Content uploaded by Timothy R Elliott
Author content
All content in this area was uploaded by Timothy R Elliott
Content may be subject to copyright.
Content uploaded by Timothy R Elliott
Author content
All content in this area was uploaded by Timothy R Elliott
Content may be subject to copyright.
Content uploaded by Timothy R Elliott
Author content
All content in this area was uploaded by Timothy R Elliott
Content may be subject to copyright.
Depression Among Parents of Children With Disabilities
J. Aaron Resch, PhD, Timothy R. Elliott, PhD, and Michael R. Benz, PhD
Texas A&M University
We examined the rate of depression among 110 parents of children with disabilities and
tested a model to determine the unique factors associated with parental depression.
Consenting parents completed measures of depression, family satisfaction, physical
health, problem-solving abilities, stress appraisals, and child functional impairment.
Participants were categorized as depressed or nondepressed based on their responses to
the Patient Health Questionnaire (PHQ9;Kroenke, Spitzer, & Williams, 2001). Nine-
teen percent of the parents met screening criteria for depression. Regression analyses
revealed that threat appraisals, poorer physical health, and lower family satisfaction
were uniquely associated with depression status with 83.3% accuracy. These findings
highlight the importance of family satisfaction, problem solving ability, physical health,
and the influence of appraisal processes on depression among parents of children with
disabilities.
Keywords: depression, children with disabilities, parents, family satisfaction, Patient Health
Questionnaire
Parents of children with disabilities appear to
be more likely to experience elevated levels of
stress and, as a result, a decrease in quality of
life (Browne & Bramston, 1998). Approxi-
mately 35% to 53% of parents of children with
disabilities may have problems with depression,
but this literature is plagued with small sample
sizes and differences in depression measure-
ment methods (Olsson & Hwang, 2001). Veis-
son (1999) points out that studies of depression
among parents of children with disabilities yield
conflicting results: There are studies indicating
higher levels of depression among parents of
children with disabilities and studies that find
no differences in depression between parents of
children with and without disabilities (Glidden
& Schoolcraft, 2003).
Many studies of depression among parents of
children with disabilities rely on samples re-
cruited in clinical settings where families seek
treatment; consequently, this work may lack
generalizeability to people living in the commu-
nity who are not actively seeking or receiving
clinical services. More importantly, many stud-
ies in this area use instruments that are not
criterion-referenced and lack specificity for a
meaningful comparison with symptoms and cri-
teria consistent with a major depressive episode.
Although popular self-report measures of de-
pressive symptoms are often used in studies of
parent adjustment (Demirtepe-Saygili & Bozo,
2011), these instruments lack specificity and
scores are inflated by general distress (Gotlib,
Lewinsohn, & Seeley, 1995). For example, in
the Singer (2006) meta-analysis of maternal de-
pression, none of the studies reviewed used a
depression measure that assessed all symptoms
by the criteria required to determine a major
depressive episode.
Generally, evidence indicates that mothers of
children with disabilities often report problems
with depressive symptoms (Bristol, Gallagher
& Schopler, 1988), and certain features may
decrease the likelihood of depression among
these parents, including education and income
(Breslau, Staruch, & Mortimer, 1982), marital
status (Olsson & Hwang, 2001), and the sever-
This article was published Online First November 12, 2012.
J. Aaron Resch, PhD, Timothy R. Elliott, PhD, and
Michael R. Benz, PhD, Center on Disability and Develop-
ment, Department of Educational Psychology, Texas A&M
University.
This study was conducted as part of a dissertation by J.
Aaron Resch. This research was supported by the Center on
Disability and Development through a grant from the De-
partment of Health and Human Services (90DD0667).
Correspondence concerning this article should be ad-
dressed to Timothy R. Elliott, PhD, 4225 TAMU, College
Station, TX 77843. E-mail: telliott@tamu.edu
Families, Systems, & Health © 2012 American Psychological Association
2012, Vol. 30, No. 4, 291–301 1091-7527/12/$12.00 DOI: 10.1037/a0030366
291
ity of the child’s impairments (Floyd & Gal-
lagher, 1997). Parents report that environmental
resources and supports that match family
needs—such as financial resources, opportuni-
ties for community and social inclusion, family
cohesion, and access to necessary information
and services for child and family—can mitigate
the stress often incurred by the child’s disability
and its impairments (Green, 2007;Resch, Mire-
les, Benz, Zhang, Peterson, & Grenwelge, 2010;
Worcester, Nesman, Mendez, & Keller, 2008).
Unfortunately, environmental resources and
supports are not well understood and are under-
studied, despite the fact that improvements in
resources and supports are emphasized in the
Affordable Care Act to benefit community-
residing families providing care to a family
member with a disability (Reinhard, Kassner, &
Houser, 2011).
Several psychological characteristics, in con-
trast, are known to be associated with the dis-
tress reported by a family member caring for a
loved one with a disability, and many of these
have considerable implications for psychologi-
cal interventions. We know that cognitive ap-
praisals of stress and growth (Hastings, 2002;
Kronenberger & Thompson, 1992;Pakenham,
2001), effective social problem-solving abilities
(Dreer, Elliott, Fletcher, & Swanson, 2005;
Dreer, Elliott, Shewchuk, Berry, & Rivera,
2007;Noojin & Wallander, 1997;Rivera, El-
liott, Berry, Grant, & Oswald, 2007), and satis-
faction with familial relationships (Glidden &
Floyd, 1997;Lightsey & Sweeney, 2008) are
predictive of distress reported by family mem-
bers who are in caregiver roles independent of
the variance attributable to care recipient dis-
ability severity. We do not know, however, the
degree to which these variables contribute to the
prediction of a possible depressive episode
among parents of children with disabilities, or
whether they remain predictive of depression
status after taking into account environmental
supports and services.
