Preventive Medicine 53 (2011) 359–363
Contents lists available at SciVerse ScienceDirect
Preventive Medicine
journal homepage: www.elsevier.com/locate/ypmed
Comparison of health outcomes among affiliated and lay disaster volunteers enrolled
in the World Trade Center Health Registry
Indira Debchoudhury a,⁎, Alice E. Welch a, Monique A. Fairclough a, James E. Cone a, Robert M. Brackbill a,
Steven D. Stellman a, b, Mark R. Farfel a
a
b
New York City Department of Health and Mental Hygiene, World Trade Center Health Registry, 2 Gotham Center, 42–09 28th Street, 7th Floor, Queens, NY 11101, USA
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
a r t i c l e
i n f o
Available online 10 September 2011
Keywords:
Volunteers
Affiliated Volunteers
Lay Volunteers
Rescue and Recovery
September 11th
Disaster
Mental Health
Physical Health
World Trade Center
a b s t r a c t
Background. Volunteers (non-professional rescue/recovery workers) are universally present at manmade and natural disasters and share experiences and exposures with victims. Little is known of their
disaster-related health outcomes.
Methods. We studied 4974 adult volunteers who completed the World Trade Center Health Registry
2006–07 survey to examine associations between volunteer type (affiliated vs. lay) and probable posttraumatic stress disorder (PTSD); new or worsening respiratory symptoms; post-9/11 first diagnosis of anxiety
disorder, depression, and/or PTSD; and asthma or reactive airway dysfunction syndrome (RADS). Affiliated
volunteers reported membership in a recognized organization. Lay volunteers reported no organizational affiliation and occupations unrelated to rescue/recovery work. Adjusted odds ratios (ORadj) were calculated
using multinomial regression.
Results. Lay volunteers were more likely than affiliated volunteers to have been present in lower Manhattan, experience the dust cloud, horrific events and injury on 9/11 and subsequently to report unmet healthcare needs. They had greater odds of early post-9/11 mental health diagnosis (ORadj 1.6; 95% CI: 1.4–2.0) and
asthma/RADS (1.8; 1.2–2.7), chronic PTSD (2.2; 1.7–2.8), late-onset PTSD (1.9; 1.5–2.5), and new or worsening lower respiratory symptoms (2.0; 1.8–2.4).
Conclusions. Lay volunteers' poorer health outcomes reflect earlier, more intense exposure to and lack of
protection from physical and psychological hazards. There is a need to limit volunteers' exposures during and
after disasters, as well as to provide timely screening and health care post-disaster.
© 2011 Elsevier Inc. All rights reserved.
Introduction
Volunteers are a universal feature of man-made and natural disasters serving as one of the main sources of outside support in postdisaster recovery work (Thormar, et al., 2010). In the last decade, volunteers have played a key role in recovery and relief efforts after the
oil spill in the Gulf of Mexico (2010), the Haitian earthquake (2010),
Hurricane Katrina (2005), the London transit bombings (2005), and
the Indian Ocean earthquake and tsunami (2004). Volunteer participation is often necessary and unavoidable due to the magnitude and
impact of such events (Thormar, et al., 2010).
⁎ Corresponding author at: New York City Department of Health and Mental Hygiene, World Trade Center Health Registry, 2 Gotham Center, 42–09 28th Street, 7th
Floor, Queens, NY 11101, USA. Fax: + 1 212 788 4127.
E-mail addresses: idebchou@health.nyc.gov (I. Debchoudhury),
awelch1@health.nyc.gov (A.E. Welch), mfairclo@health.nyc.gov (M.A. Fairclough),
jcone@health.nyc.gov (J.E. Cone), rbrackbi@health.nyc.gov (R.M. Brackbill),
sstellma@health.nyc.gov (S.D. Stellman), mfarfel@health.nyc.gov (M.R. Farfel).
On September 11, 2001, two airplanes crashed into the Twin
Towers of the World Trade Center (WTC), causing their collapse
and extensive damage to numerous other buildings. Approximately
2800 people lost their lives (Farfel, et al., 2008). Subsequent recovery and relief efforts included paid and volunteer professional rescue/
recovery workers (RRW) (fire, police or other emergency personnel),
volunteers affiliated with relief organizations and lay volunteers (i.e.,
not affiliated with a recognized response organization) (American
Red Cross, 2002, Steffen and Fothergill, 2009, Tierney, et al., 2001).
The WTC disaster exposed an estimated 409,000 individuals to potentially hazardous chemicals, environmental toxins and psychological
stressors that are risk factors for asthma and posttraumatic stress disorder (PTSD) (Landrigan, et al., 2004, Murphy, et al., 2007). A recent
review found that all WTC disaster workers including volunteers
faced an increased risk of mental health sequelae as a consequence
of their intense disaster exposures (Bills, et al., 2008). Findings from
the 2003–04 WTC Health Registry (Registry) survey demonstrate
that RRW in occupations less prepared for the type of work performed at WTC sites were more likely to develop PTSD (Perrin, et
al., 2007).
0091-7435/$ – see front matter © 2011 Elsevier Inc. All rights reserved.
doi:10.1016/j.ypmed.2011.08.034
Downloaded for Anonymous User (n/a) at Philadelphia University from ClinicalKey.com by Elsevier on September 01, 2017.
For personal use only. No other uses without permission. Copyright ©2017. Elsevier Inc. All rights reserved.
360
I. Debchoudhury et al. / Preventive Medicine 53 (2011) 359–363
To build upon previous Registry findings by expanding the range
of post-disaster outcomes, Registry data were used to compare mental and physical health outcomes and health care utilization among
affiliated and lay volunteers. We hypothesized that lay volunteers
would be more likely than affiliated volunteers to experience longterm adverse mental and physical health outcomes due to earlier arrival at WTC sites and more intense exposure to a multitude of hazards, as well as a lack of training, prior disaster experience, or
insufficient post-disaster support. We also predicted they would be
more likely to report unmet health care needs and less utilization of
post-9/11 monitoring and treatment programs.
