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Vol. 14(2), pp. 20-35, July-December 2022
DOI: 10.5897/JTEHS2022.0506
Article Number: 7476A0C69758
ISSN 2006-9820
Copyright ©2022
Author(s) retain the copyright of this article
http://www.academicjournals.org/JTEHS
Journal of Toxicology and Environmental
Health Sciences
Full Length Research Paper
Fish consumption, awareness of fish advisories, and
body burden of contaminants among the Milwaukee
urban anglers: A biomonitoring study
Xiaofei He1, Carrie Tomasallo1, Zheng Li3, Amy Schultz2, Maria Kamenetsky2, Andreas Sjodin3,
Julianne Botelho3, Jeffrey Jarrett3 and Jonathan Meiman1*
1Wisconsin Department of Health Services, 1 W Wilson St, Madison, WI 53703, United States.
2Department of Population Health Sciences, University of Wisconsin Madison Warf Office Building, 610 Walnut St #707,
Madison, WI 53726, United States.
3Division of Toxicology and Human Health Science, Agency for Toxic Substances and Disease Registry, 4770 Buford
Highway, Atlanta, GA 30341, United States.
Received 21 June, 2022; Accepted 5 September, 2022
The Milwaukee Estuary Area of Concern (AOC) has been impacted by toxic pollutants, including
polychlorinated biphenyls (PCBs) and heavy metals. During 2017-2019, the Wisconsin Department of
Health Services conducted a biomonitoring study with the Agency for Toxic Substances and Disease
Registry to examine contaminant exposures among Milwaukee urban anglers who consumed locally
sport-caught fish. Questionnaires were administered to licensed anglers living within the Milwaukee
AOC, and blood and urine samples were obtained for contaminant analysis. We conducted
multivariable logistic regression analyses to examine associations with fish consumption, awareness of
fish advisories, and protective behaviors. Contaminant concentrations among participants were
compared to the U.S. population levels in the National Health and Nutrition Examination Survey
(NHANES). Respondents completed the questionnaire (n=396) and provided biological samples (n=390).
The median age was 51 years, and the majority were male (81%) and non-Hispanic white (81%). Most
respondents were aware of safe-eating guidelines for fish caught in the Milwaukee AOC (63%) or
Wisconsin (75%), but fewer than half reported close adherence to guidelines. Women and black
respondents were less aware of Wisconsin and Milwaukee advisories than men and white respondents,
respectively. Geometric mean concentrations of perfluorooctane sulfonate (PFOS), PCBs, and mercury
were higher than NHANES estimates. Most anglers in the Milwaukee AOC did not reduce sport-caught
fish consumption to avoid exposure to contaminants in Milwaukee waterways. Milwaukee urban anglers
had higher PFOS, PCB, and mercury concentrations than those in U.S. adults. Further research is
needed to examine the factors influencing anglers’ adherence to safe-eating guidelines.
Key words: Urban anglers, fish consumption, contaminants, biomonitoring, fish advisories.
INTRODUCTION
The Great Lakes Basin has been impacted over time by
toxic chemicals, resulting in human exposure to these
contaminants via inhalation, dermal contact with water,
soil and sediments, ingestion of municipal water drawn
from the lakes, and consumption of local fish. The
International Joint Commission Water Quality Board has
identified 43 areas of concern (AOCs) in the United
States that have been severely impacted by contaminants,
five of which are located in Wisconsin. Of these, the
Milwaukee Estuary AOC is of particular concern because
the Milwaukee River Basin is located in the most densely
populated area of Wisconsin, encompassing portions of
seven counties, and is home to about 1.3 million people
(EPA, 2016). The primary contaminants in this area are
polychlorinated biphenyls (PCBs), heavy metals, and
polycyclic aromatic hydrocarbons (PAHs) (EPA, 2019).
Many of the chemicals present in the Milwaukee AOC
bioaccumulate up the food chain, making consumption of
locally caught fish a key exposure pathway. Over the past
25 years, biomonitoring studies have been conducted in
Wisconsin using convenience samples of at-risk
populations, including charter boat captains and their
families, women of childbearing age, and older males.
These studies have revealed increased body burdens of
PCB and per- and polyfluoroalkyl substances (PFAS)
consistent with exposure via consumption of Great Lakes
fish (Christensen et al., 2015; Turyk et al., 2006). These
studies have also demonstrated associations between
increased contaminant levels and adverse health effects,
including diabetes (Turyk et al., 2006, 2015), thyroid and
other hormone disorders (Turyk et al., 2015),
cardiovascular disease (Raymond et al., 2016) and
reduced birth weight (Weisskopf et al., 2005). In addition,
anglers living near the impacted waterways often
experience additional socioeconomic and environmental
stressors that further increase their vulnerability (Corburn,
2002; Kalkirtz, 2008). Milwaukee County has one of the
highest rates of children living in poverty and food
insecurity in the state (University of Wisconsin Population
Health Institute, 2015) and is a key destination for
refugee populations who have limited economic
resources and low health literacy (CDC, 2010).
Although the residents living in proximity to the
Milwaukee Estuary AOC waterways presumably have an
elevated risk of being exposed to contaminants, we have
limited knowledge of their fish consumption and body
burdens of environmental contaminants. Therefore, in
2017, the Wisconsin Department of Health Services
conducted a geographically focused community
biomonitoring study of Milwaukee urban anglers with the
purpose of examining their exposure to contaminants on
the local level and exploring how to best minimize risks
from sport-caught fish consumption. The basic
demographics of study participants were previously paper
reports fish consumption behaviors and awareness of fish
described in an overview article (Li et al., 2021). This
advisories, as well as initial descriptive analyses of
He et al. 21
biomonitoring results.
METHODS
Recruitment
Four strategies were used to recruit respondents during 2017-2018:
(1) mail recruitment of fish license registrants; (2) email recruitment
of fish license registrants; (3) peer recruitment by study participants;
and (4) shoreline recruitment at fishing venues. The study was
originally designed to recruit representative samples of anglers
using stratified random selection from a state fishing license
database, but additional sampling strategies were implemented
after a lower-than-expected response rate. Regardless of
recruitment strategies, respondents were asked to complete a
screening survey with the following: (1) “I have lived at my current
address (within 5 miles radius of Milwaukee AOC) for one year or
longer;” (2) “I am a male OR I am a female who is not currently
pregnant;” and (3) “In the past 12 months, I ate at least one fish
meal that was caught in any of the lakes, rivers, streams, or ponds
pictured in the map printed on the back of this page” (Figure 1).
