Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor D.docx

Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 11
Figure 9. Voltage waveform at the output of a PWM inverter
with and without overmodulation
(after filtering).
The Procedure is divided into the following sections:
-frequency
component
in the voltage (unfiltered) at the output of a three-phase PWM
inverter
the frequency on the induction motor speed and
magnetizing
current
Operation at motor nominal frequency. Operation at 4/3 the
motor
nominal frequency. Operation at 1/2 the motor nominal
frequency.

High voltages are present in this laboratory exercise. Do not
make or modify any
banana jack connections with the power on unless otherwise
specified.
Setup and connections
In this part of the exercise, you will set up and connect the
equipment.
1. Refer to the Equipment Utilization Chart in Appendix A to
obtain the list of
equipment required to perform the exercise.
Install the equipment in the Workstation.
Mechanically couple the Four-Pole Squirrel Cage Induction
Motor to the
Four-Quadrant Dynamometer/Power Supply using a timing belt.
PROCEDURE OUTLINE
PROCEDURE
Voltage waveform without overmodulation
Voltage waveform with overmodulation
Sine wave modulating the duty
cycle (with overmodulation)
/2
duty cycle = 100%
/2

duty cycle = 0%
Time
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12 © Festo Didactic 86368-00
2. Make sure that the ac and dc power switches on the Power
Supply are set to
the O (off) position, then connect the Power Supply to a three-
phase
ac power outlet.
Make sure that the main power switch on the Four-Quadrant
Dynamometer/
Power Supply is set to the O (off) position, then connect its
Power Input to an
ac power outlet.

3. Connect the Power Input of the Data Acquisition and Control
Interface to
a 24 V ac power supply.
Connect the Low Power Input of the Chopper/Inverter to the
Power Input of
the Data Acquisition and Control Interface. Turn the 24 V ac
power supply
on.
4. Connect the USB port of the Data Acquisition and Control
Interface to a
USB port of the host computer.
Connect the USB port of the Four-Quadrant
Dynamometer/Power Supply to
a USB port of the host computer.
5. Turn the Four-Quadrant Dynamometer/Power Supply on, then
set the
Operating Mode switch to Dynamometer.
6. Turn the host computer on, then start the LVDAC-EMS
software.
In the LVDAC-EMS Start-Up window, make sure that the Data
Acquisition
and Control Interface and the Four-Quadrant
Dynamometer/Power Supply
are detected. Make sure that the Computer-Based
Instrumentation and
Chopper/Inverter Control functions for the Data Acquisition and
Control
Interface are available. Select the network voltage and
frequency that

correspond to the voltage and frequency of your local ac power
network, then
click the OK button to close the LVDAC-EMS Start-Up
window.
7. Connect the Digital Outputs of the Data Acquisition and
Control
Interface (DACI) to the Switching Control Inputs of the
Chopper/Inverter
using a DB9 connector cable.
On the Chopper/Inverter, set the Dumping switch to the O (off)
position. The
Dumping switch is used to prevent overvoltage on the dc bus of
the
Chopper/Inverter. It is not required in this exercise.
8. Set up the circuit shown in Figure 10. Use the diodes in the
Rectifier and
Filtering Capacitors to implement the three-phase full-wave
rectifier. Use the
Chopper/Inverter to implement the Three-phase inverter.
Connect the
Thermistor Output of the Four-Pole Squirrel Cage Induction
Motor to the
Thermistor Input of the Four-Quadrant Dynamometer/Power
Supply.
Notice that the prefix IGBT
has been left out in this
manual when referring to
the IGBT Chopper/Inverter
module.

Figure 10. Three-phase, variable-frequency induction-motor
drive (with three-phase filter).
Measurement of the rms value of the fundamental-frequency
component in
the voltage (unfiltered) at the output of a three-phase PWM
inverter
In this part of the exercise, you will use the Harmonic Analyzer
in LVDAC-EMS to
measure the rms value of the fundamental-frequency component
in the unfiltered
voltage at the output of a three-phase PWM inverter. You will
also observe that
when the load connected to the three-phase PWM inverter
output is inductive
(like a motor), the current waveform at the output of the three-
phase
PWM inverter remains sinusoidal even with no filter.
a The Four-Quadrant Dynamometer/Power Supply is not used in
this part of the exercise although it is mechanically coupled to
the Four-Pole Squirrel Cage
Induction Motor.
9. In LVDAC-EMS, open the Chopper/Inverter Control window,
then make the
following settings:

-Phase PWM inverter.
-2-
3).
nominal frequency
indicated on the Four-Pole Squirrel Cage Induction Motor front
panel.
Three-phase filter
Three-phase
inverter
Three-phase
rectifier
Three-
phase
induction
machine
Switching control
signals from DACI
Brake
N

2 mH
2 mH
2 mH
5 F 5 F 5 F
5 F
the
modulation index m, i.e., it sets the amplitude of the sine-wave
signal
that modulates the duty cycle of the switching control signals.
When
the Peak Voltage parameter is set to 100%, the amplitude of the
modulating signal is set to obtain a peak output voltage
corresponding to 100% of half the dc bus voltage (100% of /2).
In other words, this sets the value of the modulation index to 1
(m = 1).
Pulse-
Width Modulation.
-Phase PWM Inverter.
10. On the Power Supply, turn the three-phase ac power source

on. The motor
should start to rotate.
11. In LVDAC-EMS, open the Oscilloscope. Use channel 1 to
display the dc bus
voltage (input E1), channels 2 and 3 to display the line voltage
at the output
of the three-phase PWM inverter before and after filtering
(inputs E2 and E3,
respectively), and channel 4 to display the current flowing in
the motor stator
windings (input I1).
Select the Continuous Refresh mode, set the time base to
display only one
complete cycle of the voltage and current waveforms (this
provides more
precision for observing the trains of rectangular bipolar pulses
produced by
the three-phase PWM inverter), and set the trigger controls so
that the
Oscilloscope triggers when the waveform of the motor stator
current passes
through 0 A with a positive slope.
Select convenient vertical scale and position settings to
facilitate observation
of the waveforms.
12. Print or save the waveforms displayed on the Oscilloscope
screen for future
reference. It is suggested that you include these waveforms in
your lab
report.
13. Describe the waveform of the voltage at the output of the

three-phase
PWM inverter before and after filtering.
14. Measure and record the rms value of the line voltage at the
output of the
three-phase PWM inverter before and after filtering.
RMS value of the line voltage at the output of the three-phase
PWM inverter before filtering: V
PWM inverter after filtering: V
15. Are the rms values of the line voltage at the output of the
three-phase
PWM inverter measured before and after filtering using the
Oscilloscope
equal? Explain briefly.
16. Describe the waveform of the current flowing in each stator
winding of the
three-phase motor (input I1).

17. In LVDAC-EMS, open the Harmonic Analyzer window, then
make the
following settings:
nominal
frequency indicated on the Four-Pole Squirrel Cage Induction
Motor
front panel.
r is set to 40.
18. Measure and record the rms value of the fundamental-
frequency
component (1f) in the line voltage at the output of the three-
phase

PWM inverter after filtering (input E3), indicated in the
Harmonic Analyzer
display.
RMS value of the fundamental-frequency component in the line
voltage
at the output of the three-phase PWM inverter after filtering: V
a Compare the rms value of the fundamental-frequency
component in the line voltage at the output of the three-phase
PWM inverter after filtering measured
using the Harmonic Analyzer with the rms value of the line
voltage measured
previously using the Oscilloscope. The values should be
identical because the
voltage waveform is sinusoidal.
19. In the Harmonic Analyzer window, set the Input parameter
to E2 to measure
the line voltage at the output of the three-phase PWM inverter
before filtering.
Measure and record the rms value of the fundamental-frequency
component
in the line voltage at the output of the three-phase PWM
inverter before
filtering (input E2), indicated in the Harmonic Analyzer
display.
voltage
at the output of the three-phase PWM inverter before filtering:
V
a The rms value of the fundamental-frequency component in the
line voltage at the output of the three-phase PWM inverter

before filtering should be slightly
higher than that after filtering because of the voltage drop
across the three-
phase filter.
20. Do your measurements confirm that the rms value of the
fundamental-
frequency component in the line voltage at the outputs of the
three-phase
PWM inverter before filtering can be measured using the
Harmonic
Analyzer? Explain briefly.
21. In the Chopper/Inverter Control window, stop the Three-
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source off.
Remove the three-phase filter from the circuit and disconnect

voltage
input E3. The circuit should be as shown in Figure 11.
Figure 11. Three-phase, variable-frequency induction-motor
drive (without three-phase filter).
22. In the Chopper/Inverter Control window, start the Three-
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source on.
Measure and record the rms value of the fundamental-frequency
component
in the line voltage at the output of the three-phase PWM
inverter indicated in
the Harmonic Analyzer display.
voltage
at the output of the three-phase PWM inverter: V
a Compare the rms value of the fundamental-frequency
component in the line voltage measured without filter to that
measured in step 19 when the filter was in
the circuit.
Three-phase
inverter
Three-phase
full-wave rectifier
Three-

phase
induction
machine
Switching control
signals from DACI
Brake
23. On the Oscilloscope, use channel 1 to display the dc bus
voltage (input E1),
channel 2 to display the voltage at the output of the three-phase
PWM inverter (input E2), and channel 3 to display the current
flowing in one
of the motor stator windings (input I1).
of the waveforms.
screen for future
your lab
report.
25. Explain why the waveform of the motor stator current
displayed on the

Oscilloscope screen is sinusoidal even if the filter is removed.
Overmodulation
In this part of the exercise, you will increase the rms value of
the line voltage at
the output of a three-phase PWM inverter by increasing the
amplitude of the sine
wave that modulates the duty cycle of the switching transistors
of the
PWM inverter to a level that causes overmodulation.
a For the remaining of this exercise, the rms value of the line
voltage at the output of the three-phase PWM inverter always
refers to the rms value of the
fundamental-frequency component in the line voltage (i.e., the
rms value of
the 1f component measured with the Harmonic Analyzer).
26. Compare the rms value of the line voltage at the output of
the three-phase
PWM inverter measured in step 22 with the nominal line voltage
of the
Four-Pole Squirrel Cage Induction Motor (indicated on its front
panel). Does
the maximum line voltage which can be obtained at the outputs
of the three-
phase PWM inverter without overmodulation (i.e., with
modulation index set
to 1) allow the induction motor to be operated at its nominal
voltage?