Methods
The Registry, created in 2002 by the NYC Department of Health and Mental Hygiene (NYCDOHMH) in collaboration with the Agency for Toxic Substances and Disease Registry, prospectively follows 71,437 individuals
highly exposed to the WTC disaster and belonging to one or more eligibility
groups: RRW and volunteers, lower Manhattan residents, area workers,
passers-by, and school children and staff. The protocol was approved by the
institutional review boards of the Centers for Disease Control and Prevention
and NYCDOHMH.
List-identified enrollees were recruited from lists of potentially eligible
individuals from governmental agencies, organizations, and employers.
Self-identified enrollees contacted the Registry via phone or pre-registered
on a website. The 2003–04 Wave 1 survey (W1) included 68,802 adults (Farfel, et al., 2008). The 2006–07 Wave 2 (W2) survey updated the health status
of 46,322 of the original adult enrollees (68% response rate) (Brackbill, et al.,
2009). Registry methods are described in detail elsewhere (Brackbill, et al.,
2009, Farfel, et al., 2008, Perrin, et al., 2007).
The present analysis focuses on 4974 enrollees who completed W1 and
W2 surveys, were aged 18 years or older on 9/11, and reported volunteering
in rescue/recovery activities between 09/11/01 and 06/30/02. Enrollees identifying as professional RRW, whether paid or unpaid for their services, were
not considered volunteers for this analysis. Volunteer status was categorized
as affiliated or lay based on the W1 question, “What organization did you
work for at the WTC site?” Affiliated volunteers reported membership in recognized organizations (e.g., American Red Cross). Lay volunteers reported no
organizational affiliation and occupations unrelated to rescue and recovery
work. Lay volunteers included members of church groups or community organizations and individuals present in the area immediately following the attack. The W2 response rates for affiliated and lay volunteers were 67.7% and
67.8% respectively. From 9/11/01 to 9/14/01 lower Manhattan south of 14th
street was considered a restricted zone, open only to credentialed emergency
management and rescue personnel (Lorber, et al., 2007). Lay volunteers may
have subsequently joined a professional organization to continue volunteering. After October 2001, only Ground Zero was restricted (Lorber, et al.,
2007). Students and school staff who worked as volunteers were excluded
due to small numbers.
We included as categorical variables: recruitment source (list- vs. selfidentified), gender, eligibility category (worker-only vs. multiple eligibility
groups), age group, race/ethnicity, education, 2002 household income, employment status, New York City residency, and state of residence on 9/11.
We assessed exposures and experiences previously shown to be associated
with increased risk of adverse mental and physical health outcomes. Presence
on 9/11 was defined as meeting at least one of the following criteria: being
south of Chambers Street between the first plane's impact and noon, being
caught in the dust cloud, witnessing horrific events, sustaining an injury, or beginning work on the pile (the construction/restricted zone composed of rubble
and remains from the collapse) on 9/11. Dust cloud exposure was classified as
intense, some, or none (Brackbill, et al., 2009). Witnessing horrific events was
assessed as having seen at least one of the following: an airplane hitting the
WTC, people falling or jumping from the Towers, buildings collapsing, people
running from a cloud or smoke, and people injured or killed. Injuries sustained
on 9/11 included burns, cuts/abrasions/puncture wounds, sprain/strain, fractured/dislocated bones and head injury. Bereavement was defined as knowing
anyone who lost his/her life on 9/11. Timing of volunteer work was classified
by the first date enrollees worked at any WTC site and time spent at all sites combined. Because 9/11-related experiences were highly correlated with volunteer
status, they were not controlled for in the data analyses.
A self-reported professional post-9/11 mental health diagnosis of depression, PTSD, and/or anxiety disorder for the first time after 9/11/01 was classified as early if diagnosed prior to 12/31/03 and late if diagnosed between
01/01/04 and 12/31/07. Date of diagnosis reflects time of presentation for
care, not disease onset. Probable PTSD was defined as a score of 44 or greater
on the stressor-specific PTSD Checklist-Civilian Version and categorized into
four groups: chronic (W1+ (present at W1), W2+ (present at W2)), late
onset (W1− (absent at W1), W2+), resolved (W1+, W2− (absent at
W2)) or no PTSD (W1−, W2−) (American Psychiatric Association, 1994,
Blanchard, et al., 1996, Dobie, et al., 2002, Koenen, et al., 2003, Perrin, et al.,
2007, Ruggiero, et al., 2003).
New or worsening lower respiratory symptoms (LRS) since 9/11 were defined as having at least one of these symptoms at W1 that began or got worse
after 9/11: wheezing, shortness of breath and/or persistent cough. Early post9/11 asthma was defined as asthma or reactive airway dysfunction syndrome
(RADS) diagnosed between 9/11/01 and 12/31/03 and late if diagnosed between
01/01/04 and 12/31/07.
Enrollees were asked at W2 if they had any unmet health care needs.
Those answering affirmatively were asked if they were unable to get care
for a problem related to 9/11. All were asked whether they had received
any services from a list of established post-disaster medical monitoring and
treatment programs.
Analyses were conducted using SAS Version 9.1 (SAS, 2005). Bivariate analyses tested differences between affiliated and lay volunteers using chisquare. Multinomial logistic analyses were performed for four outcomes:
post-9/11 mental health diagnosis (early, late vs. no post-9/11), probable
PTSD (chronic, late onset, resolved vs. no PTSD), new or worsening respiratory symptoms since 9/11, and post-9/11 asthma/RADS (early, late vs. none).
