“Examining Potential Risk Factors, Pathways and Processes ...
Transcript of “Examining Potential Risk Factors, Pathways and Processes ...
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Research Projects Section
Research and Data Management Branch
Department of Family and Community Services
“Examining Potential Risk Factors, Pathways and Processes Associated with Childhood Injury in the Longitudinal Study of Australian Children”
Tamara Blakemore
Note The views expressed in this paper are the author’s and do not necessarily reflect the opinions of the Department of Family and Community Services, the minister or of the federal government.
Acknowledgements The author is grateful to Justine Gibbings and Mark Reid for the valuable advice and assistance they provided in the preparation of this paper.
Abstract In Australia, injury including poisoning is reported as the most common cause of death from early childhood through to middle age (AIHW, 2002). Apart from its potentially fatal consequences, childhood injury can also affect children’s development via its association with disability, impairment and illness. Considerable debate exists around understandings of injury causation and the division between intentional and unintentional injury. This hampers the efforts of policy makers and practitioners to prevent injury. It is argued that the risk factors associated with physical injuries of either kind are similar, existing across multiple domains of human experience and their effect influenced by their interaction with each other. This paper builds upon the findings of preliminary work using data from first wave of Growing Up in Australia, the Longitudinal Study of Australian Children, and presents the results of work exploring potential risk factors, and the direct and indirect pathways and processes implicated in injury occurrence. The paper uses an integrated, multi-dimensional conceptual framework to guide investigation, conceptualising childhood injury as the result of exchanges and interactions between the child and their family and the broader contextual environment. Understanding the function of risk factors associated with childhood injury will aid in efforts to mitigate the risk associated with such life events and is imperative for the formation of effective injury prevention policy and practice parameters.
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Introduction
In Australia and in most industrial countries the world over, injury including
poisoning is the most common cause of child death (AIHW, 2002; Rodriguez, 1990;
Morongiello, Ondejko & Littlejohn, 2004). Amongst the world’s 26 richest nations,
injury accounts for 40 percent (40%) of child deaths for children aged between 1 and
14 years (UNICEF, 2000). In 2002, injuries accounted for the deaths of 229
Australian children aged between 1 and 14 years, representing 37 percent (37%) of all
deaths for this age group (ABS, 2004). Apart from its potentially fatal consequences,
childhood injury can also result in significant illness and impairment with around
65650 children hospitalised for injury in Australia in the year 2002 - 2003
(AIHW,2005; Hango & Houseknect, 2005).
Given the incidence of childhood injury and its potentially harmful consequences, the
task of identifying risk factors for injury is an important prerequisite to forming
effective preventative policy and practice parameters. Evidence from existing studies
in the injury literature indicates three domains of experience and characteristics are
key influences upon childhood injury (Soubhi, Raina, & Kohen, 2001). These
domains include factors specific to the child, their family, and their broader contextual
environment. Because children’s lives are shaped by their family environment, which
in turn is influenced by the broader contextual environment, it is reasonable to assume
that characteristics of each domain are intertwined, influencing injury to varying
degrees (Ramsay et al., 2003). Information about the relationships between risk
factors and the direct and indirect pathways via which risk is transmitted is however
limited.
This paper aims to address this gap in the literature by considering the potential of a
wide range of child, family and contextual characteristics to act as risk factors for
child injury and by examining inter-relationships between significant risk factors. The
multifactorial, integrated working model of child injury proposed by Peterson and
Brown (1994), is used to guide investigation and data from the four-year-old cohort of
‘Growing up in Australia’, the Longitudinal Study of Australian Children (LSAC)’ is
used.
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The following sections will review fundamental issues of injury definition and
conceptualisation as well as evidence that links characteristics and experiences of
children, their family, and the broader contextual environment to childhood injury.
Defining childhood injury
By definition, childhood injuries result in physical harm or damage to the child’s body
(KIDSAFE, 2004). The harm caused by injury may be minor, or may result in
significant illness, disability, or even death. The cause of childhood injuries may be
‘intentional’ or ‘unintentional’. Clearly, important differences exist between the risk
factors and pathways implicated in the occurrence of some forms of ‘intentional’ and
‘unintentional’ injuries, child sexual abuse related injuries being a particular case in
point. However, whether the distinction between the two types of injury is necessary,
useful, or practical in both research and prevention contexts relating to physical
injuries to young children is debatable (Peterson & Brown, 1994). In practice,
differentiating some forms of unintentional injury from intentional injury may be very
difficult. Injuries not inflicted upon the child may occur as the result of neglect and
may therefore not be purely ‘unintentional’. Perhaps as a result, the risk factors
identified for ‘intentional’ and ‘unintentional’ physical injuries to children are
numerous, wide ranging and often remarkably similar (Peterson & Brown, 1994).
Given these similarities, it may be better to conceptualise injury of either kind as a
single entity, indicated by multiple risk factors and potentially reduced by broad based
prevention efforts (Peterson & Brown, 1994; Wilson et al., 1991).
Conceptualising child injury causation
Historically, within the field of ‘unintentional’ injury, efforts to understand why
childhood injuries occur initially concentrated on identifying aspects of the
environment that posed a risk for child injury. The resulting environmental risk factor
model of child injury has been highly effective in informing the development and
adoption of safety standards to separate children from injury hazards. Subsequent
work in the field went on to reflect the direction of research in the child abuse and
neglect field and consider the role human factors play in the occurrence of child
injury. While models of injury causation within the child abuse and neglect field
focused on parent or family risk factors, models developed within the ‘unintentional’
injury field concentrated on child-specific risk factors for injury. Identified risk
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factors were conceptualised within a model of ‘accident proneness’. This model has
met with considerable criticism. Opponents argue that implying that an intrinsic trait
is responsible for injury occurrence is not constructive to the injury prevention effort,
as it diverts attention away from modifiable aspects of the environment (Klein, 1980;
Matheny, 1987).
The capacity of environmental, parent and family or child-specific risk factor models
to explain the occurrence of injury is limited because, on their own, each model relies
heavily upon the isolation of single-issue causes. These models fail to address the
reality of human experience where, rather than occur in a vacuum, childhood injuries
occur in the context of exchanges and interactions between the child and their
environment. The integrated working model of child injury proposed by Peterson and
Brown (1994) attends to this reality, and is adopted to guide the research conducted
for this paper. Informed by ecological theory (Bronfebrenner, 1979) and focussed on
physical injury, this model argues that most childhood injuries (‘intentional’ and
‘unintentional’) occur due to the influence of multiple risk factors across domains of
experience. Within the model, risk factors that affects global, ongoing, and pervasive
influences upon the child’s life, are classed ‘background contributors’ to child injury,
whereas factors that act as specific triggers for child injury are termed ‘immediate
contributors’ (Peterson & Brown, 1994). Figure 1 below illustrates the conceptual
framework, developed for this paper.
Figure 1
C o n c e p t u a l F r a m e w o r k
C o n t e x t u a l F a c t o r s
I n j u r y
F a m i l y F a c t o r s C h i l d F a c t o r s
B a c k g r o u n d I n f l u e n c e s
B a c k g r o u n d I n f l u e n c e s
B a c k g r o u n d I n f l u e n c e s
I m m e d i a t eI n f l u e n c e s
I m m e d i a t eI n f l u e n c e s
I m m e d i a t eI n f l u e n c e s
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As observed by Peterson and Brown (1994) the factors chosen to represent each
domain, and the designation of factors as background versus immediate influences,
will vary as a function of the study emphasis and adopted theoretical base. The
flexible quality of the model also inherently mirrors the real-world experiences of
children and families. Because of the inter-relationships between children’s lives,
their family environment and the broader contextual environment in which they exist,
it is reasonable to assume that characteristics of each will be intertwined, influencing
injury to varying degrees (Ramsay, et al., 2003). The following sections will briefly
review evidence that links contextual, family and child factors to childhood injury.
Contextual factors
Contextual factors describe the context within and through which childhood injury
occurs. These factors are specific to the child’s immediate home environment and
their broader community and society. While physical hazards in the child’s
environment constituted the initial focus of injury prevention research, much less
attention has been given to investigating the influence of the broader contextual
domain upon injury occurrence (Reading et al., 1999).
Studies that have assessed the relative influence of contextual factors upon childhood
injuries have mainly done so in the context of epidemiological investigations based on
area-level data (e.g., Alwash & McCarthy, 1988; Dougherty, Pless & Wilkins, 1990;
Towner & Towner; 2001). The most consistent finding reported by these studies is
that variations in the incidence of childhood injury across areas are due to the
differential experience of economic hardship and disadvantage (Jolly, Moller, &
Volkmer, 1993; Neresian, et al., 1985; Reading et al., 1999). Children who live in
low-income neighbourhoods are reported to be between two and three times more
likely than children in higher income neighbourhoods to be injured (Durkin et al.,
1994; Jolly, Moller, & Volkmer, 1993; Neresian, et al., 1985)
Children from families experiencing economic hardship may be at an increased risk of
injury because they have greater exposure to physical hazards in the home and
neighbourhood (Klein, 1980). In this way characteristics of home, neighbourhood and
community that are associated with or the result of economic hardship may act as
immediate contributors or triggers for many childhood injuries. Child pedestrian
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injuries for instance, are linked to living in poorer neighbourhoods, high-density
housing, streets with heavy traffic flow and limited access to safe playgrounds, all
characteristics of public housing estates (Burgess, 1995; Rivara & Barber, 1985;
Roberts et al., 1994). Similarly, child burns and scalds are linked to factors associated
with economic hardship including the use of old or faulty electrical equipment, lack of
smoke detectors and electrical faults indicative of poor quality housing (Burgess,
1995). Preventative efforts aimed at reducing injury whereby faulty products are
recalled or safety standards introduced may be ineffective if the experience of
economic hardship means that families use old or second-hand products (Burgess,
1995).
Social aspects of a child’s broader contextual domain may also affect injury risk.
Living in a noisy home characterised by a sense of disorganization, chaos, and
confusion has been cited as a marker for high risk for injury to young children
(Matheny, 1987). The social climate of the home may be underscored by economic
hardship, as may frequent shifts in residence, a further risk factor for child injury.
Transience in living arrangements has been found to be related to social isolation and
a lack of social support, both of which are noted to exert a significant influence on
child injury (Bronfenbrenner, 1986; Hecht & Hanson, 2001; Peterson & Stern, 1997;
Wazana, Krueger, Raina et al., 1997). While social isolation is likely to be associated
with the experience of economic hardship and unsettled living conditions, a complex
interaction may also exist between these factors, and a number of parent or family
factors, including age, marital status, education and parental mental health and well-
being.
