Individuals with occupational exposures to carcinogens are at increased risk for leukemia
(Mills, Dodge, & Yang, 2009). Blair et al. (2001) found an increased risk of leukemia
for those working in particular industries or occupations and used prior studies to
illuminate suspected carcinogens of concern: agricultural service industries (pesticides
[Brown et al., 1990]); nursing, health-care workers (ionizing radiation, antineoplastic
drugs [Skov et al., 1992], formaldehyde, and unidentified infectious agents); janitors;
cleaners (cleaning chemicals and pest control products); those in plumbing, heating,
and air-conditioning (asbestos [Schwartz et al., 1988]); and sale of nondurable goods
(like paint and varnishes).
With regard to acute myelogenous leukemia (AML) in particular, Tsai et al. (2014)
found that construction, crop production or support activities for agriculture and
forestry, and animal slaughtering and processing were the occupations most likely
to pose a risk for AML (matched odds ratios ranged from 1.13–2.09). Agricultural workers,
fishers and fishing workers; nursing, psychiatric, and home health aides; as well
as janitors and building cleaners were the occupations at highest risk for AML (matched
odds ratios ranged from 1.54–2.02; Tsai et al., 2014).
Benzene and asbestos were the suspected leukemogens in construction (Luckhaupt et
al., 2012), pesticides and solvent exposures were of concern for those in agriculture
and forestry, and exposure to retroviruses is the concern for those in animal slaughtering
and processing, although the authors warned that the evidence of how animal viruses
impact human cells needs further study (Tsai et al., 2014). For fishermen, the concern
was contaminants (like pesticides) found in fish, as well as stressful sleeping and
working conditions (Roberts, Rodgers, & Williams, 2007).
For those in nursing and home health, the concern was for viral exposure and exposure
to infectious agents in bodily fluids as well as antineoplastic agents (Skov et al.,
1992).
Finally, for those in cleaning occupations, the concern is for exposure to formaldehyde,
acetone, sodium hypochlorite, borates, and morpholine (although only formaldehyde
is a known leukemogen) as well as pesticides applied in buildings (Blair et al., 2001;
Charles, Loomis, & Demissie, 2009).
These studies provide descriptive and correlational data to investigate further the
impact of variations in occupational exposure to carcinogens. Yet occupational exposure
histories are infrequently conducted as part of an oncology patient’s history and
physical, making it difficult for researchers to understand as much as we can about
occupational exposures to carcinogens and leukemia. This is a missed opportunity by
health-care professionals in recognizing and valuing the importance of documenting
an occupational exposure.
We investigated acute leukemia cases (AML and acute lymphocytic leukemia [ALL]) treated
at a large comprehensive cancer center in North Carolina from 2007 to 2010. We set
out to explore how many of the patients with acute leukemia were in high-risk occupations
with documented occupational exposures.
METHODS
This was a retrospective study that included a convenience sample of individuals diagnosed
with acute leukemia at a large regional cancer center in North Carolina. Expedited
institutional review board approval was obtained because the data were pulled from
existing electronic health records (EHRs) and entered into a database.
Participants
Patients aged 18 or older with a diagnosis of acute leukemia receiving care between
2007 and 2010 were identified from the Carolina Data Warehouse for Health (CDW-H).
The CDW-H was initiated on July 1, 2004, and is a central repository including clinical,
research, and administrative data for patients receiving services at a large cancer
center in North Carolina.
Initially, 508 potential patient records were identified. Of them, 184 were diagnosed
outside of our study dates (2007–2010) or before 18 years of age. A total of 97 had
a diagnosis other than AML or ALL, 72 received their treatment outside of the study
center, and 40 were excluded for either insufficient clinical documentation or leukemia
secondary to another malignancy. This study included 115 patients, older than 18 years
of age at the time of diagnosis with a confirmed diagnosis of AML or ALL who received
treatment at a large cancer center in North Carolina between 2007 and 2010.
Data Collection
The first two authors (ALW and ALB) developed and entered all data into a database,
which captured information about gender, age at diagnosis, current age, race/ethnicity,
marital status, insurance status, diagnosis date, type of leukemia, subtype of leukemia,
occupation, whether pesticide exposure was assessed or other documentation of occupational
exposure was made, number of visits to the emergency department (ED), number of visits
to the hospital, number of visits to the clinic, whether or not hematopoietic stem
cell transplant (HSCT) was mentioned as a treatment option, whether or not the patient
was still in treatment, whether or not the patient had ever achieved remission, whether
or not the patient had relapsed, whether or not the patient was deceased at the time
of data collection, reason for death, and finally days from diagnosis to death.
