Food Quality and Preference 16 (2005) 401–412
www.elsevier.com/locate/foodqual
Awareness of foodborne pathogens among US consumers
Chung-Tung Jordan Lin
a
a,1
, Kimberly L. Jensen
b,2
, Steven T. Yen
b,*
Division of Market Studies, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD 20740, United States
b
Department of Agricultural Economics, University of Tennessee, Knoxville, TN 37996-4518, United States
Received 5 May 2004; received in revised form 25 June 2004; accepted 14 July 2004
Available online 11 September 2004
Abstract
Each year in the United States, microbial pathogens cause millions of cases of foodborne disease and result in many hospitalizations and deaths. Effective consumer education programs to promote safer food handling practices and other averting behaviors
may benefit from consumer awareness of microbial pathogens. This paper investigates US consumers awareness of four major
microbial pathogens (Salmonella, Campylobacter, Listeria and Escherichia coli) as food safety problems, using a multivariate probit
model. The awareness varies among pathogens and the variations appear to be related to differences in the number and severity of
illnesses associated with these pathogens. Our findings suggest that awareness of microbial pathogens is associated with food safety
perceptions, awareness of potentially risky foods and substances associated with potential food safety hazards, food safety related
behaviors, and demographics. There are differentiated effects of variables on awareness of the four pathogens.
2004 Elsevier Ltd. All rights reserved.
Keywords: Consumer awareness; Foodborne pathogens; Multivariate probit
1. Introduction
Each year, microbial pathogens cause as many as 76
million cases of foodborne illness, 324,000 hospitalizations, and 5200 deaths in the United States (Mead
et al., 1999). The more common pathogens associated
with foodborne illness include Salmonella, Campylobacter jejuni, and Escherichia coli O157:H7 (Table 1).
Some victims of E. coli O157:H7 caused illness, particularly the very young, have developed hemolytic uremic
syndrome (HUS), characterized by renal failure and
hemolytic anemia which can lead to permanent loss of
kidney function (FDA-CFSAN, 2003a). Foodborne
illness associated with Listeria monocytogenes, though
*
Corresponding author. Tel.: +1 865 974 7474; fax: +1 865 974
4829.
E-mail addresses: [email protected] (C.-T.J. Lin),
[email protected] (K.L. Jensen), [email protected] (S.T. Yen).
1
Tel.: +1 301 436 1831; fax: +1 301 436 2626.
2
Tel.: +1 865 974 3716; fax: +1 865 974 4829.
0950-3293/$ - see front matter 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.foodqual.2004.07.001
relatively low in number (Table 1), is associated with
many deaths (CAST, 1994; FDA-CFSAN, 2003a). Also,
listeriosis in pregnant women can result in miscarriage,
fetal death, and severe illness or death of a newborn infant (FDA-CFSAN, 2003d). Annual costs of foodborne
illness in the United States have been estimated between
$10 and $83 billion (FDA-CFSAN, 2003c). The US
Department of Agricultures Economic Research Service
estimates that the costs associated with five major pathogens alone (Escherichia coli O157:H7, other Shiga toxin-producing E. coli (STECs), Campylobacter, Listeria
monocytogenes, and Salmonella) amount to at least
$6.9 billion annually (USDA-ERS, 2000).