27. In order to increase the voltage at the output of the three-
phase
PWM inverter (i.e., the voltage applied to the motor windings),
set the Peak
Voltage parameter in the Chopper/Inverter Control window to
117% of the
dc bus voltage/2 (this corresponds to the maximum Peak
Voltage value
available in the Chopper/Inverter Control window). This setting
increases the
amplitude of the sine-wave signal that modulates the duty
cycles of the
switching control signals to a level that causes overmodulation.
28. Using the Harmonic Analyzer, measure and record the rms
value of the line
voltage at the output of the three-phase PWM inverter obtained
when
overmodulation is used.
PWM inverter (with overmodulation): V
29. Is the value measured in the previous step close to the
nominal line voltage
of the Four-Pole Squirrel Cage Induction Motor?

screen for future
your lab
report.
31. Compare the voltage waveforms at the output of the three-
phase
PWM inverter obtained with and without overmodulation (i.e.,
obtained in
step 30 and step 24, respectively). Do you observe that the
voltage
waveform obtained with overmodulation contains less
rectangular pulses,
indicating that overmodulation occurs?
32. Do your observations confirm that overmodulation allows
the rms value of the
maximum voltage at the output of a three-phase PWM inverter
to be
increased?
Phase
PWM Inverter.
Effect of the frequency on the induction motor speed and
magnetizing
current
In this part of the exercise, you will use the same circuit as in

the previous
section (see Figure 11) to observe the effect of the frequency on
the speed and
magnetizing current of the three-phase induction motor.
34. In LVDAC-EMS, open the Four-Quadrant
Dynamometer/Power Supply
window, then make the following settings:
Positive Constant-Torque
Prime
Mover/Brake.
This
allows the torque command of the prime mover/brake to be
controlled manually.
ensuring
that the Four-Pole Squirrel Cage Induction Motor operates with
no
mechanical load.
The first and second
numbers in this parameter specify the number of teeth on the
pulley
of the Four-Quadrant Dynamometer/Power Supply and the
number

of teeth on the pulley of the machine under test, respectively.
The
pulley ratio between the Four-Quadrant Dynamometer/Power
Supply
and the Four-Pole Squirrel Cage Induction Motor is 24:24.
is
required to match the characteristics of the thermistor in the
Four-
Pole Squirrel Cage Induction Motor with the Thermistor Input
in the
Four-Quadrant Dynamometer/Power Supply.
-Torque Primer Mover/Brake by
setting
the Status parameter to Started or by clicking the Start/Stop
button.
de of the meters by clicking
on the
corresponding button.
Operation at motor nominal frequency
35. In the Chopper/Inverter Control window, make the
following settings:
-Phase
PWM inverter.
he Switching Frequency parameter to 2000 Hz.
-2-
3).

nominal
Motor
front panel.
Pulse-
Width Modulation.
-Phase PWM Inverter.
dure
36. The induction motor should rotate at a speed slightly below
the synchronous
speed of the three-phase induction motor. Record the motor
speed of
rotation indicated by the Speed meter in the Four-Quadrant
Dynamometer/Power Supply window in Table 2.
a The synchronous speed of the Four-Pole Squirrel Cage
Induction Motor is 1500 r/min at a local ac power network
frequency of 50 Hz and 1800 r/min at
a local ac power network frequency of 60 Hz.
Table 2. Effect of the frequency on the induction motor speed
and magnetizing current.

Frequency Motor speed of
rotation
(r/min)
DC bus voltage
(V)
RMS value of the
motor magnetizing
current
(A)
Amplitude of the
motor magnetizing
current (peak value)
(A)
Nominal
frequency
4/3 the nominal
frequency
1/2 the nominal
frequency
37. On the Oscilloscope, use channel 1 to display the dc bus

voltage (input E1),
and channel 2 to display the motor stator current (input I1).
of the waveforms.
38. Measure and record the dc bus voltage as well as the rms
value and
amplitude of the motor magnetizing current in Table 2.
Operation at 4/3 the motor nominal frequency
39. In the Chopper/Inverter Control window, gradually increase
the frequency of
the three-phase PWM inverter up to about 4/3 the motor
nominal frequency
while observing the motor speed and magnetizing current.
Measure and record the motor speed, the dc bus voltage, as well
as the
rms value and amplitude of the motor magnetizing current in
Table 2.
40. Gradually decrease the frequency of the three-phase PWM
inverter to the
motor nominal frequency.
The stator current meas-
ured when an induction
motor operates with no
mechanical load is virtually
equal to the motor magnet-
izing current.

41. Compare the motor speed measured at 4/3 the nominal
frequency to that
measured at the motor nominal frequency. Does the motor speed
increase in
direct proportion with the increase in frequency?
42. Compare the motor magnetizing current measured at 4/3 the
motor nominal
frequency to that measured at the motor nominal frequency.
How does the
motor magnetizing current vary when the frequency is
increased? Explain
why.
43. How does the motor magnetizing current variation affect the
maximum
magnetic flux density in the motor?
Operation at 1/2 the motor nominal frequency
In the next steps, the current in the motor stator windings will
exceed the nominal value.

Perform your manipulations rapidly to prevent motor
overheating. Also, make sure that the
motor peak current never exceeds four times the nominal
current shown on the Four-Pole
Squirrel Cage Induction Motor front panel or the motor
temperature never exceeds 80°C
(176°F).
44. Gradually decrease the frequency of the three-phase PWM
inverter down to
about 1/2 the motor nominal frequency while observing the
motor speed and
magnetizing current.
Measure and record the motor speed, the dc bus voltage, as well
as the
rms value and amplitude of the motor magnetizing current in
Table 2.
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source off.
46. Compare the motor speed measured at 1/2 the motor
nominal frequency to
that measured at the motor nominal frequency. Does the motor
speed

decrease in direct proportion with the decrease in frequency?
47. How does the motor magnetizing current vary when the
frequency is
decreased? Explain why.
48. Compare the magnetizing current measured at 1/2 the motor
nominal
frequency to that measured at the motor nominal frequency as
well as to the
motor full-load current rating indicated on the Four-Pole
Squirrel Cage
Induction Motor front panel. What are the consequences of
decreasing the
frequency to 1/2 the motor nominal frequency while keeping the
voltage
constant?
Saturation curves
In this part of the exercise, you will plot the saturation curves
of the three-phase
induction motor at the motor nominal frequency, 2/3 the
nominal frequency,

1/2 the nominal frequency, and 1/3 the nominal frequency.
In the next steps, the current in the motor stator windings will
exceed the nominal value.
Perform your manipulations rapidly to prevent motor
overheating. Also, make sure that the
motor peak current never exceeds four times the nominal
current shown on the Four-Pole
Squirrel Cage Induction Motor front panel or the motor
temperature never exceeds 80°C
(176°F).
49. Remove the timing belt that mechanically couples the Four-
Pole Squirrel
Cage Induction Motor to the Four-Quadrant
Dynamometer/Power Supply.
50. In the Chopper/Inverter Control window, make the
following settings.
-Phase
PWM inverter.
Hz.
Phase Sequence parameter is set to Fwd (1-2-
3).

Motor
front panel.
e-Phase PWM Inverter.
51. On the Power Supply, turn the three-phase ac power source
on.
For each value of the Peak Voltage parameter shown in Table 3,
measure
the rms value of the line voltage applied to the motor stator
windings using
the Harmonic Analyzer and the peak value of the current
flowing in the motor
stator windings (peak magnetizing current) using the
Oscilloscope (use the
horizontal cursors to make the measurement). To obtain optimal
results,
begin your measurements with the highest Peak Voltage setting
as shown in
Table 3.
To reduce the data acquisition time and to prevent the motor
from
overheating, it is strongly recommended to use the Data Table
in
LVDAC-EMS to record the circuit parameters measured.
Table 3. RMS value of the motor stator winding voltage (at the
fundamental frequency) and
peak magnetizing current of the three-phase induction motor at
various frequencies.

Peak voltage
(% of dc
bus/2)
(%)
/ / /
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
Motor line
voltage
(V)

Peak
magnetizing
current
(A)
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
117
110
100
90
80
70
60
50
40
30

52. Repeat the previous step at the following three other
frequencies:
2 3⁄ , 1/2 , and 1/3 . As mentioned earlier, make sure
that the motor peak current never exceeds four times the
nominal current
indicated on the Four-Pole Squirrel Cage Induction Motor front
panel or the
temperature never exceeds 80°C (176°F), particularly at 1/2 and
1/3 the
nominal motor operating frequency.
a Use the 40 A terminal of input I1 on the DACI and set the
Range of input I1 to High in the Data Acquisition and Control
Settings window of LVDAC-EMS as
soon as the motor stator current reaches 4 A. Temporarily turn
the ac power
source off before making these changes.
53. Plot on the same graph the saturation curves (rms value of
the motor stator
winding voltage as a function of the peak magnetizing current)
of the Four-
Pole Squirrel Cage Induction Motor at each of the frequencies
in Table 3.
a You will probably not observe any saturation at nominal
frequency because the voltage applied to the motor windings
cannot be increased sufficiently.

54. Do the saturation curves you plotted in the previous step
show that, when the
motor stator winding voltage is increased, the peak magnetizing
current
increases in similar proportions until the voltage reaches a value
at which
saturation in the stator core of the induction motor starts to
occur, causing
the peak magnetizing current to increase much more rapidly
than the motor
stator winding voltage?
55. Do your observations confirm that increasing the frequency
allows higher
voltages to be applied to the motor stator windings before
saturation starts to
occur?
56. Close LVDAC-EMS, turn off all equipment, and remove all
leads and cables.
In this exercise, you learned that a three-phase, variable-
frequency induction-
motor drive consists of a power diode, three-phase full-wave

rectifier and a three-
phase PWM inverter. You learned that varying the operating
frequency of the
PWM inverter varies the frequency of the ac voltage applied to
the induction
motor, and thus, the motor speed. You also learned that varying
the modulation
index of the PWM inverter allows the voltage applied to the
motor stator windings
to be adjusted. You were introduced to the use of
overmodulation in the
PWM inverter to increase the maximum voltage that can be
applied to the motor
stator windings. You saw that for a given frequency, the
maximum flux density
( .) in the stator of the motor is directly proportional to the rms
value of
voltage applied to the motor stator windings. You also saw that
when the voltage
increases and reaches a certain value, saturation occurs in the
stator causing the
motor magnetizing current to start increasing at a rate which
largely exceeds that
of the motor voltage. You learned that for a given voltage, the
maximum flux
density is inversely proportional to the frequency of the ac
voltage applied to the
motor windings, and consequently, the magnetizing current of
the motor
decreases when the frequency increases and vice versa. You
were introduced to
the use of a harmonic analyzer to measure the rms value of the
fundamental-
frequency component of a non-sinusoidal signal (e.g., the
unfiltered voltage at
the output of a three-phase PWM inverter).

