	Mental distress	US	Relationship between ACEs and mental distress	Physical abuse only	Odds Ratio	1.82
						Domestic violence only	Odds Ratio	0.92
						Emotional abuse only	Odds Ratio	1.91
	Fowler et al	2020	Depression	Ukraine	Relationship between ACEs and depression	1 or 2 ACEs	Odds Ratio	1.15
						3 or more ACEs	Odds Ratio	1.93
	Fuller-Thompson et al	2012	Anxiety	US	Relationship between ACEs and anxiety	Physical abuse	Odds Ratio	1.61
	Hughes et al	2016	Mental illness	UK	Relationship between ACEs and low mental wellbeing	0	Odds Ratio	1.00 (REFERENT)
						1	Odds Ratio	1.35
						2 to 3	Odds Ratio	1.946
						4+	Odds Ratio	3.856
	Hughes et al	2017	Depression	UK	Relationship between ACEs and various conditions including depression	4 or more ACEs	Odds Ratio	4.40
	Lee et al	2013	PTSD	US	Relationship between ACEs and mental health	Parental incarceration	Odds Ratio	1.88
			Anxiety				Odds Ratio	1.01
			Depression				Odds Ratio	1.56
	Lu et al	2008	PTSD		Relationship between ACEs and mental health	All ACEs	Odds Ratio	1.18
			Psychiatric hospitalisation	US			Odds Ratio	1.12
	Mersky et al	2013	Depression	US	Relationship between ACEs and mental health	0	Odds Ratio	1.00 (Referent)
						1	Odds Ratio	1.47
						2	Odds Ratio	2.01
						3 to 4	Odds Ratio	3.56
						5+	Odds Ratio	8.09
			Anxiety			0	Odds Ratio	1 (Referent)
						1	Odds Ratio	0.98
						2	Odds Ratio	2.29
						3 to 4	Odds Ratio	1.77
						5+	Odds Ratio	4.19
	Oladeji et al	2010	Anxiety	Nigeria	Relationship between ACEs and mental health	2 or more	Odds Ratio	0.9
	Pettrucci et al	2019	Depression	Various	Relationship between ACEs and various conditions including depression	1	Odds Ratio	1.64
						2	Odds Ratio	2.29
						3	Odds Ratio	3.02
						4 or more ACEs	Odds Ratio	4.78
	Porter et al	2020	Borderline Personality Disorder	Various	Relationshipbetween ACEs and BPD	Physical abuse	Odds Ratio	6.82
						Emotional abuse	Odds Ratio	31.41
						Sexual abuse	Odds Ratio	6.6
						Physical neglect	Odds Ratio	7.97
						Emotional neglect	Odds Ratio	22.97
	Raposo et al	2014	Anxiety	US	Relationship between ACEs and mental health	All ACEs	Odds Ratio	1.48
			Personality disorder				Odds Ratio	2.11
	Rhee et al	2019	Psychiatric disorder		Relationship between ACEs and psychiatric disorder	At least 1 ACE	Odds Ratio	2.11
	Roustit et al	2009	Depression	France	Relationship between ACEs and various conditions including depression	Witnessing parental violence	Odds Ratio	2.01
						Sexual abuse	Odds Ratio	2.03
	Sahle et al	2001	Depression	Various	Relationship between ACEs and mental health	Childhood maltreatment	Odds Ratio	2.02
			Anxiety			Childhood maltreatment	Odds Ratio	1.86
	Subica et al	2013	Depression	US	Relationship between ACEs and depression	Physical abuse	Odds Ratio	1.41
						Sexual abuse	Odds Ratio	2.5
	Von Cheong et al	2017	Depression	Ireland	Relationship between ACEs and depression	Any ACEs	Odds Ratio	2.85
	Xiang et al	2020	Depression	US	Relationship between ACEs and depression	Physical abuse	Hazard Ratio	1.67
Circulatory	Wilson et al	2012	Stroke	US	Relationship between ACEs and myocardial infarction	Emotional neglect	Odds Ratio	1.097
	Campbell et al*	2016	Myocardial Infarction	US	Relationship between ACEs and 6 diseases inc myocardial infarction	0	Odds Ratio	1.00
						1	Odds Ratio	1.07
						2	Odds Ratio	1.12
						3	Odds Ratio	1.78
						4 or more	Odds Ratio	1.86
			Stroke	US		0	Odds Ratio	2.34
						1	Odds Ratio	2.73
						2	Odds Ratio	2.27
						3	Odds Ratio	2.69
						4 or more	Odds Ratio	2.56
	Dong et al	2004	Ischemic Heart Disease	US	Relationship between ACEs and IHD	1	Odds Ratio	1.10
						2	Odds Ratio	1.20
						3	Odds Ratio	1.60
						4	Odds Ratio	1.70
						5 to 6	Odds Ratio	2.00
						7 to 8	Odds Ratio	3.60
	Fuller-Thompson et al	2010	Heart Disease	Canada	Relationship between ACEs and heart disease	Physical abuse	Adjusted Odds Ratio	1.57
	Fuller-Thompson et al	2012	Myocardial Infarction	Canada	Relationship between ACEs and myocardial infarction	Sexual abuse	Odds Ratio	2.96
	Jacquet‑Smailovic et al	2021	Myocardial Infarction	Various	Relationship between ACEs and Myocardial Infarction	Cumulative ACEs	Odds Ratio	1.88
	White et al	2016	Myocardial Infarction	US	Relationship between ACEs and Myocardial Infarction	Family member incarcerated	Odds Ratio	1.77
Muscoskeletal
	Baiden et al	2021	Arthritis	US	Relationship between ACEs and arthritis	Physical abuse	Adjusted Risk Ratio	1.36
						Sexual abuse	Adjusted Risk Ratio	1.74
	Luiz et al	2018	Arthritis	US	Relationship between ACEs and arthritis	4 or more ACEs	Odds Ratio	1.43