The present study was conducted to achieve
two goals. First, we wanted to obtain informa-
tion about the number of parents of children
with disabilities who may be at risk for a major
depressive episode. To accomplish this, a sam-
ple of community residing parents was re-
cruited, rather than a sample of parents receiv-
ing services from a clinical settings. We also
used a criterion-referenced self-report measure
of depression that provides a reliable and valid
assessment of a probable major depressive
episode.
Second, we examined a predictive model of
depression status, using variables (i.e., apprais-
als of threat and growth, social problem solving
ability, and resources and environmental/social
supports, physical health, family satisfaction) of
theoretical and clinical importance known to be
associated with depression rates for family care-
givers in prior research. Using a model of fam-
ily adjustment after disability as a guide (cf.
Elliott & Mullins, 2004), we used a four-step
hierarchical regression equation to predict par-
ent depression status, while simultaneously
accounting for any clinically important relation-
ships that could be attributable to parent demo-
graphic information and/or the severity of the
child’s impairments.
Method
Participants
The total sample consisted of 110 parents of
at least one child with a disability recruited
through a large statewide parent organization
for parents of children with disabilities. Disabil-
ity type was determined using disability catego-
ries used in the Texas education system and
commonly understood by the participants and
the parent organization. Most of the participants
were mothers ranging in age from 27 to 68 years
(M⫽45.6). The majority of the participants
were Caucasian (83.6%); Latinos (9.1%) and
African Americans (5.5%) constituted most of
the remaining sample. Most participants (73%)
reported annual household incomes of at least
$25,000, but not more than $150,000. All par-
ticipants had at least a high school diploma, and
95% reportedly had some college experience.
About a third (34.5%) of parents reported spo-
radic or no employment, 23.6% reported part-
time employment, and 41.8% reported full-time
employment.
Child ages were normally distributed with the
majority (n⫽90) of the participant’s children
falling between ages 5 and 11 (n⫽38), 11–16
(n⫽34), and 17–21 (n⫽18). The disability
type of the child varied: 32.7% (n⫽36) had
autism, 19.1% (n⫽21) had an intellectual
disability, 20% (n⫽22) had multiple disabili-
ties (e.g., a child with both an intellectual dis-
292 RESCH, ELLIOTT, AND BENZ
ability and a visual impairment), and 27.3%
(n⫽30) had other types of disabilities (e.g.,
auditory impairments, visual impairments, trau-
matic brain injury, orthopedic impairment,
speech impairments, deaf-blind, and other types
of health impairments).
Procedure
Parents affiliated with the statewide parent
organization used as a point of recruitment for
this study were sent an initial email in which
they were invited to participate in an online
survey about their experiences as a parent rais-
ing a child with disabilities. Those parents in-
dicating a willingness to participate were sub-
sequently sent a unique link to complete the
survey. An online survey tool (Qualtrics) was
used to facilitate data collection for this study.
Because a significant portion of the state’s pop-
ulation was of Mexican descent, the survey was
also made available in Spanish. Consistent with
the requirements for informed consent, upon
accessing the link, participants were provide a
more detailed explanation about the study to
include their rights as participants, a statement
about the potential risks and rewards of partic-
ipation, information about the confidential na-
ture of any shared information, details regarding
an incentive for participating (a $10 gift card)
and the contact information for the investiga-
tors and the Institutional Review Board at
Texas A&M University.
Predictor Variables
Parent demographic data. Parent age, ed-
ucation level, employment status, and annual
household income were included as predictor
variables.
Child disability severity. To assess the se-
verity of the child’s disability, parents com-
pleted the 12 items that assess activities of daily
living (ADLs) on the Personal Care Assessment
Form (PCAF;Phillips, Patnaik, Dyer, Naiser,
Johnson, Fournier, & Elliott, 2011; available at
http://pcaf.tamu.edu/). The PCAF has been used
to assess personal and family needs among
5,000 children with special health care needs in
the Texas Medicaid Personal Care Services pro-
gram (Elliott, Phillips, Patnaik, Naiser,
Fournier, et al., 2011). The ADL items require
parents to rate the amount of assistance their
child needs to complete different ADLs on a
weekly basis. The particular areas of interest
include the following: bed mobility, eating,
transfers, toilet use, personal hygiene, bathing,
and continence. A six-item Likert response
scale ranging from total independence to total
dependence is used. Two questions regarding
bowel and bladder continence use a six-item
Likert scale ranging from continent to always/
almost always incontinent. An additional conti-
nence question uses a dichotomous response
choice (yes/no) to assess whether the child is
continent during the night. The ADL items are
added together to get a total score, with higher
scores indicating their child has less ability to
perform ADLs independently. The two bladder
and bowel continence questions are also
summed. Higher scores indicate problems with
incontinence.
Previous research using the PCAF has shown
high internal consistency (␣⫽.94; Fournier,
Davis, Patnaik, Elliott, Dyer, Jasek, & Phillips,
2010) and acceptable interrater reliability (Phil-
lips et al., 2011). Reliability analysis for this
study proved to be similar (e.g., ␣for ADLs ⫽
.95 and ␣for continence ⫽.94). Higher ADL
and incontinence scores on the PCAF are gen-
erally predictive of the hours of personal care
services requested by the family (Nineteen per-
cent of the parents met screening criteria for
depres Fournier et al., 2010) and authorized by
caseworkers for children with special health
care needs (Elliott et al., 2011), especially for
children with intellectual disabilities (Patnaik,
Elliott, Fournier, Naiser, et al., 2011).