Family Factors
The high incidence of childhood injuries within the home and whilst in the care of
their family signifies the importance of understanding the potential influence family
factors may have upon child injury (Matheny, 1988). The theoretical associations
between broad contextual factors and parental or family factors are marked, and may
provide some clue as to the pathways via which risk for child injury is transmitted.
Mothers of injured children are on average, observed to be younger, less educated and
more likely to be single when compared with mothers of non-injured children
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(Beautrais et al., 1982; McCormick, Shapiro, & Starfield, 1981; Nersesian et al.,
1985; Parker et al., 1991). As stated, these demographic characteristics may influence
child injury occurrence via their association with social isolation. Young and/or single
parents may be isolated from other mothers and a limited education may preclude
them from knowing what poses a risk to their child’s safety or what is required to
prevent injury (Ramsay et al., 2003). Parents with limited education may also not
know how to seek out alternative networks for support and information. Social
isolation is also found to be significantly associated with other adverse outcomes for
parents and families, including health and well-being, emotional coping and mental
illness, all noted risk factors for child injury (Matheny, 1987; Weissman et al., 1986).
Children of mothers who are less active and less emotionally stable have been noted
to be at high risk for child injury (Matheny, 1987) as have children of depressed
mothers. Children of depressed mothers are reported to suffer up to four times the
number of injuries experienced by children of non-depressed mothers (Brown &
Davidson, 1978). Where one or both parents have been treated for depression,
children have been found to suffer more head injuries and other health complications
than children who had parents who did not suffer from depression (Weissman et al.,
1986). These results may be indicative of the stress experienced by families where a
parent is unwell. Several studies have also noted that stress in the family increases risk
for child injury (Beautrais et al., 1982). Not only have injured children been observed
to come from families characterised by numerous stressful life experiences (Horowitz,
1988), times of great stress within the family have also been identified as key points at
which injuries are most likely to occur (Pearn & Nixon, 1977).
Increased rates of childhood injury in families characterised by ill health may also
reflect the influence such stressors have upon parenting capacity and particularly the
ability to accurately assess risk and provide adequate supervision. Research findings
indicate that parents are not always accurate in their assessment of their child’s
abilities and understanding of safety issues (Klein, 1980). The extent to which parents
overestimate their child’s competency and awareness of risk varies as a function of
parenting skill, with poorer parenting skills being associated with higher occurrence
of child injury (Glik, Kronefeld & Jackson, 1993). Radke-Yarrow et al (1985),
suggest that depression in particular, may influence a parent’s ability to moderate their
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child’s behaviour, which may have serious consequences for the provision of parental
supervision. Adequate supervision enables the parent to intervene between the child
and the physical environment to prevent exposure to hazards and to ensure safety
behaviours are learned (Peterson & Stern, 1997). Interactions between parents and
children may however, operate in a two-way fashion and as such, parenting
behaviours may also be influenced by factors specific to the child (Fox, Kimmerly, &
Schaffer, 1991).
Child factors
The role child factors play in influencing injury occurrence remains a contentious
issue. While injured children were previously thought to possess a trait that made
them ‘accident-prone’ it is now considered that child factors probably influence injury
occurrence via their association with broader family and contextual factors.
One of the most common findings in the injury literature is that boys experience more
frequent and more severe injuries than girls (Morrongiello, Ondejko & Littlejohn,
2004). This systematic variation in the injury population first emerges around 2 years
of age and persists across the life-course (Baker, O’Neill, & Ginsberg, 1992; Rivara et
al., 1982). Gender differences in injury occurrence are most commonly explained by
differences between boys and girls’ behavioural patterns and perception of risk. Both
parental reports and experimental studies observe that boys are more disruptive and
engage in more active behaviours when compared to girls (Bijur, Stewart-Brown, &
Butler, 1986; Morrongiello, et al., 2004).
Boys also exhibit more aggressive and hyperactive behaviour than girls (Bijur,
Stewart-Brown, & Butler, 1986). While the relative influence of aggressive versus
hyperactive behaviour upon injury occurrence is debated (Davidson, 1987), there is
general agreement that these two behavioural traits increase the risk of injury due to
their association with increased risk taking behaviour and impulsiveness (Bijur,
Stewart-Brown, & Butler, 1986). When children develop behaviour patterns such as
these they tend to respond to stimuli in their environment in a highly energetic and
rapid fashion, often without stopping to think before they act (Rothbart, Ahadi &
Hershey, 1994). These children may be at a greater risk of injury because the speed at
which they act limits their capacity to anticipate future adverse consequences or
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perceive immediate danger. Boys may also incur more injuries than girls, because as
well as being more likely to engage in active , impulsive and risk taking behaviour,
they also tend to underestimate injury related risks and are more likely to attribute
injuries that do occur to bad luck rather than to their own actions (Alexander, et al.,
1995; Morongeillo, 1997).
Children’s behavioural traits, while varying as a function of gender and
developmental stage, are also highly correlated with family and contextual factors that
are independently associated with injury, including maternal health and well-being,
family experience of economic hardship and dysfunction, confusion and chaos in the
home (Bijur, Stewart-Brown, & Butler, 1986; Campbell, et al., 1991). Results from a
birth cohort study of 10, 394 children indicate that children from low-income families,
who lived in crowded or poor quality housing, who moved frequently and whose
mothers were distressed and unhappy were more likely to be hyperactive and
aggressive and were also injured at a greater rate (Bijur, Stewart-Brown, & Butler,
1986). Further evidence of the influence of environmental factors is provided by
findings which show that confusion and noise in the home, and the absence of regular
sleeping and eating patterns and routines are associated with an increased injury risk
for young boys (Campbell et al., 1991).
Review of the empirical and theoretical origins of the child injury field reveals
numerous correlates of injury across multiple domains of experience. To begin to
unravel the complex relationships among and between these variables, this paper
examines the injury experiences of children in the four-year-old cohort of LSAC.
Method
LSAC is a national prospective longitudinal survey designed to measure child well-
being, health, and development. The Australian Institute of Family Studies (AIFS), on
behalf of the Commonwealth Department of Family and Community Services
conducted the first wave of the study in 2004. LSAC is a random probability sample
of Australian residential households with children born within two specified periods,
forming a infant cohort (children aged 3 months – 1 yr 7 months) and a child cohort
(children aged 4yrs 3 months and 5 years 7 months). Information was obtained from
the person most knowledgeable about the child. This informant (Parent 1) provided
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basic demographic information about all household members, socio-economic
information about her/himself and her/his spouse, and extensive information about the
selected child. Data were collected using a variety of measures including face-to-face
interview, self-complete questionnaire, direct assessment, and observational measures.
In Wave 1, complete data were collected for 5104 infants and 4976 children.
Variables and measures Variables were selected for use in this paper on the basis of their theoretical relevance
to the study of child injury. Due consideration was given to the psychometric
properties of scale scores and new variables were created where necessary. The
majority of variables selected for use in this paper were drawn from primary caregiver
report measures as these represented the most complete data.
Outcome variable Child injury was assessed by Parent 1’s answer to the question: “During the last 12
months how many times was child hurt, injured, or had an accident and needed
medical attention from a doctor or hospital?” From this data two variables were
derived, one identifying injured versus non-injured children and the second
identifying number of times injured. Information on the types of injuries sustained and
whether the child required hospitalisation as a result of their injury was also collected.
Context variables A total of twenty background and immediate variables were examined to characterise
the broader contextual domains of injured and non-injured children; these variables
(displayed in Figure 2 below) included indicators of home environment, housing
quality, tenure and stability, economic hardship and social interaction with the broader
environment. Mean scale scores are used for measures of ‘neighbourhood liveability’,
‘neighbourhood belonging’ and ‘neighbourhood facilities’ to compensate for missing
data. The total number of items endorsed on the ‘economic hardship’ scale is used as
an indicator of economic hardship. Interviewer observation was used to assess
characteristics of the home environment including clutter, noise, and the condition of
the exterior of the home. From information provided on housing tenure a further
variable was derived indicating whether the child or their family lived in public
housing. Further variable specification information may be obtained from the online
LSAC data dictionary (http://www.aifs.gov.au/growingup/home.html).
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Figure 2: Contextual factors examined
• H o u s i n g q u a l i t y• T r a f f i c • N o i s e i n t h e h o m e• C l u t t e r i n t h e h o m e• P a r e n t 1 p e r c e p t i o n o f t h e n e i g h b o u r h o o d a s a p l a c e
t o r a i s e c h i l d r e n . • N u m b e r o f h o m e s c h i l d h a s l i v e d i n s i n c e b i r t h• P a r e n t 1 r e p o r t o f n e i g h b o u r h o o d l i v e a b i l i t y• P a r e n t 1 r e p o r t o f n e i g h b o u r h o o d f a c i l i t i e s• P a r e n t 1 r e p o r t o f n e i g h b o u r h o o d b e l o n g i n g• G e n e r a l c o n d i t i o n o f b u i l d i n g s n e a r b y• D e t a i l s o f m o s t r e c e n t m o v e• D e t a i l s o f h o m e o w n e r s h i p• S t a t e a n d r e g i o n a l l o c a t i o n i n w h i c h c h i l d l i v e s• N u m b e r o f p e o p l e i n t h e h o m e• E c o n o m i c h a r d s h i p
C o n t e x t u a l F a c t o r s
B a c k g r o u n d I n f l u e n c e s
I m m e d i a t eI n f l u e n c e s
Family variables A large number of theoretically relevant variables were selected to characterise the
child’s family domain. Both background and immediate variables were selected
including socio-demographic indicators, behavioural indicators and indicators of
reported health and wellbeing. These variables are displayed in Figure 3 below.
Responses to the scale measures ‘number of stressful life events’ and ‘Parent 1 K6
depression score’ were independently summed to form variables representing ‘number
of stressful life events reported’, and ‘number of depressive symptoms endorsed’.
Only cases with complete data for each depressive scale items were used.