While practices of food suppliers and food service
establishments play an important role in reducing foodborne illness (CAST, 1994), existing research suggests
that a substantial proportion of foodborne illness is
attributable to improper in-home food handling, preparation, and consumption practices by consumers
(CAST, 1994; Redmond & Griffith, 2003). Improper
402
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
Table 1
Four major foodborne pathogens: incidence, associated foods, and symptoms
Bacterial organism
US cases annually
Associated foods
Symptoms of illness
Salmonella
2–4 million
Raw meats, poultry, eggs, milk
and dairy products, fish, shrimp,
frog legs, yeast, coconut, sauces
and salad dressing, cake mixes,
cream-filled desserts and toppings,
dried gelatin, peanut butter,
cocoa, and chocolate
Nausea, vomiting, abdominal cramps, diarrhea,
fever, and headache. Chronic
consequences—arthritic symptoms may
follow 3–4 weeks after onset of acute symptoms
Campylobacter jejuni
At least 2–4 million
Raw chicken, raw milk,
non-chlorinated water
Diarrhea, fever, abdominal pain, nausea,
headache and muscle pain. The illness usually
occurs 2–5 days after ingestion. Illness generally
lasts 7–10 days, but relapses are not uncommon
(about 25% of cases)
Escherichia coli O157:H7
20,000 and 40,000
Undercooked or raw
hamburger (ground beef),
alfalfa sprouts, unpasteurized
fruit juices, dry-cured salami,
lettuce, game meat,
cheese curds, raw milk
Severe cramping (abdominal pain) and diarrhea
that becomes grossly bloody. Occasionally vomiting
occurs. Fever is either low-grade or absent
Lasts for an average of 8 days
Listeria monocytogenes
1600 cases
(Foods with high or moderate risk)
Unpasteurized and pasteurized
fluid milk, deli meats, frankfurters
(not reheated), cheeses
(particularly soft-ripened varieties),
high-fat and other dairy products,
pâté and meat spreads, smoked seafood,
cooked and ready-to-eat crustaceans
Septicemia, meningitis (or meningo-encephalitis),
encephalitis, spontaneous abortion or stillbirth,
fever, nausea, vomiting, and diarrhea
Source: FDA-CFSAN (2003a,b,c).
practices include, but are not limited to, inadequate
cooking, inadequate cooling and storage of foods,
cross-contamination of raw and cooked foods, inadequate personal hygiene such as hand washing, and
consumption of raw, undercooked, or unsafe
foods (CAST, 1994; Doyle et al., 2000; Medeiros, Hillers, Kendall, & Mason, 2001; Redmond & Griffith,
2003). Thus, consumer food handling and preparation
behaviors are important means to reduce foodborne
illness.
Awareness of foodborne pathogens may play a positive role in reducing foodborne illness. McIntosh, Christensen, and Acuff (1994) reported that a higher number
of five bacteria that Texan consumers had heard of were
associated with (1) awareness of dangers related to the
degree of doneness in cooking hamburger patties, and
(2) preference toward hamburger patties prepared more
than less done. The five pathogens asked were Salmonella, Campylobacter, E. coli, Listeria, and Clostridium
perfringens. Similarly, US adult residents who had heard
of Salmonella as a problem in food and volunteered a
probable food vehicle related to the pathogen were more
likely than others to know that ‘‘cooking meat until
well-done reduces the risk of food poisoning’’ (Altekruse, Street, Fein, & Levy, 1996). In addition, Altekruse
et al. (1996) found that those who had heard of Salmonella and volunteered a probable food vehicle related to
Salmonella were more likely than others to (1) wash
hands after handling raw meat, (2) wash or change cutting board after cutting raw meat or poultry, (3) think
washing hands reduces risk of food poisoning, (4) think
serving steak on a plate that held raw steaks increases
risk of food poisoning, and (5) think cooking meat
‘‘well-done’’ decreases food poisoning. Nevertheless,
the hamburgers served were no more or less ‘‘done’’ in
either groups homes. Hence, these studies suggest that
awareness of foodborne pathogens goes hand in hand
with better knowledge of safe food handling and preparation principles and safer food handling and preparation practices; both should ultimately contribute to a
reduction in foodborne illness.
Consumer education programs are often used to promote safer food handling and preparation practices, and
increasing the level of awareness of foodborne pathogens appears to be helpful in enhancing the outcomes
of consumer education. In particular, consumer education programs may target individuals who are less likely
to be aware of foodborne pathogens as a food safety
problem, and thus may practice less safe food handling
and preparation behaviors. This in turn requires an
understanding of which consumers are aware of pathogens and what factors, such as, food safety perceptions,
attitudes and information, and demographic characteristics, are associated with their awareness.
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
This study built on existing research and investigated
consumer awareness of four major foodborne pathogens, Salmonella, Campylobacter, Listeria, and E. coli.