Volunteer status (with affiliated volunteers as the reference group) was the
primary predictor variable for all models. Crude and adjusted odds ratios
and 95% confidence intervals (CI) are reported. Adjustment variables were
recruitment source, gender, age on 9/11, race/ethnicity, and education.
Results
Volunteer groups differed significantly on all sociodemographic
characteristics except education (Table 1). Affiliated volunteers
were predominately list-identified, female, members of only one eligibility group, ages 45–64 years and non-Hispanic white. The largest
proportion reported a 2002 household income of $25,000–49,999.
Lay volunteers were predominantly self-identified, male, members
of more than one eligibility group, ages 25–44, and non-Hispanic
white. The largest proportion reported a 2002 household income of
$75,000–149,999. Lay volunteers were more likely to have known
someone who lost their life on 9/11 and to have lived in NYC or
New York State (NYS) on 9/11.
Lay volunteers had a wider range of exposures and experiences
than affiliated volunteers (Table 2). A substantially greater proportion
of lay volunteers were present on 9/11 (77.3% vs. 25.7%), thereby at
greater risk for acute exposures, such as: intense dust cloud exposure,
witnessing horrific events and sustaining an injury on 9/11 than affiliated volunteers. More lay volunteers began work on 9/11 (29.7%)
and worked seven days or less (74.1%) than affiliated volunteers. Almost half (48.7%) of affiliated volunteers arrived between 9/18/2001
and 12/31/2001, and the majority worked more than seven days
(54.0%).
Compared to affiliated volunteers, lay volunteers were significantly more likely to have received a post-9/11 mental health diagnosis
(30.9% vs. 18.6%), have probable PTSD at W1 or W2 (34.0% vs.
13.3%), and report new or worsening LRS since 9/11 (63.5% vs.
34.9%) as well as post 9/11 asthma/RADS (8.5% vs. 4.3%) (Table 3).
Table 4.1 shows the crude and adjusted odds ratios for mental health
outcomes. The odds of having an early post-9/11 mental health diagnosis was 1.6 times (95% CI: 1.4–2.0) greater among lay than affiliated
volunteers. Lay volunteers were 2.2 times (95% CI: 1.7–2.8) more likely to have chronic probable PTSD, 1.9 times (95% CI: 1.5–2.5) more
likely to have late-onset probable PTSD and 1.7 times (95% CI: 1.2–
2.6) more likely to have resolved probable PTSD than affiliated
Downloaded for Anonymous User (n/a) at Philadelphia University from ClinicalKey.com by Elsevier on September 01, 2017.
For personal use only. No other uses without permission. Copyright ©2017. Elsevier Inc. All rights reserved.
I. Debchoudhury et al. / Preventive Medicine 53 (2011) 359–363
Table 1
Demographic characteristics of volunteers enrolled in the World Trade Center Health
Registry, 2006–07. N = 4974.
Recruitment source
List
Self
Gender
Male
Female
Eligibility category a
Workers only
Multiple eligibility
Age group (years) on 9/11
18–24
25–44
45–64
65+
Race/ethnicity
White (non-Hispanic)
Black or African American (nonHispanic)
Hispanic or Latino (any race)
Asian
Other
Education
Less than 11th Grade
Grade 12 or GED
(high school graduate)
Some college
College graduate
Postgraduate degree
Household income in 2002 (before
taxes)
b$25,000
$25,000 to b$50,000
$50,000 to b$75,000
$75,000 to b$150,000
$150,000 +
Employment status on 9/11
Employed
New York City residents on 9/1
Yes
State lived in on 9/11
New York
New Jersey
Pennsylvania
California
Connecticut
Other
Affiliated
volunteers
(n = 3702)
Lay
volunteers
(n = 1272)
n (%)
n (%)
268 (21.1)
1004 (78.9)
b0.0001
1728 (46.7)
1974 (53.3)
869 (68.3)
403 (31.7)
b0.0001
3190 (86.2)
512 (13.8)
633 (49.8)
639 (50.2)
b0.0001
279
1482
1640
287
86
758
397
28
(6.8)
(59.7)
(31.3)
(2.2)
b0.0001
3185 (86.0)
106 (2.9)
972 (76.4)
63 (5.0)
b0.0001
231 (6.2)
68 (1.8)
112 (3.0)
141 (11.1)
51 (4.0)
45 (3.5)
69 (1.9)
555 (15.1)
33 (2.6)
212 (16.8)
879 (23.9)
1208 (32.8)
974 (26.4)
309 (24.5)
408 (32.3)
301 (23.8)
(7.6)
(40.2)
(44.5)
(7.8)
N.S
b
(14.4)
(19.4)
(20.2)
(33.5)
(12.5)
b0.0001
2941 (79.7)
1136 (89.7)
b0.0001
1043 (28.2)
809 (63.6)
b0.0001
1393
216
177
197
71
1648
987 (77.6)
153 (12.0)
14 (1.1)
9 (0.7)
22 (1.7)
87 (6.8)
b0.0001
494
969
746
863
244
(14.9)
(29.2)
(22.5)
(26.0)
(7.4)
(37.6)
(5.8)
(4.8)
(5.3)
(1.9)
(44.5)
165
222
231
384
143
Table 2
Exposures and experiences of volunteers enrolled in the World Trade Center Health
Registry, 2006–07. N = 4974.