Appraisals of threat and growth. Two
distinct types of appraisals were measured in
this study: Appraisals of threat or harm and
appraisals of positive growth. The threat ques-
tionnaire was modeled after the scale used by
Pakenham (2001). It has seven questions that
use a seven-point scale (low potential to high
potential) asking participants to appraise the
extent to which they believe raising a child with
a disability could potentially threaten or harm
key aspects of their life (e.g., important life
goals, relationships with others, and their per-
sonal physical wellbeing). Previous studies
using this questionnaire have demonstrated ac-
ceptable psychometric properties (e.g., Paken-
ham, 2001;Stanton & Snider, 1993). Internal
consistency of the threat scale for this study was
high (␣⫽.89).
293DEPRESSION AMONG PARENTS
The 21-item Post Traumatic Growth Inven-
tory (PTGI) was used (Tedeschi & Calhoun,
1996) to measure participants’ appraisals of
benefit and growth while parenting a child with
a disability. Participants used a six-point Likert
scale to rate the degree to which they believed
certain areas of their life may have positively
changed in five key areas: (a) relating to others;
(b) new possibilities; (c) personal strength; (d)
spiritual change; and (e) appreciation of life.
Higher total scores reflect a greater sense of
benefit and growth. Previous research found the
PTGI to be both reliable (e.g., full scale ␣⫽
.90) and valid in terms of measuring growth
when faced with challenges (Tedeschi & Cal-
houn, 1996). The internal consistency of the
PTGI for this study was ␣⫽.94. Notably, the
original intended use of the PTGI was to mea-
sure the existence of positive growth in individ-
uals who had experienced a traumatic event,
and parenting a child with a disability does not
necessarily constitute a “traumatic” event. Be-
cause the PTGI asks questions about how peo-
ple grow when faced when challenges, how-
ever, it is potentially a useful tool to measure
positive growth and benefit finding among the
parents in this study. Recent research supports
the assertion that the PTGI can be used in non-
trauma studies (Anderson & Lopez-Baez,
2008).
Resources and environmental/social
supports. The Resources and Environmental/
Social Supports-Questionnaire (RESS-Q) was
developed based on theoretical and empirical
evidence that suggests parents of children with
disabilities encounter several barriers related to
resources and supports in their surrounding
community and social environment (e.g., Beck-
man, 2002;Minnes, 1988;Resch et al., 2010;
Worcester et al., 2008). The purpose of the
RESS-Q was to measure the degree of match
between the family’s needs and the resources
and supports available in the community to
meet those needs. The RESS-Q has 13 items that
ask parents if they encounter problems associ-
ated with access to information and services,
financial barriers, and social/community inclu-
sion (e.g., “Important information related to the
needs of my child is usually readily available
and easy to understand”; “Our insurance plan
usually covers the majority of the health care
expenses for my child with a disability”; “I am
pleased with my social life and the number of
opportunities I have to spend with friends and
neighbors”). A five-point Likert scale ranging
from one (mostly disagree) to five (mostly
agree) is used to record responses with the total
possible questionnaire score ranging from 13 to
65. Higher scores suggest that parents feel as if
their family has greater access to environmental
and social supports available in their communi-
ties. Internal consistency of the RESS-Q was
␣⫽.79.
Social problem-solving abilities. Parents
completed the 10-item version of the Social
Problem Solving Inventory-Revised (SPSI-R-
10; Dreer, Berry, Rivera, Snow, Elliott, Miller,
& Little, 2009) in order to assess their overall
problem-solving abilities. Response choices on
the SPSI-R-10 consist of a 5-point Likert scale
ranging from 0 (not at all true of me)to4
(extremely true of me). Items are summed and a
total score is derived; higher scores suggest
better problem-solving abilities. Prior research
has found that the SPSI-R-10 is statistically
comparable to the longer, 25-item version
(Dreer et al., 2009). Internal consistency of the
SPSI-R-10 for this study was ␣⫽.74.
Physical health. Past research has consis-
tently demonstrated that physical health is an
important factor in their family caregiver well
being (e.g., Grant, Bartolucci, Elliott, & Giger,
2000;Rivera et al., 2007). To measure each
parent’s overall physical health the Physical
Component Summary (PCS) from version 1
(v.1; standard 4-week recall) of the Short
Form-12 (SF-12;Ware, Kosinski, & Keller,
1996) was used. The SF-12 v.1 has 12 items that
assess one’s mental and physical health-related
quality of life. Test–retest reliability for the
SF-12 ranges between .86 and .89 for the PCS
(Ware et al., 1996). Higher PCS scores indicate
greater overall physical health.
Family satisfaction. To measure how sat-
isfied parents are with their family functioning
the Family Satisfaction Scale (FSS;Olson &
Wilson, 1982) was used. The FSS has 14 items
to assess family cohesion and adaptability and
has been used in many studies of family adjust-
ment after disability (Johnson et al., 2010;
Lightsey & Sweeney, 2008;Perlesz, Kinsella &
Crowe, 2000). The FSS uses a Likert scale
ranging from 1 (dissatisfied)to5(extremely
satisfied). Responses are summed to yield a
total score with higher scores suggesting a
higher degree of family satisfaction. Reliability
294 RESCH, ELLIOTT, AND BENZ
and validity work by Olson and Wilson (1982)
yielded an ␣coefficient of .92. Internal consis-
tency on the FSS for this study was also high
(␣⫽.90).
Criterion Variable
The Patient Health Questionnaire (PHQ9;
Kroenke, Spitzer, & Williams, 2001) was used
to determine parent depression status. The nine
questions on the PHQ reflect the nine criteria on
which the DSM–IV depressive disorders are
based (Kroenke et al., 2001). The PHQ9 was
designed for use in clinical and medical set-
tings, and uses a four-point Likert scale (0 ⫽
not at all,1⫽several days,2⫽more than half
the days,3⫽nearly every day) to gauge re-
sponses to questions asking about the respon-
dents mental/emotional health over the previous
2-week period. Consequently, it is an excellent
tool for obtaining normative information about
depression rates among individuals who typi-
cally present their concerns about depressive
symptoms in primary care settings (Probst et al.,
2006). It is also suitable for use in screening for
depression among parents of children with se-
vere disabilities (Blucker, Elliott, Warren, &
Warren, 2011).