5.1 Part B of the systematic review

In the initial systematic search 17,936 papers were found after removing duplicates. After screening titles 373 papers were agreed upon by both reviewers to be included in the abstract screening. Following the screening of titles and abstracts a total of 246 papers were considered for full text screening of which 76 papers were included for data extraction. This is shown in a PRISMA flow chart in figure 1.4 below. The median cost for all diseases was £17,539 per person per year. Costs have been converted from respective currencies to GBP using exchange rates that prevailed at the cost year of each study. They are then inflated from the cost year to 2020 prices, the latest available data at the time of writing, using the Bank of England inflation calculator. As well as costs reported in terms of per person per year the estimated lifetime costs were also calculated. This was done by multiplying the cost with ten and the ten-year survival rates for each specific disease. Work will be done in the next chapter to extrapolate from specific disease types to whole diseases. Results have been stratified according to disease and are summarised below.

5.2 Cancer

Thirty studies were found that reported costs of cancer [59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88]. The highest annual cost was in US for bladder cancer at £711,873 per person per year [73] while the lowest cost was for prostate cancer, in Sweden, at £1,458 per person per year [79].

5.3 Circulatory disease

Twenty-three studies looked at the costs of circulatory disease, defined here as heart disease, stroke and myocardial infarction. Costs for heart failure range from £1,904 to £84,998 per person per year [89,90,91,92,93,94,95,96]. Only direct costs were reported in these studies (except [92]) and these total costs would be higher if indirect costs were included.

For stroke fourteen studies were identified reporting costs that range from £1,629 to £149,087 [97,98,99,100,101,102,103,104,105,106,107,108,109,110].

Only one study was found for the costs of myocardial infarction. It was produced by Baustein et al [111] and reported an annual cost per person of £200,054

5.4 Mental health

Sixteen studies [112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127] were included that discussed the costs of mental illness and psychotic disorders had the greatest per patient yearly cost at £87,018 [116]. The lowest cost was for brain disorders in Italy with a cost of £709 [124]. However, it must be noted that this is a cost per citizen rather than the cost per case of brain disorder.

