Indigenous Children

summary 2

SY S T EMA TIC R EVI EW Open Access
Psychosocial factors associated with the
mental health of indigenous children living
in high income countries: a systematic
review
Christian Young1,2*, Camilla Hanson1,2, Jonathan C. Craig1,2, Kathleen Clapham3 and Anna Williamson4
Abstract
Background: Indigenous children living in high income countries have a consistently high prevalence of mental
health problems. We aimed to identify psychosocial risk and protective factors for mental health in this setting.
Methods: A systematic review of studies published between 1996 and 2016 that quantitatively evaluated
the association between psychosocial variables and mental health among Indigenous children living in high
income countries was conducted. Psychosocial variables were grouped into commonly occurring domains.
Individual studies were judged to provide evidence for an association between a domain and either good
mental health, poor mental health, or a negligible or inconsistent association. The overall quality of evidence
across all studies for each domain was assessed using the Grades of Recommendation, Assessment,
Development, and Evaluation (GRADE) guidelines.
Results: Forty-seven papers were eligible (mainland US 30 [64%], Canada 8 [17%], Australia 7 [15%], Hawaii 4
[9%]), including 58,218 participants aged 4–20 years. Most papers were cross-sectional (39, 83%) and measured
negative mental health outcomes (41, 87%). Children’s negative cohesion with their families and the presence
of adverse events appeared the most reliable predictors of increased negative mental health outcomes. Children’s
substance use, experiences of discrimination, comorbid internalising symptoms, and negative parental behaviour also
provided evidence of associations with negative mental health outcomes. Positive family and peer relationships, high
self-esteem and optimism were associated with increased positive mental health outcomes.
Conclusions: Quantitative research investigating Indigenous children’s mental health is largely cross-sectional
and focused upon negative outcomes. Indigenous children living in high income countries share many of
the same risk and protective factors associated with mental health. The evidence linking children’s familial
environment, psychological traits, substance use and experiences of discrimination with mental health
outcomes highlights key targets for more concerted efforts to develop initiatives to improve the mental
health of Indigenous children.
Keywords: Indigenous, Children, Adolescent, Mental health, Psychosocial, Review
* Correspondence: christian.young@sydney.edu.au 1
Sydney School of Public Health, The University of Sydney, Edward Ford
Building (A27), Fisher Road, Camperdown, NSW 2006, Australia
2
Centre for Kidney Research, Westmead Institute for Medical Research, 179
Hawkesbury Rd, Westmead, NSW 2145, Australia
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Young et al. International Journal for Equity in Health (2017) 16:153
DOI 10.1186/s12939-017-0652-5
Background
Indigenous children living in high income countries such
as Australia, New Zealand, Canada and the United States
(US) are disproportionately affected by mental health
problems when compared to their non-Indigenous counterparts [1–5]. Childhood mental health disorders such as
anxiety, depression and externalising behaviours are associated with a range of negative outcomes that are overrepresented in Indigenous communities, including high rates
of suicidal ideation and completion [6, 7]. The long-term
sequelae of poor childhood mental health is believed to
significantly contribute to negative health and social outcomes that occur throughout the lifespan [8].
While the aetiology of childhood mental health disorders is likely to involve multiple determinants, the impact
of European colonisation constitutes an additional, pervasive risk factor for Indigenous children living in Australia,
New Zealand, Canada and the US. For these children, colonisation and subsequent cultural marginalisation are believed to be the “cause of causes” [9], impacting negatively
on children’s mental health through low socio-economic
families and communities, experiences of discrimination,
and exposure to the psychological effects of intergenerational trauma and inequality [10].
Given that Indigenous populations share a history of
colonisation, research that investigates common correlates of mental health may help to strengthen the evidence base, and contribute to the development of
effective health interventions. To date, there has been
little research that assesses risk and protective factors
among multiple Indigenous cultures. The aim of this
systematic review is to identify modifiable psychosocial
risk and protective factors, common to Indigenous children living in Australia, New Zealand, Canada and the
US that are associated with mental health outcomes typically experienced during childhood and adolescence.
The results may aid the design of initiatives to improve
the mental health of Indigenous children, reduce health
disparities, and identify areas for further research.
Methods
We followed the Meta-analysis of Observational Studies
in Epidemiology (MOOSE) guidelines to conduct this
systematic review [11].
Study inclusion and exclusion criteria
Peer-reviewed, English language studies that reported
quantified relationships between psychosocial variables
and mental health outcomes in Indigenous children were
eligible. School-aged samples (mean ages between 5 and
18 years) from the four ‘CANZUS’ (Canada, Australia,
New Zealand, United States) countries were included,
with studies including participants over 21 years excluded.
Given differences in the environmental and social
challenges Indigenous populations living within the Arctic
Circle experience compared to other Indigenous communities, studies involving these populations were excluded
[12]. Studies investigating multiple ethnic groups were included if a separate quantitative analysis was provided for
the Indigenous sample.
Due to the potential of evolving social and political
landscapes to effect changes in the health of Indigenous
minority groups, only papers published in the last
20 years (1996 to January 2016) were included. In keeping with this review’s focus of modifiable factors associated with mental health, studies measuring congenital
disorders or mental disability were excluded. Given
current controversies surrounding the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) [13], associations between psychosocial variables and an ADHD
diagnosis were not included.
Symptoms of mental health vary considerably in both
presentation and severity. This review focused on commonly measured aspects of mental health that are relevant from early childhood to late adolescence and across
a range of cultures. These included externalising and
internalising disorders, and measures of positive mental
health such as self-esteem [14]. In keeping with this
focus, outcomes that were more serious, rare and less
likely to be observed across the relevant age range such
as eating disorders, delinquency and suicidal ideation
and completion were excluded [15–18]. Studies that
used recruitment strategies that led to over-sampling
high risk populations were not included.
Search strategy
The first author (CY) conducted the literature search
using MEDLINE, PsychINFO, Embase, and Scopus databases. Results were retrieved in February, 2016. Details
of the literature search are available online (Additional
file 1: Appendix A). Author CY screened papers for eligibility by reading abstracts and, where necessary, the full
text. A second reviewer (CH) independently read 25% of
the papers and compared her findings with the first author. Disagreements were resolved by discussion. Of the
159/492 (25%) papers independently assessed by the first
and second author, four discrepancies were detected;
however on closer inspection all of these papers met exclusion criteria and no further papers were assessed by
the second author. Reference lists were examined from
included papers to identify potentially eligible studies.
Definition of variables
Psychosocial variables
Psychosocial variables were defined as any quantifiable
measure of children’s characteristics, and their family
and community environments. These were grouped into
commonly occurring domains (e.g. socioeconomic
Young et al. International Journal for Equity in Health (2017) 16:153 Page 2 of 17
status). Domains were further grouped by individual,
family and community level. Individual-level domains relate to children’s traits, attitudes or abilities; family-level
domains relate to the family/household environment, including parent’s characteristics and relationships with
children; community-level domains relate to children’s
neighbourhood and broader community, including peer
relationships and school-based variables. Domains that
were measured in fewer than four papers were not included in this analysis. This arbitrary rule was decided
by the authors in order to include domains that were
likely to provide sufficient data for comparison and
evaluation purposes. The list of domains and their definitions are given below:
Individual-level domains
Optimism
Measured children’s optimistic view of their future and
optimistic explanatory styles.
Positive attitudes towards school
Measured children’s positive view of school including
feelings of school membership.
Self-efficacy
Measured children’s belief in their ability to achieve specific goals.
Self-esteem
Measured children’s concept of their own self-worth.
Identification with white culture
Measured the extent that Indigenous children saw themselves adopting or adapting to White cultural practices.
This domain was measured primarily with ethnic identification scales. For example, the Orthogonal Cultural
Identification Scale (OCIS) [19] or the Bicultural Ethnic
Identity Scale [20].
Scholastic ability
Measured children’s academic achievement or general
cognitive ability. Grade Point Average (GPA) scores were
the most commonly used measure for this domain.
Identification with indigenous culture
Measured children’s identification with their own Indigenous culture. This domain was primarily measured
with ethnic identification scales (e.g. the OCIS), or by
assessing children’s knowledge of their Indigenous culture or language.
Substance use
Measured children’s use of illegal drugs and alcohol (tobacco use was not included).
Externalising
Measured antisocial, aggressive and oppositional behaviours.
Internalising
Measured internalising symptoms including anxiety, depression, withdrawn behaviour and suicidal ideation.
Adverse events
Measured children’s exposure to events likely to cause
substantial stress (e.g. abuse, neglect) or significant disruption to children’s lives (e.g. the loss of a close family
member).
Family-level domains
Family cohesion (positive): Measured the quality of relationships children experienced within their immediate
family including measures of family support and positive
parenting styles.
Low family SES
Measured indices of socio-economic status (SES) including family income, caregiver’s education and occupation,
household occupancy level and housing quality/tenure.
Atypical family structure
Measured whether children were raised by single caregivers or by family members other than the children’s
parents (e.g. aunts, uncles or grandparents).
Caregiver’s mental health/behaviour (negative)
Included measures of caregiver’s mental health problems, criminal activity, domestic violence and substance
abuse.
Family cohesion (negative)
Measured poor relationships children had with their
family, and harsh parenting practices.
Community-level domains
Peer support
Measured the presence and quality of prosocial relationships children had with their peers.
Community cohesion (negative)
Measured negative elements within the children’s community including violent or criminal activity in neighbourhood or school environments.
Discrimination
Measured children’s experiences of racial discrimination.
Bullying
Measured whether children had experienced recent
bullying.
Young et al. International Journal for Equity in Health (2017) 16:153 Page 3 of 17
Mental health outcomes
We defined mental health outcomes as any internalising
or externalising symptom, and/or measure of positive
mental health typically associated with school-aged children. Internalising disorders describe adverse mental
health states that are inner-directed, including depression, anxiety, and withdrawal [21]. In contrast, externalising disorders are outer-directed and manifest as
maladaptive behavioural problems including antisocial,
oppositional and aggressive behaviour [22].
Positive mental health outcomes included measures of
self-esteem, positive affect and resilience. Resilience is
commonly defined as positive adaption in the presence
of adversity [23]. Studies that measured associations between psychosocial variables and mental health outcomes in conjunction with elevated levels of adversity
were deemed to measure ‘resilient’ mental health. For
example, Hopkins et al. [24] divided a sample of Australian Aboriginal children into ‘low’ and ‘high’ risk groups
based on the number of adversities experienced. Children in the high-risk group who showed good mental
health outcomes (as measured by the Strengths and Difficulties Questionnaire) [25] were considered resilient.
Studies that did not include a measure of adversity or a
validated resilience scale were not deemed to measure
resilience. A separate summary of the psychosocial variables that were associated with resilient mental health is
given in the results.
Mental health measures that combined internalising,
externalising or positive mental health outcomes were
described as ‘Global’ measures of mental health. For example, the Strength and Difficulties Questionnaire uses
measures of ‘conduct problems’ (externalising), ‘emotional symptoms’ (internalising) and ‘prosocial behaviour’ (positive mental health) to calculate a global
measure of children’s mental health.
In order to assess comorbidity between mental health
outcomes, externalising, internalising and self-esteem
constitute both predictor variables (domains) and outcomes (mental health) in this review.