Scores on the PHQ9 can range from 0–27;
scores between 0 and 4 indicate no depression,
5–9 indicate mild depression, 10–14 indicate
moderate depression, 15–19 indicate moder-
ately severe depression, and ⱖ20 indicate se-
vere depression (Kroenke et al., 2001). In this
study depression status was coded dichoto-
mously with participants scoring ⱖ10 being
coded as depressed and participants scoring
from 0–9 coded as not depressed. Internal con-
sistency of the PHQ9 for this study was ␣⫽
.85.
Reliability and validity studies of the PHQ9
have yielded results indicating sound psycho-
metric properties. Internal consistency of the
PHQ9 has been shown to be high. A study
involving two different patient populations and
6000 total participants produced Cronbach’s al-
pha of .86 and .89. Additionally, test–retest
reliability had a high correlation at r⫽.84 and
discriminant validity was established via a ROC
analysis that produced an area under the curve
for the PHQ9 of .95 when diagnosing depres-
sion (Kroenke et al., 2001). Moreover, criterion
validity was demonstrated by both high sensi-
tivity and specificity for the PHQ9. In addition,
among the 6000 participants who completed the
PHQ9, 580 were interviewed by mental health
professionals, and results demonstrated strong
agreement between diagnoses made by the
PHQ9 and by the mental health professionals
(Kroenke et al., 2001).
Data Analysis
Preliminary data analysis steps included de-
scriptive statistics, tests of group differences,
and zero order correlations of all variables in-
cluded in the analysis. To systematically test the
relations of clinical, psychological, and envi-
ronmental variables to parents’ depression sta-
tus, a four-block hierarchical logistic regression
(HLR) analysis was conducted. Essentially,
HLR is a sequence of regression analysis where
multiple predictor variables are added at differ-
ent blocks to see if each new combination of
predictor variables can account for significant
variance in the criterion variable while still in-
cluding previously entered combinations in the
model (Hoyt, Imel, & Chan, 2008). Optimal use
of HLR occurs when independent variables
(IVs) belonging to similar categories or measur-
ing similar constructs are included in the anal-
ysis as sets of IVs, instead of being entered as
individual IVs as would be done in typical re-
gression approaches. HLR analysis calculates
how much variance in the dependent variable
(DV) is explained by each block. A regression
coefficient for each individual variable is also
calculated to measure each individual variables
distinct contribution (Hoyt et al., 2008). HLR
has been used in this fashion in previous studies
of depression among individuals caring for fam-
ily members with disabilities (Dreer et al., 2007;
Grant et al., 2004;Rivera et al., 2007).
In the first block of the equation, parent de-
mographic variables were entered to control for
any potential association they may have with
parent depression status. The second block con-
sisted of variables related to the child’s disabil-
ity (PCAF scores for ADLs, continence). Be-
cause child age and parent age were highly
correlated (r⫽.72) child age was not included
in the analysis to avoid problems with multicol-
linearity. Block three consisted of the RESSQ,
SPSI-R-10,PTGI, and Threat measures. These
variables were included in the same block be-
cause each one represents a specific psycholog-
295DEPRESSION AMONG PARENTS
ical or environmental characteristic that consti-
tutes an important protective factor for families
with members who have chronic health condi-
tions (Weihs, Fisher, & Baird, 2002), and
should, based on our hypothesis, be predictive
of parent depression (Elliott & Mullins, 2004).
The final block consisted of parent physical
health and family satisfaction. Assuming these
variables would likely have a strong inverse
relationship with depression and potentially
correlate with the other predictor variables, en-
tering these variables in the final step provided
a conservative test of any unique relationship
they may have with parent depression. The or-
der of entry for each block provides an appro-
priately stringent test of the presumed influence
of psychological, environmental, and personal
characteristics specified in the Elliott and Mul-
lins (2004) model of family adjustment follow-
ing disability.
Results
Table 1 provides information by depression
risk status on parent demographic, child char-
acteristic variables, and other predictor vari-
ables included in the equation. Chi-square tests
performed for marital status and disability type
revealed no significant differences between the
two depression risk status groups for these vari-
ables. In addition, independent ttests revealed
no mean differences between the two depres-
sion risk status groups for average parent age,
parent education level, annual household in-
come, employment status, or any of the child’s
disability variables. However, significant differ-
ences were found between the groups for other
predictor variables. The parents classified as
depressed had significantly higher threat ap-
praisals than parents who were not depressed.
Depressed parents also reported significantly
less problem-solving ability, lower family sat-
isfaction, and lower physical health. The two
groups did not differ on their reported access to
resources and environmental/social supports or
in their appraisals of positive growth.
Because of missing data on a small number of
surveys, only 86% (n⫽94) of the participants
were included in the logistic regression analy-
sis. Eighteen (19.1%) of the parents included in
this part of the analysis had depression scores at
or above 10 on the PHQ9. Therefore, the cut
value for depression classification was set at
.191 to reflect the actual rate of depression
observed among study participants. Parent de-
mographic variables, entered at the first block of
Table 1
Sample Statistics and pValues for Independent Samples tTests
Depression risk
Not depressed
(n⫽91)
Depressed
(n⫽19) Absolute mean
difference
ttest
pvalueMSDMSD
Demographics
Parent age 45.7 8.4 45.3 9.8 .4 .87
Parent education level 3.7 .6 3.6 .5 .1 .82
Household income 3.5 1.2 3.1 1.2 .4 .18
Employment status 2.0 .9 2.2 .9 .2 .63
Disability severity characteristics
ADLs 14.5 13.5 16.4 17.2 1.9 .67
Urinary/bowel continence 2.7 4.0 3.5 4.0 .8 .47
Appraisal measures
Threat 19.3 9.9 27.3 10.6 8.0 .002
ⴱⴱ
Growth 61.5 22.0 58.0 21.0 3.5 .48
Environmental/social supports 35.0 9.0 33.5 8.2 1.5 .49
Problem solving 31.0 5.1 28.1 5.0 2.9 .04
ⴱ
Parent wellbeing
Family satisfaction 50.0 11.0 40.2 9.0 9.8 .001
ⴱⴱⴱ
Physical health 53.0 9.1 44.0 14.1 9.0 .01
ⴱⴱ
ⴱ
Significant difference ⬍.05.