Respondents were eligible to participate in our study if they
answered “yes” to all three screening questions.
Data collection
All participants provided informed consent before participation.
Upon enrollment, respondents were asked to complete a
questionnaire and schedule an appointment to provide urine and
blood samples. Respondents completed a mailed or web-based
questionnaire regarding fish consumption and awareness of fish
advisories before their appointment, but they were also given the
option to complete it onsite. Respondents were asked about
consumption of sport-caught fish, store-bought fish, and shellfish in
the past 12 months. Awareness of fish advisories included
awareness of Wisconsin fish advisories and Milwaukee-specific fish
advisories. Information sources for fish advisories and safe fish
cooking practices were also examined (Appendix 1 for the list of
questions). Respondents’ urine and blood samples and body
measurements (height, weight, and waist circumferences) were
collected by certified phlebotomists. Serum samples for PCB, PFAS,
and pesticide analyses were immediately frozen and stored at -20°F;
whole blood for metals analysis was refrigerated for up to one week
prior to freezing; and urine was immediately frozen after collection.
Frozen biological samples were shipped overnight on dry ice to
analytical laboratory in batches.
All study activities were approved by the federal Office of
Management and Budget (Control Number 0923–0056). This study
was supported by a cooperative agreement from the Centers for
Disease Control and Prevention (CDC)/Agency for Toxic
Substances and Disease Registry (ATSDR).
Human Subjects Protection Committee and did not require
oversight or review by an institutional review board.
Laboratory analysis
The Division of Laboratory Sciences (DLS) within CDC’s National
*Corresponding author. E-mail: jonathan.meiman@dhs.wisconsin.gov. Tel: 608-266-1253.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
22 J. Toxicol. Environ. Health Sci.
Figure 1. Milwaukee Urban Anglers study area map. Water bodies with dark yellow shade were included in this study.
Source: Authors
Center for Environmental Health conducted all laboratory analyses,
following establish analytical methods, standard operating
procedures, and quality control and quality assurance procedures.
Five metals (manganese, lead, cadmium, selenium, and mercury)
were measured in whole blood samples using DLS Method 3040.1
by inductively coupled plasma triple quadrupole mass spectrometry.
Nine PFAS chemicals were measured in serum by on-line solid
phase extraction (SPE) liquid chromatography-tandem mass
spectrometry (LC-MS/MS) (Kato et al., 2011), including 2-(N-methyl-
perfluorooctane sulfonamido) acetate (MeFOSAA), perfluorohexane
sulfonate (PFHxS), perfluorononanoate (PFNA), sum of
perfluoromethylheptane sulfonate isomers (Sm-PFOS), n-
perfluorooctane sulfonate (n-PFOS), perfluorodecanoate (PFDA), n-
perfluorooctanoate (n-PFOA), perfluoroundecanoate (PFUnDA),
and sum of branched perfluorooctanoate isomers (Sb-PFOA). Total
PFOS and PFOA were calculated by summing the linear and
branched isomers for each participant, in accordance with CDC’s
National Report on Human Exposure to Environmental Chemicals
(CDC, 2021).
Serum samples were also analyzed for PCBs, polybrominated
diphenyl ethers (PBDEs), polybrominated biphenyl (PBBs), and
persistent pesticides (PPs) by gas chromatography isotope dilution
high resolution mass spectrometry (Jones et al., 2012). Measured
PCB congeners included PCB 28, 66, 74, 99, 105, 114, 118, 138-
158, 146, 153, 156, 157, 167, 170, 178, 180, 183, 187, 189, 194,
196-203, 199, 206, and 209. All PCBs were summed to calculate
the “total PCBs” in data analysis. Measured PBDE congeners
included PBDE 17, 28, 47, 85, 99, 100, 153, 154, and 183.
2,2',4,4',5,5'-hexabromobiphenyl (BB-153) was also measured. PPs
included hexachlorobenzene (HCB), β-hexachlorocyclohexane
(β-HCH), oxychlordane, trans-nonachlor, 2,2-Bis(4-chlorophenyl) -
1,1-dichloroethene (p,p’-DDE), 2-(4-chlorophenyl)-2-(2-
chlorophenyl)- 1,1,1-trichloroethane (o,p’-DDT), 2,2-Bis (4-
chlorophenyl)-1,1,1-trichloroethane (p,p’-DDT), and Mirex. Urinary
samples were analyzed for hydroxylated metabolites of PAHs,
including l-hydroxynaphthalene (1-NAP), 2-hydroxynaphthalene (2-
NAP), 2-hydroxyfluorene (2-FLU), 3-hydroxyfluorene (3-FLU), 1-
hydroxyphenanthrene (1-PHE), 2-hydroxyphenanthrene and 3-
hydroxyphenanthrene (2-, 3-PHE), and 1-hydroxypyrene (1-PYR).
The analytical determination was performed by on-line SPE -LC-
MS/MS (Wang et al., 2017).
Data analysis
For questionnaire results, descriptive analyses were conducted to
summarize demographic characteristics, fish consumption, and
awareness of fish advisories. Multivariable logistic regression
analyses were used to estimate the odds of fish consumption (high
fish consumption vs. low fish consumption) in relation to
demographics, the odds of awareness of fish advisories in relation
to demographics, and the odds of adopting protective fish
consumption behaviors in relation to awareness of fish advisories
and demographics. Log-linear regression was performed to
examine the association between sport-caught fish consumption
and demographics.
For biomonitoring results, descriptive analyses (that is, geometric
means, 95th percentiles, and 95% confidence intervals) were
conducted for all chemicals measured in the study. For chemicals
with analytic results below the limit of detection, a value was
imputed that is the limit of detection divided by the square root of 2
(CDC, 2018). Biomonitoring results for non-metals were corrected
for lipids or creatinine. The urinary PAH concentrations were
divided by the urinary creatinine concentration, and the creatinine-
corrected PAHs were expressed as nanograms per gram of
creatinine (ng/g). Concentrations of PCBs, PBDEs, PBB-153, and
PPs were given as ng/g lipid weight (weight of serum lipids). The
serum lipid concentration was determined using commercially
available test kits for the quantitative determination of total
triglycerides and total cholesterol.