P-value
2344 (63.3)
1358 (36.7)
361
Affiliated
volunteers
(n = 3702)
Lay
volunteers
(n = 1272)
n (%)
n (%)
P-value
Present on 9/11
Yes
952 (25.7)
983 (77.3)
b 0.0001
No
2750 (74.3)
289 (22.7)
Caught in dust cloud that resulted from the collapse of the Towers on 9/11b
Intense
259 (28.1)
397 (42.3)
b 0.0001
Some
159 (17.3)
156 (16.6)
None
503 (54.6)
386 (41.1)
Witnessed horrific events b
5 horrific events witnessed
122 (13.0)
166 (17.2)
b 0.0001a
4 horrific events witnessed
123 (13.1)
171 (17.7)
3 horrific events witnessed
156 (16.6)
183 (19.0)
2 horrific events witnessed
173 (18.4)
182 (18.9)
1 horrific event witnessed
230 (24.5)
175 (18.1)
0 horrific events witnessed
135 (14.4)
88 (9.1)
b,c
Type of horrific events witnessed
Airplane hitting WTC
351 (36.9)
410 (41.7)
b 0.05
People falling or jumping from the
305 (32.2)
386 (39.6)
b 0.001
WTC
Building collapsing
584 (61.4)
660 (67.1)
b 0.01
People running away from a
535 (56.3)
645 (65.7)
b 0.0001
cloud or smoke
People injured or killed
406 (42.9)
551 (56.7)
b 0.0001
Sustained any injury on 9/11b
Yes
160 (16.8)
247 (25.1)
b 0.0001
No
792 (83.2)
736 (74.9)
Bereavement due to 9/11
Bereaved
1070 (29.5)
777 (61.9)
b 0.0001
Worked on the pile at the WTC site
Yes
439 (11.9)
442 (34.8)
b 0.0001
No
3257 (88.1)
829 (65.2)
First day of volunteer work
September 11, 2001
162 (4.8)
360 (29.7)
b 0.0001a
September 12, 2001
196 (5.8)
300 (24.7)
September 13–17, 2001
396 (11.6)
350 (28.8)
September 18–December 31, 2001 1657 (48.7)
173 (14.3)
January 1, 2002–June 30, 2002
995 (29.2)
31 (2.6)
Number of days worked at any WTC
d
site
1–7
792 (46.0)
524 (74.1)
b 0.0001
8–30
650 (37.7)
118 (16.7)
31–90
190 (11.0)
39 (5.5)
N90
91 (5.3)
26 (3.7)
a
b
c
d
Cochran–Armitage Trend Test.
Restricted to enrollees who were present on 9/11.
Not mutually exclusive.
Worked on the pile, Staten Island, or barge.
a
Volunteers were classified as a worker only (rescue/recovery worker) or multiple
eligibility (rescue/recovery worker and at least one of the following: residents, area
workers, passers-by).
b
N.S equals not significant.
volunteers. Table 4.2 shows the crude and adjusted odds ratios for
physical health outcomes. Lay volunteers were 2.0 times more likely
(95% CI: 1.8–2.4) to report new or worsening LRS. The odds of early
post-9/11 asthma/RADS were 1.8 times (95% CI: 1.2–2.7) greater
among lay volunteers.
Discussion
After 9/11, thousands of people converged on the area to volunteer
for recovery and relief efforts. Experiences after the disaster had a substantial impact on their long-term mental and physical health. Lay volunteers had a higher prevalence of adverse mental and physical health
conditions than affiliated volunteers and both groups had a higher prevalence than the general population (Brackbill, et al., 2009). Lay volunteers were more likely to belong to more than one eligibility group
and to be NYC residents. It seems evident that most were in the area
because they lived or worked nearby on the morning of 9/11. Most affiliated volunteers enrolled in the Registry were non-NYC residents, thus
arriving later due to travel restrictions.
Previous studies have identified factors associated with increased
risk of post-disaster depression, PTSD, and anxiety: witnessing horrific
events, lack of preparation or training, sustaining an injury, personal
identification or relationship with victims and losing a loved one
(Brackbill, et al., 2009, Perrin, et al., 2007, Thormar, et al., 2010). Our
findings indicate that lay volunteers were more likely to have experienced these factors than affiliated volunteers, thereby increasing their
risk for post-disaster mental health conditions.
Lay volunteers present in lower Manhattan on 9/11 may have had
more exposure to respirable particulate matter and other substances
associated with asthma and other respiratory outcomes, especially
those working on the pile (Brackbill, et al., 2009). Lay volunteers belonging to multiple eligibility groups were potentially subject to more
9/11-related exposures than other RRW such as dust contaminated
homes or work places that have been associated with increased risk
Downloaded for Anonymous User (n/a) at Philadelphia University from ClinicalKey.com by Elsevier on September 01, 2017.
For personal use only. No other uses without permission. Copyright ©2017. Elsevier Inc. All rights reserved.
362
I. Debchoudhury et al. / Preventive Medicine 53 (2011) 359–363
Table 3
Mental and physical health outcomes among World Trade Center Health Registry volunteer groups, 2006–07. N = 4,974.
Affiliated volunteers
Lay volunteers
(n = 3.702)
(n = 1,272)
n (%)
n (%)
Post-9/11 mental health diagnoses
None
2868 (79.2)
807 (65.5)
465 (12.8)
299 (24.3)
Early a
209 (5.8)
81 (6.6)
Late b
Probable posttraumatic stress disorder
None
3086 (86.7)
791 (66.0)
Chronic (W1+, W2+)
192 (5.4)
189 (15.8)
Late onset (W1−, W2+)
205 (5.8)
166 (13.8)
Resolved (W1+, W2−)
75 (2.1)
53 (4.4)
New or worsening lower respiratory symptoms since 9/11
Yes
1275 (34.9)
797 (63.5)
Post-9/11 asthma/RADS c,d
None
2946 (95.7)
945 (91.5)
Early a
64 (2.1)
53 (5.1)
b
Late
69 (2.2)
35 (3.4)
a
b
c
d
b0.0001
b0.0001
b
b0.0001
Early post-9/11 Late post-9/11 Chronic LateResolved
mental health mental health probable onset
PTSD c
diagnosis b
diagnosis b
PTSD c
probable
PTSDc
a
b
c
d
e
f
a
2.3
(1.9–2.7)
1.4
(1.1–1.8)
3.8
3.1
2.8
(3.1–4.8) (2.5–3.9) (1.9–3.9)
1.00
1.00
1.00
1.6
(1.4–2.0)
1.00
1.1
(0.8–1.5)
1.00
2.2
1.9
1.7
(1.7–2.8) (1.5–2.5) (1.2–2.6)
1.00
1.00
1.00
1.00
c
d
b0.0001
Table 4.1
Multinomial odds ratios for mental health outcomes among lay volunteers compared
to affiliated volunteers.