By using data collected in a 2001 national telephone survey of US adults, the study examined the relationships
between the awareness and its explanatory variables
for each pathogen simultaneously and the differential
relationships among the four different pathogens. Two
major features of this study distinguish it from the literature. First, we included two categories of explanatory
variables to gain a better understanding of the awareness. One category of explanatory variables reflect consumers perceptions related to food safety, such as
whether food safety problems are most likely to occur
at homes or not and how serious of a food safety problem is contamination of food by micro-organisms. The
other category of explanatory variables represent consumers awareness of potentially risky substances in
food such as mercury and potentially risky foods such
as sprouts. We hypothesized that consumers with higher
risk perceptions or awareness of foods and substances
associated with food safety problems would pay more
attention to food safety information and therefore more
likely to be aware of foodborne pathogens. The second
distinguishing feature of this study is that we used an
econometric technique to simultaneously examine the
relationships between each of the pathogens and a common set of explanatory variables. If there are unobserved and unmeasured common factors underlying
the different awareness, then the univariate (single-equation) technique as used in previous research would be
more prone to biases caused by the common factors.
Our approach explicitly recognizes and controls for
the potential correlation among awareness and thus
can offer more accurate estimates of relationships between awareness and its explanatory variables.
403
in the household, the most-recent-birthday method was
used to select a respondent. Interviews began on April
30, 2001 and ended on August 28, 2001. A total of
4482 adults were successfully interviewed, yielding a response rate of 46.5% (per the Response Rate 5 defined
by the American Association for Public Opinion Research (AAPOR, 2004)).
We created a sample of 2992 observations, consisting
of the respondents who provided usable responses to all
survey questions included in the analysis (observations
with responses like ‘‘dont know’’ or ‘‘refuse’’ were removed from the sample). The data were weighted to adjust for probability of selection (number of residential
telephone numbers and number of adults in the household) and to adjust the sample distributions of race, education, and gender to that of the 2001 Current
Population Survey (US Census, 2001). In the current
analysis, both descriptive statistics and regression results
were based on weighted data.
As shown in Table 2, 12% of the sample are Black,
9% are Hispanic, 4% are of other races, 22% are in the
18–29-year age group, 44% are in the 30–49-year age
group, 18% have at least one child under 5 years of
age in the household, 52% are female, 52% have had
at least some college education, 18% reside in the Northeast region, 24% in the Midwest region, and 18% in the
West region. In comparison, the 2001 Current Population Survey (CPS) estimated the distributions among
US adults were 52% female, 12% Black, 11% Hispanic,
and 51% with at least some college education. The
CPS estimated that, among all US households, 18%
were in the Northeast region, 23% in the Midwest region, and 22% in the West region (US Census, 2001).
Therefore, our sample closely resembles the US population in these characteristics.
2.2. Questionnaire
2. Sample and methods
2.1. Sample
We used data from the 2001 Food Safety Survey
(FSS) sponsored jointly by the US Food and Drug
Administration (FDA) and the US Department of
Agriculture (USDA) and conducted by a private
contractor (FDA-CFSAN, 2002). The FSS is a
random-digit-dialing survey using the ComputerAssisted-Telephone-Interviewing (CATI) technique on
a nationally representative sample of American consumers. The sample is based on a single-stage sample of telephone numbers, listed and unlisted, generated from the
GENESYS Sampling System (Marketing Systems
Group, 2004). Eligible respondents were adults (18 years
of age or older) in the 48 contiguous states and the District of Columbia. When there were more than one adult
The questionnaire covered awareness of pathogens as
problems in food, food safety perceptions, food handling and consumption practices, perceived vulnerability
from unsafe food handling and consumption practices,
awareness and consumption of potentially risky foods,
awareness of new food processing technologies, food
allergies, foodborne illness experience, and demographics. In this study, we focused on the analysis of awareness of four different pathogens (Salmonella,
Campylobacter, Listeria, and E. coli). Specifically, the
survey asked ‘‘Have you ever heard of (a pathogen) as
a problem in food?’’ The possible responses were
‘‘yes’’ and ‘‘no’’.