Figure 3: Family factors examined
F a m i l y F a c t o r s
B a c k g r o u n d I n f l u e n c e s
I m m e d i a t eI n f l u e n c e s
• P a r e n t 1 a g e• P a r e n t 1 B M I• P a r e n t 1 e d u c a t i o n• P a r e n t 1 l e g a l / r e g i s t e r e d m a r i t a l s t a t u s• P a r e n t 1 r e p o r t o f p o s t n a t a l d e p r e s s i o n P a r e n t 1
D e p r e s s io n i n v e n t o r y s c o r e• P a r e n t 1 n u m b e r o f s t r e s s f u l l i f e e v e n t s• P a r e n t 1 p a r e n t i n g b e h a v io u r s • P a r e n t 1 r e l a t i o n s h i p q u a l i t y• P a r e n t 1 s e l f r e p o r t o f p a r e n t i n g s k i l l s• P a r e n t 1 r e p o r t o f b e l i e f s a b o u t s u p e r v i s i o n• P a r e n t 1 p r o b l e m s m a n a g i n g s t u d y c h i l d• P a r e n t 1 s o c i a l s u p p o r t• P a r e n t 1 m e d i c a l c o n d i t i o n s• P a r e n t 1 s l e e p p r o b l e m s• P a r e n t 1 a l c o h o l u s e
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Child variables Child-specific variables selected to characterise the sample of injured and non-injured
children included gender and age and indicators of children’s health and behaviour
and their social and emotional well-being. Behavioural patterns were assessed using
scales from the “Strengths and Difficulties Questionnaire”. Higher mean scores on the
measure of prosocial behaviour indicate positive adjustment whereas scores on
hyperactivity, and persistence, conduct and peer approval scales give a measure of
risk for emotional and behavioural problems. Child-specific factors examined are
displayed in Figure 4 below.
Figure 4: Child-specific factors examined
C h i l d F a c t o r s
B a c k g r o u n d I n f l u e n c e s
I m m e d i a t eI n f l u e n c e s
• C h i l d g e n d e r• C h i l d s l e e p p r o b l e m s• C h i l d m e d i c a l c o n d i t i o n s• C h i l d s u f f e r s f r o m A D D / A D H D• C h i l d ’ s c h o i c e o f a c t i v i t y• C h i l d a g e i n m o n t h s• C h i l d p r o s o c i a l i t y s c a l e s c o r e• C h i l d h y p e r a c t i v i t y s c a l e s c o r e• C h i l d p e r s i s t e n c e s c a l e s c o r e• C h i l d c o n d u c t s c a l e s c o r e• C h i l d r e a c t i v e s c a l e s c o r e• I n d i c a t o r o f p r e m a t u r e b i r t h• I n d i c a t o r o f s c h o o l a t t e n d a n c e• C h i l d g e n e r a l h e a l t h• P a r e n t 1 r e p o r t o f c o n c e r n o v e r c h i l d ’ s e m o t i o n a l
h e a l t h a n d / o r b e h a v i o u r a l w e l l b e i n g
Data analysis procedures
The analyses conducted included two main phases: identification of significant risk
factors for child injury and examination of the relationships between and among
significant risk factors and child injury. The first objective of the analyses was to
identify significant risk factors for child injury. This phase of the analyses included
comparison of contextual, family and child factors for injured and non-injured
children, using Pearson Chi Square analyses for binary and ordinal factors, and
analysis of variance (ANOVA) procedures for continuous factors. Logistic regression
analyses were then used to assess further the significant and net effects of
significantly differing variables on child injury. Separate regression models were
fitted for contextual, family and child factors, wherein all variables were entered
simultaneously, and models were compared against a constant only model.
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The second object of the analysis was to examine relationships among and between
significant risk factors and child injury. This phase of the analysis included testing
two potential models of relationships between risk factors and child injury; the
interaction effect model and the third variable effect model.
With the interaction effects model, the potential for significant risk factors to modify
the relationship between other potential risk factors and child injury was assessed
(Baron & Kenny, 1986). Logistic regression was used throughout the analyses with
childhood injury as the dependent variable. Once the main effects of selected
variables were examined, two-way interaction terms between the most significant
factors and other terms in the model were entered into the model and tested. Separate
regression models were first formed for each domain. After the three domains of
factors were examined, those variables within each domain that made a significant
contribution (main effects and interaction terms) to the models were entered into a
summary integrated regression model.
Third variable analyses were used to sort out the direct and indirect effects of
significant risk factors using a series of logistic regression analyses (Baron & Kenny,
1986). To illustrate, to assess the indirect influence of Parent 1 economic hardship (as
an independent variable) on injury (as a dependent variable) using Parent 1 BMI as a
third variable, the following regression equations are estimated:
Equation 1: Injury = β01 + β11 economic hardship
Equation 2: Parent 1 BMI = β02 + β12 economic hardship + error 2
Equation 3: Injury = β03 + β13 Parent 1 BMI + β23 economic hardship
To establish an indirect effect, the independent variable must be significantly related
to the dependent variable in the first equation, the independent variable must be
significantly related to the third variable in the second equation and the third variable
must be significantly related to the dependent variable in the third equation. The
magnitude and direction of the relationship between the independent variable and the
dependent variable are inspected to assess the nature of third variable effect.
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Results
In total, 883 children, representing almost 18 percent (17.7%) of the sample of four
year olds were reported by Parent 1 as having sustained a physical injury requiring
medical attention in the year prior to interview. Children who were injured sustained
between one and eight injuries, with the majority (75%) reported as having been
injured only once in the past year. The number of children hospitalised for at least one
night as a result of their injuries totalled 77 representing almost 9 percent (8.7%) of all
those injured. The frequency with which each recorded physical injury type was
reported is displayed in Chart 1 below.
Chart 1. Frequency of physical injuries experienced
0 50 100 150 200 250 300 350 400 450
Cut or scrapeOther
Broken bonesHead injury
Sprain or strainBurn or scaldDental injury
DislocationAccidental poisoningOther internal injury
Frequency (n)
Differences between injured and non-injured children
Comparative analysis characterises injured children as potentially experiencing
disadvantage and vulnerability across multiple aspects of their life. Figure 7 lists the
contextual, family and child-specific factors found to differ significantly between
injured and non-injured children. Extended data tables reporting full results are
contained in the Appendix.
Comparison of the broad contextual domains inhabited by injured and non-injured
children reveal that significant differences exist between the two groups in relation to
both background and immediate contextual factors. Injured children were more likely
to live in neighbourhoods considered by Parent 1 as less liveable and less desirable as
a place to raise children.
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Figure 7. Significant differences between injured and non-injured children
C o n t e x t u a l F a c t o r s
• N e ig h b o u r h o o d l iv e a b i l i t y• N u m b e r o f h o m e s• E c o n o m ic h a r d s h ip• H o u s in g q u a l i t y• H e a v y t r a f f ic• N o is e in t h e h o m e• C lu t t e r in t h e h o m e• N e ig h b o u r h o o d a s a p la c e t o r a is e c h i ld r e n• P u b l ic h o u s in g t e n a n c y
F a m i ly F a c t o r s
• P a r e n t 1 B M I• P a r e n t 1 a g e• P a r e n t in g s k i l l s• L if e e v e n t s• P a r e n t 1 S le e p p r o b le m s• P a r e n t 1 e d u c a t io n• P a r e n t 1 m e d ic a l c o n d i t io n s• P a r e n t 1 p o s t n a t a l d e p r e s s io n • P a r e n t 1 m a r i t a l s t a t u s
C h i ld F a c t o r s
• A g e o f c h i ld• C h i ld h y p e r a c t iv i t y• C h i ld p e r s i s t e n c e• C h i ld p r o s o c ia l i t y• C h i ld c o n d u c t• C h i ld ’ s e m o t io n a l h e a l t h o f c o n c e r n• C h i ld s le e p in g p r o b le m s• C h i ld g e n d e r• C h i ld A D D /A D H D• C h i ld m e d ic a l c o n d i t io n s• C h i ld ’ s c h o ic e o f a c t iv i t y
No significant differences were found however between the two groups in relation to
neighbourhood belonging, facilities or crowding in the home. Primary caregivers of
injured children are more likely than the caregivers of non-injured children to report
greater experience of economic hardship, were more likely to report more changes in
residence since the birth of the study child and were more likely to live in public
housing. Interviewer observations of the home environment found no differences in
the condition of buildings close to or nearby injured and non-injured children’s
homes. However children whose own homes were in poor condition were found to be
40 percent (40%) greater risk for injury than children living in homes that were in fair
or well-kept condition. Injured children’s homes were also more likely than those of
non-injured children to be cluttered, noisy, or close to heavy traffic.
The family environments of injured children were distinguished from those of non-
injured children by greater report of primary caregiver medical conditions, including
post-natal depression after the birth of the study child. No significant differences were
found between parent’s report of depressive symptoms as measured by the K6
depression scale. Primary caregivers of injured children were found to have higher
body mass index (BMI) scores when compared to parents of non-injured children and
also reported experiencing poorer quality sleep. No significant differences were found
16
between alcohol use reported by the caregivers of injured and non-injured children.
Consistent with findings regarding economic hardship, primary caregivers of injured
children were also slighter younger than those of non-injured children, had lower
education levels and were less likely to report being married. Caregivers of injured
children also reported experiencing more stressful life events in the year prior to
interview. No significant differences were found between beliefs regarding
supervision or the warmth, consistency, or hostility of parenting behaviours between
caregivers of injured and non-injured children. Caregivers of injured children were
however less likely to report confidence in their parenting skill or ability when
compared to caregivers of non-injured children.
Injured children were slightly younger than non-injured children and boys were
around 30 percent (29%) more likely to be injured than girls. Injured children were
more likely than non-injured children to have ongoing medical conditions and sleep
problems. Injured children displayed more hyperactive behaviour and evidenced
poorer adjustment across measures of persistence, conduct and prosocial behaviour.
No differences were found between the two groups’ scores on measures of peer
approval or reactivity but primary caregivers of injured children reported expending
greater worry and concern over their child’s emotional wellbeing, happiness and/or
behaviour than caregivers of non-injured children. Associated with increased rates of
hyperactivity is the finding that injured children were more likely to choose active
rather than inactive pastimes children than non-injured children and those reported as
suffering from ADD or ADHD were almost twice as likely as children without
diagnosed problems to be injured.
Risk factors for child injury
Factors identified as differing significantly between injured and non-injured children
were analysed using logistic regression to assess further their significant and net
effects on childhood injury. Separate logistic regression models were formed to assess
which factors from each domain were important in understanding the occurrence of
the dependent variable ‘child injury’.
Complete data available for analysis differed between models. Data for 1036 children
were available for analysis of contextual factors, 2509 for analysis of family factors
17
and 2208 for analysis of child factors. The number of cases with complete data for
contextual factors was diminished by the inclusion of the ‘public housing’ variable.