Data extraction strategy
Bivariate and multivariable analyses of a domain’s association with mental health were extracted from each
study, including the statistic used, the magnitude and
direction of association, the p-value and the confidence
interval (where given). When path analysis was
employed, only associations from the best fitting model
were included. Similarly, when multiple statistical
models progressively introduced confounders, only statistics from the final modal were included. Longitudinal
and cross-sectional data were both included. Interactions
were not recorded; however, because the construct of resilience can be observed through statistical interactions
between levels of adversity and other predictor variables,
interactions that were deemed to measure resilient mental
health were included. When multiple papers reported results from the same study, variables measuring the same
domain were treated as belonging to a single study.
Data synthesis and presentation
The aim was to determine the associations between psychosocial variables and childhood mental health outcomes. Due to the considerable heterogeneity in how
these variables were conceptualised and measured, and
in the statistical methods employed to assess relationships, calculation of summary estimators (meta-analysis)
was neither possible nor appropriate. Instead, a twostage process was used to assess the strength of association between psychosocial variables and mental health.
The first stage involved making an overall judgement
whether an individual study provided evidence for an association between a domain and: good mental health,
poor mental health, or showed a negligible or inconsistent association. The second stage involved assessing the
quality of evidence associating each domain with mental
health, as measured by multiple studies, using the
Grades of Recommendation, Assessment, Development,
and Evaluation (GRADE) [26].
Individual studies
Each study was independently assessed by two authors
(CY, CH) to ascertain whether it provided evidence for
an association between a psychosocial domain and: good
mental health, poor mental health, or a negligible or inconsistent association. When only one association between a psychosocial domain variable and a mental
health outcome was reported in a single study, statistical
significance was used to determine evidence for an association. When domains were measured by more than
one psychosocial variable and/or multiple mental health
outcomes were used within a single study; the number
of statistically significant associations, the magnitude
and direction of effects and the number of comparisons
were all considered before making a judgement regarding an association. Measures of both positive (e.g. selfesteem) and negative (e.g. depression) mental health
were considered together in order to determine the
overall association between domain variables and mental
health. Disagreements were resolved via discussion.
Study quality assessment
We used the Grades of Recommendation, Assessment,
Development, and Evaluation (GRADE) guidelines to
rate the quality of evidence within each domain. The
GRADE guidelines rate evidence as being ‘very low’, ‘low’,
‘moderate’ or ‘high’ depending on four categories of investigation: risk of bias, inconsistency, indirectness, and
Young et al. International Journal for Equity in Health (2017) 16:153 Page 4 of 17
if reasons to rate up the strength of evidence exist. The
GRADE category of ‘Imprecision’ was not assessed given
the relatively small number of studies that reported confidence intervals. The GRADE category of ‘Indirectness’
was also not assessed given that relevant inclusion criterion were matched directly to the research question. Observational studies start at ‘low’ quality and could be
rated up or down depending on the quality of evidence.
In accordance with the GRADE recommendations, domains that had been rated down for any reason were not
eligible to be rated up. Two authors (CY, CH) independently assessed all elements of the GRADE evidence profile, discrepancies were resolved by discussion.
Risk of bias
Risk of bias was first assessed in individual papers using
the Newcastle-Ottawa Scale (NOS) adapted for crosssectional studies [27]. This scale measures potential
sources of bias on a 10-point scale. Risk of bias is
deemed to be present if the sample size is not justified
or unsatisfactory [28], if the sample is unrepresentative
of the target population, if inappropriate or un-validated
measurement tools have been used, if theoretically important variables were not controlled for (socioeconomic
status, and age and gender), and if inappropriate or unclear statistical tests were employed. We set the following criteria for judging risk of bias: 9–10 points = low
risk; 7–8 points = medium risk; ≤6 points = high risk.
Domains that included a majority of high risk studies
were considered to be at serious risk of bias and were
rated down.
Inconsistency
Inconsistency was deemed to be present when large differences between point estimates and/or confidence
interval ranges were observed among studies that measured the same psychosocial domain. Domains were always rated as inconsistent if different studies measuring
the same domain produced statistically significant but
conflicting associations with mental health outcomes
(note: this did not include negligible associations).
Rating up the quality of evidence
Provided that there were no reasons to rate evidence
down, the quality of evidence for each domain could be
rated up if: the majority of studies reported medium or
large effect sizes, if a dose-gradient effect was observed,
or if the majority of studies controlled for confounding
variables that could plausibly reduce the magnitude of
the effect. We followed conventional rules of thumb for
effect sizes [29] and deemed medium effect sizes as:
Cohen’s d = .5, zero-order correlation coefficient r = |.3|,
and odds ratios = 2 or .5; large effect sizes were defined
as Cohen’s d = .8, zero-order correlation coefficient
r = |.5|, and odds ratios = 5 or.2. All other statistics were
interpreted within the context of the study.
Using the above heuristics two researchers (CY, CH)
independently appraised the effect sizes reported in each
study. Effect sizes were rated as being ‘small’, ‘medium’,
‘large’, ‘negligible’ or ‘inconsistent’. When more than one
statistic was reported, a summary of the range of effect
sizes was recorded, outliers were excluded. Using the
same method, a qualitative summary of the range of effect sizes, per domain, was made by the researchers, disagreements were resolved by discussion.
For example, a study by Whitbeck et al. [30] investigated substance use among American Indian children.
In this case the domain, ‘substance use’ is indicated by
three variables: “alcohol problems”, “alcohol abuse” and
“number of substances used in the past month”. Mental
health was indicated by measures of withdrawal, somatic
complaints and anxiety/depression (all symptoms of
internalising). This study provided three independent
variables and three dependent variables, yielding nine associations between the domain ‘substance use’ and mental health. The variable “number of substances used in
the past month” was found to be significantly correlated
with mental health variables: “somatic symptoms” and
“anxiety/depression” (r’s = .16 and .27, respectively). All
other correlations were positive but non-significant.
Given the absence of conflicting evidence, and the two
significant correlations, this paper is deemed to have
provided evidence of an association between the domain
‘substance use’ and poor mental health.
After appraising all other studies measuring the domain ‘substance use’, 8/9 studies measuring this domain
were deemed to provide evidence for an association with
poor mental health. Using the GRADE guidelines the
quality of evidence was rated up from ‘low’ to ‘moderate’
due to the majority of studies that adjusted for confounding factors and the absence of any reason to rate
down.
Results
Review statistics
Forty-seven papers were included in the review. Figure 1
presents the results of the literature search.
The majority of papers reported on studies conducted
in the US (mainland; 30 papers, 64%) with Native
American samples, 8 papers (17%) involved Indigenous
Canadian samples (two papers assessed both US mainland and Canadian Indigenous samples), 7 papers (15%)
involved Indigenous Australian children, and 4 (9%) papers involved Indigenous Hawaiian children. No studies
from New Zealand met inclusion criteria. All studies
were observational; 39 papers (83%) used a crosssectional design, 8 (17%) used a longitudinal design or a
mixture of longitudinal and cross-sectional designs.
Young et al. International Journal for Equity in Health (2017) 16:153 Page 5 of 17
Participants’ ages ranged from 4 to 20 years. Most studies included children aged between 11 and 18 years (i.e.
middle and/or high school-aged children). Sample sizes
ranged from 65 to 13,454 participants. Measures of
negative mental health outcomes were the most commonly assessed, measured in 41 (87%) papers. Internalising symptoms were measured in 27 papers (57%),
externalising symptoms were measured in 14 papers
(30%), global measures of mental health were measured
in 14 papers (30%), and positive mental health was measured in 13 papers (28%). Domains that appeared in the
search but were measured in fewer than four papers included: physical health, historical loss, religious involvement, level of isolation, social skills and self-regulation.
The number of publications was seen to increase over
time with half of the papers published between 2011 and
January 2016 (the last five years of the review’s 20-year
timeframe).
Individual-level domain variables were reported in 40
papers (85%), family-level domain variables were measured in 25 papers (53%) and community-level domain
variables were measured in 22 papers (47%). The median
number of associations between a single psychosocial
domain and mental health outcome per paper was two
(interquartile range: 3). Table 1 provides a summary of
the included papers.
Study quality assessment
Figure 2 presents the results of the Newcastle-Ottawa
scale assessment. Scores ranged from 4 to 10 (median:
7). 12 papers (26%) were judged to have low risk of bias,
21 papers (45%) were judged to have medium risk of
bias, and 14 papers (30%) were judged to have high risk
of bias. 23 papers (49%) failed to report information regarding non-respondents or reported a response rate
that was less than 75%, 37 papers (79%) failed to control
for age and gender, and/or any socioeconomic variables,
though most papers (36, 77%) controlled for at least one
other variable, 14 papers (30%) used measures of mental
health that were not culturally validated.
Evidence of effectiveness
Tables 2, 3 and 4 present the GRADE evidence profile
for individual, family and community level domains.