ⴱⴱ
Significant difference ⬍.01.
ⴱⴱⴱ
Significant difference ⬍.001.
296 RESCH, ELLIOTT, AND BENZ
the equation, were not significantly predictive
of parent depression status,
2
(4) ⫽2.87 (see
Table 2). At the second step of the equation the
child disability characteristics (ADLs, inconti-
nence) did not significantly contribute to the
equation,
2
(2) ⫽.44, ns.
The four psychological and environmental
variables (RESSQ,SPSI-R-10,PTGI,Threat),
entered at the third step, were significantly pre-
dictive of parent depression status,
2
(4) ⫽14.
92, p⬍.01. Parental threat appraisals signifi-
cantly contributed to the prediction of depres-
sion status (⫽.115; odds ratio ⫽1.12; Wald
[1] ⫽7.07, p⬍.01). With each unit increase in
appraisals of threat, parents had 12% greater
odds of being at risk for depression. However,
the SPSI-R-10,RESSQ,and PTGI did not sig-
nificantly contribute to the equation. Based on
the Cox and Snell, and as indicated by the
Neglkerke pseudo R-Squared estimates, the
variables entered at the third step accounted for
14.1% to 22.8% of the variance in parent de-
pression status.
The FSS and PCS were entered as a block
into the final step of the equation. This final
block,
2
[2] ⫽12.56, p⬍.01, significantly
contributed to the prediction of depression sta-
tus. Both the FSS (⫽⫺.131; odds ratio ⫽
.877; Wald [1] ⫽5.77, p⬍.01) and PCS
(⫽⫺.062; odds ratio ⫽.940; Wald [1] ⫽
4.11, p⬍.05) independently and significantly
contributed to the overall model. For each unit
decrease in the parent’s reported family sat-
isfaction, participants were 12.3% more likely
to be at risk for depression. Similarly, for
each unit decrease in the physical health
score, parents were 6% more likely to be at
risk for depression.
The pseudo R-squared values for this final
block ranged from 10.3% to 16.5%, and the
complete model was statistically significant
(
2
[12] ⫽30.79; p⬍.01), accounting for an
estimated 27.9% to 44.8% of the available vari-
ance in parent depression status. Moreover, pre-
diction accuracy for depression status using this
model was 76.3% for the nondepressed, 83.3%
for the depressed group, and 77.7% for the
entire sample (see Table 3).
Discussion
Based on responses to the PHQ9, 19.1% of
the parents in our sample were classified as
depressed. This rate of depression is much
lower than the range of 35% to 53% found in
past research on similar populations (Olsson &
Hwang, 2001), but nearly three times more than
the average for the overall U.S. population
Table 2
Hierarchical Logistic Regression Predicting Depression Risk Status
95% CI for odds
ratio
SE Wald df p Odds ratio Lower Upper
Demographics
Parent age ⫺0.05 0.06 0.79 1 .38 .95 .86 1.06
Parent education level 0.94 0.87 1.16 1 .28 .39 .07 2.16
Household income ⫺0.16 0.40 0.16 1 .69 .85 .39 1.85
Employment status 0.13 0.42 0.09 1 .76 1.13 .50 2.56
Disability characteristics
ADLs 0.05 0.03 2.21 1 .14 1.05 .98 1.12
Urinary/bowel continence ⫺0.14 0.12 1.33 1 .25 .87 .69 1.10
Psychosocial variables
Threat appraisals 0.11 0.05 4.78 1 .03
ⴱ
1.12 1.01 1.23
Growth appraisals ⫺0.01 0.16 0.22 1 .64 .99 .96 1.03
Environmental/social supports 0.11 0.06 2.99 1 .08 1.11 .99 1.25
Problem solving ⫺0.05 0.08 0.30 1 .58 .96 .81 1.12
Parent wellbeing
Family satisfaction ⫺0.13 0.06 5.71 1 .02
ⴱ
.88 .79 .98
Physical health ⫺0.06 0.03 4.11 1 .04
ⴱ
.94 .86 1.0
Constant 5.89 4.95 1.42 .23
ⴱ
p⬍.05.
297DEPRESSION AMONG PARENTS
(based on the 6.7% prevalence rate reported by
the National Institute of Mental Health, 2010).
Moreover, the percentage of depressed parents
in this study is similar to rates observed in
research that used a conservative measure of
depression among individuals caring for family
members with disabilities (15.7% among indi-
viduals caring for family members with spinal
cord injuries; Dreer et al., 2007), and substan-
tially lower than studies that used more liberal,
nonspecific measures to determine depression
status (48% among family caregivers of persons
with traumatic brain injuries, Rivera et al.,
2007; 38% among family caregivers of stroke
survivors, Grant et al., 2004). Collectively, the
results of the present study imply that parents
raising children with disabilities may be at
higher risk for depression than the general pub-
lic, but this risk may not be as high has some
studies have suggested.
The present study underscores the need for
the use of measures that closely adhere to es-
tablished criteria for diagnosing depression. The
PHQ9 is strictly modeled after the DSM–IV
depression criteria. Although not a stand alone
diagnostic tool, the PHQ9 was designed to spe-
cifically detect the presence of possible mood
psychopathology (i.e., clinical depression) and
not simply general emotional maladjustment
(i.e., mild anxiety or stress). Past studies pro-
vide support for this explanation. Dreer et al.