5.5 Muscoskeletal

Six studies were found for the costs of arthritis [128,129,130,131,132,133]. The costs ranged from £3,000 per patient per year for rheumatic disorders [132] to £33,508 for arthritis [129].

5.6 Genitourinary

No studies were found for the costs of genitourinary disease.

Table 1.2

Cost of disease estimates from systematic review

	Author	Cost Year	Disease	Currency	Country	Direct costs per person	Indirect costs per person	Costs per person	£	Inflated to 2020 (£)	Perspective		Lifetime Costs (£)
	Andreas et al	2013	Lung cancer	Euro	UK	8,377	1,414	9,791	8,314	9,744		n/a	9,744
	Babela et al	2020	Multiple Myeloma	Euro	Slovakia	155,645	127,611	283,256	252,060	252,060		Regulatory	2,192,922
	Brodsky et al	2017	Prostate cancer	Euro	Hungary	4,448	n/a	4,448	3,899	4,195		Payer	32,721
Cancer	Broekx et al	2011	Breast cancer	Euro	Belgium	12,037	95,419	107,456	70,814	127,434		Societal	968,498
	Cicin et al	2021	Lung cancer	Euro	Turkey	10,167	n/a	10,167	8,757	8,757		Payer	68,305
	Damm et al	2012	Colorectal cancer	Euro	Germany	8,750	n/a	8,750	7,098	8,572		n/a	66,862
			Breast cancer	Euro	Germany	4,300	n/a	4,300	3,488	4,212		n/a	32,011
			Prostate cancer	Euro	Germany	4,750	n/a	4,750	3,853	4,653		n/a	36,293
	Doran et al	2010	Melanoma	Australian Dollars	Australia	44,796	n/a	44,796	36,339	47,650		n/a	414,555
	Lingren et al	2002	Breast cancer	SEK	Sweden	135,135	317,077	452,212	31,022	48,213		n/a	366,419
	Tilson et al	2008	Colorectal cancer	Euro	Ireland	39,607	n/a	39,607	31,531	8,038		Healthcare Payer	45,817
	Seung et al	2017	Lung cancer	Canadian Dollars	Canada	76,816	n/a	76,816	45,989	49,476		n/a	49,476
	Bencina et al	2011	Melanoma	Euro	Croatia	88-4333	n/a	2,123	1,843	2,146		Budget Holder	18,670
	Gustavsen et al	2020	Prostate cancer	US Dollars	US	188,928	n/a	188,928	167,198	174,062		n/a	154,915
	Kontoudis et al	2014	Melanoma	GBP	UK	31,123	1,427	32,550	32,550	37,268		Healthcare Provider	331,685
	Bending et al	2005	Colorectal cancer	GBP	England	20,117	n/a	20,117	20,117	28,698		n/a	163,579
	Jung et al	2011	Bladder cancer	US Dollars	US	658,055	n/a	658,055	571,148	711,873		n/a	327,462
	Neves et al	2018	Multiple Myeloma	Euro	Portugal	31,449	n/a	31,449	27,832	28,975		NHS	252,083
	Ray et al	2010	Melanoma	US Dollars	US	281,112	n/a	281,112	182,017	238,672		n/a	2,076,446
			Breast cancer	US Dollars	US	236,496	n/a	236,496	153,129	200,792		n/a	1,526,019
			Lung cancer	US Dollars	US	204,084	n/a	204,084	132,143	173,274		n/a	173,274
	Ekwueme et al	2016	Breast cancer	USD	US	n/a	2,293	2,293	1,467	1,606		n/a	12,206
	Petrucci et al	2008	Multiple Myeloma	Euro	Italy	19,267	n/a	19,267	15,338	20,928		Hospital	182,074
	Fourcade et al	2010	Prostate cancer	Euro	UK	3,682	n/a	3,682	2,931	3,736		Healthcare Payer	29,141