Affiliated
volunteers
Model 2
(adjusted d)
Lay
volunteers
Affiliated
volunteers
Model 1 (Unadjusted)
Lay Volunteers
Affiliated
Volunteers
Model 2 (Adjusted f)
Lay Volunteers
Affiliated
Volunteers
a
for LRS and asthma/RADS (Brackbill, et al., 2009, Herbert, et al., 2006,
Lin, et al., 2005).
Both volunteer groups reported unmet health care needs at W2,
with the majority of whom were attempting to seek health care for
a 9/11-related problem. Both groups also reported low utilization of
post-9/11 medical monitoring and treatment programs. The frequency of reporting unmet health care needs was greater in lay volunteers
(29.8% vs. 16.1%, p b 0.0001). Of those reporting unmet needs, a greater proportion of lay volunteers were unable to get care for a perceived
9/11-related problem (lay 70.0% vs. affiliated 42.7%). Less than 10% of
all volunteers (8.7%) reported receiving services from a post-9/11
medical monitoring or treatment program and utilization of programs was lower among affiliated than lay volunteers (7.0% vs.
13.9%).
At the time of W2, awareness of these programs was low and
some volunteers may have been unclear about program eligibility.
The post-9/11 health care programs for RRW have strict eligibility criteria regarding duration of work at the disaster sites, which not all
Model 1
(unadjusted)
Lay
volunteers
Odds ratio (95% CI)
P-value
Diagnosed between 09/11/01 and 12/31/03.
Diagnosed between 01/01/04 and 12/31/07.
RADS—reactive airway dysfunction syndrome.
Limited to those with known date of diagnosis or known to have diagnosis.
Odds ratio (95% CI)
Table 4.2
Multinomial odds ratios for physical health outcomes among lay volunteers compared
to affiliated volunteers.
1.00
Estimated via multinomial regression.
Compared to no post-911 mental health diagnosis.
Compared to no PTSD.
Adjusted for recruitment source, gender, age on 9/11, race/ethnicity and education.
a
New or Worsening
LRS b since 9/11 c
Early Post-9/11
Asthma/RADS d, e
Late Post-9/11
Asthma/RADS d, e
3.3 (2.8-3.7)
1.00
2.6 (1.8-3.7)
1.00
1.6 (1.0-2.4)
1.00
2.0 (1.8-2.4)
1.00
1.8 (1.2-2.7)
1.00
1.4 (0.9-2.3)
1.00
Estimated via multinomial regression
LRS- Lower respiratory symptoms
Compared to no new or worsening LRS since 9/11
RADS- Reactive Airway Dysfunction Syndrome
Compared to no post-9/11 Asthma/RADS
Adjusted for recruitment source, gender, age on 9/11, race/ethnicity and education
volunteers met. Many volunteers may have lacked documentation
for acceptance into a program. During W2, most 9/11 medical monitoring and treatment programs were located in New York/New Jersey, while most affiliated volunteers (56.5%) lived outside of the
area. Project Liberty, NYS's primary post-9/11 crisis counseling program, provided services only to NYS residents (Donahue, et al.,
2006). In 2002, due to the unique circumstances of 9/11, Congress authorized $25 million for Workers' Compensation benefits for WTC
volunteers who otherwise would have been ineligible for benefits.
As such, a portion of volunteers' post-9/11 medical related needs
may have been met by the NYS Workers' Compensation System. As
of September 2009, over 1000 WTC volunteers had received benefits
(New York State Workers' Compensation Board, 2009). Enrollment
for WTC-related NYS Workers' Compensation ended 09/10/10.
One study limitation is selection bias. Despite efforts to include all
eligible rescue/recovery volunteers, volunteers in the Registry may
not be representative of all volunteers. Self-identified enrollees
were more likely to report probable PTSD, new or worsening LRS,
and newly diagnosed asthma than list-identified enrollees (Brackbill,
et al., 2009, Farfel, et al., 2008). However, the prevalence of both PTSD
and LRS was still two-fold higher among list-identified lay volunteers
compared to list-identified affiliated volunteers (data not shown).
Self-reported data collected up to five to six years after the disaster
may be subject to recall bias. Volunteers with greater exposure to
the disaster may have been more likely to recall and connect their
symptoms to the disaster than volunteers with lesser exposure. Due
to the lack of detailed documentation of actual tasks performed and
duration of time worked by volunteers who did not perform work
on the pile, we were unable to examine the full range of activities performed by all volunteers. Finally, there is a potential misclassification
of volunteer type. As previously mentioned, many lay volunteers had
to join a professional organization in order to continue volunteering.
It is possible that not all individuals self-identifying as affiliated volunteers had the level of pre-disaster experience and/or training ordinarily provided by those organizations. This may have resulted in a
differential misclassification error, which could have led to an underestimation of the differences between volunteer groups.
Conclusions
Man-made and natural disasters often require rapid deployment of
large scale rescue, recovery and cleanup efforts. Disaster relief organizations, even if well prepared, could not have responded instantaneously
to a disaster of 9/11's magnitude because of logistic and travel limitations. Urgent post-disaster needs for rescue, recovery, and cleanup are
Downloaded for Anonymous User (n/a) at Philadelphia University from ClinicalKey.com by Elsevier on September 01, 2017.