(2007) hypothesized that many studies of fam-
ily caregiver depression may base depression
prevalence rates from data obtained from non-
specific measures of distress. As a result, past
studies of parents of children with disabilities
may not have actually measured clinical depres-
sion but rather a nonspecific emotional distress.
This could account for the wide variation in
depression prevalence rates in past research.
The two groups of parents did not differ
significantly on any of the demographic vari-
ables in any of the mean difference analysis.
Furthermore, indicators of child disability se-
verity did not distinguish the two depression
risk groups. Based on these findings, demo-
graphic variables and child disability character-
istics may not be the best indicators of parent
risk for depression. In the past, such inferences
may have contributed to a negative view of
having a child with a disability. Evidence con-
tinues to suggest that parental maladjustment is
often more associated with variables not related
to the child but instead to problems accessing
information, resources, and environmental/
social supports (Green, 2007;Resch et al.,
2010;Worcester et al., 2006).
Consistent with the Elliott and Mullins
(2004) conceptualization of family adjustment
following disability, parent appraisals of their
situation were significantly associated with their
depression status. Parents at risk for depression
may have negative beliefs and fears about the
potential for harm posed by the challenges of
raising a child with a disability. Appraisals of
positive growth had no appreciable contribution
to the prediction of depression status, and con-
trary to prior work, neither did parent problem-
solving abilities. The results of the present study
imply that parents who are primed to interpret
stressful circumstances and events as poten-
tially threatening may be more likely to report
other symptoms characteristic of a major de-
pressive disorder.
Family satisfaction and physical health ap-
pear to have greater influence on parental de-
pression than the social–cognitive variables
contained in the third block of the equation. The
relationship between parental physical health
and depression may reflect, in part, a circular
relationship: It is well established that individ-
uals who care for a family member with a
disability, in general, are at risk for poor health
that subsequently increases their risk for psy-
chological problems (Vitaliano, Zhang, & Scan-
lan, 2003). A few studies have indicated a
relationship likely exists between family sat-
isfaction and overall parental adjustment, but
the family side of having a child with a dis-
ability has traditionally been neglected in re-
search (Ones, Yilmaz, Cetinkaya & Caglar,
2005). This is troubling because the unit of
society most affected by having a child with a
disability is the family and high satisfaction
with family functioning has been shown to be
Table 3
Classification Matrix for Prediction of Depression
Risk Status
Predicted group
Observed group Low risk High risk % Accurate
Not depressed 58 18 76.3%
Depressed 3 15 83.3%
Total percent accuracy 77.7%
298 RESCH, ELLIOTT, AND BENZ
associated with increased coping and more pos-
itive appraisals (Failla & Jones, 1991). The mar-
ital relationship, in particular, may be especially
important for positive child adjustment (Carr &
Springer, 2010).
Several important limitations should be men-
tioned when interpreting these results. This
study used the cut-off of ⱖ10 on the PHQ9 to
determine depression risk status for this sample,
and this could be construed as a liberal de-
pression cut-off point. Notably, however, past
studies have demonstrated that using the ⱖ10
cut-off point is equally useful as a more so-
phisticated PHQ9 scoring algorithm (Gilbody,
Richards, Brealey, & Hewitt, 2007). Moreover,
individuals with scores at or above this cut-off
point have been shown to be significantly more
likely to be diagnosed with depression follow-
ing a more in-depth clinical interview by a
mental health professional that those scoring
below the cut-off (Kroenke et al., 2001). Gil-
body et al. (2007) also found that for a commu-
nity (nonclinical) sample such as that studied
here, an even lower cut-off score (ⱖ9) may be
most appropriate. Given the cross-sectional na-
ture of this study, no statements about causality
can be made. The analytical model used here
found several predictors of depression status in
this sample of parents, but prediction in this
model should not be confused with causality in
the general population of parents raising chil-
dren with disabilities.
An additional limitation of this study is that
the majority of the sample was Caucasian, ed-
ucated, and living in mostly nonrural areas of a
single state. Importantly, no data regarding
family size or of child externalizing behavior
problems were collected. As this study dem-
onstrated, family satisfaction has a strong rela-
tionship with parental emotional wellbeing and
understanding the role of family size on this
relationship may be an important variable not
included in this study. Additionally, child ex-
ternalizing behavior problems have been asso-
ciated with parent distress in prior research
(Blacher, Neece, & Paczkowski, 2005), and
with parent requests for formal personal care
assistance in the home (Fournier et al., 2010)
and of personal care hours authorized by Med-
icaid caseworkers (Elliott et al., 2011). Future
study of parent depression should include indi-
cators of child externalizing behavior problems.
Finally, possible differences due to the parent
and/or gender of the child with the disability
were not included in this study. Future research
should attempt to capture these potential differ-
ences as they may, for example, shed additional
light on the unique challenges and perspectives
faced by fathers of children with disabilities.
Despite these limitations, the findings of this
study are informative for researchers, policy-
makers, educators, clinicians, and families of
children with disabilities. Research that builds
on these findings will continue to uncover pos-
sible predictors of parental depression that will,
in turn, inform health and education policy de-
cisions designed to assist these families.
References
Anderson, W. P., & Lopez-Baez, S. I. (2008). Mea-
suring growth with the posttraumatic growth in-
ventory. Measurement and Evaluation in Counsel-
ing and Development, 40, 215–227.
Beckman, P. J. (2002). Providing family-centered
services. In M. Batshaw (Ed.), A medical primer
(pp. 683–691). Baltimore, MD: Brookes.
Blacher, J., Neece, C. L., & Paczkowski, E. (2005).
Families and intellectual disability. Current Opin-
ion in Psychiatry, 18, 507–513.