Fig. 1 Search results
Young et al. International Journal for Equity in Health (2017) 16:153 Page 6 of 17
Table 1 Study characteristics
Region Study Sample
size
Male (%) Age (range or mean)
or school grade
Mental health outcome Mental health measure
US (mainland)
Costello [35], 1997 323 53 9–13 Symptoms of child/adolescent
psychiatric disorders
CAPA
Federman [36] 1997 431 Not
reported
9–15 Symptoms of child/adolescent
psychiatric disorders
CAPA
Cummins [45], 1999 13,454 49 14.5 Positive mental health Emotional Health scale
(bespoke measure)
Fisher [66], 1999 112 46 14.82 Psychopathological behaviour CBCL
Wall [72], 2000 96 52 8–13 Internalising and externalising
symptoms
CBCL
Whitbeck [30], 2001 195 54 9–16 Internalising symptoms YSR
Rieckmann [39], 2004 332 41 14–20 Depression CDI, DSM-IV, MMPI
Bearinger [40], 2005 569 48 9–15 Violence Bespoke measure
Newman [52], 2005 96 47 12–15 Internalising symptoms, positive
mental health
SAS, SMFQ, RSE, PANAS-X,
YSR, SEQ, FES
La Fromboise [60], 2006 212 54 10–15 Positive mental health Bespoke measure
Silmere [67], 2006 401 45 15.6 Positive mental health DIS-IV, YSR, CIS
Whitesell [70], 2006 1252 48 14–17 Self-esteem RSE
Jones [46], 2007 137 47 14–19 Self-esteem, depression RSE, CES-D
Stiffman [62], 2007 385 Not
reported
12–19 Behaviour and emotional problems YSR
Stiffman [47], 2007 401 Not
reported
12–19 Depression, conduct disorder YSR, CIS
Scott [49], 2008 112 53 13–19 Depressive symptoms IDD
Hamill [58], 2009 151 54 7-12th grade Depressive symptoms CDI
Albright [54], 2010 114 47 11–15 Hopelessness HSC
La Fromboise [55], 2010 438 46 Adolescents Hopelessness BHS
Galliher [56], 2011 137 49 14–19 Self-esteem, social functioning CASAFS, RSE
Scott [50], 2012 198 46 5-8th grade Depressive symptoms CDI
Stumblingbear-Riddle [48],
2012
196 42 14–18 Self esteem TECSES
Mileviciute [41], 2013 93 51 Grades 5–8 Depressive symptoms CDI
Mileviciute [51], 2014 146 36 13–18 Depressive symptoms,
externalising problems
CDI, YSR
Smokowski [42], 2014 1358 49 13.4 Internalising and externalising
symptoms, self-esteem
SSP, YSR, RSE
Bell [74], 2014 79 41 11–18 Depressive symptoms, self-esteem CES-DC, RSE
Tyser [43], 2014 164 47 Grades 5–12 Depressive symptoms CDI
Brokie [68], 2015 132 49 15–19 Depression and PTSD symptoms BDI-IA, Short Screen
for PTSD
US (mainland) and Canada
Hartshorn [65], 2012 692 50 10–12 at first wave Aggression DSM-IV
Whitbeck [73], 2006 656 50 9–13 Childhood mental disorders DISC-R
Canada
Mykota [57], 2006 480 51 6–18 Psychosocial functioning BRP-2
Flanagan [61], 2011 65 58 11–19 Internalising and externalising
symptoms
T-CRS, CDI, RCMAS-2,
peer report
Lemstra [53], 2011 204 44 5–8 grade Depressed mood CES-D
Young et al. International Journal for Equity in Health (2017) 16:153 Page 7 of 17
Table 1 Study characteristics (Continued)
Lemstra [75], 2011 204 44 10–16 Depressed mood CES-D
Ames [44], 2013 283 48 12 Depressive symptoms,
self-esteem
CES-D, SDQ-2
Kaspar [71], 2013 12,366 51 6–14 Psychological or nervous
difficulties
Clinical diagnosis
Australia
Silburn [31], 2007 1073 Not
reported
12–17 Clinically significant emotional
and behavioural problems
SDQ
Priest [63], 2011 345 47 16–20 Social and emotional wellbeing Strong Souls Survey
Zubrick [32], 2011 5289 Not
reported
0–17 Clinically significant emotional
and behavioural problems
SDQ
Shepherd [33], 2012 3993 51 4–17 Clinically significant emotional
and behavioural difficulties
SDQ
Askew [69], 2013 344 52 7.3 Child’s behaviour Parent report
Hopkins [34], 2013 674 50 12–17 Clinically significant emotional
and behavioural difficulties
SDQ
Hopkins [24], 2014 1021 50 12–17 Clinically significant emotional
and behavioural difficulties
SDQ
Hawaii
Makini [64], 1996 1819 45 Grades 9 to 12 Internalising and externalising
symptoms
CES-D, STAI, BADS
Goebert [37], 2000 2634 Not
reported
Grades 9 to 12 Internalising and externalising
symptoms
CES-D, STAI, BADS
Carlton [38], 2006 1173 46 Grades 9–12 Internalising and externalising
symptoms
CES-D, STAI, BADS
Hishinuma [59], 2012 3189 46 Grades 9–12 Depression CES-D
BADS Braver Aggression Detection Scale; BDI-IA amended Beck Depression Inventory; BHS Beck Hopelessness Scale; BRP-2 Behaviour Rating Profile-2nd Edition; CAPA Child
and Adolescent Psychiatric Assessment; CASAFS Child and Adolescent Social and Adaptive Functioning Scale; CBCL Child Behaviour Checklist; CDI Children’s Depression
Inventory; CES-D Centre for Epidemiology Studies-Depression; CIS Columbia Impairment Scale; DBD Disruptive Behaviour Disorders Rating Scale; DIS-IV National Institute
for Mental Health’s Diagnostic Interview Schedule; DISC-R Diagnostic Interview Schedule for Children-Revised; DSM-IV Diagnostic and Statistical Manual of Mental
Disorders-Fourth Edition; FES Family Environment Scale; HSC The Hopelessness Scale for Children; IDD Inventory to Diagnose Depression; MMPI Minnesota Multiphasic
Personality Inventory; PANAS-X Positive and Negative Affect Schedule; RCMAS-2 Revised Children’s Manifest Anxiety Scale; RSE Rosenberg Self-Esteem Scale; SAS-A Social
Anxiety Scale for Adolescents; SDQ Strengths and Difficulties Questionnaire; SDQ-2 Marsh’s Self-Description Questionnaire; SEQ Social Experiences Questionnaire; SMFQ
Short Mood and Feelings Questionnaire; SSP School Success Profile; STAI Spielberger State-Trait Anxiety Inventory; T-CRS Teacher-Child Rating Scale; TECSES Tri-Ethnic Center’s
Self Esteem Scale; YSR Youth Self-Report
Fig. 2 Risk of bias
Young et al. International Journal for Equity in Health (2017) 16:153 Page 8 of 17
Figures 3, 4 and 5 show the number of studies that
measured each individual, family, and community-level
domain’s association with mental health, respectively,
and the proportion of studies, within each domain, associated with good mental health, poor mental health, or
those that showed a negligible or inconsistent association. Five papers from Australia used data from same
large-scale study (Western Australian Aboriginal Child
Health Survey) [24, 31–34], two papers from the US
(mainland) used data from the same study (Great
Smokey Mountains Study) [35, 36], and two papers from
Hawaii used data from the same study (Native Hawaiian
Mental Health Research Development Program) [37, 38].
To avoid overinflating the number of associations, these
papers were treated as a single study when they measured the same domain.
Individual-level domains
Optimism Optimism was associated with better mental
health outcomes in all studies (7/7) that measured this
domain [38–44]. Optimism was negatively associated
with internalising symptoms in all six studies that measure this outcome.
Positive attitudes towards school Positive attitudes towards school were consistently associated with better
mental health outcomes in all studies (5/5) that
Table 2 GRADE evidence profile for individual-level domains
Domain Number
of studies
Risk of bias Inconsistency Effect size Quality Comments
Optimism 7 No serious risk No serious
inconsistency
Small-medium Moderate Rated up due to control of confounding factors
Positive attitudes
towards school
5 No serious risk No serious
inconsistency
Small-medium Low Studies from the US (mainland) only
Self-efficacy 4 No serious risk No serious
inconsistency
Small-medium Moderate Rated up due to control of confounding factors
Studies from the US (mainland) only
Self-esteem 9 No serious risk No serious
inconsistency
Small-large Moderate Rated up due to evidence of a dose-gradient
effect
Identification with
White culture
6 No serious risk No serious
inconsistency
NegligibleSmall
Low Studies from the US (mainland) only
Scholastic ability 8 No serious risk Serious inconsistency Inconsistent Very low Rated down due to inconsistent findings
Identification with
Indigenous culture
20 No serious risk Serious inconsistency Inconsistent Very low Rated down due to inconsistent findings
Substance use 9 No serious risk No serious
inconsistency
Small-Large Moderate Rated up due to control of confounding factors
Externalising 7 Serious risk of
bias
No serious
inconsistency
Medium Very low Rated down due to serious risk of bias
Internalising 7 No serious risk No serious
inconsistency
Medium-Large Moderate Rated up due to medium-large effect sizes
Adverse events 8 No serious risk No serious
inconsistency
Medium-large High Rated up due to medium-large effect sizes, a
dose-gradient effect and satisfactory control
of confounding factors
GRADE Grades of Recommendation, Assessment, Development, and Evaluation
Table 3 GRADE evidence profile for family-level domains
Domain Number
of studies
Risk of bias Inconsistency Effect size Quality Comments
Family cohesion (positive) 12 No serious risk No serious inconsistency Small-large Moderate Rated up due to evidence of a
dose-gradient effect
Low family SES 8 No serious risk Serious inconsistency Inconsistent Very low Rated down due to inconsistent
findings
Atypical family structure 6 No serious risk No serious inconsistency Negligiblesmall
Moderate Rated up due to control of
confounding factors
Caregiver mental
health/behaviour (negative)
8 No serious risk No serious inconsistency Small-large Moderate Rated up due to control of
confounding factors
Family cohesion (negative) 6 No serious risk No serious inconsistency Medium-large High Rated up due to medium-large
effect sizes and a dose-gradient effect
GRADE Grades of Recommendation, Assessment, Development, and Evaluation; SES Socioeconomic Status
Young et al. International Journal for Equity in Health (2017) 16:153 Page 9 of 17
measured this domain [40, 45–48]. This domain was
only assessed in studies conducted in the US (mainland).
Self-efficacy Self-efficacy was associated with good mental health in all studies (4/4) that measured this domain
[43, 49–51]. Using a cross-sequential longitudinal design
one study found increases in self-efficacy predicted decreases in depressive symptoms over a three-year period
[50]. This domain was only assessed in studies conducted
in the US (mainland).
Self-esteem High self-esteem was associated with better
mental health outcomes in 7/9 (78%) of the studies that
measured this domain [24, 42, 44–46, 52, 53]. One study
of Aboriginal Australian children showed a dosegradient effect linking higher levels of self-esteem to
greater odds of positive psychosocial functioning [24].
Medium to high negative correlations between selfesteem and depressive symptoms were reported (correlation coefficients ranged from −.26 to −.71).
Identification with white culture Greater identification
with White culture was significantly associated with
better mental health outcomes in 4/6 (67%) studies
[46, 54–56]. This domain was only assessed in studies
conducted in the US (mainland).
Scholastic ability Greater scholastic ability was significantly associated with better mental health outcomes in
4/8 (50%) studies [38, 43, 48, 57], however this domain’s
relationship with mental health was inconsistent with
one study showing that higher GPA was significantly
Table 4 GRADE evidence profile for community-level domains
Domain Number
of studies
Risk of bias Inconsistency Effect size Quality Comments
Peer support 5 No serious
risk
No serious
inconsistency
Small-Medium Low
Community cohesion
(negative)
4 No serious
risk
Serious inconsistency NegligibleLarge
Very low Rated down due to inconsistent findings Studies
from US (mainland) and Canada only
Discrimination 8 No serious
risk
No serious
inconsistency
Small-Medium Moderate Rated up due control of confounding variables
Bullying 4 No serious
risk
No serious
inconsistency
Small-Large Low Studies from US (mainland) and Canada only
GRADE Grades of Recommendation, Assessment, Development, and Evaluation
Fig. 3 Individual-level associations
Young et al. International Journal for Equity in Health (2017) 16:153 Page 10 of 17
associated with increased depressive symptoms [58]. The
highest quality study, a cohort-sequential design, provided evidence that depression negatively affects scholastic ability, not the other way around [59].
Identification with indigenous culture Children’s identification with their own Indigenous culture was found
to be significantly associated with better mental health
outcomes in 10/20 (50%) studies [39, 42, 43, 46, 48, 52,
55, 56, 60, 61]. Conversely, two studies conducted in the
US (mainland) and Hawaii found this domain to be associated with poor mental health [38, 47]. Identification with
Indigenous culture appeared more strongly associated
with measures of positive mental health (i.e. self-esteem,
significantly associated in 6/9 studies) than measures of
negative mental health (significantly negatively associated
in 5/14 studies).
Substance use Substance use was associated with
poorer mental health in 8/9 (88.9%) studies [30, 36, 40,
46, 51, 62–64]. Substance use was consistently associated
with externalising and global measures of poor mental
health (5/5 studies) [36, 40, 51, 62, 63], but was less
consistently associated with depressive symptoms (4/8
studies) [30, 46, 63, 64].