These descriptive results were compared with the U.S.
population (20 years and older) from the National Health and
Nutrition Examination Survey (NHANES) 2015-2016. In the
NHANES 2015-2016 laboratory data, PCBs, PBDEs, PBB-153, and
PPs were presented as pooled samples; following the procedures
proposed by Caudill et al. (2007), Caudill (2010, 2012) and Mee
and Owen (1983), we estimated the bias-corrected geometric
He et al. 23
means, 95th percentiles, and their 95% confidence intervals (CIs).
For other chemicals measured in the study with individual samples
in NHANES 2015-2016, including metals, PFAS and PAH
metabolites, we used the geometric means, 95th percentiles and
95% CIs reported in CDC’s National Report on Human Exposure to
Environmental Chemicals (CDC, 2021). All statistical analyses were
performed using SAS (Statistical Analysis Software 9.4, SAS
Institute Inc, Cary, North Carolina, USA).
RESULTS
Survey respondents
Among the 2,239 screening survey responses we
received, 949 respondents were eligible for participation.
Of the eligible respondents, 396 completed the
questionnaire, 390 provided blood samples, and 389
provided urine samples. The inclusion and exclusion
steps for respondents are illustrated in Figure 2. Among
the 396 respondents who completed the questionnaire,
more than half were 50 years or older (51.7%, n=199,
median: 51 years). The overwhelming majority were male
(80.1%, n=314), white (86.2%, n=337), had bachelor’s
degree or higher education (88.2%, n=346), were married
(73.6%, n=287), and lived in the Milwaukee area for more
than 20 years (77.5%, n=303). Demographics of
respondents who completed the questionnaire are
summarized in Table 1.
Fish consumption
Respondents reported fish consumption in terms of
number of meals, with a single meal equaling 6 ounces.
The majority of our respondents (64.4%, n=255) ate less
than one meal (6 ounces) of fish per week, which is lower
than the Wisconsin fish advisory recommended fish
consumption (1-2 fish meals per week) and the EPA/FDA
recommended amount (8-12 ounces per week). Only a
quarter of respondents (25.8%, n=102) reported
consuming one to two meals (6-12 ounces) of fish per
week. About one-tenth of respondents’ (9.9%, n=39) fish
consumption exceeded the Wisconsin fish advisory
recommended amount.
Respondents (n=386) consumed an average of 53.7
fish meals annually (Median=39). In addition, the median
number of store-bought fish meals (n=364, Median=16.0,
Mean=26.1) was higher than that of their sport-caught
fish meals (n=366, Median=12.0, Mean=22.3) and more
than twice that of their shellfish meals (n=287, Median=7,
Mean=10.71).
Multivariable logistic regression was conducted to
examine the association between demographic
characteristics and fish consumption. None of the
demographic characteristics (that is, age, race, education,
years in Milwaukee, and household income) were
24 J. Toxicol. Environ. Health Sci.
Figure 2. Respondents’ inclusion and exclusion flowchart.
Source: Authors
Table 1. Demographics of respondents in the Milwaukee Urban Anglers Study who completed
the questionnaire (n=396).
Variable
N (%)
Age (years)
18-29
45 (11.7)
30-39
75 (19.5)
40-49
66 (17.1)
≥50
199 (51.7)
Unknown
11
Sex
Male
314 (80.1)
Female
78 (19.9)
Unknown
4
Race
White
337 (88.7)
He et al. 25
Table 1. Contd.
Black or African American
32 (8.4)
Asian
10 (2.6)
American Indian or Alaska Native
1 (0.3)
Unknown
11
Hispanic or Latino
Yes
11 (2.9)
No
368 (97.1)
Unknown
4
Household income
Less than $25,000
27 (7.8)
$25,000 to less than $50,000
73 (21.2)
$50,000 to less than $100,000
132 (38.4)
$100,000 or more
112 (32.6)
Unknown
47
Education
High school or less
45 (11.5)
Bachelor, associate, or some college
259 (66.0)
Postgraduate, professional, or doctoral
87 (22.3)
Unknown
4
Employment outside home
Yes
249 (66.0)
No
128 (34.0)
Unknown
13
Marital status
Married or living as married
287(74.4)
Not married
99 (25.6)
Unknown
4
Years lived in the Milwaukee, Wisconsin area
1-20
88 (22.5)
21-40
119 (30.4)
41-60
118 (30.2)
≥61
66 (16.9)
Unknown
5
Living with household members
Living with women of child-bearing age
124 (58.2)
Living with children <15 years
89 (41.8)
Unknown
2
Current smoker
Yes
44 (24.9)
No
133 (75.1)
Unknown
1
Use smokeless tobacco
Yes
17 (4.4)
26 J. Toxicol. Environ. Health Sci.
Table 1. Contd.
No
371 (95.6)
Unknown
1
There were rounding errors, percentages may not add up exactly to 100%.
Source: Authors
Table 2. Number of fish consumption meals by high vs. low mercury concentrations.
Category of fish (high vs. low mercury)
N
Median
Mean
Min
25th quartile
75th quartile
Max
Sport-caught fish
366
12
22.3
1
5.5
24
507
High mercury
352
6
13.7
1
3
15
365
Low mercury
310
6
10.8
0
2
10
142
Store-bought fish
364
16
26.1
1
8
33.5
336
High mercury
248
5
10.0
1
3
10.5
272
Low mercury
352
14
19.8
1
6.3
25
186
Shellfish1
287
7
10.7
1
4
12
200
All fish
386
39
53.7
1
22
64
555
High mercury
369
12
19.8
1
5
21
365
Low mercury2
377
26
35.5
1
14
46
290
1Shellfish are low mercury. 2Low mercury “all fish” include shellfish.
Source: Authors
associated with total fish consumption (≥104 fish meals
per year or ≥2 fish meals per week vs. <104 fish meals
per year or <2 fish meals per week), nor with sport-
caught fish consumption. A log-linear model was also
conducted to examine the association between sport-
caught fish consumption and demographics and did not
identify significant associations.
The fish species that participants reported consuming
was summarized. The sport-caught fish species most
consumed by participants was bluegill (n=232), followed
by chinook or coho salmon (57.1%), walleye (55.1%),
and yellow perch (39.1%). The store-bought species
most consumed by respondents was cod (70.5%),
salmon (58.1%), canned light tuna (52.3%), canned white
tuna (n=41.2%), and tilapia (n=39.1%). The overwhelming
majority of respondents consumed shellfish (n=72.5%).