1. How can the ac voltage at the output of a three-phase PWM
inverter be
varied?
2. How does the magnetizing current vary when saturation starts
to occur in the
stator of an induction motor?
3. What should be done for an induction motor to be able to
produce the
highest possible torque?
4. How do the maximum flux density ( .) and peak magnetizing
current of an
induction motor vary when the PWM inverter …
Rates and Predictors of Postpartum Depression by Race
and Ethnicity: Results from the 2004 to 2007 New York City
PRAMS Survey (Pregnancy Risk Assessment Monitoring
System)
Cindy H. Liu • Ed Tronick
Published online: 25 October 2012
� Springer Science+Business Media New York 2012

Abstract The objective of this study was to examine
racial/ethnic disparities in the diagnosis of postpartum
depression (PPD) by: (1) identifying predictors that account
for prevalence rate differences across groups, and (2) com-
paring the strength of predictors across groups. 3,732 White,
African American, Hispanic, and Asian/Pacific Islander
women from the New York City area completed the Preg-
nancy Risk Assessment Monitoring System from 2004 to
2007, a population-based survey that assessed sociodemo-
graphic risk factors, maternal stressors, psycho-education
provided regarding depression, and prenatal and postpartum
depression diagnoses. Sociodemographic and maternal
stressors accounted for increased rates in PPD among Blacks
and Hispanics compared to Whites, whereas Asian/Pacific
Islander women were still 3.2 times more likely to receive a
diagnosis after controlling for these variables. Asian/Pacific
Islanders were more likely to receive a diagnosis after their
providers talked to them about depressed mood, but were less

likely than other groups to have had this conversation. Pre-
natal depression diagnoses increased the likelihood for PPD
diagnoses for women across groups. Gestational diabetes
decreased the likelihood for a PPD diagnosis for African
Americans; a trend was observed in the association between
having given birth to a female infant and increased rates of
PPD diagnosis for Asian/Pacific Islanders and Whites. The
risk factors that account for prevalence rate differences in
postpartum diagnoses depend on the race/ethnic groups
being compared. Prenatal depression is confirmed to be a
major predictor for postpartum depression diagnosis for all
groups studied; however, the associations between other
postpartum depression risk factors and diagnosis vary by
race/ethnic group.
Keywords Postpartum depression � Health status
disparities � Asian Americans � Prenatal depression �
Gestational diabetes
Introduction
Postpartum depression (PPD) is a serious health concern

affecting approximately 13 % of all women [1]. At least
19.2 % of women experience depression within 12 months
after giving birth [2]. The associations between prenatal
depression and PPD depression are well documented [3–5].
Psychosocial factors including high stress, low social sup-
port, and low marital satisfaction are also predictors [4, 5].
Surprisingly little is known about the extent to which
postpartum depression varies by race and ethnicity, given the
effects of culture on the experiences and manifestations of
depression [6, 7]. This dearth of information on postpartum
depression in ethnic minorities is well recognized. In a
published review of maternal depression, the Agency for
Healthcare Research and Quality found ‘‘screening instru-
ments [to be] poorly representative of the U.S. population,’’
and that ‘‘populations [from studies] were overwhelmingly
Caucasian’’ [8]. A review by O’Hara found that meta-anal-
yses on postpartum depression had omitted race and eth-
nicity as risk factors for postpartum depression [4].

Research studies on postpartum depression that have
included ethnic minorities generally compare African
C. H. Liu (&)
Beth Israel Deaconess Medical Center, Harvard Medical School,
75 Fenwood Road, Boston, MA 02115, USA
e-mail: [email protected]
E. Tronick
Child Development Unit, University of Massachusetts,
100 Morrissey Blvd, Boston, MA 02125, USA
e-mail: [email protected]
123
Matern Child Health J (2013) 17:1599–1610
DOI 10.1007/s10995-012-1171-z
Americans and Hispanics with Whites. In these studies,
group differences in prevalence rates have shown to be
inconsistent. Across studies, the rates of postpartum
depression in African American and Hispanic women were
found to be higher [9], lower [10], or no different [11]

compared to Whites. What accounts for observed racial and
ethnic differences in prevalence is unclear. In some studies,
sociodemographic risk variables were associated with
higher levels of depressive symptomatology among Afri-
can Americans, raising the possibility that sociodemo-
graphic variables rather than race and ethnicity account for
different levels of postpartum depression [12–14]. In con-
trast, others have shown greater levels of depressive
symptomatology among African Americans and Hispanics
than Whites, after accounting for sociodemographic factors
[9]. While certain social factors could increase risk, some
factors might buffer against postpartum depression within
groups. For instance, low income foreign-born Hispanic
women with social support exhibited lower rates of post-
partum depression [15], whereas bilingual Hispanic women
were at greater risk than those who spoke only Spanish
[11]. It is possible that factors such as social support or
nativity and its effect on the likelihood of postpartum

depression differ by race/ethnicity because they express
different meanings or incur different implications for each
group. Moreover, stigmas about psychological problems
and help-seeking may have an effect on identifying post-
partum depression, resulting in a subsequent effect on
reported prevalence of postpartum depression rates [6, 16].
Given the mixed picture across groups, this study aimed to
systematically determine the extent to which prevalence
rates across race and ethnicity are explained by factors
associated with postpartum depression.
This study uniquely includes Asian/Pacific Islander
(API) women within the U.S. As the fastest growing ethnic
minority group, over 16 million APIs are estimated to be
living in the U.S [17, 18]. The research on API postpartum
experiences is limited, which is striking given that API
women may hold several risk factors.
If psychiatric history is a major predictor, API women
may be at greatest risk: those between the ages of

15–24 years have the highest rate of depression and su-
icidality compared to any other ethnicity, gender, or age
[19–21]. One study showed APIs to be at lower risk for
postpartum depressive symptoms compared to Whites,
African Americans, and Hispanics [14], while another
study reported a greater percentage of APIs with post-
partum symptoms compared to White Americans [22].
Analyses conducted by the New York City Department of
Health and Mental Hygiene on data from the 2004 to 2007
New York City (NYC) Pregnancy Risk Assessment Mon-
itoring System (PRAMS) revealed a higher rate of PPD
diagnoses among APIs compared to other groups [23–25].
From the most recent sample in 2007, 10.4 % of API
received a PPD diagnosis compared to 1.7 % of non-His-
panic White women [26]. These findings suggest a poten-
tial risk for postpartum depression in APIs.
This study examines racial/ethnic disparities in PPD
diagnosis by identifying predictors accounting for preva-

lence differences. Because previous studies have either
focused mostly on small samples of one group, or did not
examine these risk factors by race/ethnicity, we hypothe-
size that associations of risk factors and PPD differ by race/
ethnic group. The risk factors evaluated were selected
based on the current literature [27–31]. Our study also
sought to explain disparities in PPD rates from a published
report by the NYC Department of Health and Mental
Hygiene. We utilized the study’s comprehensive popula-
tion-based dataset. We also sought to determine the
strength of predictors within each group and differences
across groups. Accordingly, we stratified our analyses by
race/ethnicity. Determining the strength of predictors by
group is essential for identifying individuals most at risk,
and may inform the possible causes of depression for dif-
ferent groups. Unique to this study was the use of diagnosis
as an outcome measure, the inclusion of information on
whether providers talked to women about depressed mood,

and an adequate sample size of APIs. This allowed us to
also examine disparities in psycho-education and diagnosis
across groups.
Methods
Sample
This study used the NYC PRAMS from 2004 to 2007, a
population-based survey administered to postpartum
women from NYC. Coordinated by the Centers for Disease
Control and Prevention and state health departments,
PRAMS’ goal is to monitor maternal behaviors and expe-
riences of women before, during, and after live birth
pregnancies. The dataset was provided by the NYC
Department of Health and Mental Hygiene (DOHMH).
The participants were part of an ongoing population-
based random sampling of NYC live births. NYC mothers
of approximately 180 infants with registered birth certifi-
cates that gave birth during the previous 2–4 months were
contacted for participation monthly. Eighty-three percent

responded by mail and 17 % by phone. The sample was
randomized without replacement and stratified by birth
weight. The final dataset was weighted for stratification,
nonselection, and nonresponse.
According to the DOHMH, a total of 4,813 responses
were received with response rates of at least 70 % from
July to December of 2004, May to December of 2005, and
1600 Matern Child Health J (2013) 17:1599–1610
123
January to December of 2006. A rate of 65 % was achieved
from January to December of 2007. For 2004–2005,
responses were weighted to represent 138,266 live births.
For 2006 and 2007, responses represented 119,079 and
122,222 live births, respectively. Based on the DOHMH
analysis, respondents differed from non-respondents on
some key sociodemographic variables (p .05). APIs
compared to other racial and ethnic groups, younger
women, and women with less education were less likely to

respond to the survey.
Measures
The birth certificate provided information on maternal race/
ethnicity and nativity (i.e., U.S. or non-U.S. born mothers).
Women were classified as Hispanic or non-Hispanic based
on self-report. Non-Hispanic women were categorized in
one of the following groups: White, African American,
Asian/Pacific Islander, and American Indian/Alaskan
Native. Maternal age, nativity (U.S. Born versus Foreign
Born) and education (categorized as: 0–8, 9–11, 12, 13–15,
and [16 years) were based at the time of infant birth from
information in the birth certificate. Mean infant age at the
time of survey completion was 9.7 months; there were no
significant differences in infant age across groups.
The PRAMS survey itself provided information for
remaining variables. To obtain income, women were asked
to indicate ‘‘total household income before taxes in the
12 months before the new baby was born’’ by checking off

one of the following options: $10,000, $10,000–$14,999,
$15,000–$19,999, $20,000–$24,999, $25,000–$34,999,
$35,000–$49,999, $50,000–$74,999, and [$75,000. Stress-
ful events during pregnancy were obtained by ‘‘yes’’ or
‘‘no’’ responses to events that may have occurred during
the last 12 months before the new baby was born. Exam-
ples include ‘‘I moved to a new address,’’ ‘‘I had a lot of
bills to pay,’’ ‘‘I got separated or divorced from my hus-
band or partner,’’ and ‘‘Someone very close to me died.’’
These events were counted and categorized into the fol-
lowing: 0, 1–2, 3–5, and 6–13 events. A ‘‘yes’’ or ‘‘no’’
response was also used to obtain information on following:
gestational diabetes (‘‘High blood sugar (diabetes) that
started during this pregnancy’’), social support from partner
(responses of ‘‘My husband or partner’’ to the question
‘‘During your most recent pregnancy, who would have
helped you if a problem had come up’’), NICU (Neonatal
Intensive Care Unit) (‘‘After your baby was born, was he or
she put in an intensive care unit?’’), unintended pregnancy

(‘‘When you got pregnant with your new baby, were you
trying to get pregnant?’’). The NYC PRAMS included
additional questions related to depression. Mothers were
asked to respond ‘‘yes’’ or ‘‘no’’ regarding prenatal
depression (‘‘At any time during your most recent
pregnancy, did a doctor, nurse, or other health care worker
diagnose you with depression?’’), and discussion about
mood (‘‘At any time during your most recent pregnancy or
after delivery, did a doctor, nurse, or other health care
worker talk with you about ‘‘baby blues’’ or postpartum
depression?’’). In addition, mothers were asked about PPD
diagnosis (‘‘Since your new baby was born, has a doctor,
nurse, or other health care worker diagnosed you with
depression?’’). The response to this item was the outcome
variable used for the analyses in this study.
The language of the survey (English or Spanish version)
was also noted.
Variables

Covariates included maternal age, household income,
maternal education, nativity, and infant age at the time the
mother completed the questionnaire. Variables considered
as potential stressors included: gestational diabetes,
stressful events, social support, NICU, intention for preg-
nancy, and prenatal depression. Discussion about mood
served as an additional predictor of PPD diagnosis.
Responses with missing variables of interest for this
study were eliminated. Variables with less than a 100 %
response rate included household income (86.9 %),
maternal education (99.3 %), maternal age (97.0 %), and
PPD diagnosis (99.4 %) resulting in an unweighted study
sample of 3,732.
Statistical Analyses
To account for the stratified and weighted sample, the data
was analyzed using the complex samples module of SPSS
version 17.0 (SPSS Inc., Chicago, IL). A non-race stratified
model was conducted to determine the likelihood of

receiving a PPD diagnosis for each race/ethnic group with
Whites as the reference group. A series of four logistic
regression models were employed where the variables of
interest (race/ethnicity, sociodemographic, stressors, and
discussion about mood) were sequentially added to the
model, allowing incremental examination of the variables’
effects in identifying factors that explain racial/ethnic
disparities in PPD.
Prevalence estimates within each group were generated
according to predictors. To compare the characteristics of
those with and without PPD and to understand associated
predictors, race-stratified logistic regressions incorporated
all predictors, with sociodemographic variables as covari-
ates. Adjusted odds ratios for each predictor were gener-
ated by race/ethnic group. Note that our models failed to
converge with the inclusion of language, nativity, and
NICU variables because of low cell sizes; thus, these
variables were dropped from our analyses. Unless