Externalising All studies (7/7) that measured externalising symptoms found a positive association between
this domain and other negative mental health outcomes
[30, 46, 51, 52, 61, 64, 65]. Externalising symptoms were
associated with symptoms of depression in 5/5 studies
[30, 46, 51, 52, 64], with other symptoms of externalising
in 2/2 studies [61, 65], and negatively associated with
positive mental health in 1/2 studies [46]. The evidence
for externalising was rated down due to 4/7 (57%) studies having a high risk of bias [51, 52, 61, 64].
Internalising All studies (7/7) that measured internalising symptoms found a positive association between this
domain and other negative mental health outcomes
[30, 40, 44, 45, 51, 62, 64]. Internalising symptoms
were associated with symptoms of externalising symptoms in 3/3 studies [40, 51, 64], with global measures
of poor mental health in 2/2 studies [45, 62], with
Fig. 4 Family-level associations
Fig. 5 Community-level associations
Young et al. International Journal for Equity in Health (2017) 16:153 Page 11 of 17
other internalising symptoms in 2/2 studies [30, 64],
and were negatively associated with positive mental
health in one study [44].
Adverse events Children’s experience of adverse events
was associated with poorer mental health in all (9/9) papers that measured this domain [31, 32, 41, 53, 62, 66–
69]. Two papers used data from the same study [31, 32],
therefore, 8/8 studies were ultimately recorded as showing an association between adverse events and mental
health. The evidence linking adverse events and negative
mental health included large effect sizes (maximum odds
ratio: 8.9; Cohen’s d: 1.55), and two studies that reported
a dose-gradient response between the number of adversities and prevalence of poor mental health [31, 68].
Family-level domains
Family cohesion (positive) This domain was significantly associated with better mental health outcomes in
12/13 papers [37, 38, 40, 45, 48, 53, 60, 62, 66, 67, 70,
71]. Two papers used data from the same study [37, 38],
therefore, 11/12 (92%) studies were ultimately recorded
as showing an association between positive family cohesion and mental health.
Low family SES Low family SES was significantly associated with poor mental health in 4/11 papers [33, 34,
37, 65]. Four papers using data from the same study
found an inconsistent relationship with mental health
[24, 31, 33, 34], with two papers showing low SES was
associated with less odds of emotional and behavioural
problems [24, 31], and two further papers reporting that
low SES was associated with increased odds of emotional
or behavioural problems [33, 34]. These four papers
were treated as one study showing inconsistent outcomes. Therefore, 2/8 (25%) studies were ultimately recorded as showing an association between low family
SES and poor mental health [37, 65]. A Canadian study
found that children of caregivers who had some postsecondary education were more likely to have a diagnosed
psychological or nervous condition than those who did
not have any post-secondary education [71]. The
remaining studies found negligible associations.
Atypical family structure Atypical family structure was
associated with poor mental health in 4/8 papers [31, 32,
34, 37]. Three papers used data from the same study [31,
32, 34], therefore, 2/6 (33%) studies were ultimately
recorded as showing an association between atypical
family structure and poor mental health.
Caregiver’s mental health/behaviour (negative) This
domain was associated with poor mental health outcomes
in 9/10 papers [24, 31, 34, 35, 37, 40, 68, 72, 73]. Three papers used data from the same study [24, 31, 34], therefore,
7/8 (88%) studies were recorded as showing an association
between caregiver’s negative mental health or behaviour
and children’s mental health. Violence between caregivers,
and caregiver’s anti-social behaviour produced the strongest association with poor mental health (bivariate odds
ratios: 5.6 and 7.1, respectively) [40, 68].
Family cohesion (negative) Negative family cohesion
was associated with poor mental health in 7/7papers [31,
34, 52, 53, 62, 67, 68]. Two papers used data from the
same study [31, 34], therefore, 6/6 studies were recorded
as showing an association between this domain and poor
mental health. Effect sizes were medium to large in all
studies that reported them (one study did not report effect sizes [67]). Children who stated that they rarely had
someone who showed them love and affection [53] or
who reported more family conflict [52] showed the
strongest associations with poor mental health (odds ratio: 4.8, correlation coefficient: .55, respectively).
Community-level domains
Peer support All studies (5/5) that investigated peer
support found an association between this domain and
better mental health outcomes [34, 40, 48, 52, 71].
Community cohesion (negative) Negative community
cohesion was associated with poor mental health in 2/4
(50%) studies [62, 67]. Only studies from the US (mainland) and Canada assessed this domain.
Discrimination Discrimination was observed to be associated with poor mental health in 8/9 papers [24, 30,
56, 60, 63, 65, 67, 68]. Two papers used data from the
same study [24, 63], therefore, 7/8 (88%) studies were
recorded as showing an association between discrimination and mental health. Using an auto-regressive crosslagged path design, a study of Native American and
Canadian Indigenous groups concluded that discrimination caused subsequent aggression and not the other
way around [65].
Bullying Bullying was associated with poor mental
health in 4/4 papers [52, 53, 74, 75]. Only studies from
US (mainland) and Canada assessed this domain.
Resilience
Five studies provided a quantitative measure of both adversity and mental health, fitting the inclusion criteria
for ‘resilience’. These included one Australian, one
Hawaiian, and three studies from the US (Mainland)
[24, 37, 41, 56, 60].
Young et al. International Journal for Equity in Health (2017) 16:153 Page 12 of 17
Of the three studies conducted with Native American
youths, resilient mental health was significantly associated with identification with Indigenous culture, maternal warmth, not experiencing discrimination, optimistic
explanatory styles, and identification with White culture
(females only) [41, 56, 60]. One Australian study found
resilient Aboriginal youths were more likely to have
higher self-esteem, be less likely to be involved in fights,
have a prosocial friend, and be less likely to live in the
top 50% of neighbourhoods, as rated by an index of
neighbourhood SES [24]. Identification with Aboriginal
culture was not found to be significantly related to resilience in this study. A study of Hawaiian youths found
that family support lessened the likelihood of internalising symptoms in children experiencing multiple family
adversities [37].
Discussion
Any discussion of Indigenous disadvantage must first acknowledge the longstanding inequalities many Indigenous people continue to face, and the subsequent
influence this can have on all aspects of their lives [76].
Within this context, many risk factors may also be considered as downstream effects of historical trauma.
Moderate to high level evidence exists for associations
between a number of psychosocial domains and the
mental health of Indigenous children living in high income countries. Of these, domains associated with better
mental health outcomes included: children’s positive cohesion with their family, higher self-efficacy, self-esteem
and optimism. Domains associated with poorer mental
health outcomes included: caregiver’s negative mental
health/behaviour, discrimination, co-morbid internalising
symptoms, and substance use. The highest quality evidence indicated that negative family cohesion and children’s experiences of adversity predicted poorer mental
health, with both domains consistently producing
medium to large effect sizes. Studies focused on adolescents, and predominantly measured symptoms of poor
mental health. Despite a growing body of work in this
area, the amount of research that investigates the aetiology of Indigenous children’s mental health appears
small relative to need.
The association between children’s identification with
their Indigenous culture and mental health was the most
commonly assessed association, reflecting the importance that community-led research and Indigenous mental health initiatives place on this relationship [77–79].
This domain generally predicted better mental health
outcomes however evidence for this association was inconsistent. Children’s identification with their Indigenous culture was seen to be a factor that promoted
resilient mental health in a sample of American Indian
children [60], indicating that cultural identification may
be a protective factor when adversity is present, however
this finding was not replicated in Australian Aboriginal
children [24]. Differences in the way cultural constructs
are operationalized, and difficulties measuring this construct have been previously reported and may account
for the heterogeneous findings [80, 81]. Research that
can identify the specific processes that allow Indigenous
children’s identification with their culture and with
White culture to protect against poor mental health is
suggested as an area for more detailed investigation.
In contrast, relationships between individual-level psychological factors and mental health outcomes appeared
more stable, indicating the importance of fostering optimistic attitudes, self-esteem and self-efficacy in Indigenous
young people. These results suggest that community initiatives that seek to empower Indigenous children are
likely to prevent some occurrences of poor mental health.
Our results are consistent with findings from nonIndigenous research that show the important influence
the familial environment has on children’s mental health
[82–85]. Of the 18 studies that measured family
cohesion, 17 were judged to provide evidence for an
association with mental health, including medium to
large effect sizes reported in studies from all regions.
Moreover, our results illustrate the clear correlation family cohesion has with mental health outcomes: positive
cohesion predicted better mental health, whereas negative cohesion predicted worse mental health. Negative
caregiver behaviour, such as criminal activity or the
presence of domestic violence and poor mental health
was also robustly associated with poorer mental health
outcomes in children, as was the domain ‘adverse events’,
which often included adversities that were directly related to parent’s behaviour (e.g. neglect). Taken together,
these results provide strong evidence that the quality of
familial relationships and the presence of stable, supportive family environments are highly predictive of the
mental health of Indigenous children.
Low family SES and atypical family structures appeared less consistently associated with mental health.
There is a large body of evidence that shows SES is
linked to children’s mental health in non-Indigenous
populations [86–88]. While the results provide some
evidence in support of this research, socioeconomic
and family structure factors do not appear to be as
reliable predictors of mental health as the types of relationships and stability caregivers are able to provide
for Indigenous children. It is possible that limited
variation in Indigenous family’s SES, due to ongoing
disadvantage, reduced the strength of associations
with mental health, resulting in negligible or weak associations. Additionally, variation in the way SES variables
were measured may also account for inconsistencies
in the results.
Young et al. International Journal for Equity in Health (2017) 16:153 Page 13 of 17
At the community level, experiences of discrimination
were consistently associated with poor mental health, including evidence from a longitudinal study that suggested a causal relationship with aggressive behaviour
[65], however, effect sizes were small to medium. This
magnitude of effect is consistent with a recent meta-analysis that found an overall zero-order correlation of
−.20 (95% CI: −.22 to −.17) between perceived discrimination (predominantly racial) and mental health in adults
[89]. We note that the effect sizes reported in this review
refer only to explicit discrimination and are not necessarily reflective of the impact of implicit discriminatory
attitudes/behaviours, as well as the historical effects
of systemic racism [90].
Despite the growing call from Indigenous groups for
more strengths-based research [91, 92], we found that a
comparatively small amount of studies measured positive mental health outcomes, including studies that were
specifically designed to assess resilience. Of these, significant associations were identified at the individual,
family and community level, supporting common theoretical frameworks that define resilience as a combination
of proximal and distal influences [93]. ‘Positive family
cohesion’ was the only domain significantly associated
with resilience in more than one study.
Limitations
This review contains a number of limitations. The heterogeneous manner in which both independent and
dependent variables were conceptualised and measured
prevented a more fine-grained analysis from being performed, and meant qualitative judgements of quantitative data were employed, potentially introducing bias.
This review is vulnerable to publication bias that may result in an overestimate of the number of studies that
show significant associations between psychosocial variables and mental health. Most studies were crosssectional and therefore the results may not be indicative
of causal relationships; it is also possible that a bidirectional or reverse causation process may underlie associations. Given similarities between the samples (e.g.
socioeconomic status), and that much of the data was
self-report, this review may also incur common method
bias. Using statistical significance as a primary indicator
of an association is problematic as studies that use large
samples or employed multiple comparisons are more
likely to report significant results. It is therefore likely
that this method increased the chance of making a type
I error and potentially contributed to a ‘best case’ scenario for detecting associations. Further, we acknowledge
that the reliance on arbitrary p value thresholds has been
widely criticised [94, 95]. We believe the inclusion of the
GRADE evidence table and reporting effect sizes help to
provide a more thorough description of associations that
is not based on p values alone. Most studies were conducted in the US (mainland) restricting the generalizability
of some domains to other Indigenous groups, similarly
some domains were only measured in a small number of
studies, this is most notable at the community level. Finally,
it is possible that Western ideas and measures of psychopathology do not adequately map onto Indigenous concepts
of mental health [96]. Given that the majority of studies
used culturally validated measurement tools (measuring
both risk/protective factors and mental health outcomes)
we are confident that Indigenous concepts of mental health
were, for the most part, adequately measured.