Testing revealed however, that omission of this factor from the model significantly
reduced the model’s effectiveness in explaining child injury. Regression models from
each domain were found to be statistically significant indicating that contextual,
family and child-specific risk factors, as a set, had some impact on the dependent
variable (child injury). The proportion of the variance accounted for by risk factors
from each domain was limited, with adjusted estimates ranging from around 2 to 5
percent. Figure 8 below identifies variables within each domain identified as
significant risk factors for child injury.
Figure 8. Significant risk factors for child injury
S i g n i f i c a n t C o n t e x t u a l
R i s k F a c t o r s
E c o n o m i c h a r d s h i p
H e a v y t r a f f i c
S i g n i f i c a n t F a m i l y
R i s k F a c t o r s
P a r e n t 1 a g e
P a r e n t 1 B M I
S i g n i f i c a n t C h i l d
R i s k F a c t o r s
M a l e g e n d e r
H y p e r a c t i v i t y
I n j u r y
Controlling for the effect of all other contextual domain variables, the factors
‘economic hardship’, and ‘heavy traffic’, were statistically significant risk factors for
child injury. As a significant background contributor, the experience of economic
hardship may have global, pervasive, and ongoing effects upon the child’s home and
family environment whereas heavy traffic, as an immediate risk factor may act to
trigger injury events.
The factors ‘Parent 1 age’ and ‘Parent 1 BMI’ were found to be significantly
associated with child injury when the effect of all other family factors was controlled
for. The direction of these associations differed however. The factor ‘Parent 1 age’
was negatively associated with child injury, with higher parental age decreasing the
18
odds of child injury. The factor ‘Parent 1 BMI’ was positively associated with child
injury with higher BMI scores associated with greater injury risk. The factor ‘number
of stressful life events’ neared but did not reach, statistical significance.
Controlling for the effect of all other child-specific variables the factors ‘male gender’
and ‘hyperactivity’ were both found to be significant risk factors for child injury. The
association between child injury and a number of other child-specific factors
including ‘child sleep problems’, ‘child prosociality’, ‘child conduct’ and ‘child’s
choice of activity’ neared but did not reach statistical significance.
Across the individual domain specific regression models, the factor most strongly
associated with child injury was ‘heavy traffic’, with living in a street with heavy
traffic increasing the odds for child injury by 50 percent (50%). However, when the
significant variables from each domain were entered simultaneously into an integrated
summary regression model, the variables ‘heavy traffic’ and ‘economic hardship’
failed to reach significance. All other significant risk factors retained their association
with child injury.
Investigating relationships between risk factors
Interaction effects Evidence from the child injury literature suggests that the effect of some variables
indicated as risk factors for child injury are likely to interact with the effect of others.
The hypothesis examined in testing interaction models is that the effect of some risk
factors may be more strongly related to injury for some people or in some
circumstances than for others.
The model building strategy suggested by Hosmer and Lemeshow (1989) guided the
selection of covariates for the interaction models formed for each domain. Variables
were included based upon their theoretical importance and their demonstrated
statistical significance (p< 0.05, 95% confidence intervals). Changes to the scale of
some variables were done whenever necessary after verification of the assumption of
linearity in the logit (Hosmer & Lemeshow, 1989). Two-way interaction terms
between the most significant factors and all other variables for that domain were
19
entered into the model and tested. Extended data tables reporting full results are
contained in the Appendix.
For the contextual domain interaction terms included the product of the background
risk factor ‘economic hardship’ and all other contextual variables and the product of
the immediate risk factor ‘heavy traffic’ and all other contextual variables. Addition
of these terms to the regression model found no significant interaction effects.
Significant interaction effects were however observed within the family and child
domains.
Interaction terms for the family domain included the product of ‘Parent 1 BMI’ and
all other family variables, the product of ‘Parent 1 age’ and all other family variables
and the product of ‘number of stressful life events’ and all other family variables.
Addition of the interaction terms to the model of contextual factors revealed a
significant interaction between the variables ‘Parent 1 BMI’ and ‘Parent 1 self report
of parenting skills’. The effect of ‘Parent 1 BMI’ upon child injury was greater when
parents reported less confidence in their parenting skills. The interaction between
number of stressful life events and parenting skills neared but did not reach
significance. Inclusion of the interaction terms in the model meant that the variable
‘number of stressful life events’ reached statistical significance, but that ‘Parent 1
BMI’ was no longer significantly associated with the dependent variable. The variable
‘Parent 1 age’ maintained its significant negative association with child injury when
interaction terms were included in the model.
Interaction terms for the child domain included the product of ‘male gender’ and all
other child variables and the product of ‘hyperactivity’ and all other child variables.
Addition of these interaction terms to the child domain regression model revealed a
significant interaction between the variables ‘hyperactivity’ and ‘child sleep
problems’ and also between ‘emotional health a worry’ and ‘male gender’. The effect
of the variable ‘hyperactivity’ upon child injury was greater for children whose sleep
problems were reported to be problematic rather than non-problematic. The combined
effect of ‘emotional health a worry’ and ‘male gender’ decreased the odds of child
injury. Inclusion of these significant interaction terms meant that the variable ‘child’s
choice of activity’ reached statistical significance. The variables ‘male gender’ and
20
‘hyperactivity’ retained their significant effect upon child injury when the effect of all
other child factors and significant interaction terms were controlled for.
Variables significantly associated within child injury were then assessed for their
potential to interact with factors from other domains. Interaction terms including the
product of ‘male gender’, ‘hyperactivity’, ‘heavy traffic’, ‘Parent 1 age’, ‘Parent 1
BMI’, ‘number of stressful life events’, ‘economic hardship’ with all other variables
were added to a logistic regression model containing their component terms. All
variables (main effects and interaction terms) found to make a significant contribution
to the dependent variable (child injury) were simultaneously entered into a summary
logistic regression model. As a whole the factors in this final summary model were
significant predictors of child injury, accounting collectively for around 7 percent
(7%), of the variance in the dependent variable. Figure 9 below displays factors and
interaction terms in the final summary model. Factors and terms marked with an
asterisk were significantly associated with child injury.
Figure 9. Final summary model
E c o n o m ic h a r d s h i pT ra ff icP a re n t 1 a g e *P a re n t 1 B M IL ife e v e n tsP a re n ti n g S k il lsH yp e ra c t iv it yM a le g e n d e rC h o ic e o f a c t iv it yE m o t io n a l h e a l th c o n c e rn sC h il d s le e p p ro b le m s *
E m o t io n a l h e a l th c o n c e rn s x T ra f f ic *E c o n o m ic h a r d s h i p x C h i ld s le e p p r o b s .*
H yp e ra c tiv it y x T ra ffic *C h o ic e o f a c t iv it y x M a le g e n d e r
P a re n t 1 B M I x P a re n t i n g s k ills *L ife e v e n ts x P a re n ti n g s k il ls
I n ju r y
As previously found, the variable ‘Parent 1 age’ was negatively associated with child
injury with older parent age associated with a decreased risk of child injury. The
variable ‘child sleep problems’ was positively associated with child injury with
children reported as having problematic sleep being at greater risk for injury than
those with non-problematic sleep patterns. The direct association between injury and
21
several other variables including ‘heavy traffic’, ‘number of stressful life events’,
‘hyperactivity’, ‘child’s choice of activity’ and ‘self report of parenting skills’ neared
but did not reach statistical significance. The interactive effects of the variables
‘child’s emotional health a worry’ and ‘heavy traffic’ and the variables ‘economic
hardship’ and ‘child sleep problems’ both reduced the odds of child injury whereas
the interactive effects of the variables ‘Parent 1 BMI’ and ‘self report of parenting
skills’ and ‘hyperactivity’ and ‘heavy traffic’ both acted to increase the odds of child
injury.
Third variable effects Third variable analyses were used to examine the potential of significant risk factors
to influence injury occurrence through indirect or ‘third variable’ effects. To do so, a
series of three logistic regression analyses are performed for each investigation.
Providing the conditions of third variable analysis are met, the magnitude and
direction of the relationship between the independent variable and the dependent
variable are inspected to assess the nature of third variable effect. Where addition of
the third variable diminishes the relation between the independent variable and the
dependent variable, mediation or confounding pathways may be indicated. Mediation
pathways, by definition, imply that the independent variable causes the third variable
(the mediator), which, in turn causes the dependent variable (MacKinnon, Krull, &
Lockwood). By contrast confounding pathways suggest that the third variable (the
confounder), explains the relationship between the independent and dependent
variable, but a causal relationship is not necessarily implied (MacKinnon, Krull, &
Lockwood). Where addition of the third variables results in an increased relation
between the independent variable and the dependent variable, pathways may involve
suppression effects. For the purpose of these analyses where necessary continuous
variables were reduced to a binary form with 0 indicating below mean scores and 1
indicating above mean scores. Significance of third variable effects were assessed
using Sobel’s test method (Sobel, 1986). Full details of all analyses conducted are
presented in the Appendix.
Significant third variable effects Analyses revealed that two background risk factors, ‘economic hardship’ and
‘stressful life events’, were involved in third variable or indirect pathways to injury.
22
Third variable effects involving ‘economic hardship’ Third variable analyses reveal that the background risk factor ‘economic hardship’ is
implicated in numerous indirect pathways to child injury. Including economic
hardship as a third variable in the regression model significantly reduced the
magnitude of the relation between child injury and the contextual factors; ‘public
housing’ ‘how you feel about your neighbourhood as a place to raise children’, and
‘background noise in the home’, the family factors; ‘Parent 1 education’, ‘self report
of parenting skills’ ‘number of stressful life events’ and the child factor ‘emotional
health a worry’. These results suggest economic hardship may influence child injury
through mediation or confounding pathways.
An example of a mediating pathway may be where low levels of education lead to
economic hardship, which in turn is associated with injury. The magnitude of the
relationship between education and child injury is reduced because economic hardship
explains part or all of the relationship between education and injury. Economic
hardship may confound the relationship between parenting skill and child injury in the
following way; parents experiencing greater economic hardship may express less
confidence in their parenting skill than parents who were under less economic strain,
and children from families experiencing economic hardship may also more likely to
be injured. Parenting skills and child injury are thus related through a common
confounder, economic hardship. Parenting skills do not cause economic hardship,
which then causes injury, but the relationship between parenting skills and child
injury is reduced in magnitude because the distortion due to economic hardship is
removed.