Matern Child Health J (2013) 17:1599–1610 1601
123
otherwise noted, all reported proportions represent weigh-
ted averages.
Results
Compared to other groups, API women showed the highest
rate for PPD, followed by Hispanics and African Ameri-
cans. White women had the lowest rate of PPD. The high
rate of a PPD diagnosis among API women is consistent
with previous analyses from this dataset, which utilized a
larger sample size than the dataset here, as this set includes
only women with complete data on the predictor variables.
Other racial/ethnic differences among assessed variables
are presented (Table 1).
A major objective was to determine whether sociode-
mographic variables, stressor variables, and discussion
about mood accounted for PPD differences. In the unad-

justed model, likelihood estimates indicate that API women
were 4.6 times more likely and Hispanic women 2.7 times
more likely than Whites to receive a PPD diagnosis.
African American were 1.7 times more likely to receive the
diagnosis than Whites, although this was not statistically
significant (Table 2). Once sociodemographic factors were
entered, African Americans were no more likely to receive
a diagnosis than Whites. For Hispanics, the greater likeli-
hood for a diagnosis compared to Whites was less pro-
nounced after accounting for sociodemographic factors and
was eliminated with the inclusion of stressors. The diag-
nosis likelihood was slightly reduced for APIs after
accounting for sociodemographic factors, and significantly
reduced with stressor variables, although diagnosis likeli-
hood was still more than double the rate of Whites and
African Americans. In contrast to the other groups, diag-
nosis likelihood for APIs increased to 3.2 times relative to
Whites, after accounting for reports of having discussed

mood with a provider. Prenatal depression was by far the
strongest predictor for all women compared to other
stressors, although women who gave birth to females were
more likely to receive a diagnosis than women with male
infants. Overall, those who had a discussion about mood
were also more likely to receive a diagnosis.
Profiles of women with PPD diagnoses compared to
women without a diagnosis differed by race/ethnicity. The
majority of White women reporting a PPD diagnosis
received a postgraduate education, while API and African
American women with the diagnosis tended to be high
school graduates. Approximately half of the White women
with PPD had household incomes above $75,000 per year.
Among APIs, Hispanics, and African Americans, more
women with PPD had less than $15,000 of household
income per year than those without a diagnosis (Table 3).
With regard to stressors, we found a significantly higher
rate of gestational diabetes among those with PPD than

those without PPD, but only for White women. However,
after controlling for sociodemographic variables through
our race-stratified adjusted model, gestational diabetes did
not significantly predict PPD in White, API, or Hispanic
women (Table 4). In fact, African American women with
gestational diabetes were less likely to receive a diagnosis
of PPD.
Compared to those without PPD, there was a higher
percentage among APIs and Hispanics with the diagnosis
who had an unintended pregnancy. In addition, the
majority of APIs with PPD had a diagnosis of prenatal
depression compared to the other groups. Stressful events
were not associated with greater likelihood for PPD, but
API women who reported having 6–13 stressful events
were significantly more likely to have PPD, a rate that was
statistically significant. The association between prenatal
depression and PPD persisted for all groups, even after
controlling for sociodemographic variables.

Overall, there was a higher rate of women with PPD
who had a discussion about mood with their providers than
women without the diagnosis. However, the association
between PPD and discussion about mood with providers
was specific to only API and African American women in
the adjusted model.
Women from all groups who received a diagnosis of
PPD were more likely to have given birth to females
although the differences were not statistically significant.
However, having a female infant seemed to slightly
increase the likelihood of a PPD diagnosis among White
and API women based on the race-stratified analyses.
Discussion
This study assessed PPD estimates and identified predictors
of PPD as defined by women’s reports of receiving a
diagnosis from a health care provider. We included API
women and used race-stratified analyses, allowing us to
determine whether predictors varied by race/ethnicity.

This study also sought to identify factors that explained
racial/ethnic disparities obtained in a previous analysis of
the dataset by the NYC Department of Health and Mental
Hygiene. As with other studies, we found that sociode-
mographic factors accounted for the higher rates of PPD
among African Americans and Hispanics. Based on such
findings, some have argued for prevention or intervention
programs to provide resources (e.g., financial support,
education) in addressing the racial/ethnic disparities of
PPD for African Americans and Hispanics [12]. However,
unlike other studies that primarily assessed reported
symptoms [9, 12, 14], we used the diagnosis of PPD as the
123
outcome measure. This raises the possibility that sociode-
mographic status accounts for the rates at which one
receives a diagnosis; in our study, African Americans and

Hispanics with lower sociodemographic statuses were less
likely to receive a diagnosis compared to Whites. If race/
ethnic disparities are found among rates of diagnosis, then
the diagnostic process may be another area to target for
improvement among lower sociodemographic status
groups.
Among ethnic minorities in our study, API women were
the most likely to receive a PPD diagnosis, and unlike
African Americans and Hispanics, the likelihood of
receiving a PPD diagnosis for APIs remained significantly
higher even after accounting for other variables (e.g.,
sociodemographic factors). Prenatal depression was asso-
ciated with PPD for all groups in our study, but the like-
lihood was highest for APIs. Although psychiatric history
for depression was not available, the strong association
between prenatal depression and PPD observed among the
API women in our sample adds to the growing concern of
depression experiences and its effects on API women

during motherhood [19–21]. A number of factors specific
to API women’s experiences are potentially associated
with later postpartum mood. The high rate of depression
and suicidal ideation during adolescence and young
adulthood may reflect family and societal pressures faced
by young women to uphold high academic standards and
traditional gender roles [32]. These young women likely
must negotiate their cultural values and beliefs when
assuming a mother’s identity [33, 34]. In addition, the
cultural preference for male infants may affect PPD.
Table 1 Weighted percentage distribution of mothers who
recently
gave birth that completed the NYC PRAMS from 2004 to 2007,
by
characteristic, according to race/ethnicity
White Asian/
Pacific
Islander
Hispanic Black
(n = 1,043) (n = 425) (n = 1,253) (n = 1,027)

Maternal age
20 2.4a 0.9a 9.9b 6.9c
20–34 70.1
a
75.4
b
76.8
b
73.8
a,b
C35 27.5
a
23.7
a,b
13.3
c
19.3
b,d
Maternal education
0–8 1.7
a
2.7
a

11.7
b
1.6
a
9–11 4.2
a
10.7
b
19.6
c
15.8
d
12 22.6
a
26.1
a
34.4
b
32.1
b
13–15 16.2
a
14.7
a

21.1
b
28.1
c
C16 55.4
a
45.8
b
13.2
c
22.4
d
Income
10,000 10.0a 20.4b 40.3c 29.2d
10,000–14,999 6.7
a
15.1
b
14.3
b
10.3
c
15,000–19,999 4.6

a
8.0
b
8.8
b
8.6
c
20,000–24,999 4.7
a
5.8
a
6.8
b
9.2
c
25,000–34,999 6.8
a
5.7
a
9.5
b
13.2
c
35,000–49,999 8.7

a
6.0
a
6.7
a
10.1
a
50,000–74,999 12.1
a
9.9
a
6.3
a
10.2
a
C75,000 46.4
a
29.1
b
7.1
c
9.0
d
Maternal nativity

U.S. born 68.4
a
11.1
b
34.1
c
56.3
d
Non-U.S. born 31.1 88.9 65.6 43.0
Missing data 0.5 0 0.3 0.7
Language of questionnaire
English 99.1
a
99.5
a
51.2
b
98.8
a
Spanish 0 0 48.5 0
Missing data 0.5 0.5 0.3 1.2
NICU

Yes 5.1 5.9 6.4 14.4
No 94.9
a
94.1
a
93.6
a
85.5
b
Don’t know 0 0.1 0 0.1
Gender
Male 49.3
a
52.1
a
51.1
a
52.0
a
Female 50.7 47.9 48.9 48.0
Diabetes
No 92.4 85.1 89.9 89.9

Yes 7.6
a
14.9
b
10.1
c
10.1
c
Stresses
0 45.1
a
49.1
a
31.6
b
26.5
c
1–2 41.8
a
38.7
a
41.5
a

42.8
a
3–5 12.1
a
11.3
a
23.3
b
25.2
b
6–13 1.1
a
0.8
a
3.6
b
5.5
c
Social support
No 90.4 90.8 76.9 75.2
Yes 9.6
a
9.2
a

23.1
b
24.8
b
Table 1 continued
White Asian/
Pacific
Islander
Hispanic Black
(n = 1,043) (n = 425) (n = 1,253) (n = 1,027)
Intention for pregnancy
No 30.9
a
35.1
a
59.0
b
66.5
c
Yes 69.1 64.9 41.0 33.5
Prenatal depression diagnosis

No 97.2 87.6 92.4 94.5
Yes 2.8
a
12.4
b
7.6
c
5.5
d
Discussion about mood
No 46.0 61.4 42.7 39.3
Yes 54.0
a
38.6
b
57.3
a,c
60.7
c
Postpartum depression diagnosis
No 97.4 89.3 93.6 96.3
Yes 2.6

a
10.7
b
6.4
c
3.7
a
Lower case superscripts that differ across each row represent
statistically
different values across racial/ethnic groups. Conversely, groups
within a
row that share the same superscript demonstrate no statistically
significant
difference in values within p .05
Matern Child Health J (2013) 17:1599–1610 1603
123
Table 2 Logistic regression models of race/ethnicity, other
sociodemographic factors, stressors, and discussion of mood
with provider, with
adjusted odds of postpartum depression diagnosis
Model 1 Model 2 Model 3 Model 4
OR CI OR CI OR CI OR CI

Race
White 1.0 1.0 1.0 1.0
Asian/Pacific Islander 4.6*** 2.6–8.2 4.0*** 2.2–7.2 2.7** 1.4–
4.9 3.2*** 1.7–6.0
Hispanic 2.7*** 1.7–4.5 1.8* 1.0–3.1 1.5 0.9–2.7 1.5 0.9–2.7
Black 1.7
�
1.0–3.0 1.2 0.6–2.2 0.9 0.5–1.8 0.9 0.4–1.8
Maternal age
20 1.0
20–34 0.5 0.3–1.1 0.5 0.2–1.1 0.5 0.2–1.2
C35 0.7 0.3–1.6 0.7 0.3–1.7 0.7 0.3–1.9
Maternal education
0–8 1 1 1
9–11 0.8 0.3–1.9 1.2 0.4–3.2 1.1 0.4–3.0
12 1.0 0.5–2.1 1.6 0.7–4.1 1.6 0.7–4.0
13–15 1.1 0.5–2.5 1.6 0.6–4.2 1.6 0.6–4.3
C16 0.8 0.4–1.8 1.5 0.6–4.0 1.6 0.6–4.2
Income