Blucker, R. T., Elliott, T. R., Warren, R. H., &
Warren, A. M. (2011). Psychological adjustment
of family caregivers of children with severe neu-
rodisabilities requiring chronic respiratory man-
agement. Families, Systems & Health, 29, 215–
231.
Breslau, N., Staruch, K. S., & Mortimer, E. A.
(1982). Psychological distress in mothers of dis-
abled children. American Journal of Diseases of
Children, 636, 682–686.
Bristol, M., Gallagher, J., & Schopler, E. (1988).
Mothers and fathers of young developmentally dis-
abled and nondisabled boys: Adaptation and spou-
sal support. Developmental Psychology, 24, 441–
451.
Browne, G., & Bramston, P. (1998). Stress and qual-
ity of life in the parents of young people with
intellectual disabilities. Journal of Psychiatric and
Mental Health Nursing, 5, 415–421.
Carr, D., & Springer, K. W. (2010). Advances in
families and health research in the 21st century.
Journal of Marriage and Family, 72, 743–761.
Demirtepe-Saygili, D., & Bozo, Ö. (2011). Predicting
depressive symptoms among the mothers of chil-
dren with leukaemia: A caregiver stress model
perspective. Psychology & Health, 26, 585–599.
Dreer, L. E., Berry, J., Rivera, P., Snow, M., Elliott,
T. R., Miller, D., & Little, T. D. (2009). Efficient
assessment of social problem-solving abilities in
299DEPRESSION AMONG PARENTS
medical and rehabilitation settings: A Rasch anal-
ysis of the Social Problem-Solving Inventory-
Revised. Journal of Clinical Psychology, 65, 653–
669.
Dreer, L. E., Elliott, T. R., Fletcher, D. C., & Swan-
son, M. (2005). Social problem-solving abilities
and psychological adjustment of persons in low
vision rehabilitation. Rehabilitation Psychology,
50, 232–238.
Dreer, L. E., Elliott, T. R., Shewchuck, R., Berry,
J. W., & Rivera, P. (2007). Family caregivers of
persons with spinal cord injury: Predicting care-
givers at risk for probable depression. Rehabilita-
tion Psychology, 52, 351–357.
Elliott, T. R., & Mullins, L. L. (2004). Counseling
families and children with disabilities. In D. At-
kinson & G. Hackett (Eds.), Counseling diverse
populations, 3rd Ed. (pp. 151–170). New York,
NY: McGraw-Hill.
Elliott, T. R., Phillips, C., Patnaik, A., Naiser, E.,
Fournier, C., Miller, T., Hawes, C., & Dyer, J.
(2011). Medicaid personal care services and care-
givers’ reports of children’s health: The dynamics
of a relationship. Health Services Research, 46,
1803–1821.
Failla, S., & Jones, L. C. (1991). Families of children
with developmental disabilities: An examination
of family hardiness. Research in Nursing and
Health, 14, 41–50.
Floyd, F. J., & Gallagher, E. M. (1997). Parental
stress, care demands, and the use of support ser-
vices for school-age children with disabilities and
behavior problems. Family Relations, 46, 359–
371.
Fournier, C. J., Davis, M., Patnaik, A., Elliott, T. R.,
Dyer, J. A., Jasek, E., & Phillips, C. D. (2010).
Modeling caregivers’ perceptions of children’s
need for formal care: Physical function, intellec-
tual disability level, and behavior. Disability and
Health Journal, 3, 213–221.
Gilbody, S., Richards, D., Brealey, S., & Hewitt, C.
(2007). Screening for depression in medical set-
tings with the Patient Health Questionnaire (PHQ):
A diagnostic meta-analysis. Journal of General
Internal Medicine, 22, 1596–1602.
Glidden, L. M., & Floyd, F. J. (1997). Disaggregating
parental depression and family stress in assessing
families of children with developmental disabili-
ties: A multisample analysis. American Journal of
Mental Retardation, 102, 250–266.
Glidden, L. M., & Schoolcraft, S. A. (2003). Depres-
sion: Its trajectory and correlates in mothers rear-
ing children with intellectual disability. Journal of
Intellectual Disability Research, 47, 250–263.
Gotlib, I. H., Lewinsohn, P. M., & Seeley, J. R.
(1995). Symptoms versus a diagnosis of depres-
sion: Differences in psychosocial functioning.
Journal of Consulting and Clinical Psychology,
63, 90–100.
Grant, J. S., Bartolucci, A., Elliott, T. R., & Giger,
J. N. (2000). Sociodemographic, physical, and
psychosocial characteristics of depressed and non-
depressed family caregivers of stroke survivors.
Brain Injury, 14, 1089–1100.
Grant, J. S., Weaver, M., Elliott, T. R., Bartolucci,
A., & Giger, J. N. (2004). Family caregivers of
stroke survivors: Characteristics of caregivers at
risk for depression. Rehabilitation Psychology, 49,
172–179.
Green, S. E. (2007). “We’re tired, not sad”: Benefits
and burdens of mothering a child with a disability.
Social Science and Medicine, 64, 150–163.
Hastings, R. P. (2002). Parental stress and behaviour
problems of children with developmental disabil-
ity. Journal of Intellectual and Developmental
Disability, 27, 149–160.
Hoyt, W. T., Imel, Z. E., & Chan, F. (2008). Multiple
regression and correlation techniques: Recent con-
troversies and best practices. Rehabilitation Psy-
chology, 53, 321–339.
Johnson, C. L., Resch, J. A., Villarreal, V., Elliott,
T. R., Kwok, O.-M., Berry, J. W., & Underhill,
A. T. (2010). Family satisfaction predicts life sat-
isfaction trajectories over the first five years after
traumatic brain injury. Rehabilitation Psychology,
55, 180–187.
Kroenke, K., Spitzer, R. L., & Williams, J. B. W.