Including economic hardship in the regression model, significantly increased the
magnitude of the relationship between ‘child sleep problems’ and child injury,
suggesting economic hardship may also operate as suppressor variable. The increase
in the magnitude of the relationship between child sleep problems and child injury
may be because economic hardship explains the variability in sleep problems, or that
sleep problems are more common among children from families experiencing
economic hardship.
23
Economic hardship also significantly influences child injury through indirect effects
where; ‘Parent 1 BMI’, ‘neighbourhood liveability’, ‘clutter in the home’ ‘heavy
traffic in the street’ ‘Parent 1 sleep quality’ and ‘housing quality’ are entered as third
variables. In these instances the relationship between economic hardship and child
injury is explained by the causal relationship between economic hardship and factors
such as clutter in the home etc. Figure 10. illustrates the pathways via which
economic hardship may be associated with child injury. The letters (M), (C), and (S)
indicate that economic hardship significantly mediates (M), confounds (C), or
suppresses (S) the influence of the independent variable upon the dependent variable
(child injury). Variables marked with an asterisk were found to significantly mediate
the relation between economic hardship and child injury.
Figure 10. Third variable effects involving ‘economic hardship’
E c o n o m i c h a r d s h i p
• P u b l ic h o u s in g ( C )• N e i g h b o u r h o o d a s a p la c e t o r a is e c h ild r e n ( C )• N o is e ( C )• P a r e n t in g s k i l l ( C )• C h i ld ’ s e m o t io n a l h e a lt h a w o r r y ( C )• P a r e n t 1 e d u c a t io n ( M )• S t r e s s f u l l if e e v e n t s ( M )• C h i ld s l e e p p r o b l e m s ( S )• N e i g h b o u r h o o d l iv e a b i l it y *• C lu t t e r in t h e h o m e *• H e a v y t r a f f ic *• H o u s in g q u a l it y *• P a r e n t 1 B M I *• P a r e n t 1 s le e p q u a l it y *• H y p e r a c t iv it y *
I n ju r y
Third variable effects involving ‘number of stressful life events’ The family risk factor ‘number of stressful life events’ was also found to be involved
in fewer indirect pathways to injury than ‘economic hardship’. Including ‘stressful life
events’ as a third variable in the regression model significantly reduced the magnitude
of the relation between child injury and the family factors ‘Parent 1 sleep quality’ and
‘self report of parenting skill’. Similar effects were observed for the child factors
‘child sleep problems’ and ‘emotional health a worry’. Including ‘stressful life events’
in the regression model, significantly increased the magnitude of the relationship
between ‘Parent 1 BMI’ and child injury, suggesting ‘stressful life events’ may also
operate as suppressor variable. The factor ‘stressful life events’ was also found to
24
significantly influence child injury through indirect effects where the factors ‘marital
status’, and ‘Parent 1 education’ were entered as third variables. Figure 11. illustrates
the pathways via which ‘stressful life events’ may be associated with child injury,
again the letters (M), (C), and (S) indicate that ‘stressful life events’ significantly
mediate (M), confound (C), or suppress (S) the influence of the independent variable
upon the dependent variable (child injury). Variables marked with one asterisk were
found to significantly mediate the relation between ‘stressful life events’ and child
injury, whereas those marked with two asterisks were found to significantly confound
the relation between the ‘stressful life events’ and child injury.
Figure 11. Third variable effects involving ‘stressful life events’
S tr e s s f u l l if e e v e n ts
• P a re n t 1 s l e e p q u a l i t y (C )• P a re n t i n g s k i l l (C )• C h i l d sl e e p p ro b l e m s (C )• C h i l d e m o ti o n a l h e a l t h a w o r ry (C )• P a re n t 1 B M I (S )• M a r t i a l s t a t u s *• P a re n t 1 e d u c a t i o n * *
In j u r y
Discussion
Recognition of the prevalence and potentially fatal consequences of childhood injury
have driven researchers, practitioners and policy makers alike to search for those
factors associated with increased risk for child injury. While evidence suggests that
factors specific to the child, their family, and their broader contextual environment are
key influences upon childhood injury, few studies have assessed the relationships
between risk factors and the direct and indirect pathways via which risk is transmitted.
Using data from the four year-old-cohort of LSAC this paper presents an empirical
application of Peterson and Brown’s (1994) integrated working model of child injury.
It considered the potential of a wide range of child, family and contextual
25
characteristics to act as risk factors for child injury and for inter-relationships between
significant risk factors to provide some insight into the pathways and processes via
which injury occurs.
Results of analyses conducted indicate that apart from their injury experience, injured
children significantly differed from non-injured children across contextual, family and
child specific factors. These differences collectively characterise injured children in
the study sample as potentially experiencing disadvantage and vulnerability in many
aspects of their life. Injured children’s homes were more likely than those of non-
injured children to be cluttered, noisy and close to heavy traffic. The primary
caregivers of injured children reported greater economic hardship than those of non-
injured children and also described their neighbourhoods as less liveable and less
desirable as a place to bring up children. Children living in homes that were in poor
condition were 40 percent (40%) greater risk for injury compared to children living in
homes in better condition. Children living in public housing were almost 30 percent
(28%) more likely to be injured than children living in other rental accommodation.
The primary caregivers of injured children were slightly younger and less educated
than those of non-injured children. They were also generally less healthy than
caregivers of non-injured children, being more likely to have ongoing medical
conditions, poorer sleep quality, and higher BMI scores. When compared to their non-
injured counterparts, injured children were more likely to choose active pastimes and
display more hyperactive behaviour. Injured children were also less healthy than non-
injured children, having more ongoing medical conditions and more problematic sleep
patterns. Boys were at around 30 percent (29%) greater risk of injury than girls and
children with ADD or ADHD were almost twice as likely as those without attention
problems to be injured.
Further examination identified that within each domain of life experience, statistically
significant background and immediate risk factors for childhood injury exist. These
findings provide support to the Peterson and Brown (1994) integrated working model
of child injury and indicate that rather than caused by any one single factor, child
injury is potentially associated with multiple risk factors across contextual, family and
child specific domains. Significant contextual risk factors identified for childhood
injury included ‘heavy traffic’ and ‘economic hardship’, and significant family risk
26
factors included ‘Parent 1 age’ and ‘Parent 1 BMI’. Significant child risk factors for
injury were ‘male gender’ and ‘hyperactivity’.
Given the nested nature of child, family and contextual domain factors, and the fact
that childhood injuries invariably occur within and through the context of multiple
risk factors, the manner in which risk factors influence injury may not be direct or
linear. Assessment of the relationships among and between significant risk factors and
child injury revealed both interaction and third variable models as likely pathways or
processes via which injury risk was transmitted.
Significant interaction models identified that the effect of some risk factors was more
strongly related to injury for some people or in some circumstances than for others.
For example, the effect of ‘Parent 1 BMI’ upon injury was greater when less
confidence was reported in the parenting role and ‘hyperactivity’ posed a greater risk
for child injury when the child lived in a street with heavy traffic. Interactive effects
also reduced the likelihood of injury. When the effects of worrisome emotional health
and heavy traffic were combined a decreased risk of child injury was observed.
Similar results were found when the effects of problematic sleep and economic
hardship were combined. This is likely to be due to the fact that when combined, the
individual effects of these risk factors cancel each other out.
Third variable analyses revealed that the risk factors, ‘economic hardship’ and
‘stressful life events’, were implicated in numerous third variable or indirect pathways
to child injury. These factors influenced the occurrence of child injury via their inter-
relationships with other significant risk factors. Importantly, economic hardship had
an indirect influence upon child injury via its causal relationship with a number of
other significant risk factors across domains of experience and characteristic. This
finding is consistent with other studies that suggest economic disadvantage has
moderate to strong influence upon the injuries experienced by Australian children
(Jolly, Moller & Volkmer, 1993) and that economic disadvantage may influence the
occurrence of child injury through a combination of mechanisms and processes
(Jencks & Mayer, 1990; Platt & Pharaoh, 1996).
27
The findings presented in this paper support the general conclusion that childhood
injury is likely to be influenced by multiple risk factors, and that these risk factors are
likely to influence the occurrence of injury via interactive and indirect pathways.
These findings should, however, be considered in light of limitations related to
measurement and methodology. Firstly, while commonly used in the injury literature,
caregiver report of child injury requiring medical attention may be subject to recall
bias (Morongiello, 1997; Schwebel et al., 2004). The measure may also be biased by
the effect of factors such as geographical location, access to services, caregiver health
and wellbeing and caregiver knowledge of injury care. Secondly, the outcome
measure used was limited to the occurrence of any injury in the 12 months prior to the
survey. Future research could further refine the analyses conducted to needed to take
into account the types of injuries involved, as some risk indicators may be related to
injuries in general but not to specific types of injuries (Wazana, 1997). Finally, the
research conducted did not consider the potential for factors related to Parent 2 to
influence injury. It may be that Parent 2 characteristics and experiences also affect
injury through both direct and indirect pathways and processes.
Conclusion
The findings presented in this paper support the conceptualisation of childhood injury
as the result of exchanges and interactions between the child and their family and their
broader contextual environment. These findings may also warrant examination of the
most effective mix of programs and interventions to prevent childhood injury. An
important implication of the findings presented may be that aspects of the broader
contextual environment should not be considered in isolation from family and child
factors. Because interrelationships across domains may explain the pathways via
which injury occurs, preventative strategies that focus on risk factors from any one
domain without paying attention to related risk factors from other domains may be
limited in their capacity to reduce injury incidence. Government early childhood and
parenting programs that target some of the family and child risk factors identified in
this paper may be an important complement to existing preventative programs
addressing contextual risks.
28
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Comparative Analysis ~ Parent/family characteristics, injured vs. non-injured children Non-Injured Children -Injured Children Parent/family variables (continuous).
Significance calculated using unweighted data Unweighted sample Weighted sample Unweighted sample Weighted sample N Mean S.D N Mean S.D N Mean S.D N Mean S.D Parent 1 “Body Mass Index Measure”(banded measure) Significance: F(1, 2812) =9.375, p=.002.