10,000 1.0 1.0 1.0
10,000–14,999 1.2 0.7–2.1 1.5* 0.8–2.8 1.5* 0.8–2.8
15,000–19,999 0.8* 0.3–1.6 1.1 0.5–2.4 1.0 0.5–2.2
20,000–24,999 0.5 0.2–1.2 0.6 0.3–1.4 0.6 0.2–1.3
25,000–34,999 0.6 0.3–1.3 0.7 0.3–1.7 0.7 0.3–1.6
35,000–49,999 0.3 0.1–0.7 0.3 0.1–0.9 0.3 0.1–0.8
50,000–74,999 0.4 0.2–0.9 0.5 0.2–1.3 0.5 0.2–1.3
C75,000 0.5 0.3–1.0 0.7 0.3–1.5 0.7 0.3–1.4
Gender
Male 1.0 1.0
Female 1.6* 1.1–2.4 1.7* 1.1–2.5
Diabetes
No 1.0 1.0
Yes 0.8 0.4–1.5 0.8 0.4–1.6
Stresses
0 1.0 1.0
1–2 0.8 0.5–1.3 0.8 0.5–1.3
3–5 1.0 0.6–1.8 1.0 0.6–1.8

6–18 1.8
�
0.7–4.9 2.0
�
0.8–5.1
Social support
No 1.0 1.0
Yes 1.1 0.7–1.9 1.2 0.7–2.0
Intention for pregnancy
No 1.0 1.0
Yes 1.2 0.8–1.8 1.2 0.8–1.8
Prenatal depression diagnosis
No 1.0 1.0
Yes 17.3*** 10.9–27.5 15.0*** 9.4–23.8
Discussion about mood
No 1.0
Yes 2.6*** 1.6–4.1
�
p 0.1; * p .05; ** p .01; *** p .001

123
Table 3 Weighted percentage of mothers who completed the
NYC PRAMS from 2004 to 2007, by characteristic according to
race/ethnicity and
postpartum depression diagnosis
White Asian/Pacific Islander Hispanic Black
No PPD PPD No PPD PPD No PPD PPD No PPD PPD
(n = 1,010) (n = 33) (n = 383) (n = 42) (n = 1,162) (n = 91) (n =
979) (n = 48)
Maternal age
20 2.3 5.9 1 0 9.6 13.4 6.2 25.2***
20–34 70.4 62 74.1 86.1
�
77.7 63.7** 74.2 63.4
�
C35 27.4 32.1 24.9 13.9 12.6 22.9** 19.6 11.5
Maternal education
0–8 1.7 0 2.4 4.9 11.5 15.8 1.7 0.7
9–11 4 9 10.6 11.4 19.3 23 15.9 12.5

12 22.9 12.1 23 52.8*** 34.9 25.9
�
31.4 49.3***
13–15 16.5 5.3 14.5 16.5 20.7 27.7 28.2 26.2
C16 54.9 73.6
�
49.5 14.3*** 13.6 7.6 22.8 11.4
Income
10,000 9.9 15.4 18.7 34.2* 40.1 44.2 28.6 46.9***
10,000–14,999 6.7 4.7 14.8 18.2 13.5 27*** 9.8 21.8***
15,000–19,999 4.8 0 7.3 13.5 9.1 5.3 8.4 14.9*
20,000–24,999 4.8 0 6 4.5 6.7 8.5 9.6 0.6**
25,000–34,999 7 0.5 4.6 15.3 9.7 5.8 13.5 4.8*
35,000–49,999 8.5 15.1 6.7 0.3 7.1 0.5* 10.5 1.7**
50,000–74,999 12.1 11.5 10.7 3.6 6.5 4.4 10.6 0.5**
C75,000 46.2 52.7 31.3 10.3** 7.3 4.3 9 8.8
Maternal nativity
U.S. born 68.4 66.7 12.3 100*** 35.0 23.1* 56.4 54.2
Non-U.S. born 31.1 30.3 87.7 0 64.7 76.9 42.9 45.8
Missing data 0.5 0.3 0 0 0.3 0 0.7 0

Language of questionnaire
English 99.2 97.0 99.5 100 50 46.2 98.8 100
Spanish 0 0 0.1 0 50 53.8 1.1 0
Missing data 0.8 3.0 0.4 0 0 0 0.1 0
NICU
No 94.9 94.2 93.6 98.1 93.7 91.9 85.7 82.3
Yes 5.1 5.8 6.3 1.9 6.3 8.1 14.3 17.3
Don’t know 0 0 0.1 0 0 0 0.1 0.5
Gender
Male 49.5 40.2 53.2 43 51.7 43.8 52.3 44.7
Female 50.5 59.8 46.8 57 48.3 56.2 47.7 55.3
Diabetes
No 92.5 90 85.6 81.4 90.2 86.2 89.6 98.9
Yes 7.5 10* 14.4 18.6 9.8 13.8 10.4 1.1
Stresses
0 45.6 26.4* 47.6 61.2 31.6 30.8 27 12.5**
1–2 41.4 54.3 39.9 28.8 42.6 25 42.9 39.4
3–5 12 14.2 12.1 5.4 22.7 32.1 24.6 41.2

6–13 1 5.1* 0.4 4.5** 3 12.1 5.4 6.9
Social support
No 9.4 15.3 8.6 13.7 22.7 28.3 24.6 31.4
Yes 90.6 84.7 91.4 86.3 …
CLINICAL ISSUES
The effect of nurse–patient interaction on anxiety and
depression in
cognitively intact nursing home patients
Gørill Haugan, Siw T Innstrand and Unni K Moksnes
Aims and objectives. To test the effects of nurse–patient
interaction on anxiety and depression among cognitively intact
nursing home patients.
Background. Depression is considered the most frequent mental
disorder among the older population. Specifically, the
depression rate among nursing home patients is three to four
times higher than among community-dwelling older people,
and a large overlap of anxiety is found. Therefore, identifying
nursing strategies to prevent and decrease anxiety and depres-
sion is of great importance for nursing home patients’ well-
being. Nurse–patient interaction is described as a fundamental

resource for meaning in life, dignity and thriving among nursing
home patients.
Design. The study employed a cross-sectional design. The data
were collected in 2008 and 2009 in 44 different nursing
homes from 250 nursing home patients who met the inclusion
criteria.
Methods. A sample of 202 cognitively intact nursing home
patients responded to the Nurse–Patient Interaction Scale and
the Hospital Anxiety and Depression Scale. A structural
equation model of the hypothesised relationships was tested by
means of LISREL 8.8 (Scientific Software International Inc.,
Lincolnwood, IL, USA).
Results. The SEM model tested demonstrated significant direct
relationships and total effects of nurse–patient interaction on
depression and a mediated influence on anxiety.
Conclusion. Nurse–patient interaction influences depression, as
well as anxiety, mediated by depression. Hence, nurse–
patient interaction might be an important resource in relation to
patients’ mental health.
Relevance to clinical practice. Nurse–patient interaction is an
essential factor of quality of care, perceived by long-term nurs-
ing home patients. Facilitating nurses’ communicating and
interactive skills and competence might prevent and decrease
depression and anxiety among cognitively intact nursing home

patients.
Key words: anxiety, depression, nurse–patient interaction,
nursing home, structural equation model analysis
Accepted for publication: 11 September 2012
Introduction
With advances in medical technology and improvement in the
living standard globally, the life expectancy of people is
increasing worldwide. The document An Aging World (US
Census Bureau 2009) highlights a huge shift to an older popu-
lation and its consequences. Within this shift, the most rapidly
growing segment is people over 80 years old: by 2050, the per-
centage of those 80 and older would be 31%, up from 18% in
1988 (OECD 1988). These perspectives have given rise to the
notions of the ‘third’ (65–80 years old) and the ‘fourth age’
(over 80 years old) in the lifespan developmental literature
(Baltes & Smith 2003). These notions are also referred to as
the ‘young old’ and the ‘old old’ (Kirkevold 2010).
Authors: Gørill Haugan, PhD, RN, Associate Professor, Faculty
of

Nursing, Research Centre for Health Promotion and Resources,
Sør-Trøndelag University College, HIST, Trondheim; Siw T
Innstrand, PhD, Associate Professor, Research Centre for Health
Promotion and Resources Norwegian University of Science and
Technology, NTNU, Trondheim; Unni K Moksnes, PhD, RN,
Associate Professor, Faculty of Nursing, Research Centre for
Health Promotion and Resources, Sør-Trøndelag University
College, HIST, Trondheim, Norway
Correspondence: Gørill Haugan, Associate Professor, Research
Centre for Health Promotion and Resources, HIST/NTNU,
NTNU,
SVT/ISH, 7491 Trondheim, Norway. Telephone:
+47 73 55 29 27.E-mail: [email protected]
© 2013 Blackwell Publishing Ltd
2192 Journal of Clinical Nursing, 22, 2192–2205, doi:
10.1111/jocn.12072
For many of those in the fourth age, issues such as physi-
cal illness and approaching mortality decimates their func-
tioning and subsequently lead to the need for nursing home

(NH) care. A larger proportion of older people will live for
shorter or longer time in a NH at the end of life. This
group will increase in accordance with the growing popula-
tion older than 65, and in particular for individuals older
than 80 years. Currently, 1�4 million older adults in the
USA live in long-term care settings, and this number is
expected to almost double by 2050 (Zeller & Lamb 2011).
In Norway, life expectancy by 2050 is 90�2 years for men
and 93�4 years for women (Statistics of Norway 2010).
Depression is one of the most prevalent mental health
problems facing European citizens today (COM 2005);
and, the World Health Organization (WHO 2001) has esti-
mated that by 2020, depression is expected to be the high-
est ranking cause of disease in the developed world.
Moreover, depression is described to be one of the most
frequent mental disorders in the older population and is
particularly common among individuals living in long-term
care facilities (Choi et al. 2008, Karakaya et al. 2009,
Lattanzio et al. 2009, Drageset et al. 2011, Phillips et al.
2011). A linear increase in prevalence of depression with

increasing age is described (Stordal et al. 2003); the three
strongest explanatory factors on the age effect of depression
are impairment, diagnosis and somatic symptoms, respec-
tively (Stordal et al. 2001, 2003). Worse general medical
health is seen as the strongest factor associated with depres-
sion among NH patients (Djernes 2006, Barca et al. 2009).
A review that included 36 studies from various countries,
reported a prevalence rate for major depression ranging
from 6–26% and from 11–50% for minor depression.
However, the prevalence rate for depressive symptoms ran-
ged from 36–49% (Jongenelis et al. 2003). Twice as many
women are likely to be affected by depression than men
(Kohen 2006), and older people lacking social and emo-
tional support tend to be more depressed (Grav et al.
2012). A qualitative study on successful adjustment among
women in later life identified three main areas as being the
main obstacles for many; these were depression, maintain-
ing intimacy through friends and family and managing the

change process associated with older age (Traynor 2005).
Significantly more hopelessness, helplessness and depres-
sion are found among patients in NHs compared with those
living in the community (Ron 2004). Jongenelis et al.
(2004) found that depression was three to four times higher
in NH patients than in community-dwelling adults. Moving
to a NH results from numerous losses, illnesses, disabilities,
loss of functions and social relations, and approaching mor-
tality, all of which increases an individual’s vulnerability
and distress; in particular, loneliness and depression are iden-
tified as risks to the well-being of older people (Routasalo
et al. 2006, Savikko 2008, Drageset et al. 2012). The NH
life is institutionalised, representing loss of social relation-
ships, privacy, self-determination and connectedness.
Because NH patients are characterised by high age, frailty,
mortality, disability, powerlessness, dependency and vulner-
ability, they are more likely to become depressed. A recent
literature review showed several studies reporting prevalence

of depression in NHs ranging from 24–82% (Drageset et al.
2011). Also, with a persistence rate of more than 50% of
depressed patients still depressed after 6–12 months, the
course of major depression and significant depressive symp-
toms in NH patients tend to be chronic (Rozzini et al.
1996, Smalbrugge et al. 2006a).
Moreover, studies in NHs report a large co-occurrence of
depression and anxiety (Beekman et al. 2000, Kessler et al.
2003, Smalbrugge et al. 2005, Van der Weele et al. 2009,
Byrne & Pachana 2010). A recent review concerning anxi-
ety and depression reports a paucity of findings on anxiety
in older people (Byrne & Pachana 2010). Hence, more
research is urgently required into anxiety disorders in older
people, as these are highly prevalent and associated with
considerable disease burden (ibid.).
Depression and anxiety in NH patients are associated
with negative outcomes such as poor functioning in
activities of daily living and impaired quality of life (QoL)