Fish consumption was also summarized by mercury
concentrations. We categorized sportfish according to the
Wisconsin fish advisory (Wisconsin Department of
Natural Resources, 2020) and categorized store-bought
fish and shellfish according to FDA commercial fish and
shellfish testing results (FDA, 2017). Low-mercury
sportfish include bluegill, crappies, yellow perch,
bullheads, and inland trout, while high-mercury sportfish
include the remaining sportfish species in the
questionnaire. High-mercury store-bought fish include
king mackerel, shark, swordfish, canned white or albacore
tuna, and halibut. Low-mercury store-bought fish include
salmon (including canned salmon), canned light tuna,
tilapia, and cods. Shellfish was categorized as having
low-mercury concentration.
It was found that respondents’ median number of high-
mercury sport-caught fish meals was the same as that of
low-mercury sport-caught fish meals. However, the
median number of their high-mercury store-bought fish
meals was about one-third that of their low-mercury
store-bought fish meals. Because respondents consumed
more store-bought fish than sport-caught fish, and
shellfish was categorized as a low-mercury fish meal,
their total fish meals reflect a greater number of low-
mercury fish meals (Table 2).
Awareness of fish advisories for sport-caught fish
This study found that the majority of respondents were
aware of Wisconsin fish advisories for fish caught in
Wisconsin (72.8%) or Milwaukee fish advisories for fish
caught in the Milwaukee and surrounding waterbodies
(60.1%). However, only one-fifth of the respondents
reported knowing “quite a bit” or “a great deal” about
Wisconsin fish advisories (19.9%) or Milwaukee fish
advisories (24.3%). Fewer than half of the respondents
reported following the Wisconsin fish advisories (27.0%)
or Milwaukee fish advisories (43.0%) very closely (Table
3).
He et al. 27
Table 3. Awareness of Wisconsin and Milwaukee fish advisories.
Awareness
Wisconsin fish advisory
[n (%)]
Milwaukee fish advisory
[n (%)]
Yes
286 (72.8)
235 (60.1)
How much would you say that you know about these guidelines
Nothing/Little
113 (39.5)
95 (40.4)
Some
116 (40.6)
83 (35.3)
Quite a bit/A great deal
57 (19.9)
57 (24.3)
How closely do you follow the advice provided in these guidelines
Not at all/A little bit
90 (31.6)
56 (23.8)
Somewhat
118 (41.4)
77 (32.8)
Very/extremely
77 (27.0)
101 (43.0)
No
93 (23.7)
139 (35.6)
Don’t know
14 (3.6)
17 (4.4)
(1) There were rounding errors and (2) the number of respondents who preferred not to answer was not reported in the table, percentages
may not add up exactly to 100%.
Source: Authors
Licensed anglers’ information source of fish advisories
was the booklet accompanying the fishing license
(68.0%), Wisconsin Department of Natural Resources
website or publications (60.2%), signs at fishing locations
(58.9%), friends or family (52.3%), and mass media
(29.6%). The overwhelming majority of the respondents
considered the information sources easy to understand
and reported willingness to use them when making
decisions about eating fish.
The associations between demographics and
awareness of Wisconsin and Milwaukee fish advisories,
respectively were further examined (Table 4). Logistic
regression results showed that sex, race, and years living
in Milwaukee were statistically significantly associated
with awareness of Wisconsin and Milwaukee fish
advisories. Specifically, compared to men, women were
less likely to be aware of Wisconsin fish advisories (ORadj
=0.3, 95% CI: 0.2, 0.5) or Milwaukee fish advisories
(ORadj =0.4, 95% CI: 0.2, 0.8). Compared to white
respondents, black respondents were about one-third as
likely to be aware of Wisconsin fish advisories (ORadj =0.3,
95% CI: 0.1, 0.7) or Milwaukee fish advisories (ORadj =0.4,
95% CI: 0.1, 0.9). Compared to respondents who lived in
Milwaukee for 1-20 years, respondents who lived in
Milwaukee for 41-60 years were over six times as likely to
be aware of Wisconsin fish advisories (ORadj =6.5, 95%
CI: 2.4, 17.5) and over three times as likely to be aware
of Milwaukee fish advisories (ORadj =3.6, 95% CI: 1.6,
7.8).
It was also surveyed whether to what extent
respondents ate specific parts of fish or prepared meals
using fish parts, because Wisconsin fish advisories
provide steps people can take to reduce their contaminant
intake (Table 5). It is worth noting that although almost all
respondents reported never eating fish head (90.4%) or
guts, organs, or other innards (97.2%), more than half of
the respondents reported eating fish skin (57%).
Regarding the cooking methods, the overwhelming
majority of respondents reported never using fish or fish
parts to make broth/stock, curry, or soup (83.6%) or fish
paste (93.2%). However, most respondents reported
deep-frying fish (78%).
Of particular note, although most respondents reported
being aware of Wisconsin or Milwaukee fish advisories,
only about one-fifth of the respondents reported reducing
fish consumption (21.7%) or eating different types or
species of fish (24.2%) to avoid contaminants. Only
42.9% of the respondents reported avoiding certain parts
of the fish (n=170) or avoiding eating fish from certain
locations (51%). About one-fifth of respondents (20.5%)
reported not performing any protective behaviors to avoid
contaminants in fish.
A series of multivariable logistic regression analyses
were conducted to examine the impact of awareness of
Wisconsin and Milwaukee fish advisories on the adoption
of protective behaviors, adjusted for demographic
characteristics (that is, age, sex, race, income, education,
and years in Milwaukee). The results showed that
respondents were more likely to perform protective
behaviors in general and avoid eating fish head, fat, belly,
and skin if they were aware of Wisconsin or Milwaukee
guidelines. Respondents were more likely to eat different
types of fish or avoid eating fish from certain fishing
locations if they were aware of Milwaukee guidelines.
28 J. Toxicol. Environ. Health Sci.
Table 4. Association between demographics and awareness of Wisconsin (n=379)a and Milwaukee fish advisory guidelines (n=374)b.