2317 2.58 1.090 149625 2.59 1.100 496 2.74 1.147 25102 2.78 1.167
Parent 1 “Age in years” Significance: F(1, 4974) =14.279, p=<.001
4093 34.89 5.481 207064 34.78 5.545 882 34.12 5.435 45707 33.98 5.454
Parent 1 “Self report of parenting skills” Significance: F(1, 4961) =4.053, p=.044
4081 3.94 .885 206382 3.95 .895 881 3.88 .899 45655 3.89 .899
Parent 1 “Number of live events in past year” Significance: Kruskal Wallis ( ²=9.456, df(1), N(4225),p..002)
3492 1.57 1.716 175607 1.59 1.746 733 1.86 2.042 38168 1.92 2.089
Non-Injured Children Injured Children Unweighted Weighted Unweighted Weighted
Parent/family variables (categorical)
N % N % N % N %
Significance
(calculated using unweighted data) Parent 1 “Sleep problems” Very good Fairly good Fairly bad Very bad
777 2043 898 376
19.0% 49.9% 21.9% 9.2%
39398 102552 45383 19771
19.0% 49.5% 21.9% 9.5%
139 424 222 97
15.8% 48.1% 25.2% 11.0%
7177 21839 11479 5194
15.7% 47.8% 25.1% 11.4%
Pearson ( ²=10.462, df(3), N(4976),p.015)
Relative Risk using recoded variable (very or fairly good vs. fairly or very bad)
= 1.20 (95% c.i <1.06, 1.36>) Parent 1 “Highest year of education” Year 12 Year 11 Year 10 Year 9 Year 8 or below.
2431 532 847 174 107
59.4% 13.0% 20.7% 4.3% 2.6%
102516 33467 53251 10742 7005
49.5% 16.2% 25.7% 5.2% 3.4%
461 144 214 39 23
52.3% 16.3% 24.3% 4.4% 2.6%
18909 8659 14011 2613 1527
41.1% 18.9% 30.6% 5.7% 3.3%
Pearson ( ²=16.647, df(4), N(4972),p.002) Relative Risk using recoded variable
( Yr 12 vs. Below Yr 12) = 1.26, (95% c.i. <1.12, 1.43>)
Parent 1 “Any medical conditions” No Yes
3023 1071
73.8% 26.2%
152348 54758
73.6% 26.4%
621 262
70.3% 29.7%
31707 14060
69.3% 30.7%
Pearson ( ²=4.567, df(1), N(4977),p.033)
Relative Risk = 1.15 (95% c.i.<1.01, 1.32>) Parent 1 “Post natal depression after birth of study child” No Yes
2634 449
85.4% 14.6%
131652 22764
85.3% 14.7%
533 119
81.7% 18.3%
27605 6215
81.6% 18.4%
Pearson ( ²=5.676, df(1), N(3735),p.017)
Relative Risk = 1.24 (95% c.i.<1.04, 1.49>)
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Comparative Analysis~ Child characteristics, injured vs. non-injured children Non-Injured Children -Injured Children Child Variables (continuous).
Significance calculated using unweighted data Unweighted sample Weighted sample Unweighted sample Weighted sample N Mean S.D N Mean S.D N Mean S.D N Mean S.D “Age of child in months” Significance: F(1,4976) =3.869, p=.049.
4094 56.94 2.670 207105 57.06 2.683 883 46.75 2.509 45766 56.87 2.527
“Child Hyperactivity (mean SDQ scale score)” Significance: F(1, 4963) =25.632, p=.<.001
4082 3.43 2.270 206335 3.50 2.274 882 3.86 2.336 45734 3.97 2.320
“Child Persistence (mean SDQ scale score)” Significance: F(1, 4194) =6.455, p=.011
3472 3.937 .945 174588 3.92 .962 723 3.839 .965 37545 3.892 .982
Child Prosociality (mean SDQ scale score)” Significance: F(1, 4963) =4.790, p=.029
4082 7.76 1.780 206335 7.75 1.788 882 7.62 1.824 45734 7.62 1.856
“Child Conduct (mean SDQ scale score)” Significance: Kruskal Wallis ( ²=20.093, df(1), N(4964),p001)
4082 7.57 1.979 206335 7.53 2.000 882 7.21 2.127 45734 7.14 2.133
Non-Injured Children Injured Children Unweighted Weighted Unweighted Weighted
Child variables (categorical)
N % N % N % N %
Significance
(calculated using unweighted data) “Child’s emotional health is a worry?” None A little bit Somewhat Quite a lot A lot
1880 1338 517 263 96
45.9% 32.7% 12.6% 6.4% 2.3%
96395 66393 25549 13675 5094
46.5% 32.1% 12.3% 6.6% 2.5%
365 272 127 85 34
41.3% 30.8% 14.4% 9.6% 3.9%
18641 13959 6468 4744 1954
40.7% 30.5% 14.1% 10.4% 4.3%
Pearson ( ²=22.860, df(4), N(4977),p<.001)
Relative Risk using recoded variable (No concerns vs.Cconcerns)
= 1.17, (95% c.i <1.03, 1.32>)
“Child’s sleeping patterns a problem?” Large problem Moderate problem Small problem No problem
174 356 815 2747
4.3% 8.7% 19.9% 67.1%
9142 17878 40943 139057
4.4% 8.6% 19.8% 67.2%
52 90 185 556
5.9% 10.2% 21.0% 63.0%
2743 4988 9120 28915
6.0% 10.9% 19.9% 63.2%
Pearson ( ²=8.372, df(3), N(4975),p..039)
Relative Risk using recoded variable (No problems vs. Problems)
= 1.16, (95% c.i. <1.03, 1.31>) “Child gender” Female Male
2065 2029
50.4% 49.6%
103593 103512
50.0% 50.0.%
378 505
42.8% 57.2%
19773 25994
43.2% 56.8%
Pearson ( ²=16.925, df(1), N(4977), p<.001) Relative Risk = 1.29 (95% c.i.<1.14, 1.45>)
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Non-Injured Children Injured Children
Unweighted Weighted Unweighted Weighted Child variables (categorical) cont.
N % N % N % N %
Significance
(calculated using unweighted data) “Child has ongoing medical conditions No Yes
3294 800
80.5% 19.5%
165719 41386
80.0% 20.0%
668 215
75.7% 24.3%
34298 11468
74.9% 25.1%
Pearson ( ²=10.343, df(1), N(4977),p.001) Relative Risk = 1.26 (95% c.i.<1.10, 1.44>)
“Child has ADD or ADHD” No Yes
4058 36
99.1% 0.9%
205104 2001
99.0% 1.0%
865 18
98.0% 2.0%
44729 1037
37.7% 2.3%
Pearson ( ²=9.094, df(1), N(4977),p.003)
Relative Risk = 1.90 (95% c.i.<1.29, 2.78>) “Child’s choice to spend free time” Inactive pursuits Either inactive or active pursuits Active pursuits
1030 1898 1162
25.2% 46.4% 28.4%
54764 92771 59362
26.5% 44.8% 28.7%
186 413 282
21.1% 46.9% 32.0%
10666 20594 14372
23.4% 45.1% 31.5%
Pearson ( ²=8.177, df(2), N(4971),p..017) Relative Risk using recoded variable
(Inactive vs. Active pursuits) = 1.28, (95% c.i. <1.08, 1.51>)
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Multivariate Logistic Regression Models.
Model 2. All significantly differing parent / family variables Parent/family variables: model sig. (Wald χ²= 33.485, df(9), N(2509), p<.001), Nagelkerke R Square =.022 Variables Entered B SE Prob. Odds Ratio 95% C.I Parent 1 age -.024 .011 .028 .976 .956, .997 Parent 1 legal / registered marital status .045 .040 .257 1.046 .967, 1.132 Parent 1 BMI .119 .046 .010 1.126 1.029, 1.233 Parent 1 education .084 .053 .112 1.088 .980, 1.207 Number of stressful life events in past year .051 .029 .075 1.053 .995, 1.214 Parent 1 report of parenting skills .074 .061 .226 1.077 .955, 1.114 Parent 1 medical conditions -.125 .123 .311 .883 .694, 1.124 Parent 1 post natal depression with study child .142 .143 .324 1.152 .870, 1.526 Parent 1 sleep problems .118 .114 .302 1.125 .899, 1.408
Model 3, Significant parent / family variables plus significant interaction terms. Parent/family variables: model sig. (Wald χ²= 43.554, df(9), N(2805), p<.001), Nagelkerke R Square =.025 Variables Entered B SE Prob. Odds Ratio 95% C.I Parent 1 age -.030 .010 .003 .970 .951, .990 Parent 1 legal / registered marital status .032 .038 .406 1.032 .958, 1.112 Parent 1 BMI -.216 .120 .071 .806 .637, 1.019 Parent 1 sleep problems .112 .108 .298 1.119 .906, 1.381 Parent 1 education .059 .050 .237 1.061 .962, 1.170 Parent 1 report of parenting skills -.249 .152 .102 .779 .578, 1.050 Number of stressful life events in past year (parent) .170 .066 .010 1.185 1.041, 1.348 Parent 1 BMI x Self report of parenting skills .148 .051 .003 1.160 1.051, 1.281 Number of life events x parenting skills -.055 .028 .053 .947 .895, 1.001
Model 1. All significantly differing context variables Contextual variables: model sig. (Wald χ²= 31.414, df(9), N(1036), p<.001), Nagelkerke R Square =.048 Variables Entered B SE Prob. Odds Ratio 95% C.I Number of homes child has lived in .082 .108 .449 1.085 .878, 1.342 Neighbourhood liveability .003 .182 .988 1.003 .702, 1.432 Economic hardship .184 .050 .000 1.202 1.089, 1.326 Badly deteriorated or poor housing .055 .280 .845 1.056 .610, 1.827 Neighbourhood poor or very poor to raise children. .231 .197 .240 1.260 .857, 1.854 Heavy traffic in the street .425 .163 .009 1.530 1.111, 2.107 Clutter in the home .065 .250 .794 1.067 .654, 1.742 Loud or moderate background noise -.027 .176 .879 .974 .690, 1.374 Public housing tenant .149 .202 .463 1.160 .780, 1.725
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Model 3. All significantly differing child variables Child variables: model sig. (Wald χ=35.227, df(11), N(2208), p<.001), Nagelkerke R Square =.027 Variables Entered B SE Prob. Odds Ratio 95% C.I Male gender (child) .261 .119 .028 1.298 1.029, 1.639 Child’s emotional health a worry to parent 1 .048 .121 .690 1.049 .828, 1.329 Child’s sleep patterns problematic .217 .123 .077 1.242 .977, 1.580 Child has a medical condition -.038 .139 .783 .962 .733, 1.264 Child has ADD / ADHD .668 .411 .104 .1951 .872, 4.362 Child’s age in months -.09 .022 .681 .991 .950, 1.034 Child’s mean prosociality score .062 .036 .084 1.063 .992, 1.140 Child’s mean hyperactivity score .082 .031 .009 1.085 1.020, 1.154 Child’s mean conduct score -.022 .033 .513 .979 .917, 1.044 Child’s mean persistence score .024 .067 .727 1.024 .897, 1.169 Child chooses active ways to spend free time. .194 .117 .096 1.214 .966, 1.527
Model 4. Significant child variables plus significant interaction terms Child variables plus interactions: model sig. (Wald χ²= 47.290, df(8), N(2650), p<.001), Nagelkerke R Square =.029 Variables Entered B SE Prob. Odds Ratio 95% C.I Child’s emotional health a worry to parent 1 .100 .108 .357 1.105 .893, 1.367 Child’s sleep patterns problematic -.228 .220 .300 .796 .517, 1.226 Male gender (child) .358 .108 .001 1.430 1.158, 1.766 Child chooses active ways to spend free time. .251 .105 .017 1.285 1.046, 1.580 Child’s mean hyperactivity score .069 .023 .003 1.071 1.024, 1.120 Hyperactivity x Sleep problem .101 .045 .023 1.106 1.014, 1.207 Emotional health a worry x male gender -.457 .212 .031 .633 .418, .959
Model 5. Significant variables across domains including significant interaction terms from each domain. Integrated model sig. (Wald χ²= 63.072, df(17), N(1443), p<.001), Nagelkerke R Square =.070 Variables Entered B SE Prob. Odds Ratio 95% C.I Economic hardship -.029 .066 .658 .971 .853, 1.106 Heavy traffic in the street -.558/ .303 .066 .572 .316, 1.037 Parent 1 Age -.035 .014 .012 .966 .940, .993 Parent 1 BMI -.263 .168 .118 .769 .552, 1.070 Number of stressful life events in past year (parent) .161 .087 .066 1.174 .990, 1.394 Parent report of parenting skills -.391 .214 .068 .676 .445, 1.029 Child’s mean hyperactivity score .061 .033 .066 1.063 .996, 1.135 Male gender (child) .161 .148 .279 1.174 .878, 1.570 Child chooses active ways to spend free time. .255 .146 .081 1.290 .969, 1.719 Child’s emotional health a worry to parent 1 -.061 .155 .693 .941 .695, 1.374 Child’s sleep patterns problematic .419 .190 .028 1.520 1.047, 2.207 Parent 1 BMI x Self report of parenting skills .189 .071 .008 1.208 1.051, 1.389 Number of life events x parenting skills -.053 .039 .169 .948 .879, 1.023 Child’s emotional health a worry x heavy traffic -.676 .303 .026 .509 .281, .920 Economic hardship x child sleep problems -.298 .120 .013 .742 .587, .938 Child’s mean hyperactivity score x heavy traffic .187 .064 .003 1.206 1.064, 1.366 Child chooses active past time x male gender .518 .292 .076 1.679 .947, 2.977
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Investigation of third variable effects ~ contextual domain.