For personal use only. No other uses without permission. Copyright ©2017. Elsevier Inc. All rights reserved.
I. Debchoudhury et al. / Preventive Medicine 53 (2011) 359–363
often filled by individuals who volunteer spontaneously, and it is likely
that volunteers will continue to play an integral role in these efforts, despite exposure to potential health hazards.
Protecting disaster responders should be a major public health
priority. Many cities and states have extensive disaster management
plans, but few are equipped for long-term health surveillance on
the scale of 9/11. The experience of both professional and volunteer
responders to 9/11 indicates a need for the provision of site-specific
training, regardless of prior disaster experience, to limit exposure
to specific hazards and familiarize volunteers with safe operating
procedures.
When exposure to toxic substances is suspected, agencies charged
with monitoring long-term health of professional responders should
extend this function to volunteers. For illnesses with long latency
periods, monitoring activities should follow a cohort design, by establishing and maintaining a roster containing: names, contact information, baseline health screening, tasks performed, and timing and
duration of work. An example is the database of clean-up workers
and volunteers created after the 2010 oil spill in the Gulf of Mexico.
These activities should be followed up with regular assessments of
mental and physical health and health care needs, including a mechanism for making appropriate referrals to care. Rosters of volunteers
will provide an avenue for outreach and education promoting awareness of post-disaster services such as monitoring and treatment programs and Workers' Compensation benefits. Inevitably, monitoring
and treatment costs will emerge as potential obstacles to maintaining
such programs. The data presented in this study represent a first step
in determining the magnitude of existing needs.
Conflict of interest statement
There is none to declare.
Acknowledgments
This study was supported by Cooperative Agreement 1U50/
OH009739 from the National Institute for Occupational Safety and Health
(NIOSH) of the U.S. Centers for Disease Control and Prevention (CDC); Cooperative Agreement U50/ATU272750 from the Agency for Toxic Substances and Disease Registry (ATSDR) which included support from the
National Center for Environmental Health (NCEH); and the New York
City Department of Health and Mental Hygiene (NYC DOHMH). We
would like to thank Dr. Carey Maslow, Dr. JieHui Li, and Daniel
Wallingford for their technical assistance. We would like to thank the
enrollees in the World Trade Center Health Registry for their participation
in our surveys. We would also like to acknowledge the volunteers and
rescue/recovery workers for their service on 9/11 and thereafter. The
views expressed in this article are those of the authors and do not
necessarily represent the views of ATSDR, NIOSH, NCEH, or NYC DOHMH.
363
References
American Psychiatric Association, 1994. Diagnostic and statistical manual of mental
disorders (4th ed., text rev.). Washington, DC: Author.
American Red Cross, 2002. September 11, 2001: Unprecedented Events, Unprecedented
Response. American Red Cross.
Bills, C.B., Levy, N.A., Sharma, V., Charney, D.S., Herbert, R., Moline, J., Katz, C.L., 2008.
Mental health of workers and volunteers responding to events of 9/11: review of
the literature. Mt. Sinai J. Med. 75, 115–127.
Blanchard, E.B., Jones-Alexander, J., Buckley, T.C., Forneris, C.A., 1996. Psychometric
properties of the PTSD checklist (PCL). Behav. Res. Ther. 34, 669–673.
Brackbill, R.M., Hadler, J.L., DiGrande, L., Ekenga, C.C., Farfel, M.R., Friedman, S., Perlman,
S.E., Stellman, S.D., Walker, D.J., Wu, D., Yu, S., Thorpe, L.E., 2009. Asthma and
posttraumatic stress symptoms 5 to 6 years following exposure to the World
Trade Center terrorist attack. JAMA 302, 502–516.
Dobie, D.J., Kivlahan, D.R., Maynard, C., Bush, K.R., McFall, M., Epler, A.J., Bradley, K.
A., 2002. Screening for post-traumatic stress disorder in female Veteran's Affairs
patients: validation of the PTSD checklist. Gen. Hosp. Psychiatry 24, 367–374.
Donahue, S.A., Lanzara, C.B., Felton, C.J., Essock, S.M., Carpinello, S., 2006. Project Liberty: New York's crisis counseling program created in the aftermath of September 11,
2001. Psychiatr. Serv. 57, 1253–1258.
Farfel, M., DiGrande, L., Brackbill, R., Prann, A., Cone, J., Friedman, S., Walker, D.,
Pezeshki, G., Thomas, P., Galea, S., Williamson, D., Frieden, T., Thorpe, L., 2008.
An overview of 9/11 experiences and respiratory and mental health conditions
among World Trade Center Health Registry enrollees. J. Urban Health 85,
880–909.
Herbert, R., Moline, J., Skloot, G., Metzger, K., Baron, S., Luft, B., Markowitz, S., Udasin, I., Harrison, D., Stein, D., Todd, A., Enright, P., Stellman, J.M., Landrigan, P.J., Levin, S.
M., 2006. The World Trade Center disaster and the health of workers: five-year assessment of a unique medical screening program. Environ. Health Perspect. 114,
1853–1858.
Koenen, K., Stellman, J.M., Stellman, S.D., Sommer, J.F., 2003. Risk factors for course of
posttraumatic stress disorder among Vietnam veterans: a 14-year follow-up of
American Legionnaires. J. Consult. Clin. Psychol. 71, 980–986.
Landrigan, P.J., Lioy, P.J., Thurston, G., Berkowitz, G., Chen, L.C., Chillrud, S.N., Gavett, S.
H., Georgopoulos, P.G., Geyh, A.S., Levin, S., Perera, F., Rappaport, S.M., Small, C.,
2004. Health and environmental consequences of the world trade center disaster.
Environ. Health Perspect. 112, 731–739.