(Smalbrugge et al. 2006b, Diefenbach et al. 2011, Drageset
et al. 2011), substantial caregiver burden and worsened
medical outcomes (Bell & Goss 2001, Koenig & Blazer
2004, Sherwood et al. 2005), increased risk of hospital
admission (Miu & Chan 2011), a risk of increased demen-
tia (Devanand et al. 1996) and a higher mortality rate
(Watson et al. 2003, Ahto et al. 2007). Accordingly, efforts
to prevent and decrease depression and anxiety are of great
importance for NH patients’ QoL.
Social support and relations to significant others are
found to be a vital resource for QoL and thriving among
NH patients (Bergland & Kirkevold 2005, 2006, Drageset
et al. 2009a, Tsai et al. 2010, Tsai & Tsai 2011), as well
as the nurse–patient relationship (Haugan Hovdenes 2002,
Cox & Bottoms 2004, Franklin et al. 2006, Medvene &
Lann-Wolcott 2010, Burack et al. 2012). The perspective
of promoting health and well-being is fundamental in nurs-
ing and a major nursing concern in long-term care (Nakrem

et al. 2011, Drageset et al. 2009b). However, low rates of
recognition of depression by staff nurses is found (Bagley
et al. 2000, Volkers et al. 2004).
Through the last decades, the importance of establishing
the nurse–patient relationship as an integral component of
Journal of Clinical Nursing, 22, 2192–2205 2193
Clinical issues Nurse-patient-interaction, depression, and
anxiety
nursing practice has been well documented (Nåden &
Eriksson 2004, Arman 2007, Carpiac-Claver & Levy-
Storms 2007, Granados Gámez 2009, Rchaidia et al. 2009,
Fakhr-Movahedi et al. 2011). Excellent nursing care is
characterised by a holistic view with inherent human values
and moral; thus, excluding the patient as a unique human
being should be regarded as noncaring and amoral practice
(Haugan Hovdenes 2002, Nåden & Eriksson 2004, Aust-
gard 2008, Watson 2008). NH patients are in general

extremely vulnerable and hence the nurse–patient relation-
ship and the nurse–patient interaction are critical to their
experience of dignity, self-respect, sense of self-worth and
well-being (Dwyer et al. 2008, Harrefors et al. 2009,
Heliker 2009). NH patient receiving self-worth therapy
showed statistically significantly reduced depressive symp-
toms relative to control groups members 2 months after
receiving the intervention (Tsai et al. 2008). Self-worth
therapy comprised establishment of a therapeutic relation-
ship offering feedback and focusing the patient’s dignity,
emotional and mental well-being (ibid.).
Caring nurses engage in person-to-person relationships
with the NH patients as unique persons. Good nursing care
is defined by the nurses’ way of being present together with
the patient while performing nursing activities, in which
attitudes and competence are inseparately connected. ‘Pres-
ence’, ‘connectedness’ and ‘trust’ are described as funda-
mental cores of holistic nursing care (McGilton & Boscart

2007, Potter & Frisch 2007, Carter 2009) in the context of
the nurse–patient relationship in which the nurse–patient
interaction is taking place. Trust is seen as a confident
expectation that the nurses can be relied upon to act with
good will and to secure what is best for the individuals
residing in the NH. Hence, trust is the core moral ingredi-
ent in nurse–patient relationships; even more basic than
duties of beneficence, respect, veracity, and autonomy
(Carter 2009).
Caring is a context-specific interpersonal process that is
characterised by expert nursing practice, interpersonal sen-
sitivity, and intimate relationships (Finfgeld-Connett 2008)
which increases patient’s well-being (Nakrem et al. 2011,
Hollinger-Samson & Pearson 2000, Cowling et al. 2008,
Rchaidia et al. 2009, Reed 2009). The relationship between
NH staff attention and NH patients’ affect and activity par-
ticipation have been assessed among depressed NH
patients, showing that positive staff engagement was signifi-

cantly related to patients’ interest, activity participating,
and pleasure (Meeks & Looney 2011). These results suggest
that staff behaviour and engagement could be a reasonable
target for interventions to increase positive affect among
NH patients (ibid.).
In summary, the literature suggests depression as a com-
mon mental disorder among older people characterised by
high age, impairment, and somatic symptoms. In addition,
a large overlap of anxiety is reported. The patients’ sense
of loss of independency and privacy, feelings of isolation
and loneliness, and lack of meaningful activities are risk
factors for depression in NH patients. Nurse–patient inter-
action might be a resource for preventing and decreasing
depression among NH patients. To the authors’ knowl-
edge, previous research has not examined these relation-
ships in NHs by means of structural equation modelling
(SEM).
Aims

The main aim of this study was to investigate the relation-
ships between nurse–patient interaction, anxiety and
depression among cognitively intact NH patients by means
of SEM. Based on the theoretical and empirical knowledge
of depression, anxiety and nurse–patient interaction our
research question was: ‘Does the nurse–patient interaction
affect anxiety and depression in cognitively intact NH
patients?’ The following hypotheses were formulated:
� Hypothesis 1 (H1): nurse–patient interaction positively
affects anxiety.
� Hypothesis 2 (H2): nurse–patient interaction positively
affects depression.
� Hypothesis 3 (H3): depression negatively affects anxiety.
Methods
Design and ethical considerations
The study employed a cross-sectional design. The data was
collected in 2008 and 2009 in 44 different NHs from 250
NH patients who met the inclusion criteria: (1) local
authority’s decision of long-term NH care; (2) residential

time six months or longer; (3) informed consent compe-
tency recognised by responsible doctor and nurse; and (4)
capable of being interviewed. Two counties comprising in
total 48 municipalities in central Norway were selected,
from which 25 (at random) were invited to contribute in
this study. In total, 20 municipalities were partaken. Then,
all the NHs in each of the 20 municipalities was asked to
participate. A total of 44 NHs took part in the study. To
include as many participants from rural and central NHs,
respectively, the NHs was one by one invited to participate,
until the minimum of n = 200 was reached. The NH
patients were approached by a head nurse they knew
well. The nurse presented them with oral and written
2194 Journal of Clinical Nursing, 22, 2192–2205
G Haugan et al.
information about their rights as participants and their

right to withdraw at any time. Each participant provided
informed consent. Because this population has problems
completing a questionnaire independently, three trained
researchers conducted one-on-one interviews in the patient’s
room in the actual NH. Researchers with identical profes-
sional background were selected (RN, MA, trained and
experienced in communication with older people, as well as
teaching gerontology at an advanced level) and trained to
conduct the interviews as identically as possible. Inter-rater
reliability was assessed by comparing mean scores between
interviewers by means of Bonferroni-corrected one-way
ANOVAs. No statistically significant differences were found
that were not accounted for by known differences between
the areas in which the interviewers operated.
The questionnaires relevant for the present study were part
of a questionnaire comprising 130 items. The interviews
lasted from 45–120 minutes due to the individual partici-
pant’s tempo, form of the day, and need for breaks. Inter-

viewers held a large-print copy of questions and possible
responses in front of the participants to avoid misunder-
standings. Approval by the Norwegian Social Science Data
Services was obtained for a licence to maintain a register
containing personal data (Ref. no. 16443) and likewise we
attained approval from The Regional Committee for
Medical and Health Research Ethics in Central Norway
(Ref. no. 4.2007.645) as well as the directory of the 44 NHs.
Participants
The total sample comprised 202 (80�8%) of 250 long-term
NH patients representing 44 NHs. Long-term NH care was
defined as 24-hour care; short-term care patients, rehabilita-
tions patients, and cognitively impaired patients were not
included. Participants’ age was 65–104, with a mean of
86 years (SD = 7�65). The sample comprised 146 women
(72�3%) and 56 men (27�7%), where the mean age was
87�3 years for women and 82 years for men. A total of 38
(19%) were married/cohabitating, 135 (67%) were widows/
widowers, 11 (5�5%) were divorced, and 18 (19%) were
single. Duration of time of NH residence when interviewed

was at mean 2�6 years for both sexes (range 0�5–13 years);
117 were in rural NHs, while 85 were in urban NHs. In
all, 26�1% showed mild to moderate depression, only one
woman scored >15 indicating severe depression, 70�4%
was not depressed, and nearly 88% had no anxiety disor-
der. Missing data was low in frequency and was handled
by means of the listwise procedure; for the nurse–patient
interaction 4�0% and for anxiety and depression 5�0% had
some missing data.
Measures
The Nurse–Patient Interaction Scale (NPIS) was developed
to identify important characteristics of NH patients’ experi-
ences of the nurse–patient interaction. The NPIS comprises
14 items identifying essential relational qualities stressed in
the nursing literature (Watson 1988, Martinsen 1993,
Eriksson 1995a,b, Nåden & Eriksson 2004, Nåden &
Sæteren 2006, Levy-Malmberg et al. 2008). Examples of
NPIS-items include ‘Having trust and confidence in the staff
nurses’; ‘The nurses take me seriously’, ‘Interaction with
nurses makes me feel good’ as well as experiences of being

respected and recognised as a person, being listened to and
feel included in decisions. The items were developed to
measure the NH patients’ ability to derive a sense of well-
being and meaningfulness through the nurse–patient inter-
action (Haugan Hovdenes 1998, 2002, Hollinger-Samson
& Pearson 2000, Finch 2006, Rchaidia et al. 2009). The
NPIS has shown good psychometric properties with good
content validity and reliability among NH patients;
(Haugan et al. 2012). The NPIS is a 10-points scale from 1
(not at all)–10 (very much); higher numbers indicating
better nurse–patient interaction (Appendix 1). Cronbach’s
Table 1 Means (M), standard deviations (SD), Cronbach’s
alpha, and correlation coefficients for the study variables
Construct M SD Cronbach’s alpha NPIS HADS-A HADS-D
NPIS (10 items) 8�19 1�73 0�92 –
HADS-A (5 items) 0�40 0�50 0�79 �0�114 –
HADS-D (5 items) 0�74 0�58 0�66 �0�294* 0�340* –
HADS (14 items) 2�85 0�34 0�78
*p < 0�01.
NPIS, Nurse–Patient Interaction Scale; HADS, Hospital Anxiety
and Depression Scale; HADS-A, Hospital Anxiety and
Depression Scale -