(2001). Validity of a brief depression severity in-
ventory. Journal of General Internal Medicine, 16,
606–613.
Kronenberger, W. G., & Thompson, R. J. (1992).
Medical stress, appraisal stress, and the psycholog-
ical adjustment of mothers of children with myelo-
meningocoele. Journal of Developmental and Be-
havioural Pediatrics, 13, 405–411.
Lightsey, O. R., & Sweeney, J. (2008). Meaning in
life, emotion-oriented coping, generalized self-
efficacy, and family cohesion as predictors of fam-
ily satisfaction among mothers of children with
disabilities. The Family Journal: Counseling and
Therapy for Families and Couples, 16, 212–221.
Minnes, P. M. (1988). Family stress associated with
a developmentally handicapped child. Interna-
tional Review of Research in Mental Retardation,
15, 195–226.
National Institute of Mental Health. (2010). The
numbers count: Mental disorders in America. Re-
trieved at http://www.nimh.nih.gov/health/
publications/the-numbers-count-mental-disorders-
in-america/index.shtml#MajorDepressive
Noojin, A. B., & Wallander, J. A. (1997). Perceived
problem-solving ability, stress, and coping in
mothers of children with physical disabilities: Po-
tential cognitive influences on adjustment. Inter-
300 RESCH, ELLIOTT, AND BENZ
national Journal of Behavior Medicine, 4, 415–
432.
Olson, D. H., & Wilson, M. (1982). Family invento-
ries: Inventories used in a national survey of fam-
ilies across the family life cycle. In D. H. Olson, H.
I. McCubbin, H. Barnes, A. Larsen, M. Muxen, &
M. Wilson (Eds.), Family social science (pp. 25–
31). St. Paul, MN: University of Minnesota.
Olsson, M. B., & Hwang, C. P. (2001). Depression in
mothers and fathers of children with intellectual
disability. Journal of Intellectual Disability Re-
search, 45, 435–443.
Ones, K., Yilmaz, E., Cetinkaya, B., & Calgar, N.
(2002). Assessment of the quality of life of moth-
ers of children with cerebral palsy (primary care-
givers). Neurorehabilitation and Neural Repair,
19, 232–237.
Pakenham, K. I. (2001). Application of a stress and
coping model to caregiving in multiple sclerosis.
Psychology, Health and Medicine, 6, 13–27.
Patnaik, A., Elliott, T. R., Fournier, C., Naiser, E.,
Moudouni, D., Miller, T.,...Phillips, C. (2011).
Severity of children’s intellectual disabilities and
Medicaid personal care services. Rehabilitation
Psychology, 56, 383–390.
Perlesz, A., Kinsella, G., & Crowe, S. (2000). Psy-
chological distress and family satisfaction follow-
ing traumatic brain injury: Injured individual and
their primary, secondary, and tertiary carers. Jour-
nal of Head Trauma Rehabilitation, 15, 909–929.
Phillips, C., Patnaik, A., Dyer, J., Naiser, E., John-
son, J., Fournier, C., & Elliott, T. R. (2011). Reli-
ability and functional status measurement (ADLs)
for children with special health care needs
(CSHCN) living in the community. Disability and
Rehabilitation, 33, 2013–2022.
Probst, J. C., Laditka, S. B., Moore, C. G., Harun, N.,
Powell, M. P., & Baxley, E. G. (2006). Rural-
urban differences in depression prevalence: Impli-
cations for family medicine. Family Medicine, 38,
653–660.
Reinhard, S. C., Kassner, E., & Houser, A. (2011).
How the Affordable Care Act can help move states
toward a high-performing system of long-term ser-
vices and supports. Health Affairs, 30, 447–453.
Resch, J. A., Mireles, G., Benz, M. R., Zhang, D.,
Peterson, R. L., & Grenweldge, C. (2010). Giving
parents a voice: A qualitative study of the chal-
lenges experienced by parents of children with
disabilities. Rehabilitation Psychology, 55, 139–
150.
Rivera, P., Elliott, T. R., Berry, J. W., Grant, J. S., &
Oswald, K. (2007). Predictors of caregiver depres-
sion among community-residing families living
with traumatic brain injury. NeuroRehabilitation,
22, 3–8.
Singer, G. (2006). Meta-analysis of comparative
studies of depression in mothers of children with
and without developmental disabilities. American
Journal of Mental Retardation, 111, 155–169.
Stanton, A. L., & Snider, P. R. (1993). Coping with
a breast cancer diagnosis: A prospective study.
Health Psychology, 12, 16–23.
Tedeschi, R. G., & Calhoun, L. G. (1996). The post-
traumatic growth inventory: Measuring the posi-
tive legacy of trauma. Journal of Traumatic Stress,
9, 455–471.
Veisson, M. (1999). Depression symptoms and emo-
tional states in parents of disabled and non-
disabled children. Social Behavior and Personal-
ity, 27, 87–98.
Vitaliano, P. P., Zhang, J., & Scanlan, J. (2003). Is
caregiving hazardous to one’s physical health? A
meta-analysis. Psychological Bulletin, 129, 946–
972.
Ware, J. E., Jr., Kosinski, M., & Keller, S. D. (1996).
A 12 item short form health survey: Construction
of scales and preliminary tests of reliability and
validity. Medical Care, 34, 220–233.
Weihs, K., Fisher, L., & Baird, M. (2002). Families,
health and behavior. Families, Systems, & Health,
20, 7–46.
Worcester, J. A., Nesman, T. M. Raffaele Mendez,
L. M., & Keller, H. R. (2008). Giving voice to
parents of young children with challenging behav-
ior. Exceptional Children, 74, 509–525.
Received January 12, 2012
Revision received August 22, 2012
Accepted September 4, 2012 䡲
301DEPRESSION AMONG PARENTS