Occupations were captured in free text in the database and then coded into 1 of 13
codes, including 1 for not obtained and 1 for unknown. Furthermore, each patient’s
occupation was coded as to whether or not there was an increased risk for leukemia
based on industry or occupation using the findings of Blair et al. by Standard Industrial
Classification Code or Dictionary of Occupational Title (Blair et al., 2001). We also
classified military personnel as an employment associated with leukemia based on classifications
found in the peer-reviewed Cancer Research Program Fiscal Year 2012 Report to Congress
(U.S. Army Medical Research and Material Command, 2012), as military personnel were
not a focus of the Blair et al. study.
Analysis
As this was a descriptive study, power analysis was not undertaken prior to the start
of the study. The Statistical Package for the Social Sciences (SPSS) version 22 was
used to code all responses, conduct all data analyses, and compute all summary scores
where appropriate (IBM Corporation, 2013). Descriptive statistics including frequency
counts and percent statistics were computed for the demographic variables.
RESULTS
Sample
Initially, 508 potential patient records were identified. Of them, 184 were diagnosed
outside of our study dates (2007–2010) or before 18 years of age. A total of 97 had
a diagnosis other than AML or ALL, 72 received their treatment outside of the study
center, and 40 were excluded for either insufficient clinical documentation or leukemia
secondary to another malignancy. Of the 115 patients remaining, 57 were women, and
58 were men (age range, 18–82 years). There were 50% (n = 57) non-Latino whites, 20%
(n = 23) blacks or members of another race, and 30% (n = 34) Latinos; one patient
of an unknown race was included. The majority of patients were married/partnered (73%),
and 75% had either no insurance or public insurance (Table 1).
Table 1
Findings by Race/Ethnicity
Occupation
Occupation was noted for 98 of the 115 patients in this sample (Table 2). Of the 17
patients missing occupational data, 10 were women. Although occupation was commonly
reported, an assessment of occupational exposures to carcinogens was found in the
medical record of only two patients (pesticides for a farm worker and asbestos for
a factory worker). Our analyses showed that 35% of our sample for whom occupation
was known were at increased risk for leukemia according to their industry or occupational
code (Blair et al., 2001).
Table 2
Variation in Occupation of Study Participants
DISCUSSION
Although it was difficult to answer our original research question with the lack of
occupational health information collected, several interesting findings emerged with
regard to race/ethnicity (Table 1). Significant differences in age at diagnosis and
type of leukemia were found by race/ethnicity. Latinos were younger at diagnosis,
with a mean age of 40 (range 18–81) vs. 49 for blacks/members of other races (range,
19–82) and 50 for whites (range, 18–72; p = .02). Latinos were more likely to have
ALL than were members of other races, with 18 (53% of ALL cases) for Latinos vs. 8
(35% of ALL cases) for blacks/members of other races and 15 (26% of ALL cases) for
whites (p = .04). Latinos were less likely than non-Latino whites (although slightly
more likely than blacks and those of other race/ethnicities) to undergo HSCT. Whites
were more likely to receive HSCT (20 [36%]), than Latinos (5 [16%]) and blacks/members
of other races (2 [9%], p = .01). Nonsignificant statistical differences existed in
gender, insurance status, number of patients deceased at the time of the study, length
of time from diagnosis to death, and whether or not HSCT was discussed as a treatment
option.
More of this sample were Latino than may have been expected compared with the National
Cancer Institute’s Surveillance, Epidemiology, and End Results Program (SEER) data
on leukemia incidence. According to SEER from 2008 to 2012, the incidence rates by
ethnicity and gender per 100,000 were 17.9 male, 10.9 female for whites, 13.5 male
and 8.5 female for blacks, and 12.6 male and 8.9 female for Hispanics (National Cancer
Institute, 2013). One plausible explanation for this difference is that the state
of North Carolina, where this research was conducted, has one of the fastest-growing
Hispanic populations in the United States, up 120% from 2000 to 2011 (Brown & Lopez,
2013).
It is worthy to note that more of every racial/ethnic group were un/underinsured than
would have been expected compared with insurance data for the state of North Carolina.
One plausible explanation may be the institution where this research took place is
a not-for-profit health-care system owned by the state. As such, we may see more un/underinsured
people than other institutions in our state.