Conclusions
This review highlights several important implications for
policy makers, clinicians and Indigenous health researchers. Indigenous children’s family environment appeared a strong universal risk or protective factor for
mental health outcomes and comprises a clear target for
greater initiatives to promote mental health. Indigenous
parents face a number of well-documented stressors that
can lead to poor family environments [97, 98]. Further,
they face significant cultural and socioeconomic barriers
that can prevent them from seeking and receiving adequate health services [99, 100]. While there are programs in place to support caregivers of Indigenous
children, given the high rates of mental illness, more
needs to be done to enable caregiver’s provision of positive, stable parenting for their children in safe, supportive family environments. This review also supports
initiatives that seek to foster positive psychological attributes such as children’s self-esteem, and aim to reduce
the incidence of substance use and experiences of discrimination. We identified only three studies that
employed research methodologies specifically designed
to assess the direction of causality [50, 59, 65]. While
study designs of this type often require greater resources
to conduct, more research designed to assess causality
can provide a richer understanding of the aetiology of
Indigenous mental health that can, in turn, aid the construction of effective mental health initiatives.
Large disparities between Indigenous and nonIndigenous health are unacceptable in high income
countries that have both the resources and the responsibility to address this inequality. The results of this review
emphasise important individual, family and community
level factors that comprise potential targets for health interventions. In particular, the strong evidence linking positive familial relationships and environments to better
mental health outcomes support the design and implementation of more initiatives to strengthen Indigenous
families. However, the lack of Indigenous mental health
research, including the small number of longitudinal
designs and strength-based research does not appear
Young et al. International Journal for Equity in Health (2017) 16:153 Page 14 of 17
commensurate with the research and health needs of
Indigenous communities. Given the disproportionately
high rates of Indigenous mental health disorders and
youth suicide, there is an urgent need to address this research gap and develop more evidence-based strategies to
reduce the burden of poor mental health for Indigenous
children and their families.
Additional file
Additional file 1: Appendix A. Search strategy. (DOCX 12 kb)
Abbreviations
ADHD: Attention Deficit Hyperactivity Disorder; CANZUS: Canada, Australia,
New Zealand and United States; CI: Confidence interval; GPA: Grade Point
Average; GRADE: Grades of Recommendation, Assessment, Development,
and Evaluation; MOOSE: Meta-analysis of Observational Studies in
Epidemiology; NOS: Newcastle-Ottawa Scale; OCIS: Orthogonal Cultural
Identification Scale; SES: Socio-Economic Status; SDQ: Strengths and
Difficulties Questionnaire; US: United States
Acknowledgements
Not applicable.
Funding
This study was funded through the Study of Environment on Aboriginal
Resilience and Child Health (SEARCH). SEARCH is funded by the Australian
National Health and Medical Research Council (grant numbers 358457,
1035378, 1023998), and an Australian Primary Health Care Research Institute
Centre for Research Excellence grant. The first author is supported by an
Australian Post-Graduate Award (APA) through the University of Sydney.
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
Authors’ contributions
CY contributed to the study conception and design, conducted the literature
search, collected and analysed the data and drafted the manuscript. CH
contributed to the literature search, data analysis and reviewed the manuscript.
JC, KC and AW contributed to the study design and reviewed the manuscript.
All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Author details
1
Sydney School of Public Health, The University of Sydney, Edward Ford
Building (A27), Fisher Road, Camperdown, NSW 2006, Australia. 2
Centre for
Kidney Research, Westmead Institute for Medical Research, 179 Hawkesbury
Rd, Westmead, NSW 2145, Australia. 3
Australian Health Services Research
Institute, Innovation Campus, University of Wollongong, Building 234 (iC
Enterprise 1), Wollongong, NSW 2522, Australia. 4
The Sax Institute, Level 13,
Building 10, 235 Jones Street, Ultimo, NSW 2007, Australia.
Received: 3 April 2017 Accepted: 15 August 2017
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Archives of Academic Emergency Medicine. 2020; 8(1): e74
REVIEW ARTICLE
The Potential Role of Super Spread Events in SARS-COV-2
Pandemic; a Narrative Review
Anthony M. Kyriakopoulos1∗
, Apostolis Papaefthymiou2,3, Nikolaos Georgilas4
, Michael Doulberis3,5, Jannis
Kountouras3
1. Department of Research and Development, Nasco AD Biotechnology Laboratory, Piraeus 18536, Greece.
2. Department of Gastroenterology, University Hospital of Larisa, Larisa 41110, Greece.
3. Department of Internal Medicine, Second Medical Clinic, Ippokration Hospital, Aristotle University of Thessaloniki, Thessaloniki, 54642
Macedonia, Greece.
4. Department of Nephrology, Agios Pavlos Hospital of Thessaloniki, Thessaloniki 55134, Macedonia, Greece.
5. Division of Gastroenterology and Hepatology, University Medical Department Kantonsspital Aarau, Aarau 5001, Switzerland.
Received: August 2020; Accepted: August 2020; Published online: 21 September 2020
Abstract: Coronaviruses, members of Coronaviridae family, cause extensive epidemics of vast diseases like severe acute
respiratory syndrome (SARS) and Coronavirus Disease-19 (COVID-19) in animals and humans. Super spread
events (SSEs) potentiate early outbreak of the disease and its constant spread in later stages. Viral recombination
events within species and across hosts lead to natural selection based on advanced infectivity and resistance.
In this review, the importance of containment of SSEs was investigated with emphasis on stopping COVID-19
spread and its socio-economic consequences. A comprehensive search was conducted among literature available in multiple electronic sources to find articles that addressed the “potential role of SSEs on severe acute
respiratory syndrome coronavirus 2 (SARS-COV-2) pandemic” and were published before 20th of August 2020.
Overall, ninety-eight articles were found eligible and reviewed. Specific screening strategies within potential super spreading host groups can also help to efficiently manage severe acute respiratory syndrome coronavirus 2
(SARS-COV-2) epidemics, in contrast to the partially effective general restriction measures. The effect of SSEs on
previous SARS epidemics has been documented in detail. However, the respective potential impact of SSEs on
SARS-COV-2 outbreak is composed and presented in the current review, thereby implying the warranted effort
required for effective SSE preventive strategies, which may lead to overt global community health benefits. This
is crucial for SARS-COV-2 pandemic containment as the vaccine(s) development process will take considerable
time to safely establish its potential usefulness for future clinical usage.
Keywords: Pandemics; epidemics; coronavirus; severe acute respiratory syndrome coronavirus 2; disease outbreaks; cost
of illness; mass vaccination
Cite this article as: Kyriakopoulos AM, Papaefthymiou A, Georgilas N, Doulberis M, Kountouras J. The Potential Role of Super Spread Events
in SARS-COV-2 Pandemic; a Narrative Review. Arch Acad Emerg Med. 2020; 8(1): e74.
1. Introduction
Severe acute respiratory syndrome (SARS) has periodically
emerged as epidemics and its natural history could be utilized as a “compass” to comprehend and manage the current pandemic of SARS-COV-2. SARS-COV-2 the etiologic
agent of the novel coronavirus disease 2019 (COVID-19), be-
∗Corresponding Author: Anthony M. Kyriakopoulos; Department of Research
and Development, Nasco AD Biotechnology Laboratory, 11 Sachtouri Str, Piraeus 18536, Greece. Email: antkyriak@gmail.com, Fax : 00309210818032
longs to RNA coronavirus family (Coronaviridae) and is a
zoonotic coronavirus that has crossed species barriers to infect human (1-3). The initially investigated strains of COVID19 exhibited low potential for transmissibility and infectivity, similar to SARS coronavirus (SARS-COV) (1-4). Moreover,
SARS epidemic was potentiated due to super spread events
(SSEs), which led to unexpected elevation of the basic reproduction numbers as calculated via associated epidemiology equations (5). Specifically, SSEs resulted from secondary
contacts of carriers (6, 7). Infected individuals, as mediators
of SSEs, represent the initial cluster of viral transmission (8);
thus, inducing an exponential secondary contamination (4).
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A. M. Kyriakopoulos et al. 2
Although the prediction and subsequently the prevention of
SSEs seems to be complicated, the virus, host, environmental, and mass behaviors determine relative approaches to
prevent and control SSEs; core community health programs
can inhibit and decrease the incidence and the effect of SSEs
(9).
Nevertheless, horizontal austerity measures, such as recommending or compelling individuals to self-isolate at home,
which might cause serious social and psychological burden,
and quarantine, also leading to loss of income due to social distancing, are associated with negative psychological
and religious effects, which can be long lasting (10), thereby
leading to serious instability of the global society. Prolonged
social isolation and loneliness are associated with increased
mortality (11).
Currently, limited piece of information exists regarding the
effect of SSEs on coronavirus epidemics. The aim of this narrative review is to mainly focus on the potential impact of
SSEs on large outbreaks of coronavirus. The development of
an emergency SARS-COV-2 vaccine has its potential usefulness and/or limitations and may result in severe health outcomes, which prompts better screening for SSEs in order to
control coronavirus pandemics.
2. Method
2.1. Methodological approach
To avoid, in most respects, literature selection bias (12), multiple electronic sources: Medline/PubMed, SciFinder, Science Direct and Goggle Scholar as well as ResearchGate and
General (Google) were investigated via queries with a nonrestricted time frame reaching the 20th of August 2020. Initial investigation of SSEs and SARS, SSEs and MERS, and
SSEs and COVID-19, gave narrative results from PubMed.
The selected literature, which is included in the study, is
presented in table 1. Same items were also searched in
all other mentioned sources. The scope of the study was
not only to investigate the transmission of SARS-COV-2 due
to SSEs, its comparison with SARS-COV-1 and MERS-COV,
but also to assess the general global impact due to SSEs by
COVID-19. Therefore, further literature investigation was
performed using the same electronic sources. Further investigation was made on: a) the prevention of SSEs by
coronaviruses causing SAR-1, MERS and COVID-19, b) the
socio-economic relation of SARS-COV-1, MERS and COVID19 due to SSEs, c) the austerity caused by SSEs of COVID19, and d) the relation of SSEs containment to future vaccination programs. For further investigation, the following items were searched: “SARS, MERS and COVID-19 Epidemic Prevention”, “SARS MERS and COVID-19 Infectivity and Pathogenicity”, “Coronavirus SSE Prevention”, SSE
Coronavirus Crisis and Socio-economics”, “Holy Cup Religion and Transmission of Pathogens and SSEs”, and “Coronavirus Immunity and Vaccination”.