	Tingstedt et al	2007	Pancreatic Cancer	Euro	Sweden	16,066	n/a	16,066	10,997	15,604		n/a	7,802
	Hao et al	2016	Prostate cancer	Euro	Sweden	1,510	271	1,781	1,458	1,625		Societal	12,675
	Leal et al	2016	Bladder cancer	Euro	UK	32,615	29,313	61,928	50,236	56,673		n/a	283,365
			Cancer			19,209	38,585	57,793	46,882	52,890		n/a	264,450
	Stokes et al	2008	Pancreatic Cancer	US Dollars	US	34,432	n/a	34,432	27,410	37,400		Payer	18,700
	Gerace et al	2017	Bladder cancer	Euro	Italy	3,591	n/a	3,591	3,148	3,387		Societal	15,580
	Geenen et al	2017	Prostate cancer	Euro	Netherlands	17,931	n/a	17,931	14,461	16,557		n/a	129,145
	Vallejo-Torres et al	2008	Melanoma	GBP	England	2,607	n/a	2,607	2,607	3,557		n/a	30,946
	Yue et al	2020	Ovarian Cancer	US Dollars	US	13,566	n/a	13,566	8,239	9,433		n/a	33,016
			Uterine Cancer	US Dollars	US	6,852	n/a	6,852	4,162	4,765		n/a	34,308
			Cervical Cancer	US Dollars	US	2,312	n/a	2,312	1,404	1,607		n/a	8,196
	Verleger et al	2018	Lung cancer	GBP	England	17,761	n/a	17,761	17,761	18,490		n/a	18,490
	Tinghog et al	2008	Skin cancer	Euro	Sweden	2,074	1,636	3,710	2,545	362		Societal	3,149
	Biermann et al	2009	Heart failure	Euro	Germany	3,150	n/a	3,150	2,809	3,600		Societal	10,620
	Czech et al	2013	Heart failure	PLN	Poland	7,739	n/a	7,739	1,635	1,904		Public payer	5,617
Circulatory	Pavlusova et al	2018	Heart failure	CZK	Czech Republic	85,414	n/a	85,414	72,525	84,998		Healthcare System	250,744
Heart	Delgado et al	2014	Chronic heart failure	Euro	Spain	18,220	n/a	18,220	15,642	19,161		Societal	56,525
	Bungaard et al	2016	Heart failure	Euro	Denmark	11,926	5,113	17,039	13,952	15,548		n/a	45,867
	Stalhammar et al	2012	Heart failure	SEK	Sweden	72,613	n/a	72,613	58,904	71,133		n/#	209,842
	Baustein et al	2012	Myocardial Infarction	CZK	Czech Republic	204,217	n/a	204,217	165,662	200,054		n/a	600,162
	Kruse et al	2008	Heart disease	Euro	Denmark	3,195	n/a	3,195	2,187	3,734		n/a	11,015
	Alvarez-Sabine et al	2017	Stroke	Euro	Spain	27,134	276	27,410	22,235	25,084		Societal	80,269
	Asil et al	2008	Stroke	US Dollars	Turkey	1,677	n/a	1,677	1,148	1,629		n/a	5,213
	Bottachi et al	2012	Stroke	Euro	Italy	9,044	n/a	9,044	7,337	8,860		Healthcare System	28,352
	Girotra et al	2016	Stroke	US Dollars	US	4,317	n/a	4,317	3,197	3,563		n/a	11,402
	Gloede et al	2020	Stroke	US Dollars	Australia	5,207	n/a	5,207	4,059	4,059		Societal	12,989
	Godwiin et al	2010	Stroke	US Dollars	US	34,162	n/a	34,162	22,120	22,120		Insurer	70,784
	Chinthammit et al	2012	Stroke	US Dollars	US	18,796	n/a	18,796	11,863	14,326		n/a	45,843
	Patel et al	2015	Stroke	GBP	UK	45,409	n/a	45,409	45,409	51,481		Societal	164,739
	Jakobsen et al	2012	Stroke	US Dollars	Denmark	19,989	n/a	19,989	12,616	15,235		Societal	48,752