2.3. Methods
Our statistical model postulated that pathogen awareness is related to food safety perceptions, awareness of
404
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
Table 2
Variable definitions and weighted sample means
Variable
Definition and code
Mean
Dependent variables
AWARE_SAL
AWARE_CAM
AWARE_LIS
AWARE_ECO
1 = ever
1 = ever
1 = ever
1 = ever
0.94
0.07
0.32
0.90
heard
heard
heard
heard
of
of
of
of
Salmonella as a problem in food, 0 = otherwise
Campylobacter as a problem in food, 0 = otherwise
Listeria as a problem in food, 0 = otherwise
E. coli as a problem in food, 0 = otherwise
Explanatory variables—risk perceptions
FROMHOME
1 = homes are where food safety problems are most likely to occur; 0 = otherwise
HOMERISK
How common it is for people in the US to become sick because of the way food is handled
or prepared in their homes; 1 = very common or somewhat common, 0 = not very common
GERMRISK
How serious of a food safety problem is contamination of food by micro-organisms; 1 = very
serious or serious, 0 = somewhat a problem or not a problem at all
VULNERABLE
Average of perceived likelihood of getting sick from unsafe food handling practices ((a)
forgetting to wash hands before beginning cooking, (b) letting vegetables that will be eaten
raw touch raw meat or chicken, (c) eating meat or chicken not thoroughly cooked, and
(d) leaving cooked food out of the refrigerator for more than 2 h after it has finished cooking);
each likelihood is measured on a 1 (not at all likely) to 5 (very likely) scale; recoded as 1
if average = 3 or higher, and 0 otherwise
Food safety practices
WASHHANDS
HAMBURGER
STOPBUY
Frequency of washing hands with soap before preparing food, 1 = all of the time, 0 = otherwise
1 = hamburgers are usually served well-done or medium (brown all the way through) at home;
0 = served medium (still have some pink in the middle) or rare
1 = has stopped buying specific kinds of food that the respondent is especially worried about
because of their safety, 0 = otherwise
Awareness of other food safety issues
SPROUTS
1 = has heard or read about possible health problems related to eating sprouts,
such as alfalfa or bean sprouts, 0 = otherwise
JUICES
1 = has heard or read about possible health problems related to drinking juice
that has not been pasteurized or unpasteurized juice, 0 = otherwise
MERCURY
1 = has heard or read about possible health problems related to mercury
as a problem in some fish, 0 = otherwise
Foodborne illness and other diseases
ILLNESS
1 = respondent or someone in the household has had some kind of sickness that the respondent
thought might be caused by eating spoiled or unsafe food (except food allergies), 0 = otherwise
HEALTH
1 = has one or more of the following health conditions: liver disease, diabetes, reduced gastric acidity, HIV,
AIDS, a weakened immune system, or currently receiving chemotherapy or radiation therapy, 0 = otherwise
0.17
0.62
0.54
0.67
0.76
0.83
0.23
0.15
0.29
0.69
0.30
0.12
Meal preparation
MEALPREP
1 = prepares the main meal in respondents household all or nearly all of the time, 0 = otherwise
0.55
Demographics
INCOME2
INCOME3
INCOME4
HHSIZE2
HHSIZE3
HHSIZE4
CHILD5
FEMALE
COLLEGE
BLACK
HISPANIC
OTHER RACE
AGE1829
AGE3049
MIDWEST
WEST
NORTHEAST
Household income is $20,001–$50,000 (reference = $20,000 and under)
Household income is $50,001–$100,000
Household income is more than $100,000
Household size is two persons (reference = household with one person)
Household size is 3–4 persons
Household size is more than 4 persons
1 = if children under age 5 in household, 0 = other
1 = female, 0 = male
1 = at least some college education, 0 = other
1 = black race, 0 = otherwise (reference = White)
1 = Hispanic origin, 0 = otherwise
1 = Other race, 0 = otherwise
1 = age 18–29, 0 = other (reference = 50 and older)
1 = age 30–49, 0 = other
1 = Midwest region, 0 = other (reference = south region)
1 = West region, 0 = other
1 = Northeast region, 0 = other
0.42
0.32
0.10
0.31
0.41
0.19
0.18
0.52
0.52
0.12
0.09
0.04
0.22
0.44
0.24
0.18
0.18
Source: FDA Food Safety Survey, 2001.