Characteristics
Wisconsin fish advisory
Milwaukee fish advisory
Awareness [% (n)] Total n=286
ORadj (95% CI)
Awareness [% (n)] Total n=235
ORadj (95% CI)
Age (years)
18-29
59 (26)
Ref
44 (18)
Ref
30-39
70 (52)
1.9 (0.8, 4.6)
60 (43)
2.2 (0.95, 4.9)
40-49
68 (43)
0.8 (0.3, 2.0)
61 (38)
1.4 (0.6, 3.5)
≥50
84 (160)
2.0 (0.8, 5.1)
70 (134)
2.1 (0.9, 5.0)
Sex*** c
Male
81 (247)
Ref
54 (203)
Ref
Female
53 (39)
0.3 (0.2, 0.5)
9 (32)
0.4 (0.2, 0.8)
Race*
White
79 (258)
Ref
65 (209)
Ref
Black
48 (14)
0.3 (0.1, 0.7)
41 (12)
0.4 (0.1, 0.9)
Other
55 (6)
0.6 (0.1, 2.2)
55 (6)
0.8 (0.2, 3.0)
Income ($)
<25,000
63 (17)
Ref
59 (16)
Ref
25,000-49,999
65 (43)
0.8 (0.3, 2.3)
58 (40)
0.6 (0.2, 1.7)
50,000-99,999
75 (95)
0.8 (0.3, 2.2)
66 (82)
0.7 (0.3, 1.9)
100,000 or more
84 (92)
1.4 (0.4, 4.2)
65 (70)
0.6 (0.2, 1.7)
Education
High school or less
76 (34)
Ref
66 (29)
Ref
Some college
69 (94)
0.7 (0.3, 1.8)
58 (79)
0.8 (0.3, 1.7)
College graduate
81 (91)
1.8 (0.6, 5.1)
70 (77)
1.5 (0.6, 3.7)
Postgraduate
79 (66)
1.3 (0.4, 3.7)
60 (50)
0.9 (0.4, 2.1)
Years in Milwaukee*
1-20 years
68 (57)
Ref
52 (42)
Ref
21-40 years
64 (75)
1.1 (0.6, 2.2)
56 (65)
1.3 (0.7, 2.5)
41-60 years
90 (100)
6.5 (2.4, 17.5)
78 (87)
3.6 (1.6, 7.8)
≥61 years
84 (54)
2.1 (0.7, 6.2)
63 (40)
1.3 (0.6, 3.2)
a17 respondents who did not answer whether they were aware of Wisconsin guidelines were excluded from the analysis. b22 respondents who did not answer whether they
were aware of Milwaukee guidelines were excluded from the analysis. c*** indicates p<0.001 and * indicates p<0.05.
Source: Authors
He et al. 29
Table 5. How often respondents ate specific parts of fish or prepared meals using fish parts.
Variable
Responses
[n]
Never
[n (%)]
Sometimes
[n (%)]
Always
[n (%)]
Don’t know or prefer not to answer [n (%)]
Skin
391
163 (41.7)
189 (48.3)
34 (8.7)
5 (1.3)
Head
386
349 (90.4)
29 (7.5)
4 (1.0)
4 (1.0)
Guts, organs, or other innards
388
377 (97.2)
6 (1.6)
3 (0.8)
2 (0.5)
Belly fat
388
275 (70.9)
94 (24.2)
9 (2.3)
10 (2.6)
Pan fry, grill, roast
364
22 (6.03)
330 (90.7)
12 (3.3)
0
Deep fry
372
82 (22.0)
238 (64.0)
52 (14.0)
0
Boil or poach
357
247 (69.2)
100 (28.0)
6 (1.7)
4 (1.1)
Use fish or fish parts to make broth/stock, curry, or soup
360
301 (83.6)
55 (15.3)
1 (0.3)
3 (0.8)
Use fish to make fish paste
351
327 (93.2)
20 (5.7)
3 (0.9)
1 (0.3)
Source: Authors
Detailed results were summarized in Appendix 2.
Biomonitoring results
The descriptive results of chemical measurement
were summarized in Table 6. The geometric mean
of serum PFOS in our study population was 8.64
(95% CI: 7.98-9.36) ng/mL which was twice of
national estimate for adult population based on
NHANES 2015-2016 [5.02, (95% CI: 4.64-5.43)].
PFDA also had higher geometric mean (0.222,
95% CI: 0.207-0.238) than NHANES (0.160, 95%
CI: 0.144, 0.178). The geometric means of the
remaining PFAS compounds were similar to those
in the NHANES. The geometric mean of blood
mercury [1.4, (95% CI: 1.3-1.6)] was nearly twice
that seen in the NHANES [0.8, (95% CI: 0.7-0.9)],
while the other four blood metals measured were
at similar levels as NHANES. The geometric
mean of the sum of PCB congeners [77.8, (95%
CI: 71.4-84.8)] in serum was higher than that in
the NHANES sample [55.2, (95% CI: 47.1-64.7)],
although it should be noted that our participants
were older than the NHANES adult sample.
Overall, the geometric means of serum PBDE and
most urinary PAHs were similar to those in the
NHANES samples, except for the phenanthrene
metabolites that were higher in this cohort. The
pesticide concentrations in serum in our study
were similar to or lower than those in the
NHANES sample.
DISCUSSION
The overwhelming majority of the Milwaukee
urban anglers in the present study were non-
Hispanic white men with college or higher
educational level. Our study participants ate an
average of 53.7 fish meals (the median number of
39 total fish meals including a median number of
12 sport-caught fish meals) per year. Previous
studies on Wisconsin older male anglers showed
higher fish consumption. For example, a 2011-
2012 cohort of anglers whose average age was
60 years old consumed a median number of 74
fish meals including a median number of 28 sport-
caught fish meals per year (Imm et al., 2013); a
2012-2013 cohort of anglers whose average age
was 61.7 years old consumed a median number
of 66.5 fish meals per year (Christensen et al.,
2016a). As the average age of the study
participants was 49.6, which is about 10 years
younger than the other two samples of Wisconsin
anglers, it is possible that older people consumed
more fish. The present study results showed that
the average fish meals consumed by participants
aged over 50 years was significantly higher than
that consumed by participants aged 50 and
younger. However, it is also worth noting that the
study population was not a random sample and
may not represent the fish consumption behaviors
of all Wisconsin licensed anglers.
Previous studies show that urban anglers with
lower income and less education tend to depend
30 J. Toxicol. Environ. Health Sci.
Table 6. Contaminant concentrations in Milwaukee Urban Anglers Study vs. NHANES aged 20 years and older (2015-2016).