Finding: Economic hardship confounds the relation between the number of homes lived in and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Number of homes lived in ª .126 .077 .103 1.134 .975, 1.319 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: Number of homes lived inª .416 .059 <.001 1.516 1.350, 1.703 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardship .268 .075 <.001 1.308 1.130, 1.514 Predictor: Number of homes lived inª .099 .078 .201 1.104 .949, 1.286 Tests of significance: Sobel’s Test = 3.19, p<.001 Note : C.I = Confidence interval ; Number of homes lived in ª :[ 0= one home only, 1= two or more homes] Economic hardshipª [0= below mean number of hardship items endorsed, 1= above mean number of items endorsed.]
Finding: Neighbourhood liveability mediates the relation between economic hardship and child injury . Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Economic hardshipª .277 .074 <.001 1.319 1.140, 1.526 Testing Step 2 (Path a) Third variable: :Neighbourhood liveability Predictor: Economic hardshipª .599 .060 <.001 1.820 1.616, 2.048 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Neighbourhood liveabilityª .161 .077 .036 1.174 1.010, 1.365 Predictor: Economic hardshipª .255 .075 .001 1.290 1.114, 1.495 Tests of significance: Sobel’s Test = 2.05, p=.004 Note : C.I = Confidence interval ; Economic hardshipª [0= below mean number of hardship items endorsed. 1= above mean number of items endorsed.] Neighbourhood liveabilityª [mean neighbourhood liveability score]
Finding: Economic hardship confounds the relation between public housing and child injury . Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Public housing -.312 .156 .045 .732 .539, .994 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: Public housingª -.668 .147 <. 001 .513 .384, .684 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardshipª .458 .149 .002 1.582 1.181, 2.118 Predictor: Public housingª .251 .158 .111 1.286 .944, 1.751 Tests of significance: Sobel’s Test = -2.54, p=.001 Note : C.I = Confidence interval ; Public housingª [0= renting but not in public housing. 1 = renting from a government authority] Economic hardshipª [ 0=below mean number of hardship items endorsed, 1= above mean number of items endorsed.].
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Finding: Economic hardship confounds the relation between ‘how you feel about your neighbourhood’ and injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: How you feel about your neighbourhoodª -.235 .107 .028 .791 .641, .975 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: How you feel about your neighbourhoodª -.601 .086 <.001 .548 .463, .649 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardshipª .277 .082 .001 1.319 1.122, 1.550 Predictor: How you feel about your neighbourhoodª .197 .108 .067 1.218 .986, 1.505 Tests of significance: Sobel’s Test = -2.48 p=.001 Note : C.I = Confidence interval ; How you feel about your neighbourhood as a place to raise childrenª [ 0= very good or good, 1= fair, poor or very poor] Economic hardshipª [ 0=below mean number of hardship items endorsed, 1= above mean number of items endorsed.]
Findings: Clutter in the home mediates the relationship between economic hardship and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Economic hardshipª .277 .074 <. 001 1.319 1.140, 1.526 Testing Step 2 (Path a) Third variable: Clutterª Predictor: Economic hardshipª .705 .104 <.001 2.025 1.653, 2.481 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Clutterª -.326 .123 .008 .722 .568, .918 Predictor: Economic hardshipª .253 .076 .001 1.288 1.110, 1.494 Tests of significance: Sobel’s Test = -.247 p=.001 Note : C.I = Confidence interval ; Economic hardshipª [ 0= below mean number of hardship items endorsed, 1= above mean number of items endorsed] Clutterª (home is cluttered) [0 = no, 1= yes.]
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Finding: Housing quality mediates the relation between economic hardship and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Economic hardshipª .277 .074 <. 001 1.319 1.140, 1.526 Testing Step 2 (Path a) Outcome: Housing qualityª Predictor: Economic hardshipª 1.016 .136 <. 001 2.762 2.118, 3.602 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Housing qualityª .345 .151 .022 1.412 1.051, 1.897 Predictor: Economic hardshipª .249 .075 .001 1.282 1.106, 1.486 Tests of significance: Sobel’s Test = 2.18, p=.020 Note : C.I = Confidence interval ; Economicª = 0= below mean number of hardship items endorsed 1= above mean number of items endorsed] Housing qualityª [0= fair condition or well kept dwelling, 1= badly deteriorated or poor condition dwelling]
Findings: Heavy traffic in the street mediates the relation between economic hardship and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Economic hardshipª .277 .074 <. 001 1.319 1.140, 1.526 Testing Step 2 (Path a) Third variable: Trafficª Predictor: Economic hardshipª .367 .061 <. 001 1.444 1.281, 1.627 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Trafficª .226 .078 .004 1.253 1.076, 1.460 Predictor: Economic hardshipª .257 .075 .001 1.293 1.117, 1.497
Tests of significance: Sobel’s Test = 2.967 p=.003 Note : C.I = Confidence interval ; Economic hardshipª [ 0= below mean number of hardship items endorsed ,1= above mean number of items endorsed.] Trafficª (There is heavy traffic on our street.)[ 0 = strongly disagree or disagree, 1= strongly agree or agree]
Finding: Economic hardship confounds the relation between background noise in the home and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Noiseª .167 .082 .042 1.182 1.006, 1.389 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: Noiseª .420 .065 <.001 1.521 1.340, 1.727 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardshipª .265 .075 <.001 1.303 1.125, 1.509 Predictor: Noiseª .142 .083 .087 1.152 .980, 1.356 Tests of significance: Sobel’s Test = 3.10, p=.002 Note : C.I = Confidence interval ; Noiseª(amount of background noise in the home) [0 =limited or no noise, 1= moderate or loud noise.] Economicª [ 0= below mean number of hardship items endorsed 1= above mean number of items endorsed.]
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Investigation of third variable effects ~ parent/family domain.
Finding: Marital status mediates the relation between number of stressful life events in past year and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Life eventsª .213 .082 .009 1.238 1.055, 1.453 Testing Step 2 (Path a) Third variable: Marital statusª Predictor: Life eventsª -.808 .075 <.001 .446 .385, .517 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Life eventsª .168 .083 .043 1.183 1.005, 1.393 Predictor: Marital statusª -.317 .094 .001 .728 .605, .875 Tests of significance: Sobel’s Test = 3.22, p=.001 Note : C.I = Confidence interval ; Life eventsª (number of stressful life events endorsed) [0= below mean number of events, 1= above mean number of events] Marital statusª (Legal/registered marital status)[ 0= married, 1= separated, divorced, widowed or never married.]