Anxiety; HADS-D, Hospital Anxiety and Depression Scale -
Depression.
Journal of Clinical Nursing, 22, 2192–2205 2195
Clinical issues Nurse-patient-interaction, depression, and
anxiety
a = 0�92 (Table 1) and composite reliability = 0�92
(Table 2) of the NPIS construct was good.
Anxiety and depression were assessed by the Hospital
Anxiety and Depression Scale (HADS), comprising 14 items
(Appendix 2), with subscales for anxiety (HADS-A; seven
items) and depression (HADS-D seven items). Each item is
rated from 0–3, where higher scores indicate more anxiety
and depression. The maximum score is 21 on each subscale.
The ranges of scores for cases are as follows: 0–7 normal,
8–10 mild disorder, 11–14 moderate disorder, and 15–21
severe disorder (Snaith & Zigmond 1994). HADS has been
tested extensively and has well-established psychometric
properties (Herrmann 1997). To increase acceptability and

avoid individuals feeling as though they are being tested for
mental disorders, symptoms of severe psychopathology
have been excluded. This makes HADS more sensitive to
milder psychopathology (Stordal et al. 2003). HADS is
translated into Norwegian and found to be valid for older
people (Stordal et al. 2001, 2003).
Examples of sample-items are for depression: ‘I still enjoy
the things I used to enjoy’, ‘I can laugh and see the funny side
of things’, ‘I feel cheerful’, ‘I have lost interest in my appear-
ance’, and ‘I look forward with enjoyment to things’, and for
anxiety: ‘I feel tense and wound up’, ‘I get a sort of frightened
feeling as if something awful is about to happen’, ‘Worrying
thoughts go through my mind’, ‘I get a sort of frightened feel-
ing like ‘butterflies’ in the stomach’, and ‘I get sudden feeling
of panic’. The items were scored on a four-point scale ranging
from totally disagrees to totally agree. The internal consis-
tence of the anxiety and depression constructs (Table 1) was
satisfactory; a = 0�79 and a = 0�66, respectively. Composite

reliability (qc) displayed values between 0�70–0�92 (Table 2);
values >0�60 are desirable, whereas values >0·70 are good
(Diamantopolous & Siguaw 2008, Hair et al. 2010).
Statistical analysis
A structural equation model (SEM) of the hypothesised
relations between the latent constructs of depression and
self-transcendence was tested by means of LISREL 8.8 (Scien-
tific Software International Inc., Lincolnwood, IL, USA)
(Jøreskog & Sørbom 1995). Using SEM accounts for ran-
dom measurement error and the psychometric properties of
the scales in the model are more accurately derived. Since
the standard errors are estimated under non-normality, the
Satorra–Bentler scaled chi-square statistic was applied as a
goodness-of-fit statistic, which is the correct asymptotic
mean even under non-normality (Jøreskog et al. 2000). In
line with the rules of thumb of conventional cut-off criteria
(Schermelleh-Engel et al. 2003), the following fit indices
were used to evaluate model fit: chi-square (v2); a small v2
and a non-significant p-value corresponds to good fit

(Jøreskog & Sørbom 1995). Further we used the root mean
square error of approximation (RMSEA) and the standar-
dised root mean square residual (SRMS) with values below
0�05 indicating good fit, while values smaller than 0�08 are
interpreted as acceptable (Hu & Bentler 1998, Schermelleh-
Engel et al. 2003). The comparative fit index (CFI) and the
non-normed fit index (NNFI) with an acceptable fit at 0�95,
and good fit at 0�97 and above were used, and the normed
fit index (NFI) with an acceptable fit at 0�90, while a good
fit was set to 0�95 (ibid.).
Before examining the hypothesised relationships in the
SEM analysis, the measurement models were tested by con-
firmatory factor analysis (CFA). The CFA provided a good
fit to the observed data for the nurse–patient interaction
construct comprising ten items (v2 = 92�32, df = 77,
Table 2 Measurement models included in Model 1: nurse–
patient
interaction (NPIS) to anxiety (HADS-A) and depression
(HADS-D)
Items Parameter Lisrel estimate t-value R2
NPIS
NPIS1 kx1,1 0�63 6�04** 0�39

NPIS2 kx2,1 0�74 8�99** 0�55
NPIS3 kx3,1 0�74 10�41** 0�55
NPIS4 kx4,1 0�81 12�84** 0�65
NPIS5 kx5,1 0�66 6�16** 0�43
NPIS7 kx6,1 0�72 8�25** 0�51
NPIS9 kx7,1 0�77 14�39** 0�60
NPIS11 kx8,1 0�77 11�36** 0�59
NPIS12 kx9,1 0�69 8�18** 0�47
NPIS13 kx10,1 0�78 9�45** 0�61
HADS-A
HADS1 ky5,2 0�62 – 0�39
HADS3 ky7,2 0�73 7�04** 0�53
HADS5 ky11,2 0�62 4�65** 0�39
HADS9 ky13,2 0�69 5�60** 0�40
HADS13 kx14,2 0�66 6�00** 0�43
HADS-D
HADS2 ky1,1 0�74 – 0�54
HADS4 ky2,1 0�67 7�43** 0�45
HADS6 ky3,1 0�65 5�86** 0�42
HADS10 ky5,1 0�20 2�33* 0�04
HADS12 ky6,1 0�51 4�94** 0�26
qc NPIS 10 items qc 0�92 – –
qc HADS-A 5 items qc 0�80 – –
qc HADS-D 5 items qc 0�70 – –
Standardised factor loadings and t-values. Squared multiple
correla-
tions (R2).
†Composite reliability, qc ¼
P
kð Þ2

P
kð Þ2þP hð Þ
� � (Hair et al. 2010).
*p < 0�05; **p < 0�01.
HADS, Hospital Anxiety and Depression Scale; NPIS, Nurse–
Patient Interaction Scale.
2196 Journal of Clinical Nursing, 22, 2192–2205
G Haugan et al.
p < 0�0110, RMSEA = 0�032, SRMR = 0�045, NFI = 0�97,
NNFI = 0�99, CFI = 1�00) and the two-factor construct
(HADS) of anxiety and depression comprising 10 items
(v2 = 54�22, df = 34, p < 0�015, RMSEA = 0�056,
SRMR = 0�071, NFI = 0�93, NNFI = 0�96, CFI = 0�97).
All parameter estimates were significant (p < 0�05) and
loaded positively and clearly on their intended latent vari-
able with standardised factor loadings between 0�20–0�81.
For scaling, the first factor loadings of each dependent
latent variable were set to 1.
Results
Descriptive analysis
Table 1 displays the means (M), standard deviations (SD),

Cronbach’s a and Pearson’s correlation matrix for the con-
structs of nurse–patient interaction, anxiety and depression.
The correlations between the measures were in the expected
direction. Moderate correlations were found between the
latent constructs included in the SEM model (Table 1). The
a-levels for the various measures indicate an acceptable
level of inter-item consistency in the measures (Nunally &
Bernstein 1994) with Cronbach’s a coefficients of 0�66 or
higher.
Structural equation modelling (SEM)
To investigate how the nurse–patient interaction related to
anxiety and depression, model-1 was estimated. Figure 1
shows Model-1 with its measurement and structural
models, while Table 2 displays the factor loadings, R2 and
t-values. All estimates were significant (p < 0�05) and the
factor loadings ranged between 0�51–0�81 (except from
item HADS10 ‘I have lost interest in my appearance’ with
factor loading = 0�20 and R2 = 0�04) and R2 values
between 0�26–0�65. Model-1 fit well with the data:
v2 = 211�44, p = 0�011, df = 167, RMSEA = 0�037, p-

value = 0�92, NFI = 0�94, NNFI = 0�99, CFI = 0�99, and
SRMR = 0�060.
Table 3 shows the standardised regression coefficients of
the directional relationships and the total and indirect
effects between the latent constructs in Model-1. As
hypothesised, the directional paths from nurse–patient
interaction to depression displayed a significant negative
relationship (c1,1 = �0�37). The path between nurse–
patient interaction and anxiety was not significant
(c1,2 = �0�09); however, a significant path from depression
to anxiety (b1,2 = 0�55) was found, indicating a mediated
effect (by depression) on anxiety (Table 3).
A scrutiny of the total effects of nurse–patient interaction
revealed statistical significant total effects on depression
(�0�37), as well as a significant total effect on anxiety from
depression (0�55). Also, a significant indirect (mediated)
effect from nurse–patient interaction on anxiety (�0�20)
was displayed (Table 3).
Discussion
The aim of this study was to explore the associations
between nurse–patient interaction, anxiety, and depression
in cognitively intact NH patients. By doing so we sought to

contribute to a holistic nursing perspective of promoting
well-being in NH patients in …
J O U R N A L O F T R A U M A N U R S I N G
WWW.JOURNALOFTRAUMANURSING.COM 17
R E S E A R C H
ABSTRACT
A retrospective study examined in-hospital antidepressant
medication (ADM) use in adult trauma patients with an
intensive care unit stay of 5 or more days. One fourth of
patients received an ADM, with only 33% of those patients
having a documented history of depression. Of patients
who received their first ADM from a trauma or critical care
physician, only 5% were discharged with a documented
plan for psychiatric follow-up. The study identified a need for
standardized identification and management of depressive
symptoms among trauma patients in the inpatient setting.
Key Words
antidepressant medication , critical care , depression ,
injury ,

psychiatry , trauma
Author Affiliations: UnityPoint Health, Des Moines, Iowa
(Ms Spilman
and Drs Smith and Tonui); and Fort Sanders Regional Medical
Center,
Knoxville, Tennessee (Dr Schirmer).
The abstract was presented at 47th Annual Society for
Epidemiological
Research (SER) Meeting, Seattle, Washington, June 24–27,
2014.
None of the authors have any conflicts of interest to disclose.
Correspondence: Sarah K. Spilman, MA, Trauma Services,
Iowa Methodist
Medical Center, 1200 Pleasant St, Des Moines, IA 50309 (
[email protected]
unitypoint.org ).
Evaluation and Treatment of Depression in Adult
Trauma Patients
Sarah K. Spilman , MA ■ Hayden L. Smith ,
PhD ■ Lori L. Schirmer , PharmD ■ Peter M.
Tonui , MD
approaches require resources and training of hospital
personnel. 5 Regardless of the method, however, assess-
ment of depression is often confounded by the variable
nature of depressive symptoms. Some depressive symp-

toms (eg, fatigue, insomnia, weight loss) can be similar
to symptoms of other medical illnesses or may resemble
temporary conditions, such as delirium or adjustment dis-
order. 6 , 7 In addition, trauma patients in the intensive care
unit (ICU) may often lack the ability to display or report
classic depressive symptoms due to the effects of medica-
tion, pain, or sleep deprivation. 8 , 9
A major issue, though, is that many hospitals do not
routinely screen for depression or assess depressive
symptoms during hospitalization. To our knowledge,
there is no consensus as to when assessments (and re-
assessments) are appropriate. Symptoms of depression
most often are noted through subjective observation by
family or nurses and reported to physicians. Because of
limited resources, mental health experts are often only
involved in the most severe or complicated cases. This is
a fundamental problem in that large numbers of patients
may be overlooked because of the subjective nature and
timing of these observations. Findley and colleagues 4
found that when a psychiatrist was actively involved in
the trauma service, identification and treatment of psy-
chopathology were increased by 78%. While the rate of
mood and anxiety disorders recognized by trauma phy-
sicians remained unchanged, involvement of psychiatry
resulted in a broader range of psychiatric diagnoses and
more than doubled the treatment of substance abuse or
dependence.
Complicating matters further, many trauma patients
present with preexisting depression. Traumatic injury is
related to depression as both a causal factor and a result-
ing condition. 2 , 4 , 10 If patients are unable to self-report
their
health history, the trauma team relies on family report
or pharmacy records. This presents challenges in timely