	Demaerschalk	2012	Stroke	US Dollars	US	74,353	n/a	74,353	46,928	56,670		n/a	181,344
	Lopez-Bastida et al	2004	Stroke	Euro	Canary Islands	15,691	1,926	17,617	11,957	17,539		Societal	56,125
	Snozzi et al	2005	Stroke	Euro	Switzerland	40,090	n/a	40,090	27,445	41,909		n/a	134,109
Stroke	Saka et al	2009	Stroke	GBP	UK	79,428	25,083	104,511	104,511	149,087		Societal	477,078
	Smith et al	2012	Stroke and transient ischemic attack	Euro	Ireland	12,186	5,065	17,251	11,808	15,652		Societal	50,086
	Roodenrijs et al	2019	Arthritis	Euro	Netherlands	37,605	n/a	37,605	33,013	33,508		n/a	268,064
	Klimes et al	2013	Arthritis	Euro	Czech Republic	8,968	2,307	11,275	9,574	11,221		n/a	89,768
	Eriksson et al	2015	Arthritis	Euro	Sweden	23,147	n/a	23,147	19,873	26,059		n/a	208,472
Muskosceletal	Hamuryudan et al	2016	Arthritis	Euro	Turkey	4,954	2,802	7,756	6,732	8,391		n/a	67,128
	van den Akker-van Marle	2012	Rheumatic Disorders	Euro	Netherlands	2,665	n/a	2,665	2,288	3,000		Societal	24,000
	Turchetti et al	2013	Arthritis	Euro	Italy	13,595	n/a	13,595	11,800	14,707		n/a	117,656
	Bode et al	2017	Borderline Personality Disorder	Euro	Germany	8,508	n/a	8,508	7,384	9,203		n/a	29,450
	Sobocki et al	2007	Depression	Euro	Sweden	1,900	3,600	5,500	3,765	5,371		Societal	32,226
	Ekman et al	2013	Depression	Euro	Sweden	21,500	n/a	21,500	17,115	23,353		Primary Care	142,453
	Ekman et al	2013	Bipolar	Euro	Sweden	21,008	7,003	28,011	22,300	28,426		Societal	90,963
	Ekman et al	2013	Schizophrenia	Euro	Sweden	55,100	n/a	55,100	43,863	59,850		n/a	191,520
Mental Health	Evensen et al	2012	Schizophrenia	US Dollars	Norway	106,000	n/a	106,000	66,902	80,791		n/a	258,531
	Neil et al	2014	Psychotic disorders	US Dollars	Australia	36,356	40,941	77,297	66,362	87,018		Societal	278,458
	Gustavsson et al	2011	Anxiety	Euro	UK	n/a	n/a	1,426	1,224	1,499		Societal	4,797
			Personality disorders			n/a	n/a	9,613	8,253	10,110		Societal	32,352
			Psychotic disorders			n/a	n/a	28,487	24,456	29,957		Societal	95,862
	Soeteman et al	2008	Personality disorders	Euro	Netherlands	7,399	3,727	11,126	7,617	10,865		Societal	34,768
	Rovira et al	2012	Anxiety	Euro	Spain	1,329	3,810	5,139	3,504	5,185		Societal	16,592
	Tanner et al	2020	Depression	Canadian Dollars	Canada	8,244	n/a	8,244	12,152	19,080		Public Payer	116,388
	Chevreul et al	2013	Mental disorders	Euro	France	1,642	2,033	3,675	2,516	3,334		Societal	10,669
	Pletscher et al	2014	Schizophrenia	Euro	Switzerland	14,300	25,108	39,408	31,968	38,605		Societal	123,536
	Wagner et al	2014	Personality disorders	Euro	Germany	28,026	n/a	28,026	24,060	31,549		n/a	100,957
	Cai et al	2016	Depression	US Dollars	US	19,626	n/a	19,626	16,070	17,908		n/a	109,239
	Pugliatti et al¹	2008	Brain disorders	Euro	Italy	297	409	706	483	709		Societal	2,269