In fiscal year 2010, the hospital system within which the cancer center is located
provided $283 million in uncompensated care, which includes indigent care, bad debts,
and care costs not reimbursed by Medicare or Medicaid. Uncompensated care was expected
to exceed $300 million in the hospital system in fiscal year 2011 (University Gazette,
2011). This particular hospital system was also recognized for providing charity-care
levels that exceeded the cost of living for its region (Linker, 2010). In this study,
39 whites (68%), 18 (78%) blacks/members of other races, and 28 (82%) Latinos were
un/underinsured. North Carolina state data from 2010 to 2011 illustrated the percentages
of each of those same racial/ethnic groups that were uninsured and found that 14.5%
of all whites in the state, 41% of all blacks/members of other races, and 41% of Latinos
were uninsured (North Carolina Institute of Medicine, 2013).
Extracting data on occupation itself and then determining whether a patient was in
an occupation at increased risk for acute leukemia was challenging. The codes that
we initially chose for occupations did not match those used by Blair et al., and we
compared ours against those they deemed to be at higher risk for any leukemia. There
was also variation of risk within our codes, which made it necessary to recode to
determine whether there was risk per the Blair article.
For example, housekeeping was complicated. We listed housekeeping under "Other" in
Table 2. In our sample, one person cleaned hospitals (considered increased risk per
the Blair article), one person cleaned homes (considered not increased risk per the
Blair article), and yet another had just "housekeeping" listed as occupation by the
provider. Housekeeping in private homes vs. in industry/lodging is associated with
a different risk per the Blair article and makes meaningful results challenging.
Determining the set of codes to use was also challenging. We used those Blair provided
as being at higher risk for all leukemia, although some of the subanalyses used in
the Blair article broke out histologic type, and some work published after our study
was complete conducted analyses for particular subtypes as well (Tsai et al., 2014).
Finding only two occupational exposure assessments completed in the workups of 115
patients with leukemia demonstrates the lack of awareness by clinicians of the potential
value in collecting this information. Even though 30% (n = 34) of the total sample
were in occupations at increased risk for leukemia, in only two charts were there
documented exposures.
The questions posed to obtain this information from the individual patient are unknown
and underscore that oncologists and advanced practice oncology nurses may not know
what to ask. Wider distribution of a resource published by the Agency for Toxic Substances
& Disease Registry (2001) called the "I PREPARE," a pocket guide card for primary
care providers, may be beneficial for oncologists as well and provide a practical
and clinically relevant tool to assess environmental exposures and contribute to the
body of knowledge for research (Paranzino, Butterfield, Nastoff, & Ranger, 2005).
The tool was tested and revised based on the input of 159 health-care providers in
2004 and was developed in response to the findings that little time was spent on occupational
health in the nursing or medical curricula despite the Institute of Medicine’s strong
urgings to the contrary (Paranzino et al., 2005). The tool cues the provider to "Investigate
potential exposures"; ask questions about "Present work," "Residence," "Environmental
concerns," "Past work," and "Activities"; as well as provide "Referrals and Resources"
and "Educate" the patient on strategies to prevent or minimize exposures. Examples
of questions in each of those areas can be found in Table 3, and the full PDF is available
on the Agency for Toxic Substances and Disease Registry’s website (http://www.atsdr.cdc.gov/asbestos/site-kit/docs/IPrepareCard.pdf).
Table 3
Select Questions Included in the I PREPARE Assessment
Although this tool has not been trialed in the oncology setting, it seems that each
of the areas would contribute not only to the data that can be accessed by researchers
but to the quality of care patients receive and education for working more safely.
In this era, which emphasizes the importance of the learning health system, utilizing
the EHR to inform and improve outcomes in patients with cancer, it is paramount that
oncology providers understand their significant role in the careful documentation
of environmental exposures and the impact that documentation has on data for research.
As EHRs are further customized, tools that allow clinicians to quickly collect relative
exposure data should be incorporated and will be invaluable to the study of occupational
exposures to carcinogens.
Acknowledgements
Dr. Walton was supported by the National Institute of Nursing Research of the National
Institutes of Health under Award Numbers T32NR007091 and T32NR013456. Dr. Walton was
also supported by the Doctoral Scholarship in Cancer Nursing Renewal DSCNR-13-276-03
from the American Cancer Society. Dr. Bryant was supported by the National Cancer
Institute of the National Institutes of Health under Award Number R25CA116339 (Bryant)
and NCI 5K12CA120780-07 (Bryant). This project was also supported by the NC TraCS
Institute, NIH Clinical and Translational Science Award from the National Center for
Research Resources UL1TR001111.