2.2. Selection process
Screening Process and Eligibility Criteria
Studies providing an adequate determination of an SSE related to SARS, MERS and COVID-19 were primarily screened
and selected by two reviewers (authors) blinded to one another. The results were thereafter cross-matched and duplicates were removed. Based on this primary search, the
socio-economic impact of coronavirus, produced by SSEs,
was extrapolated by two other reviewers (authors). Following
this initial selection stage, further screening was performed
by all reviewers, using the previously described search items
to identify parameters determining the global impact of
COVID-19 due to SSEs. Identified parameters included the
global impact of immunity and vaccination, the holy cup and
religion transmission, and the austerity caused by COVID-19
and other coronavirus epidemics due to restrictions applied.
All search results were cross-matched to remove duplicates
and thereafter, exclusion and inclusion criteria were applied.
Exclusion and Inclusion Criteria
After removing the duplicates, review was conducted on titles
and abstracts. Also, a decision was made to remove “news
press opinions”. Computational model methodologies producing contradictory results, studies with wrong interpretation of SSEs, and studies with non-clear-cut results were also
removed. Studies using the interpretation “a super spreading individual, known as the index case, produces a cluster
of SARS, MERS, and COVID-19 secondary infections” were
included. A second exclusion criterion was applied. In this
stage, peer reviewed literature of recent dates, studies assessing SARS, MERS, and COVID-19 epidemiology measures,
studies on COVID-19 restriction measures producing social
and economic austerity, articles discussing the perspective
for future vaccination and population immunity, and finally
genetic studies on coronaviruses causing SARS, MERS, and
COVID-19.
3. Results
By following the described methodology, on Medline/PubMed: a) 23 articles were found on SARS and MERS
and SSE, and b) 11 articles were found on COVID-19 and
SSEs. Out of: a) 13 of the 23 articles on SARS and MERS and
SSE, and b) 7 out of the 11 articles on COVID-19 and SSE were
deemed relevant hits. After applying the exclusion criteria,
12 articles from the first category, and 4 from the second
category were included in the study. Suitable articles found
by searching, which were selected and reviewed for each
part, are illustrated in figure 1. Further investigation in all
other electronic sources described, using the same methodThis open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).
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3 Archives of Academic Emergency Medicine. 2020; 8(1): e74
Table 1: Literature included from PubMed search for SSEs* in relation to coronavirus outbreaks
Literature included in review for SARS∧ and MERS! in relation to SSEs*
Authors and Year Type of article** Study description
Chowell et al. (2015)(51) Comparative research Investigation of relation between SSEs for SARS and MERS transmission in nosocomial outbreaks
Al Tawfig et al. (2020) (38) Commentary Demonstration of a stochastic model of transmission of SARS virus
Shaw (2006) (55) Perspective review Implementing efficient intensive care practices to avoid hospital
transmission
Chen et al. (2006) (96) Original research Case
control study Investigation of SSE likelihood during hospital transmission
Sung et al. (2009) (97) Original research Case
control study
Investigation of SSEs occurring in hospital and prevention strategies
Li et al. (2004) (54) Original research Case
control study
Investigation of factors contributing to SSEs for prevention and
control of disease
Riley (2003) (5) Original research Cross
sectional study Analysis of SARS epidemiology in Hong Kong
Stein (2011) (98) Perspective review Analysis of SARS transmission leading to SSE
Gormely et al. (2017) (52) Original research Model for prevention of pathogen transmission via sanitary plumping systems
Lau (2004) (53) Perspective review Implementation of SSE containment with vaccination programs
Literature included in review for COVID-19 in relation to SSEs
Cave (2020) (56) Perspective review Call for clear epidemiologic definition for SSEs
Xu (2020) (57) Original research Retrospective cohort study Analysis of SSEs during COVID-19 in China
Kwok (2020) (58) Original research Analysis of SSE influence in the nature of COVID-19 epidemic
Zhang (2020)(21) Original research Description of SSE importance in COVID-19 epidemic
! Severe Acute Respiratory Syndrome; ∧ Middle East Respiratory Syndrome; &Coronavirus Disease-19; *Super Spread Events;
**When clearly indicated in article, the type of study is also mentioned.
Table 2: The search results of literature related to COVID-19& global impact due to SSEs*
Search Item Medline/PubMed Other electronic
sources**
Number of articles retrieved
Number of articles included
SARS!, MERS∧, and COVID-19 epidemic prevention 42 3589 139 17
SARS, MERS, and COVID-19 infectivity and pathogenicity 672 3812 145 18
Coronavirus SSE prevention 10 627 151 22
SSE, coronavirus crisis, and socio-economics 4 3181 89 11
Holy Cup religion and transmission of pathogens and SSEs 0 15 4 4
Coronavirus immunity and vaccination 73 20975 1175 9
&Coronavirus Disease-19; *Super Spread Events; ** Science Direct, SciFinder, and Google Scholar;
!Severe Acute Respiratory Syndrome; ∧Middle East Respiratory Syndrome
ology, increased the number of the included literature to a)
17 and b) 14, for their respective categories of search. Studies
included from PubMed in these categories of searches are
briefly described and listed in table 1. Further, assessing the
general global impact of SSEs related to COVID-19, using all
the mentioned sources, via the same methodology, led to the
inclusion of a) 10 articles related to genetic analysis of SARSCOV-1 and MERS-COV and SARS-COV-2, b) 5 articles related
to super spread events, c) 2 articles related to austerity, d)
18 articles related to infectivity and pathogenicity of SARS,
MERS and COVID-19, e) 17 articles related to prevention of
SSEs concerning human coronaviruses, f ) 9 articles related
to socio-economic impact, and g) 9 articles related to immunity and future vaccination. Table 2 illustrates the initial
numbers of hits using all search items in all sources, and the
final number of articles reviewed in each category.
4. Discussion
4.1. Insights to SSEs
The involvement of SSEs in SARS extensive outbreaks (1, 4, 5,
13-17), necessitates urgent elucidation as global tranquility is
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A. M. Kyriakopoulos et al. 4
Figure 1: Method followed for PubMed search and literature selection regarding SSE in relation to a) SARS-1, MERS and b) COVID-19 outbreaks.
disturbed by COVID-19 pandemic. Epidemiological research
has proposed that the outbreak was related to a seafood market in Wuhan (Hubei, China), underlining the ongoing risk
of viral transmission from animals to induce severe diseases
in humans. Metagenomic RNA sequencing of bronchoalveolar lavage fluid from a patient with pneumonia identified a
novel RNA virus strain from the Coronaviridae family (called
SARS-COV-2); and phylogenetic analysis (by introducing the
widely used in silico protein screening) (18-21) of the complete viral genome (29,903 nucleotides) disclosed that the
virus was most closely connected (89.1% nucleotide similarity) with a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) formerly isolated from bats
in China (18-22). Insights from previous reports by Menachery et al. (23) (Menachery et al., 2015), pointed out that the
2002-2003 emergence of SARS-CoV introduced the possibility of viruses of animal origin causing epidemics in human
populations. Conclusions from their study revealed, as previous studies had demonstrated (1, 5, 13, 15), that closely
related SARS-like viral genes were traceable in Chinese bat
populations. Authors claimed that these viruses were capable of infecting humans, by selective adaptations or adjustments, and thereby, causing a new epidemic (23). Enhancement of virulence is also attributed to these adaptations due
to acquisition of spike protein via adaptive mutations (24).
Continuous viral random mutations are possible through inThis open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).
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5 Archives of Academic Emergency Medicine. 2020; 8(1): e74
Figure 2: Flow of genetic variation of coronaviruses leading to increase of virulence and pathogenicity; epidemiologic steps for specific and
targeted diagnosis to prevent super spread events. Blue arrows point to the flow of genetic variation across gene pools where genetic variation
occurs, i.e. a) between hosts of the same species, b) between hosts of different species by crossing the species barrier, spreading to c) humans
and subsequently to super spreader individuals, where disease transmission is potentiated. Grey arrows point to specific identifications that
can lead to effective interventions with the potential to control the disease spread.
termediate host transmission, until a deadly virus develops,
as illustrated in Figure 2. Recent evidence revealed that recombination within intermediate hosts has contributed to
development of SARS-COV-2 (1, 24). Asian outdoor markets
could constitute the ideal places for continuous viral mutation exchanges (25). As presented in Table 3, the best way to
circumvent continuous virus production is targeted surveillance; to at least stop the overspreading by SSEs (2, 3, 22, 26).
This has also been proposed by Menachery et al. (23).
4.2. SARS epidemics and SARS-COV-2 pandemic
SARS-COV-2 is accountable for the unprecedented COVID19 pandemic (27), and the interplaying mechanisms involved
in the pathophysiology of COVID-19 include SARS-COV-2
virulence, host immune response, and complex inflammatory reactions (28). Emerging data, also, imply that the reservoirs of SARS-COV-1 infection may be similar to COVID-19
(1, 4, 5, 13, 29), as remarkable similarities exist between SARS
and swine acute diarrhea syndrome (SADS) in topographical, temporal, environmental and etiological backgrounds.
However, the increasing coronavirus variety and spread in
bats were recognized as a potential target to diminish future
epidemics that might impend livestock, community health,
and financial progress (30). Probably, identification of animal and insect vectors that transmit the disease, identification and control of alternative routes of transmission like
fecal-oral route, and identification of super spreader patient
groups could help minimize the epidemiological extent compared to the one observed for SARS-COV-2 infection worldwide. Lessons from SARS epidemic taught us that the key to
control is minimizing the time from the diagnosis of infection
to prompt hospital isolation and diminishing the probability
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A. M. Kyriakopoulos et al. 6
Table 3: Key clinical and laboratory screening functions to appropriately forecast, prevent, and confront SARS! Coronavirus 2, and future
coronavirus epidemic waves
Specific Clinical and Laboratory Investigation Validated techniques to be used
Forecasting of pre-symptomatic infection To estimate the probability of a major outbreak, use simulations
of stochastic compartmental epidemic models. Use of diagnostic
tests to detect asymptomatic susceptibility and pre-symptomatic
infectivity
Estimation of Super Spread Events of current and previous coronavirus epidemics
Introduction of individual reproductive number. Integrated and
computational analysis of the influence of individual variation by
binomial distribution and use of branching process analysis of disease data.
Genetic characterization of inpatient viral isolates to identify intermediate animal hosts facilitating the infection
Next generation sequencing of samples and cultured viral isolates
to obtain full sequence and phylogenetic analysis application.
Environmental detection and continuous sewage monitoring RT-qPCR# screening on sewage systems, vectorsÙ´L and potential
air transmission. Autopsies and detection of serology conversion
of potential vectors.
Heptad repeat region screening for positive selection Computer simulation models to detect positive selection events
e.g. codeml branch-site Test coupled with Bayes empirical Bayes
procedure, and mixed effects model of evolution.
Receptor recognition analysis of ACE-II+ to identify origin of crossspecies and human to human transmissions coronaviruses
Genetic sequencing and phylogenetic analysis of ACE-II to provide
origin and efficiency of cross-species and human to human transmission and identification of intermediate hosts.
!Severe Acute Respiratory Syndrome; #Reverse Transcriptase Quantitative Polymerase Chain reaction; +Angiotensin-converting enzyme-II.