Parameter
This Study (n=389)
NHANES 2015-2016
% below
LOD
Geometric mean
(95% CI)
95th percentile
(95% CI)
% below LOD
Geometric mean
(95% CI)
95th percentile
(95% CI)
Serum PFAS (ng/mL)a1
PFOA
N/A
1.53 (1.46, 1.60)
3.24 (2.82, 3.65)
N/A
1.60 (1.51, 1.71)
4.27 (4.07, 4.97)
PFOS
N/A
8.64 (7.98, 9.36)
40.5 (32.4, 48.6)
N/A
5.02 (4.64, 5.43)
19.1 (15.8, 24.4)
PFDA
15.7
0.222 (0.207, 0.238)
0.942 (0.767, 1.16)
33.9
0.160 (0.144, 0.178)
0.700 (0.500, 1.00)
PFHxS
1.0
1.29 (1.21, 1.38)
4.08 (3.44, 4.73)
1.4
1.22 (1.11, 1.34)
5.00 (4.10, 6.20)
PFNA
1.3
0.632 (0.596, 0.670)
1.96 (1.72, 2.20)
1.3
0.591 (0.546, 0.640)
1.90 (1.60, 2.30)
PFUnDA
49.9
* a2 (<LOD)
0.387 (0.336, 0.437)
62.3
* (<LOD)
0.400 (0.300, 0.500)
MeFOSAA
57.1
* (<LOD)
0.550 (0.326, 0.774)
60.7
* (<LOD)
0.600 (0.500, 0.700)
Blood metals (µg/L or µg/dL)b1-4
Cadmium (µg/L)
3.9
0.245 (0.228, 0.264)
1.25 (0.602, 1.90)
25.6
0.300 (0.280, 0.310)
1.35 (1.22, 1.48)
Manganese (µg/L)
0
9.40 (9.15, 9.66)
15.9 (14.0, 17.7)
0
9.34 (9.11, 9.58)
16.1 (15.6, 16.9)
Lead (µg/dL)
0
1.18 (1.11, 1.25)
3.64 (3.01, 4.26)
0.1
0.920 (0.860, 0.980)
2.89 (2.65, 3.07)
Selenium (µg/L)
0
197 (195, 199)
242 (235, 249)
0
194 (190, 198)
237 (230, 243)
Mercury (µg/L)
2.1
1.44 (1.31, 1.56)
6.94 (5.73, 8.16)
25.5
0.810 (0.740, 0.890)
4.66 (3.91, 5.96)
Serum PCBs (ng/g lipid)c1-2
PCB sum
N/A
77.8 (71.4, 84.8)
3680 (304, 431)
N/A
55.2 (47.1, 64.7)
393.0 (348.0, 454.0)
PCB105
4.9
0.926 (0.851, 1.008)
5.13 (3.94, 6.32)
5.3
0.494 (0.415, 0.86)
4.17 (3.64, 4.86)
PCB114
36.0
0.345 (0.318, 0.373)
1.76 (1.52, 1.99)
41.9
0.257 (0.220, 0.300)
1.75 (1.55, 2.01)
PCB156
2.3
2.00 (1.82, 2.21)
10.0 (8.94, 11.2)
5.3
1.43 (1.18, 1.73)
15.8 (13.6, 18.8)
PCB157
28.5
0.460 (0.4, 0.5)
2.36 (2.14, 2.57)
38.3
0.359 (0.304, 0.425)
2.89 (2.54, 3.37)
PCB167
27.5
0.479 (0.437, 0.524)
3.00 (2.71, 3.28)
31.5
0.361 (0.306, 0.426)
2.71 (2.39, 3.15)
PCB189
46.0
0.263 (0.246, 0.280)
0.978 (0.881, 1.075)
54.0
0.209 (0.187, 0.234)
0.832 (0.764, 0.922)
5 highest PCBs (ng/g lipid)
PCB153
0
15.5 (14.2, 16.9)
81.7 (69.7, 93.6)
0
12.1 (10.3, 14.2)
89.4 (79.0, 104)
PCB180
0
11.4 (10.4, 12.6)
61.6 (53.9, 69.2)
0
7.34 (6.08, 8.87)
76.1 (65.7, 90.4)
PCB138_158
0.8
8.82 (8.04, 9.66)
50.5 (42.1, 58.8)
0
5.88 (4.98, 6.95)
46.5 (40.8, 54.0)
PCB187
0.8
4.18 (3.79, 4.61)
23.5 (19.2, 27.8)
1.5
2.60 (2.17, 3.12)
24.3 (21.1, 28.7)
PCB118
0
4.10 (3.80, 4.43)
21.2 (16.4, 26.0)
0
2.87 (2.46, 3.36)
19.8 (17.6, 22.8)
Serum BFRs (ng/g lipid)d1-d2
PBDE47
4.4
4.95 (4.47, 5.49)
33.7 (22.0, 45.3)
0
5.98 (4.85, 7.39)
81.8 (69.1, 98.4)
He et al. 31
Table 6. Contd.
PBDE99
26.0
0.900 (0.810, 0.990)
6.95 (4.71, 9.18)
0
1.07 (0.848, 1.35)
18.6 (15.4, 22.7)
PBDE153
0
4.50 (4.16, 4.87)
28.9 (12.2, 45.6)
0
5.51 (4.58, 6.63)
54.4 (46.9, 64.0)
PBDE209
32.2
1.38 (1.30, 1.47)
4.69 (3.99, 5.39)
3.8
1.34 (1.17, 1.55)
7.69 (6.87, 8.70)
PBB153
6.7
1.33 (1.20, 1.46)
7.17 (4.68, 9.65)
17.0
0.960 (0.763, 1.21)
16.5 (13.8, 20.2)
Urine PAH metabolites (µg/g creatinine or ng/g creatinine)e1-2
2-FLU (ng/g creatinine)
0.3
2.14 (1.97, 2.32)
17.3 (12.5, 22.2)
0.1
2.06 (2.00, 2.12)
13.4 (12.2, 14.7)
3-FLU (ng/g creatinine)
0.8
0.934 (0.851, 1.02)
8.18 (4.67, 11.7)
2.5
0.925 (0.893, 0.958)
9.13 (8.37, 9.89)
1-NAP (µg/g creatinine)
0.5
16.6 (148, 187)
189 (125, 253)
0.2
16.6 (15.9, 17.3)
190 (172, 209)
2-NAP (µg/g creatinine)
0
54.5 (50.3, 59.1)
253 (186, 319)
0
51.6 (50.2, 53.1)
224 (212, 236)
1-PHE (ng/g creatinine)
0.5
1.40 (1.33, 1.49)
4.00 (3.41, 4.60)
1.2
1.01 (0.99, 1.04)
3.30 (3.07, 3.54)
2-, 3-PHE (ng/g creatinine)
0.3
1.64 (1.54, 1.74)
5.77 (4.89, 6.66)
0.5
1.32 (1.29, 1.35)
4.56 (4.26, 4.86)
1-PYR (ng/g creatinine)
28.5
1.46 (1.37, 1.56)
6.10 (5.07, 7.13)
23.6
1.53 (1.49, 1.56)
5.51 (5.15, 5.88)
Serum Pesticides (ng/g lipid)f1-2
-HCH
55.2
1.10 (1.02, 1.18)
5.91(3.44, 8,38)
26.42
1.80 (1.41. 2.31)
38.2 (31.6, 43.8)
HCB
0.3
8.08 (7.84, 8.33)
13.3 (11.9, 14.8)
0
8.02 (7.53, 8.55)
17.7 (16.8, 18.7)
Mirex
41.8
1.49 (1.39, 1.61)
6.41 (5.05, 7.77)
41.1
1.23 (1.02, 1.49)
13.2 (11.4, 15.7)
Oxychlordane
6.2
4.60 (4.26,4.97)
16.8 (14.6, 19.0)
2.3
5.64 (4.85, 6.55)
36.1 (32.2, 41.4)
p,p’- DDE
0
77.3 (72.2, 82.8)
329 (234, 424)
0
103 (84.4, 126)
1180 (1010, 1420)
p,p’-DDT
24.5
1.57 (1.47, 1.68)
5.72 (3.86 7.58)
5.0
1.702 (1.39, 2.09)
18.7 (16.0. 22.5)
Trans-Nonachlor
1.6
7.04 (6.49, 7.64)
32.3 (27.0, 37.7)
0.8
8.97 (7.65, 10.5)