reinitiation of medications.
STUDY RATIONALE
A review of the medical literature found no relevant
published research on physician and medical team re-
sponse to depressive symptoms during the patient’s ini-
tial hospitalization within settings where mental health
screening is not the standard of care. Current research DOI:
10.1097/JTN.0000000000000102
I
t is well-established in the literature that critically ill
trauma patients can often suffer from depression and
posttraumatic stress disorder in the months and years
following hospitalization. 1-3 Many hospitals may not
have a standardized process for assessing and treat-
ing trauma patients with depressive symptoms. 3-5 During
the acute phase of recovery, the trauma team is primarily
in charge of treating the injuries and preparing to dis-
charge the patient to the next phase of recovery. With-
out a standardized process for recognizing, screening,
and treating the psychological and emotional needs of
the patient, there may be increased risk that depression
will go unrecognized and untreated or misinterpreted and
improperly treated.
Formal assessment of depression can be accom-
plished through clinical interview or screening tools; both
Copyright © 2015 Society of Trauma Nurses. Unauthorized
reproduction of this article is prohibited.
JTN-D-14-00071.indd 17JTN-D-14-00071.indd 17 06/01/15
8:45 PM06/01/15 8:45 PM

18 WWW.JOURNALOFTRAUMANURSING.COM Volume 22 |
Number 1 | January-February 2015
that examines depression screening has been primarily
funded by grant dollars, which provide hospitals with
resources to staff special assessment teams (eg, Dicker
et al 2 ) and may not represent practices at many hospi-
tals. These studies have established the importance of
early detection of depression, although this may be ex-
tremely difficult in hospitals that do not have protocols
for managing depression in the critically ill or special
teams for assessing, treating, and reassessing mental
health symptoms.
The purpose of this study was to examine how a trau-
ma team recognizes and treats depression in the absence
of a screening tool and to document antidepressant medi-
cation (ADM) usage and prescribing patterns. Study data
can assist in the evaluation and understanding of institu-
tion processes and possibly help design protocols to miti-
gate some of the long-term mental health issues that can
result from traumatic injury.
METHODS
Study Design and Patient Sample
A retrospective study was performed at an urban tertiary
hospital in the Midwestern region of the United States.
The hospital’s trauma registry was used to identify adult
patients (aged 18 years or older) who met trauma criteria
during the 5-year study period of 2008 to 2012. A trauma
patient was defined as an individual who sustained a
traumatic injury with an International Classification of
Diseases, 9th Revision, Clinical Modification code rang-

ing from 800 and 959.9, excluding codes for late effects
of injury (905-909.9), superficial injuries (910-924.9), and
foreign bodies (930-939.9). Patients were included in the
study if they were admitted to the hospital and stayed in
the ICU for 5 or more days. The study was approved by
the hospital’s institutional review board.
Study Data
Study variables were grouped into 3 categories: patient
and injury characteristics, depression diagnoses, and
ADM use. Patient characteristics included gender, race,
age, hospital length of stay (LOS), ICU LOS, and mechani-
cal ventilator days. Discharge status was coded as alive or
deceased, while discharge location was coded as home
or institutional setting (including hospice facility, rehabili-
tation facility, skilled nursing facility, federal hospital, or
intermediate care facility).
Injury characteristics included the Injury Severity Score,
which is an anatomical coding system ranging from 0 (no
injury) to 75 (most severe). Finally, mechanism of injury
was recorded on the basis of the External Causes of In-
jury and Poisoning Code (E-Code): Vehicle accident (810-
848), Accidental Fall (880-888), or Other.
Depression diagnoses were assessed retrospectively
through chart review. Patients were classified as having
a documented history of depression if it was specifically
noted in the medical history or if the patient was taking
an ADM at the time of hospital admission. If the patient’s
history was not obtained at admission, the patient was
considered to be on a prior ADM if he or she received
a dose within the first 72 hours of the hospital stay. We
also noted if a patient received a psychiatric consultation
during their stay and if the patient was discharged with
a plan for psychiatric follow-up. The latter was used to

indicate whether or not discharge instructions included
directions for psychiatry follow-up.
The ADM use was ascertained through pharmacy dis-
pensing records. Specifically, it was recorded if a patient
received any of the following drugs: selective seroto-
nin reuptake inhibitors (SSRIs; citalopram, escitalopram,
fluoxetine, fluvoxamine, paroxetine, sertraline); selective
norepinephrine reuptake inhibitors (SNRIs; desvenlafax-
ine, duloxetine, venlafaxine); dopamine reuptake inhibi-
tors (bupropion); and alpha-2 antagonists (mirtazapine).
Some ADMs were excluded from the study, including
tricyclics and monoamine oxidase inhibitors, which can
be used to treat other diagnoses in addition to depres-
sion; vilazodone, which was not approved by the Food &
Drug Administration until 2011; trazodone because it can
be prescribed as a sleep aid; and milnacipran because its
Food & Drug Administration indication is for fibromyalgia.
The first dispensed ADM was used for basic descrip-
tive purposes. For example, if a patient received multiple
ADMs during the stay, only the first ADM was used to
describe patient treatment. If an ADM was not a medica-
tion taken prior to admission, it is hereafter referred to as
a new ADM. Days between hospital admission and first
ADM dose were used to calculate time of initiation. If an
ADM medication was listed in the discharge summary or
the patient received a dose of the medication on the last
day of the stay, then the patient was classified as being
discharged on an ADM.
Statistical Procedures
Descriptive statistics were reported for continuous data as
medians with interquartile ranges; normality was tested
using the Shapiro-Wilk test. Categorical data were re-
ported as counts with percentages. Comparative statistics

were conducted by stratifying patients based on whether
or not they received an ADM during their hospital stay.
These groups were examined using the Fisher exact test
(nominal variables) and the independent samples Mann-
Whitney U Test (2-group medians). All analyses were
2-tailed and based on a 0.05 significance level. Analy-
ses were performed with IBM SPSS Basic Statistics for
Windows, version 19.0 (IBM Corp, 2010; Armonk, NY).
8:45 PM06/01/15 8:45 PM
J O U R N A L O F T R A U M A N U R S I N G
WWW.JOURNALOFTRAUMANURSING.COM 19
Trauma Patients Admitted During
Study Period (N=4947)
ICU Length of Stay < 5 Days
(n=4635)
ICU Length of Stay > 5 Days
(n=312)
Received No Antidepressant
Medication (n=230)
Restarted Antidepressant
Medication (n=55)
Received New Antidepressant

Medication (n=27)
Figure 1. Trauma patients admitted to the hospital during the
study period, 2008-2012. ICU indicates intensive care unit.
RESULTS
There were 4947 trauma patients admitted to the hospital
during the 5-year study period, with 312 (6.3%) staying in
the ICU for 5 or more days (see Figure 1 ). Patient char-
acteristics are presented in Table 1 . More than two-thirds
of the patients in the study sample were male, and the
majority of patients were white. Fifteen percent of the
patients died.
There were 82 patients (26.3%) who received an ADM
during the hospital stay (see Table 2 ). Bivariate analy-
ses (not shown) revealed significant differences in age,
with older patients more likely to receive an ADM than
younger patients ( P = .002). Men were less likely to re-
ceive an ADM. There were no significant bivariate differ-
ences between patients based on hospital LOS, ICU LOS,
ventilator days, Injury Severity Score, discharge location,
or injury mechanism.
Patients who received an ADM during the hospital stay
were significantly more likely to have a documented his-
tory of depression upon admission to the hospital. Specif-
ically, 67.1% of patients who received an ADM during the
hospital stay were taking an ADM prior to admission and
19.5% had depression mentioned in their medical history.
Patients who received an ADM were also significantly
more likely to receive a psychiatric consultation during
the hospital stay and were more likely to be discharged
with a plan for psychiatric follow-up.
Of the 82 trauma patients who received an ADM dur-

ing hospitalization, 9 (11.0%) were initiated by a psychia-
trist and 73 (89.0%) were initiated by a critical care or
other nonpsychiatric physician (see Table 3 ). One-third
of patients who received an ADM during their stay were
prescribed a new ADM; 29.6% of these new prescriptions
were initiated by psychiatry and 70.3% were initiated by
a nonpsychiatric physician. There were no significant
differences in ADM choice based on the physician who
initiated the medication.
Patients whose ADM was prescribed by a psychiatrist
received their first dose many days later in the hospital
stay than those patients whose ADM was prescribed by
a critical care or other physician. Patients whose ADM
was prescribed by psychiatry were also more likely to be
discharged with a plan for psychiatric follow-up. Nearly
all patients who received an ADM during hospitalization
were discharged with the medication, regardless of the
provider who initiated it.
DISCUSSION
Study data revealed that 26.3% of trauma patients spend-
ing 5 of more days in the ICU received an ADM during
the hospital stay; 33% of these patients did not have a
documented history of depression or ADM use upon ad-
mission. This is considerably higher than ADM use in the
general population, which is estimated at 10% to 11%. 11 , 12
Female trauma patients were more likely to receive an
ADM than male patients, which is consistent with trends
in the general population. 12
Trauma or critical care physicians were the practition-
ers most likely to continue home ADMs and initiate new
ADMs, compared with psychiatry physicians. However,

TABLE 1 Descriptive Characteristics for
Trauma Patients With Intensive Care
Unit Length of Stay 5 or More Days,
2008-2012 (N = 312) a
All Trauma Patients
(N = 312)
Male 218 (70.1%)
White 271 (86.9%)
Median age, y 55.00 (39.75-69.00)
Median hospital length of stay, d 17 (10-25)
Median intensive care unit length
of stay, d
8.5 (6-14)
Median ventilator days 5 (1.5-10)
Deceased 48 (15.4%)
Discharged to home 68 (25.8%)
Median injury severity score 25 (15.5-33.25)
Injury mechanism
Vehicle accident 174 (55.8%)
Fall 105 (33.7%)
Other 31 (9.9%)

a Data are presented in medians (interquartiles) and counts
(percentages).
8:45 PM06/01/15 8:45 PM
20 WWW.JOURNALOFTRAUMANURSING.COM Volume 22 |
Number 1 | January-February 2015
leads to oversights in posttrauma care. Primary care
physicians or other health care providers may not be
alerted to the task of titrating the medication, monitor-
ing adherence to the medication, and/or evaluating the
appropriateness of the ADM in the months and years
after the trauma.
when the ADM was initiated by trauma or critical care
physicians, patients were frequently discharged without
a plan for psychiatric follow-up. This may be a critical
omission, especially for patients started on new ADMs.
Since the trauma or critical care physician manages care
during the acute phase of the injury, this potentially
TABLE 2 Prescription of Antidepressant Medication in
Trauma Patients (N = 312) a
ADM Received During
Stay (n = 82)
ADM Not Received During

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