Table 4: Potential groups of coronavirus super spreaders within the human population*
Population Group Potential route of transmission
Hepatitis B and C virus positive patients Airborne
Pulmonary tuberculosis positive patients Airborne
HIV! positive patients Airborne, urine & fecal-oral (98)
Patients receiving hemodialysis Airborne (droplets by nebulizer) and fecal-oral
MRSA# Staphylococcus aureus acquisition Constant Worn Glove Contact Transmission
Rhinovirus co-infections Airborne
Gastrointestinal (Salmonella enteritis) co-infections Fecal – oral
Frequent contact with wild animal reservoirs (including domestic animals) and
birds**
Airborne and fecal – oral
Construction area workers Air particles
Sewage system workers*** Fecal – oral
*In both community and hospital environments.
**Including slaughter houses, pet shops, animal and bird collectors and breeders, cow, and pig farmers.
***Including workers coming in contact with environment contamination.
!Human Immunodeficiency Virus, #Methicillin Resistant Staphylococcus aureus.
of another SSE (5).
4.3. The 20/80 rule as applied to SARS
The typically recognized 20–80 rule or the so-called “Pareto
rule”, states that 20% of efforts lead to 80% of results (31).
More specifically, this comprises a principally convenient
state when tackling infectious diseases and is applied to investigate infection transmission, and initially among cattle
farms. In this regard, Woolhouse et al. (17) reported that targeted actions concerning disease control and prevention in
20% of the farms that mainly supplied the basic reproduction number (Ro) decreased spread by 80% (32). Focusing
on the COVID-19 virus, Ro is a sign of virus transmissibility,
denoting the average figure of novel infections caused by an
infectious individual in a totally naive population. For R0 >
1, the number of infected people tends to increase, whereas
for R0 < 1, transmission is likely to stop; Ro represents a
chief model in the epidemics, signifying the risk of an infectious mediator with regard to epidemic spread (33). Recent
data indicate that the estimated mean Ro for COVID-19 is almost 3.28, with a median of 2.79 and the interquartile range
(IQR) of 1.16, which is substantially higher than WHO’s estimation of 1.95. However, due to biased methodology, Ro
for COVID-19 is expected to be about 2–3, which is approxiThis open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).
Downloaded from: http://journals.sbmu.ac.ir/aaem
7 Archives of Academic Emergency Medicine. 2020; 8(1): e74
mately consistent with the WHO estimate (33). SSEs appear
to be a main limitation of the Ro concept. Ro, when calculated as a mean or median value, does not include the heterogeneity of transmission between infected individuals (4);
two infective agents with equal R0 estimates might have noticeably diverse patterns of transmission. Moreover, the goal
of a Health Care System is to achieve Ro <1, which is probably only phenomenally feasible in certain conditions without scheduled prevention, recognition, and response to SSEs
(9). Naturally, epidemics follow the aforementioned 20 / 80
rule (17). Specifically, in human population, due to heterogeneous exposure to infectious agent, the 20% core population
may transmit the disease, widely. For SARS, the rate might
have been even lower than 20% (4). The increased infectious potential of a small population subgroup seems to be
related to immunodeficiency, such as in hemodialysis, cancer, immunosuppressive therapies (4, 5, 15, 34). Additionally,
facilitation of disease spread and transmission due to vector exposure has been investigated in relation to cockroaches
(35). Possible mechanical transportation by rats and cat (13,
36) and air transmission (37) in SARS-COV-1 have also been
studied. Other animals capable of being SARS-COV-2 carriers (excluding mice and rats), like pigs, ferrets, cats, and nonhuman primates have recently been introduced (3), and contamination of sewage with SARS-COV-2, has probably preceded COVID-19 outbreak in France (29). All these agents
may contribute to a minimum of 80% of the total transmission potential (17), maybe even more (4, 5). Table 4 displays
possible super spreader groups; thus, indicating screening
targets to prevent SSEs. SARS epidemic taught us that control
programs were inefficient in controlling the epidemic within
a population, and failed to identify and provide a targeted
infection diagnosis in groups causing potential SSEs (5, 17).
On the other hand, SARS-COV-2 having the ability to cause a
pandemic rather than an epidemic, resulted in an increased
number of cases and deaths; albeit having a lower mortality rate than SARS coronavirus (2). SSEs during COVID-19
may involve not only one city, but also a whole country or
many countries, requiring investigation of their effects on a
national or international level (2, 38, 39).
4.4. Prevention of SSEs
Preventing and decreasing COVID-19-related SSEs necessitates the decryption of the mechanism through which SARSCOV-2 spreads through super spreader individuals, for example within healthcare facilities (7, 9). Healthcare facilities are
essential for prevention and control of SSEs (9). SSE prevention may enable us to even overcome initial low COVID-19
virus infectiveness. The capability of the virus to produce
SSEs troubles the epidemiological attempts to restrict viral
spread only by isolating individuals at high risk and performing obsolete isolation at home for the general population as
carried out in countries such as Greece (5). During the SARS
epidemic in China (Beijing) and Singapore, the vast majority of infected individuals were barely infective and only 6%
of the population was highly infectious, in contrast to many
published SARS models (4, 5). Other ways of potential coronavirus transmission between hosts may provide explanations for enormous outbreaks (16). It should not be disregarded that coronaviruses cause both respiratory and intestinal infections and share common evolutionary roots with
hepatitis viruses (40, 41). Passing the cross-species barrier
and genetic adaptation within hosts may promote virulence
of coronaviruses in humans (14). This, prompts to specifically identify potential super spreader groups within populations through targeted diagnosis. Some of these groups are
listed in Table 2. For this purpose, a usual infection must
be distinguished from a super spread infection (4, 5). During SARS epidemic, the coronavirus infectiousness mostly
occurred in the late stages of infection (5, 17), whereas in
COVID-19, viruses are transmitted even in pre-symptomatic
stages (42). As with Influenza A virus subtype H1N1 transmission (43), accurate diagnosis of COVID-19 in potentially
asymptomatic super spreaders may help contain the magnitude of large outbreaks (44).
In the case of Diamond Princess Cruise ship, an earlyassessed R0 of 14.8 (âL’´L4 times higher than the R0 in the epicenter of the outbreak in Wuhan, China) was decreased to an
assessed effective Ro of 1.78 following on-board isolation and
quarantine processes (45). Similarly, in China (Wuhan) the
application of non-pharmaceutical interventions in the society, including a cordon sanitaire of the town; interruption
of community transport, school, and most employment; and
termination of all community events decreased the Ro from
3.86 to 0.32 over a 5-week period (46). Nevertheless, these
strategies could not be maintained.
Emerging research evidence (29) regarding sewage contamination that preceded Paris COVID-19 epidemic is pointing
to the reports of 2003 from the health department of Hong
Kong (35, 36), the noble work by Ng (13), and urge for extensive environmental monitoring (29, 37) to prevent future
COVID-19 relapses. However, the flow of genetic variation
may be even more complex as illustrated in Figure 2. Therefore, advanced clinical and laboratory monitoring is required
to prevent SSEs and thereafter, new coronavirus epidemics.
Assembly of key functions and screening techniques of reference centers is presented in Table 3. Newer therapeutic agents and protocol applications are promising (47), although probably carrying the possibility of resistance state
(48). First, these also require specific diagnostic and surveillance strategies to overcome any unknown adverse epidemiology consequences (48). Inhibiting wild meat markets and
related consumption of wild meat by creating vivid campaigns could be a critical for interrupting the introduction of
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A. M. Kyriakopoulos et al. 8
coronaviruses crossing from animals to the human population, as was the case for SARS (1, 4, 5) and Middle East respiratory syndrome (MERS) (49) epidemics, and probably now
for COVID-19 pandemic (1-3). Furthermore, the food production process requires radical reconsideration, concerning
the industrial environment of current food production and
serious violations of natural ecosystems (50). Current industrial procedures for preparing food increasingly favor conditions where viral evolution produces new mutations and increased rates of mutations (25), thus raising the probability
of new and more infectious viral strains. In SARS and MERS
epidemics, the role of SSEs in vigorously distributing the
epidemics has been substantially proven (51-55). The new
COVID-19 epidemiology evidence also adequately highlights
the important role of SSEs in homeland of China (21, 56,
57), although surprising evidence from neighboring countries show the unlikely role of SSE in the spread of the disease
(58).
4.5. Effective molecular screening of SARS-COV-2
and socioeconomic relations
The Coronaviridae family is characterized by a positive-sense
single-stranded RNA genome. Mouse hepatitis virus is a representative member of the family (41). Additionally, human
hepatitis E virus also has a positive-sense single stranded
RNA genome and shares a common evolution pathway with
coronaviruses (40). Hepatitis-related incidents were described for SARS (59). The genetic recombination of these
viruses within arbitrary intermediate hosts produced contagious strains that are extremely pathogenic to humans (40,
60). In this respect, the relation of SARS-COV genetic sequences isolated from human, civets, and bats permitted us
to find the reason for such a dangerous epidemic, which affected people on a worldwide scale in 2003 (61). Moreover,
the unpredictable epidemic of MERS-COV posed a serious
risk to the health of communities worldwide. These underscored the necessity for further research of the virus epidemiology and pathophysiology to develop successful therapeutic and preventive medications against MERS-COV infection
(62). While SARS-COV-2 is genetically and structurally connected with MERS-COV, it has its own exclusive structures
which are responsible for its quick spread throughout the
world (60).
Specifically, variations in coronavirus pathogenicity within
different species (63) make the understanding of SARS epidemics even more unclear through their capability to overcome the barrier for cross species transmission, which also
alters their infectivity status (14, 64). As a result, boosting the
pathogenic behavior of coronavirus strains, within species
(65), and across species barriers (49), which is a reflection
of their positive adaptation to rapid recombination events
(49). The recent MERS epidemic revealed the tendency of
the strain to genetically adapt and produce greater outbreaks
(49) as occurred in SARS epidemic in 2003 (66). However,
mainly for socioeconomic reasons, alarm signals were ignored until recently (67). A new phylogenetic analysis technique employed on clustered COVID-19 strains displayed a
geographic variation preference in infectivity and pathogenesis (39). This is probably due to predominating strain’s tendency to cause an SSE as an outcome of a multi- factorial epidemic process presented in Figure 2 (23, 24). Marked SSEs for
COVID-19 have already been fully characterized and warrant
urgent investigation (23, 24). As presented in Tables 3 and
4, each way of transmission should be investigated. Heterogeneity of epidemic characteristics across nations (39) implies that in this way we may minimize coronavirus transmission. Therefore, salvation of national economic catastrophes will also be achieved in this way (66). Thus, the whole
Biomedical Science machinery needs to perform targeted diagnosis of SSEs and share the obtained experience. Subsequently, central authorities will no longer need excessive
non-specific contact measures, which will in turn normalize
both societal and economic activities.