Lin, S., Reibman, J., Bowers, J.A., Hwang, S.A., 2005. Upper respiratory symptoms and
other health effects among residents living near the World Trade Center site
after September 11, 2001. Am. J. Epidemiol. 162, 499–507.
Lorber, M., Gibb, H., Grant, L., Pinto, J., Pleil, J., Cleverly, D., 2007. Assessment of inhalation exposures and potential health risks to the general population that resulted
from the collapse of the World Trade Center towers. Risk Anal. 27, 1203–1221.
Murphy, J., Brackbill, R.M., Thalji, L., Dolan, M., Pulliam, P., Walker, D.J., 2007. Measuring
and maximizing coverage in the World Trade Center Health Registry. Stat. Med. 26,
1688–1701.
New York State Workers' Compensation Board, 2009. World Trade Center Cases in the
New York Workers' Compensation System. New York State.
Perrin, M.A., DiGrande, L., Wheeler, K., Thorpe, L., Farfel, M., Brackbill, R., 2007. Differences in PTSD prevalence and associated risk factors among World Trade Center
disaster rescue and recovery workers. Am. J. Psychiatry 164, 1385–1394.
Ruggiero, K.J., Ben, K.D., Scotti, J.R., et al., 2003. Psychometric properties of the PTSD
checklist—civilian version. J. Trauma. Stress 16, 495–502.
SAS, 2005. Statistical Analysis System, Cary, NC.
Steffen, S.L., Fothergill, A., 2009. 9/11 volunteerism: a pathway to personal healing and
community engagement. Soc. Sci. J. 46, 29–46.
Thormar, S.B., Gersons, B.P., Juen, B., Marschang, A., Djakababa, M.N., Olff, M., 2010. The
mental health impact of volunteering in a disaster setting: a review. J. Nerv. Ment.
Dis. 198, 529–538.
Tierney, K., Lindell, M.K., Perry, R.W., 2001. Facing the unexpected:disaster preparedness and response in the United States. Joseph Henry Press, Washington, DC.
Downloaded for Anonymous User (n/a) at Philadelphia University from ClinicalKey.com by Elsevier on September 01, 2017.
For personal use only. No other uses without permission. Copyright ©2017. Elsevier Inc. All rights reserved.
Probability
What is a P value?
Why do we need statistical calculations?
When analyzing data, your goal is simple: You wish to make the strongest possible conclusion
from limited amounts of data. To do this, you need to overcome two problems:
• Important differences can be obscured by biological variability and experimental
imprecision. This makes it hard to distinguish real differences from random variability.
• The human brain excels at finding patterns, even from random data. Our natural inclination
(especially with our own data) is to conclude that differences are real, and to minimize the
contribution of random variability. Statistical rigor prevents you from making this mistake.
Statistical analyses are most useful when you are looking for differences that are small compared
to experimental imprecision and biological variability. If you only care about large differences,
you may follow these aphorisms:
If you need statistics to analyze your experiment, then you’ve done the wrong experiment.
If your data speak for themselves, don’t interrupt!
But in many fields, scientists care about small differences and are faced with large amounts of
variability. Statistical methods are necessary.
P values
Definition of a P value
Consider an experiment where you’ve measured values in two samples, and the means are
different. How sure are you that the population means are different as well? There are two
possibilities:
• The populations have different means.
• The populations have the same mean, and the difference you observed is a coincidence of
random sampling.
The P value is a probability, with a value ranging from zero to one. It is the answer to this
question:
If the populations really have the same mean overall, what is the probability that random
sampling would lead to a difference between sample means as large (or larger) than you
observed?
1
How are P values calculated? There are many methods, and you’ll need to read a statistics text to
learn about them. The choice of statistical tests depends on how you express the results of an
experiment (measurement, survival time, proportion, etc.), on whether the treatment groups are
paired, and on whether you are willing to assume that measured values follow a Gaussian bellshaped distribution.
Common misinterpretation of a P value
Many people misunderstand what question a P value answers.
If the P value is 0.03, that means that there is a 3% chance of observing a difference as large as
you observed even if the two population means are identical. It is tempting to conclude, therefore,
that there is a 97% chance that the difference you observed reflects a real difference between
populations and a 3% chance that the difference is due to chance. Wrong. What you can say is
that random sampling from identical populations would lead to a difference smaller than you
observed in 97% of experiments and larger than you observed in 3% of experiments.
You have to choose. Would you rather believe in a 3% coincidence? Or that the population means
are really different?
“Extremely significant” results
Intuitively, you probably think that P=0.0001 is more statistically significant than P=0.04. Using
strict definitions, this is not correct. Once you have set a threshold P value for statistical
significance, every result is either statistically significant or is not statistically significant. Some
statisticians feel very strongly about this.
Many scientists are not so rigid, and refer to results as being “very significant” or “extremely
significant” when the P value is tiny. Often, results are flagged with a single asterisk when the P
value is less than 0.05, with two asterisks when the P value is less than 0.01, and three asterisks
when the P value is less than 0.001. This is not a firm convention, so you need to check the figure
legends when you see asterisks to find the definitions the author used.
One- vs. two-tail P values
When comparing two groups, you must distinguish between one- and two-tail P values.
Start with the null hypothesis that the two populations really are the same and that the observed
discrepancy between sample means is due to chance.
•
•
The two-tail P value answers this question: Assuming the null hypothesis, what is the
chance that randomly selected samples would have means as far apart as observed in this
experiment with either group having the larger mean?
To interpret a one-tail P value, you must predict which group will have the larger mean
before collecting any data. The one-tail P value answers this question: Assuming the null
2
hypothesis, what is the chance that randomly selected samples would have means as far
apart as observed in this experiment with the specified sample having the larger mean?