Finding: Number of stressful life events in past year suppresses the relation between parent 1 BMI and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: BMIª -.275 .099 .006 .760 .626, .923 Testing Step 2 (Path a) Third variable: BMIª Predictor: Life eventsª -.279 .077 <.001 .757 .650, .881 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Life eventsª .232 .100 .020 1.262 1.067, 1.575 Predictor: BMIª .260 .099 .020 1.262 1.037, 1.535 Tests of significance: Sobel’s Test = 1.96, p=.05. Note : C.I = Confidence interval ; Parent 1 BMIª [0 = BMI within the underweight or normal weight ranges. 1= BMI within the overweight or obese weight ranges] Life eventsª (number of stressful life events endorsed)[ 0= below mean number of events, 1= above mean number of events]
Finding: Number of stressful life events in past year confounds the relation between parent sleep quality and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Parent sleep qualityª .226 .078 .004 1.254 1.077, 1.461 Testing Step 2 (Path a) Third variable: Life eventsª Predictor: Parent sleep qualityª .566 .067 <.001 1.761 1.545, 2.008 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Life eventsª .197 .082 .017 1.218 1.036, 1.432 Predictor: Parent sleep qualityª .131 .087 .131 1.140 .962, 1.351 Tests of significance: Sobel’s Test = 2.31, p=.02 Note : C.I = Confidence interval ; Parent sleep qualityª [0= sleep quality is very good or fairly good, 1= sleep quality is fairly bad or very bad] Life eventsª (number of stressful life events endorsed) [0= below mean number of events, 1= above mean number of events]
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Finding: Parent 1 education confounds the relation between number of stressful life events in the past year and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Life eventsª .213 .082 .009 1.238 1.055, 1.453 Testing Step 2 (Path a) Third variable: Educationª Predictor: Life eventsª .247 .064 <.001 1.334 1.731, 1450 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Educationª .272 .082 .001 1.312 1.117, 1.541 Predictor: Life eventsª .200 .082 .015 1.221 1.040, 1.434 Tests of significance: 2.51, p=.01 Note : C.I = Confidence interval ; Life eventsª (number of stressful life events endorsed)[ 0= below mean number of events, 1= above mean number of events] Educationª (Parent 1) [0= completed year 12, 1= did not complete year 12] Finding: Number of stressful life events in the past year confound the relation between parenting skill and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Parenting skillª .084 .042 .044 1.088 1.002, 1.181 Testing Step 2 (Path a) Third variable: Life eventsª Predictor: Parenting skillª .125 .036 <.001 1.134 1.057, 1.216 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Life eventsª .204 .082 .013 1.226 1.044, 1.440 Predictor: Parenting skillª .065 .046 .163 1.067 .974, 1.168 Tests of significance: 2.02, p=.002 Note : C.I = Confidence interval ; Parenting skillª [self report of parenting skill (very good , above average, average, some trouble & not very good)] Life eventsª (number of stressful life events endorsed) [0= below mean number of events, 1= above mean number of events] Investigation of third variable effects ~ child domain. Finding: Hyperactivity confounds the relation between child’s emotional health and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Emotional health .187 .075 .013 1.205 1.040, 1.396 Testing Step 2 (Path a) Third variable: Hyperactivity ª Predictor: Emotional healthª .555 .058 <.001 1.742 1.556, 1.951 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable Hyperactivity ª .316 .075 <.001 1.372 1.184,1.590 Predictor: Emotional healthª .140 .076 .065 1.151 .991, 1.336 Tests of significance: 3.86, p<.001 Note : C.I = Confidence interval ; Child’s emotional healthª (Worry over child’s emotional health?)[ 0 = none, 1= a little bit, somewhat, quite a lot or a lot] Hyperactivityª (mean hyperactivity scale score) [0 = below mean , 1= above mean]
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Investigation of third variable effects ~ across domains.
Finding: Parent 1 BMI mediates the relation between economic hardship and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Economic hardshipª .277 .074 .<.001 1.319 1.140, 1.526 Testing Step 2 (Path a) Third variable: BMIª Predictor: Economic hardshipª .384 .077 <.001 1.468 1.262, 1.708 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: BMIª .256 .100 .010 1.292 1.063, 1.570 Predictor: Economic hardshipª .201 .100 .045 1.222 1.055, 1.487 Tests of significance: Sobel’s Test = 2.28, p=.02 Note : C.I = Confidence interval ; Economic hardshipª [0= below mean number of hardship items endorsed 1= above mean number of items endorsed] Parent 1 BMIª [0 = BMI within the underweight or normal weight ranges. 1= BMI within the overweight or obese weight ranges]
Finding: Number of stressful life events in past year confounds the relation between child sleep problems and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Child sleep problemsª .183 .077 .018 1.201 1.032, 1.397 Testing Step 2 (Path a) Third variable: Life eventsª Predictor: Child sleep problemsª .389 .066 <.001 1.475 1.296, 1.678 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Life eventsª .200 .082 .015 1.221 1.040, 1.435 Predictor: Child sleep problemsª .149 .085 .081 1.160 .982, 1.371 Tests of significance: Sobel’s Test = 2.25, p=.02 Note : C.I = Confidence interval ; Child sleep problemsª [0= sleeping patterns are not problematic, 1= Sleeping patterns are a small, moderate or large problem]. Life eventsª (number of stressful life events endorsed)[ 0= below mean number of events, 1= above mean number of events]
Finding: Economic hardship suppresses the relation between child sleep problems and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Child sleep problemsª .183 .077 .018 1.201 1.032, 1.397 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: Child sleep problemsª .317 .060 <.001 1.372 1.219, 1.544 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardshipª .265 .075 <.001 .1.304 1.126, 1.509 Predictor: Child sleep problemsª .163 .078 .036 1.177 1011, 1.370 Tests of significance: Sobel’s Test = 2.94, p=.003 Note : C.I = Confidence interval ; Child sleep problemsª [0= sleeping patterns are not problematic, 1= sleeping patterns are a small, moderate or large problem]. Economic hardshipª [0= below mean number of hardship items endorsed, 1= above mean number of items endorsed].
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Finding: Parent 1 sleep quality mediates the relation between economic hardship and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Economic hardshipª .277 .074 .<.001 1.319 1.140, 1.526 Testing Step 2 (Path a) Third variable: Parent sleep qualityª Predictor: Economic hardshipª .550 .061 <.001 1.733 1.537, 1.955 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Sleep qualityª .193 .079 .014 1.213 1.040, 1.414 Predictor: Economic hardshipª .251 .075 .001 1.285 1.110, 1.489 Tests of significance: 2.36, p=.01 Note : C.I = Confidence interval ; Economic hardshipª [0= below mean number of hardship items endorsed, 1= above mean number of items endorsed.] Parent sleep qualityª[ 0= sleep quality is very good or fairly good, 1= sleep quality is fairly bad or very bad]
Finding: Economic hardship mediates the relationship between parent 1 education and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Educationª .288 .075 <.001 1.334 1.153, 1.544 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: Educationª .715 .058 <.001 2.043 1.822, 2.292 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardshipª .236 .076 .002 .1.266 1.092, 1.469 Predictor: Educationª .248 .076 .001 1.282 1.105, 1.487 Tests of significance: 3.01, p=.003 Note : C.I = Confidence interval ; Educationª (Parent 1)[ 0= completed year 12, 1= did not complete year 12] Economic hardshipª [0= below mean number of hardship items endorsed, 1= above mean number of items endorsed]
Finding: Economic hardship confounds the relation between parenting skill and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Parenting skillª .084 .042 .044 1.088 1.002, 1.181 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: Parenting skillª .150 .032 <.001 1.162 1.091, 1.238 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardshipª .265 .075 <.001 1.303 1.126, 1.508 Predictor: Parenting skillª .073 .042 .082 1.075 .991, 1.167 Tests of significance: 2.82, p=.004 Note : C.I = Confidence interval ; Parenting skillª [self report of parenting skill (very good , above average, average, some trouble & not very good)] Economic hardshipª[ 0= below mean number of hardship items endorsed, 1= above mean number of items endorsed]
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Finding: Economic hardship mediates the relation between number of stressful life events in past year and child injury** Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Life eventsª .213 .082 .009 1.238 1.055, 1.453 Testing Step 2 (Path a) Third variable: Economic hardshipª Predictor: Life eventsª 1.025 .065 <.001 2.786 2.455, 3.161 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardshipª .246 .084 .003 1.279 1.085, 1.508 Predictor: Life eventsª .151 .084 .073 1.164 .986, 1.373 Tests of significance: 2.88, p=.004 Note : C.I = Confidence interval ; Life eventsª (number of stressful life events endorsed)[ 0= below mean number of events, 1= above mean number of events] Economic hardshipª [0= below mean number of hardship items endorsed, 1= above mean number of items endorsed] **(This relationship may alternately be termed a confounding one).
Finding: Hyperactivity mediates the relation between economic hardship and child injury. Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Economic hardshipª .277 .074 .<.001 1.319 1.140, 1.526 Testing Step 2 (Path a) Third variable: Hyperactivity ª Predictor: Economic hardship ª .432 .057 <.001 1.540 1.376, 1.724 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Hyperactivity ª .309 .075 <.001 1.362 1.176, 1.578 Predictor: Economic hardship ª .244 .075 .001 1.277 1.117, 1.479 Tests of significance: 3.62 p<.001 Note : C.I = Confidence interval ; Economic hardshipª [0= Below mean number of hardship items endorsed, 1= above mean number of items endorsed] Hyperactivityª (mean hyperactivity scale score) [0 = below mean, 1= above mean)].
Finding: Number of stressful life events in the past year confound the relation between child’s emotional health and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Child’s emotional health ª .187 .075 .013 1.205 1.040, 1.396 Testing Step 2 (Path a) Third variable: Life events ª Predictor: Child’s emotional health ª .626 .064 <.001 1.871 1.650, 2.121 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Live events ª .195 .083 .018 1.216 1.034, 1.429 Predictor: Child’s emotional health ª .121 .083 .147 1.129 .959, 1.329 Tests of significance: 2.28, p=.02 Note : C.I = Confidence interval ; Child’s emotional healthª (Worry over child’s emotional health?) [0 =none, 1= a little bit, somewhat, quite a lot or a lot] Life eventsª (number of stressful life events endorsed) [0= below mean number of events, 1= above mean number of events].
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Finding: Economic hardship confounds the relation between child’s emotional health and child injury Testing steps in third variable effect model B Std Error Prob. Odds Ratio 95% C.I Testing Step 1 (Path c) Outcome: Injury status (non-injured vs. injured). Predictor: Child’s emotional health ª .187 .075 .013 1.205 1.040, 1.396 Testing Step 2 (Path a) Third variable: Economic hardship ª Predictor: Child’s emotional health ª .363 .058 <.001 1.437 1.284, 1.609 Testing Step 3 (Path b and ć) Outcome: Injury status (non-injured vs. injured). Third variable: Economic hardship ª .263 .075 <.001 1.301 1.124, 1.506 Predictor: Child’s emotional health ª .158 .076 .036 1.171 1.010, 1.359 Tests of significance: 3.06 p=.02 Note : C.I = Confidence interval ; Child’s emotional healthª (Worry over child’s emotional health?) [0=–none, 1= a little bit, somewhat, quite a lot or a lot] Economic hardshipª[ 0= below mean number of hardship items endorsed, 1= above mean number of items endorsed]