65.3 (57.7, 75.6)
a1 For those PFAS compounds whose values were below the limit of detection, the limit of detection (LOD)/sqrt(2) was imputed. LOD for PFAS compounds: 0.1 (Reference:
https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/PFAS_I.htm). Geometric mean and 95th percentile for NHANES 2015-2016 aged 20 years and older were cited from CDC, 2021. a2 * Not calculated:
proportion of results below limit of detection was too high to provide a valid result. b1For those whose value was below the limit of detection; the limit of detection (LOD)/sqrt(2) was imputed. LOD
for cadmium: 0.1; lead: 0.07; manganese: 0.99; mercury: 0.28; selenium: 24.48 (Reference: https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/PBCD_I.htm). Geometric mean and 95th percentile for
NHANES 2015-2016 aged 20 years and older were cited from CDC, 2021. b2The number of samples with large clots and was not analyzed: 27. b3The number of samples with micro-clots and was
not analyzed in our study population: n=7. b4We treated all zero values as the values that were below the limit of detection (i.e., limit of detection/square(2)). c1For those whose PCBs values were
below the limit of detection; the limit of detection (LOD)/sqrt(2) was imputed. LOD for PCB28:0.61; PCB66: 0.46; PCB74: 0.44; PCB99, PCB114, PCB138, PCB146, PCB153, PCB156, PCB157,
PCB167, PCB170, PCB178, PCB180, PCB183, PCB187, PCB189, PCB194, PCB196/203, PCB199, and PCB209: 0.18; PCB105: 0.21; PCB118: 0.27; PCB206: 0.36. c2 # of non-reportable due to
interference or co-elution for PCBs: PCB15: n=2; PCB66: n=1; PCB74: n=24; PCB99: 1; PCB105: n=2; PCB114: n=2; PCB118: n=2; PCB146: n=4; PCB153: n=1; PCB157: n=1; PCB167: n=4;
PCB170: n=2; PCB178: n=8; PCB183: n=10; PCB187: n=8; PCB189: n=2; PCB194: n=1; PCB199: n=6; PCB206: n=2; PCB138_158: n=6; PCB196_203: n=8. d1For those whose BFRs values
were below the limit of detection, the limit of detection (LOD)/sqrt(2) was imputed. LOD for BFRs: PBDE47: 1.3; PBDE153: 1.0; PBB153: 1.0; PBDE209: 3.5; PBDE99: 1.1 # of non-reportable due
to interference or co-elution for BRFs: PBDE47: n=2; PBDE99: n=2; PBDE153:n=2; PBB153:n=2; PBDE209: n=9. d2 # of non-reportable due to interference or co-elution for PAHs: 2-FLU: n=1; 3-
FLU: n=6; 1-NAP: n=11; 2-NAP: n=1. e1PAHs. For those whose PAHs values were below the limit of detection, the limit of detection (LOD)/sqrt(2) was imputed. LOD for NAP_1: 60; 2-NAP: 90; 3-
FLU: 8; 2-FLU: 8; 1-PHE:9; 2-, 3-PHE:10; 1-PYR: 70 (Reference: https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/PAH_I.htm). Geometric mean and 95th percentile for NHANES 2015-2016 aged 20
years and older were cited from CDC, 2021. e2Non-reportable due to interference or co-elution: n=13 . f1For those whose pesticides values were below the limit of detection; the limit of detection
(LOD)/sqrt(2) was imputed. LOD for pesticides: 0.92. f2Non-reportable due to interference or co-elution: -HCH: n=3; p,p’-DDT: n=1; Trans-Nonachlor: n=1; Oxychlordane: n=3.
Source: Authors
32 J. Toxicol. Environ. Health Sci.
on sport-caught fish for food and nutritional sources
(Silver et al., 2007; Stevens et al., 2018). However, in the
present study, only about 10% of the anglers’ fish
consumption exceeded 12 ounces per week (the upper
limit of EPA/FDA recommended fish consumption amount)
and about two-thirds of the anglers ate fewer than one
fish meal (6 ounces) per week, which is lower than the
EPA/FDA lower limit of recommended fish consumption
amount (8 ounces). This is similar to results from
previous studies that found 78%-87% of the Great Lakes
region participants ate fewer fish than the EPA/FDA
recommended amount (Connelly et al., 2012, 2019).
Although eating fewer fish may reduce exposure to
contaminants in fish, it also reduces intake of nutritional
sources in fish (e.g., omega-3 fatty acids). Previous
research on awareness of risks and benefits of fish
consumption found that women were more likely to
perceive fish as a healthy food that reduces the risk for
coronary heart disease (Verbeke et al., 2005). The
findings highlight the need for a clear messaging to
educate anglers on how to balance the risks and benefits
of fish consumption (Sherer et al., 2008; Engelberth et al.,
2013).
Most participants in the present study reported being
aware of Wisconsin or Milwaukee fish advisories.