A one-tail P value is appropriate only when previous data, physical limitations or common sense
tell you that a difference, if any, can only go in one direction. The issue is not whether you expect
a difference to exist that is what you are trying to find out with the experiment. The issue is
whether you should interpret increases and decreases the same.
-
You should only choose a one-tail P value when you believe the following:
•
Before collecting any data, you can predict which group will have the larger mean (if the
means are in fact different).
•
If the other group ends up with the larger mean, then you should be willing to attribute
that difference to chance, no matter how large the difference.
It is usually best to use a two-tail P value for these reasons:
•
•
•
The relationship between P values and confidence intervals is more clear with two-tail P
values.
Some tests compare three or more groups, which makes the concept of tails inappropriate
(more precisely, the P values have many tails). A two-tail P value is more consistent with
the P values reported by these tests.
Choosing a one-tail P value can pose a dilemma. What would you do if you chose a onetail P value, but observed a large difference in the opposite direction to the experimental
hypothesis? To be rigorous, you should conclude that the difference is due to chance, and
that the difference is not statistically significant. But most people would be tempted to
switch to a two-tail P value or to reverse the direction of the experimental hypothesis.
You avoid this situation by always using two-tail P values.
Independent or mutually exclusive event?
One of the important steps you need to make when considering the probability of two or more
events occurring is to decide whether they are independent or related events.
Examples:
Mutually Exclusive vs. Independent
It is not uncommon for people to confuse the concepts of mutually exclusive events and
independent events.
Definition of a mutually exclusive event
If event A happens, then event B cannot, or vice-versa. The two events "it rained on Tuesday"
and "it did not rain on Tuesday" are mutually exclusive events. When calculating the probabilities
for exclusive events you add the probabilities.
3
Independent events
The outcome of event A has no effect on the outcome of event B. Such as "It rained on Tuesday"
and "My chair broke at work.” When calculating the probabilities for independent events you
multiply the probabilities. You are effectively saying, what is the chance of both events
happening bearing in mind that the two were unrelated. To be or not to be.....?
So, if A and B are mutually exclusive, they cannot be independent. If A and B are independent,
they cannot be mutually exclusive. However, if the events were “it rained today" and "I left my
umbrella at home" they are not mutually exclusive, but they are probably not independent either,
because one would think that you'd be less likely to leave your umbrella at home on days when it
rains. That fact aside, use the following to understand the definition.
Example of a mutually exclusive event
What happens if we want to throw 1 and 6 in any order? This now means that we do not mind if
the first die is either 1 or 6, as we are still in with a chance. But with the first die, if 1 falls
uppermost, clearly it rules out the possibility of 6 being uppermost, so the two Outcomes, 1 and 6,
are exclusive. One result directly affects the other. In this case, the probability of throwing 1 or 6
with the first die is the sum of the two probabilities, 1/6 + 1/6 = 1/3.
The probability of the second die being favorable is still 1/6 as the second die can only be one
specific number, a 6 if the first die is 1, and vice versa.
Therefore the probability of throwing 1 and 6 in any order with two dice is 1/3 x 1/6 = 1/18. Note
that we multiplied the last two probabilities as they were independent of each other!!!
Example of an independent event
The probability of throwing a double three with two dice is the result of throwing three with the
first die and three with the second die. The total possibilities are, one from six outcomes for the
first event and one from six outcomes for the second, Therefore (1/6) * (1/6) = 1/36th or 2.77%.
The two events are independent, since whatever happens to the first die cannot affect the throw of
the second the probabilities are therefore multiplied, and remain 1/36th.
Statistical hypothesis testing
The P value is a fraction. In many situations, the best thing to do is report that number to
summarize the results of a comparison. If you do this, you can totally avoid the term “statistically
significant”, which is often misinterpreted.
In other situations, you’ll want to make a decision based on a single comparison. In these
situations, follow the steps of statistical hypothesis testing.
1. Set a threshold P value before you do the experiment. Ideally, you should set this value
based on the relative consequences of missing a true difference or falsely finding a
4
difference. In fact, the threshold value (called alpha) is traditionally almost always set to
0.05.
2. Define the null hypothesis. If you are comparing two means, the null hypothesis is that the
two populations have the same mean.
3. Do the appropriate statistical test to compute the P value.
4. Compare the P value to the preset threshold value. If the P value is less than the threshold,
state that you “reject the null hypothesis” and that the difference is “statistically
significant”. If the P value is greater than the threshold, state that you “do not reject the null
hypothesis” and that the difference is “not statistically significant”.
Note that statisticians use the term hypothesis testing very differently than scientists.
Statistical significance
The term significant is seductive, and it is easy to misinterpret it. A result is said to be statistically
significant when the result would be surprising if the populations were really identical. A result is
said to be statistically significant when the P value is less than a preset threshold value.
It is easy to read far too much into the word significant because the statistical use of the word has
a meaning entirely distinct from its usual meaning. Just because a difference is statistically
significant does not mean that it is important or interesting. And a result that is not statistically
significant (in the first experiment) may turn out to be very important.
If a result is statistically significant, there are two possible explanations:
•
•
The populations are identical, so there really is no difference. You happened to randomly
obtain larger values in one group and smaller values in the other, and the difference was
large enough to generate a P value less than the threshold you set. Finding a statistically
significant result when the populations are identical is called making a Type I error.
The populations really are different, so your conclusion is correct.
There are also two explanations for a result that is not statistically significant:
•
•
The populations are identical, so there really is no difference. Any difference you
observed in the experiment was a coincidence. Your conclusion of no significant
difference is correct.
The populations really are different, but you missed the difference due to some
combination of small sample size, high variability and bad luck. The difference in your
experiment was not large enough to be statistically significant. Finding results that are not
statistically significant when the populations are different is called making a Type II
error.
5
Purchase answer to see full
attachment