4.6. SSE-related large outbreaks and uncontrolled austerity
On the other hand, improper understanding of how COVID19 spreads resulted in societal imbalance due to arbitrary restriction of social and religious life including Holy Communion Cup. It has been consecutively demonstrated by expert research that the Holy Cup (Chalice) and the Holy Cloth
are not sources or pathways, for potential spreading of infectious diseases including Human Immunodeficiency virus
(HIV) (68), Hepatitis B virus (HBV) (69) as well as other communicable pathogens (70). Specifically, a review (69), considered other 129 relative studies. In this review, the possibilities that the shared communion cup can act as a vehicle
for indirect transmission of human immunodeficiency virus,
since it was detected in the saliva of infected individuals, was
investigated. It was emphasized that although for bacterial
contamination, the alcoholic content of the wine, the material that the cup is made of, or the practice of partially rotating the cup, cannot stop the occasional transmission of
microbes, the microbial transmission was considerably reduced by the intervening use of a cloth to swab the lip of
the cup between communicants. Notably, it was emphasized
that transmission means not an obligatory inoculation or infection. Furthermore, it was also emphasized that out of the
epidemiology of microbes transmitted via saliva, particularly
for the transmission of the herpes viruses, the indirect transmission is rare, and indeed transmission is highly possible by
other means than by the saliva. It was also emphasized that
neither hepatitis B virus nor human immunodeficiency virus
infection can be transmitted by saliva, rendering their indiThis open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).
Downloaded from: http://journals.sbmu.ac.ir/aaem
9 Archives of Academic Emergency Medicine. 2020; 8(1): e74
rect transmission also less likely by inorganic objects. Finally,
the study concluded that no episode of disease transmission
has ever been reported as a result of the shared communion
cup use, and that there was not any scientific evidence that
the communion cup practice should be abandoned due to
the possible risk of spreading of any infection (71, 72). Likewise, Kingston et al. (68), by considering 44 relative papers,
also concluded that there is no evidence that the Holy Communion Cup spreads infections. Moreover, more recent estimations also demonstrated that no infections have ever been
observed as a result of religious rituals including Christian
Common Communion chalice practice (70); whereas, data of
previous studies implied that saliva could play a role in HBV
transmission, are likely to be trivial (69). Similarly, recent evidence indicate that, although HBV DNA and HCV RNA can
be discovered in the saliva of infected patients, they seem unlikely to transmit infection (72). It should be noted that, as in
the case of coronavirus (73, 74), HBV also exists in many body
fluids including saliva, nasopharyngeal fluid or tears by measures of qualitative and PCR methods (75).
The detection of HBV DNA in saliva motivated our study
group to investigate the potential viral transmission through
the Holy Communion Cup. Two successive retrospective
studies were conducted to investigate the role of Holy Communion as an independent risk factor of HBV dispersion.
The first preliminary study included patients from our registry of those with chronic hepatitis B under entecavir (Jannis
Kountouras-personal communication) treatment (76), and
in the next step, the relative registry of another Department of the same Hospital was incorporated. Other parameters studied, the substantial independent categorical variable to evaluate our hypothesis was the patients’ occupation,
thereby introducing two sub-groups; priests and non-priests.
This classification was performed based on a standard active
and perpetual exhibition (at least once weekly) of priests to
many people’s saliva, as a part of the grounded process of
the Holy Communion Cup. The control group comprised of
the aggregate of Orthodox priests in Greece (10,338) and the
rest general population (10,680,866) at that timeframe. Approval of the Institutional Ethics Committee was obtained
and all predispositions of the Helsinki Declaration were fulfilled. The reservoir database did not include any personified
information (name, ID number, etc.) and thus no informed
consent was required. Pearson’s chi-squared test with 1 degree of freedom was performed to evaluate whether there
was a statistically significant difference between the frequencies of HBV infection in case and control groups and statistical significance was set at p <0.05.
The first single-centre registry included 71 patients and one
(1.4%) of them was a priest. Chronic hepatitis B was significantly more frequent among non-priests compared to priests
(x2 (1, N=71)=12.65, p <0.05). The extended sample (N=429)
included the registry of another Department and an aggregate of four (0.93%) priests were diagnosed with chronic hepatitis B. Likewise, the chi-square test revealed that non-priest
subjects were more likely to suffer from chronic hepatitis
from HBV infection compared to priests (x2 (1, N=429) =
31, p <0.001). In conclusion, both of our analyses indicated
a lower prevalence of HBV chronic hepatitis among priests
when compared to other occupations.
4.7. Coronavirus vaccination and relationship
with SSEs
Currently, vaccines for COVID-19 are in pre-clinical development, and no final clinical phase has been ended due
the recent emergence of the disorder. Many global entities have stated their plans to produce a vaccine for COVID19. According to the WHO, 41 candidate vaccines are being produced for COVID-19 as of March 13, 2020 (77). Importantly, for production of highly effective and safe COVID19 vaccines, features such as the possibility of the induction
of antigen-dependent enhancement (ADE) and additional
severe opposing effects previously detected with SARS and
MERS should be considered. ADE is a phenomenon that occurs when non-neutralizing antibodies against proteins of a
virus increase, also increasing virus infectivity (78). In this
regard, coronaviruses can escape the immunity provided by
inactivated or recombinant protein vaccines via fast evolution (79). The problem with live attenuated vaccines is that
the coronavirus can recover its virulence via serial passages
in cell culture or in vivo (80). Moreover, vaccination in animals and humans could facilitate, rather than inhibit, the
pathogenesis of the targeted viruses. This can be the consequence of an ADE phenomenon. This underlines a mechanism by which specific antibodies facilitate infection with
the targeted virus, or cell-based augmentation, a process resulting in an allergic inflammatory response induced by immunopathology (81, 82).
Many experimental SARS-CoV-1 vaccines have been formulated from whole inactivated viruses, due to their advantage of large-scale production, multiple epitope presentation
and high conformation stability (83). One such vaccine uses
viruses from AY71A217 strain of SARS-CoV-1, which are double inactivated using formalin and UV irradiation, the socalled double-inactivated virus (DIV) vaccine (84). Although
DIV had initially been demonstrated to induce neutralizing
antibodies and to protect against SARS-CoV-1 viral replication, both in tissue culture and in young mice, it soon became
apparent that older mice suffered from vaccine-induced immune pathologies, including failure to contain viral replication, augmented clinical disease and associated symptoms,
and increased inflammatory response and eosinophilic influx (84, 85). In this respect, there is an overlap between the
immunopathologic responses connected with coronavirus
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A. M. Kyriakopoulos et al. 10
disease and vaccination, and the role of T helper (Th) 17
cells in immune augmentation and eosinophilic lung immunopathology; host Th17 polarized inflammatory reactions portray an important role in the pathophysiology of
COVID-19 pneumonia and edema (86, 87). Eosinophilic
pathology, indicating increased pathogenesis and disease
severity in the elderly, has been attributed to the nucleocapsid (N) protein, despite the incorporation of multiple SARSCoV-1 antigens in the DIV (82, 84). This is on grounds that
the N protein is a strong modulator of innate immunity, also
acting as an interferon antagonist, and therefore, it has the
capability to induce inflammation with subsequent immune
pathology in situations of heterologous viral challenge or in
immune senescence, where patients fail to mount effective
immune responses against the disease (84, 88). The route of
transmission is important to be established for SARS COV2. As seen with other important infectious diseases of a)
air borne transmission such as tuberculosis (89), b) orofecal transmission such as HEV (90) and c) blood transmission such as HBV and hepatitis D virus (91), even if efficient vaccination is established, understanding of SSEs is
still important. Recent research data on the immune receptors used by coronaviruses, which reflect their ability to
propagate in the human population, imply that complex immune reactions are responsible for a cell to cell transmission. In addition to ACE-II receptor, as is the case with SARSCOV, MERS-COV (92) and possibly for SARS-COV-2 (92, 93),
viruses use complex receptor recognition systems common
to immunopathology damage mechanisms in coronavirusinfected individuals, which clearly define the clinical outcome (94). Therefore, application of vaccines that may interfere with antibody-mediated infection by coronaviruses (95)
without true epidemiologic containment of coronaviruses, to
restrict genetic adaptation events and inevitably producing
an SSE, may be a miscellaneous attempt. However, synergy
of SSE prevention measures with proper vaccination can provide a robust attempt for disease containment.
5. Limitations
This study aimed to perform a literature review. Although
effort was made to decrease the risk of bias of results via
double-blind screening of literature and employment of multiple electronic search engines, bias cannot be eliminated
due to incomplete retrieval of identified research and biased
estimations of included literature conclusions and methods
used. Outcome of the study may also contain biased estimations originating from wrong interpretation of super spreading individuals in literature reviewed for SARS, MERS, and
COVID-19 outbreaks. Although the importance of SSEs in
COVID-19 was recognized by this study, more data from future accumulated epidemiology studies are needed to justify
these findings.
6. Conclusion
Taken all together, management of SSEs is mandatory to
yield efficient control over SARS-CoV-2. This is achievable
through early diagnosis of pre/asymptomatic infected individuals within potential super spreading groups. Prevention
of outbreaks is more essential, especially due to the lack of
efficient vaccination and therapeutic protocols, which necessitates efficient monitoring, as SARS-COV-2 virus follows
complex infectious patterns. The SARS-COV-2 epidemiological models that do not take SSEs into consideration seem to
lead to confusing results with high uncertainty. SARS-CoV2 causes prolonged “pandemics” through complex adaptation routes. Currently, in addition to the high technology utilized for diagnosis, clinical observation is indispensable to
deeply comprehend SSEs and prohibit further outspread of
COVID-19. Reference laboratories with efficient and accredited molecular and serological diagnosis must be inter-linked
between countries. All these parameters could contribute to
avoiding a second blind unjustified response that characterized the first COVID-19 pandemic spread. Understanding the
epidemiology of COVID-19 through SSEs could be preventive
for future epidemics. A systematic meta-analysis research
methodology, when COVID-19 epidemiology data accumulate further, would be advisable to confirm the conclusions
of this study.
7. Declarations
7.1. Ethics approval and consent to participate
This study did not involve the participation of any humans or
animals as it was based only on literature research.
7.2. Consent for publication
All authors agree to publish this manuscript.
7.3. Availability of data and materials
All data used for this manuscript are available upon request
7.4. Competing interests
All authors declare that they have no competing interests.
7.5. Funding
No funding or grant was received for this study.
7.6. Acknowledgements
We thank our families for providing moral assistance to accomplish this study.
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11 Archives of Academic Emergency Medicine. 2020; 8(1): e74
7.7. Author contribution
AK inspired the conception and drafted the initial
manuscript. JK revised substantially the manuscript, intellectual content and provided disclosed data for HBV
investigation. AK and JK made the primary double-blind
search. AP, JK, AK, MN, and NG, made all other searches.
AP contributed to the immunology aspect of manuscript.
AP and NG aided in the clinical part and preparing the
final version of the manuscript. MD contributed to bibliographical search and revision of the manuscript. All authors
contributed to the English editing of the manuscript. âA˘Cˇ
7.8. Abbreviations
SARS: Severe acute respiratory syndrome.
SARS-COV-2: Severe acute respiratory syndrome coronavirus
-2.
SSEs: Super spread events.
COVID-19: Coronavirus disease 2019.
SADS: Swine acute diarrhea syndrome.
Ro: Basic reproduction number.
IQR: Interquartile range.
MERS-COV: Middle East Respiratory Syndrome coronavirus.
HIV: Human Immunodeficiency virus
HBV: (Human) Hepatitis B virus
HCV: (Human) Hepatitis C virus
ADE: Antigen dependent enhancement
DIV: Double Inactivated virus
Th: T helper (cell)
RT-qPCR: Reverse transcriptase quantitative polymerase
chain reaction
ACE-II: Angiotensin converting enzyme II
MRSA: Methicillin resistant Staphylococcus aureus
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