Encouragingly, it was found that awareness of Wisconsin
or Milwaukee fish advisories were associated with some
protective health behaviors such as eating different types
of fish, avoiding eating fish from certain contaminated
locations, avoiding eating fish parts (e.g., head, fat, belly,
and skin) that tend to accumulate contaminants. However,
only about one-fifth of the participants reported knowing
“quite a bit” or “a great deal” about these advisories.
Moreover, we found that being aware of fish advisories
did not necessarily lead to a reduction in sport-fish
consumption, which was consistent with previous findings
among other residents in the Great Lakes basin
(Krabbenhoft et al., 2019). Participants reported eating
the same median number of high-mercury sport-caught
fish as that of low-mercury sport-caught fish. The
biomonitoring results corroborated this finding by showing
that participants’ blood mercury concentration was more
than two times that reported in the NHANES.
Comparable elevations in blood mercury concentrations
were observed among licensed anglers who participated
a biomonitoring project in New York state (Hsu et al.,
2022). The implications of these findings are two-fold.
First, anglers may not have adequate knowledge about
safe fish consumption. Second, even when anglers had
adequate knowledge, they may not necessarily act upon
their knowledge, as a previous study showing that
people’s perceived risk of being exposed to mercury did
not significantly impact their fish consumption amount
(Birch and Lawley, 2012; Verbeke et al., 2005).
Given the frequent consumption of high mercury-
containing caught fish, it is plausible that the present
study participants did not have adequate knowledge of
how to choose fish with low mercury, even with high
awareness of fish advisories. A study of New York Bight
anglers showed that people lack knowledge of which fish
are high in PCBs or mercury and therefore cannot make
informed decisions when eating the fish they catch
(Burger and Gochfeld, 2009). The choice of low-mercury
fish species is particularly important because the
contaminant cannot be removed by cooking. Field
experiment results showed that anglers often imperfectly
recalled which were the most highly contaminated fish
and thus failed to avoid these (Roosen et al., 2009;
Verger et al., 2007). A clear and simple fish guide is
needed for respondents to make healthy choices.
In addition, the study participants did not perform
recommended fish cleaning and cooking practices. Of the
participants, 71% reported that they “never” trimmed fish
belly fat and 42% “never” removed fish skin when they
cooked. Only 2% “always” trimmed fish belly fat and 9%
of them “always” removed fish skin. These findings
indicated that the lack of knowledge about safe cooking
practices persisted over time among Wisconsin anglers
as reported in studies conducted in the past twenty years
(Anderson et al., 2004; Christensen et al., 2016a; Gliori et
al., 2006), highlighting the need to enhance anglers’
knowledge about safe fish cleaning and cooking practices.
Besides mercury, PFOS concentration among the
study participants was twice the concentration in the
NHANES. Currently, there is still limited advisory
information regarding this emerging contaminant in
Wisconsin. Since cooking is not effective in removing
PFAS from fish in a consistent way (Bhavsar et al., 2014;
Taylor et al., 2019), choices of fishing locations and fish
species/sizes are particularly important. In addition, we
found that the concentrations of persistent pesticides in
the present study population were lower than or similar to
those in the NHANES sample; similarly low pesticide
concentrations were also observed in a biomonitoring
study of Michigan shoreline anglers (Wattigney et al.,
2019). Plausibly, this is due to the steady decline of
persistent pesticides in Great Lakes fish (Zhou et al.,
2018).
In the present study, it was observed that Milwaukee
anglers consumed fewer fish than the amount
recommended by EPA/FDA, which means that they had
a decreased intake of nutrients from fish; however, at the
same time, the study participants had higher levels of
PFOS, PCBs, and mercury compared to the U.S.
population. There is a clear need to develop health
messaging that could help people avoid contaminated
fish while still benefiting from nutrients in fish. Well-
defined communication is needed to educate target
populations on risks and benefits of fish consumption and
safe fish-eating practices (Frewer et al., 2016). Messaging
must incorporate continually evolving scientific
understanding of the health risks associated with
contaminants, particularly PFAS (Christensen et al.,
2016b; National Academies of Sciences, Engineering,
and Medicine, 2022). Further efforts (e.g., focus groups)
will be needed to understand anglers’ risk/benefits
perceptions regarding safe fish consumption and
consider these when developing actionable and concrete
recommendations.
One limitation of the current study was that the study
sample was not representative of the urban anglers living
near the Milwaukee impacted waterways. Although the
study design originally planned for a statistically
representative sampling of licensed anglers based on
random selection of fish license registrants, the study had
to adapt the recruitment strategy after a significantly
lower than expected response rate over a prolonged
period. This change in recruitment method limits
generalizability of the results, as it may have biased our
sample toward certain demographics and resulted in
underrepresentation by persons identifying as female.
Another limitation was that the study was cross-sectional,
and we were not able to determine the temporality of
being aware of fish advisories and adopting safe fish
consumption behaviors or the temporality of consuming
fish and accumulating contaminants in the body. Third,
the higher levels of PCBs observed in the study
population may be due to the fact that the study
population was older than the NHANES population, and
the higher PCBs concentrations may reflect our
participants’ past exposures to PCBs from sportfish or
other sources over the years. Further analysis needs to
be conducted to examine whether Milwaukee licensed
anglers consumed more PCBs-contaminated fish and
whether this consumption contributes to higher PCBs
concentrations compared to other U.S. adults with the
same age distribution. Finally, recall bias around
participants recalling their fish consumption and social
desirability (when participants reported being aware of
fish advisories when they were not) may affect the
accuracy of the self-reported results on fish consumption
and awareness.
Conclusion
Most urban anglers in the Milwaukee area were aware of
Wisconsin and Milwaukee advisories in this study, and
their awareness was associated with protective behaviors,
such as avoidance of eating particular parts of the fish,
eating different type of fish, or avoidance of eating fish
from certain areas. However, the anglers in this study did
not necessarily reduce their sport-caught fish
consumption, especially the high-mercury containing
species, to avoid exposure to contaminants in the
Milwaukee waterways. Biomonitoring results show that
the Milwaukee urban anglers had higher body burden
levels of mercury, PCBs, and PFOS, and lower persistent
He et al. 33
pesticide levels than the U.S. population. More effective
health education strategies need to be developed to
increase anglers’ knowledge about the risks and benefits
of fish consumption and promote safe fish consumption
behaviors.
CONFLICT OF INTERESTS
The authors have not declared any conflicts of interest.
ACKNOWLEDGEMENT
This study was supported by a cooperative agreement
with Agency for Toxic Substances and Disease Registry
[Grant number: NU61TS000269].
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