This study reports the results of a systematic search for studies that consider the link between certain ACEs and subsequent disease and report this link in terms of a risk or odds ratio. This was done by searching for relevant studies in the different disease fields identified. In addition to this an estimate of the costs of diseases is also identified. The odds ratios varied in terms of how certain ACEs affect the likelihood of developing certain diseases. The highest odds ratio was 15.47 for the relationship between sexual abuse and psychosis, which provides evidence that there is a very strong relationship and that the prevalence of psychosis could be substantially lowered if effective interventions to tackle sexual abuse were successful. There were some odds ratios that were less than 1 which indicates that having that particular ACE reduces the odds of developing the disease. This was the case for domestic violence affecting mental distress at 0.92 [40], domestic violence affecting lung cancer at 0.92 [11] and having 1 ACE affecting lung cancer at 0.73 [14]. This may suggest that interventions aimed at reducing domestic violence, for example, may not lead to reductions in the disease prevalence. There is no clear association.

In terms of costs the evidence is as varied. The median lifetime cost per annum associated with having these diseases was £17,539 per person per year. Where costs for certain diseases from certain countries seem low it can usually be attributed to the fact that only direct hospital costs or indirect costs such as loss of productivity have been measured and not both types of costs.

The information gathered in this review will in theory then enable the calculation of the population attributable fractions (PAF) of certain ACEs as they relate to the disease in question. These PAFs, when applied to the total cost of diseases, will then allow, using the costs identified in this review, the estimation of the lifetime costs associated with ACEs.

Cost information is essential for conducting economic evaluations of health interventions to reduce the prevalence of ACEs. The aim of this study is to make it possible to describe the costs that are attributable to ACEs of the diseases identified here. It was decided that specific diseases should be investigated as they come from the ‘biological embedding’ or direct impacts of ACEs on physical health in adulthood. Diseases were looked at rather than health harming behaviour because there is an established literature on the topic of health harming behaviour [13]. This review has provided the available published information on odds ratios and costs of diseases as they relate to ACEs. However, there was some surprise as to the paucity of evidence especially in terms of the cost and the odds ratios for genitourinary disease. As mentioned, using the information in this systematic review will enable analysts to calculate the attributable costs of ACEs in order to ascertain the costs associated with ACEs when it comes to specific diseases.

Ethics approval and consent to participate

– n/a

Consent for publication

– n/a

Availability of data and materials

– n/a

Competing interests

– n/a

Funding

– Public Health Wales

Authors' contributions

– Huw Lloyd-Williams wrote the manuscript and conducted the review with Llinos Haf Spencer acting as second reviewer. RT Edwards provided advice.

Acknowledgements

I would like to than my PhD supervisors Prof. Rhiannon Tudor Edwards and Prof. Mark Bellis for guidance. Also I’d like to thank Llinos Haf Spenser for her assistance and her work on this paper. Lastly I’d like to thank Public Health Wales for funding.

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Systematic review of the costs of adverse childhood experiences (ACEs): presenting the evidence on odds ratios and costs.

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Abstract

Figures

1 Background

2 Methodology

2.1 Inclusion and exclusion criteria – Part A of systematic review

2.2 Inclusion and exclusion criteria – Part B of systematic review

2.3 Types of outcome measures

2.4 Selection of studies

3 Data Extraction

4 Results – Part A

4.1 Part A of the systematic review

4.2 Cancer

4.3 Circulatory disease

4.4 Mental health

4.5 Muscoskeletal disease

4.6 Genitourinary disease

5 Results – Part B

5.1 Part B of the systematic review

5.2 Cancer

5.3 Circulatory disease

5.4 Mental health

5.5 Muscoskeletal

5.6 Genitourinary

6 Discussion

7 Conclusion

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

References

Additional Declarations

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