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
potentially risky foods and substances associated with
potential food safety hazards, food safety related behaviors and experience, and demographics.
2.3.1. Food safety perceptions
We hypothesized that consumers who perceive higher
risk of foodborne illness are more likely to be aware of
the pathogens because they may pay more attention to
food safety information and be more motivated to learn
about food safety, such as the causes of foodborne illness. Answers to the following survey questions were
used to construct four binary explanatory variables to
represent food safety perceptions. First, ‘‘Where do
you think food safety problems are most likely to occur,
would you say farms, food processing plants, warehouses, supermarkets, restaurants, or homes’’ (FROMHOME). Second, ‘‘How common do you think it is
for people in the United States to become sick because
of the way food is handled or prepared in their homes’’
(HOMERISK). Third, ‘‘Do you think contamination of
food by micro-organisms, such as germs, is a very serious food safety problem, a serious food safety problem,
somewhat of a problem, or not a food safety problem at
all’’ (GERMRISK). The fourth variable, VULNERABLE, is a binary variable constructed from responses
to four questions: (1) ‘‘If you forget to wash your hands
before you begin cooking, how likely are you to get
sick’’, (2) ‘‘If vegetables you will eat raw happen to
touch raw meat or chicken, how likely are you to get
sick’’, (3) ‘‘If you eat meat or chicken that is not thoroughly cooked, how likely are you to get sick’’, and
(4) ‘‘If you happen to leave cooked food out of the
refrigerator for more than 2 hours after it has finished
cooking, how likely are you to get sick’’ (see Table 2
for variable codes).
2.3.2. Awareness of potentially-risky foods and substances
We hypothesized that consumers who are aware of
potentially risky foods and substances are also more
likely to be aware of the pathogens. Again, this relationship is expected because these consumers may pay more
attention to food safety information and are more
motivated to learn about food safety. The association
between pathogen awareness and awareness of high
risk foods may also arise because when consumers hear
or read about certain potentially risky foods, they are
likely to hear or read about the source of the risk, i.e.,
the pathogens. The survey asked respondents whether
they had heard or read about any possible health
problems related to ‘‘eating sprouts (such as alfalfa or
bean sprouts)’’ (SPROUTS), ‘‘drinking juice that has
not been pasteurized, that is, unpasteurized juice’’
(JUICES), and ‘‘mercury as a problem in some fish’’
(MERCURY). In the late 1990s and early 2000s, raw
sprouts and unpasteurized juices were implicated in a
number of foodborne illness outbreaks in the United
405
States. For instance, from 1996 to 2000, four major outbreaks were related to unpasteurized juices contaminated with Salmonella or E. coli O157:H7 (USDHHS,
2001). A Salmonella-related outbreak in four Western
states in early 2001 was related to sprouts (CDC,
2002). Mercury occurs naturally in the environment
and can also be released into the air through industrial
pollution; the pollutant falls from the air and can accumulate and is turned into methylmercury in the water.
Major foodborne illness outbreaks receive a lot of
media attention and news stories often mention the
pathogens implicated (see, e.g., Ollinger-Synder & Matthews, 1994). Food safety authorities, such as the FDA
and the USDA, also issue consumer advisories and food
recall notices that often mention the specific pathogen(s)
related to a risk (FDA-CFSAN, 1998; USDHHS, 1999).
Hence, consumers who have heard or read of the high
risk foods or substances are hypothesized to have higher
likelihood of having heard of foodborne pathogens.
2.3.3. Other explanatory variables
Previous studies have suggested that awareness of
foodborne pathogens is related to better knowledge of
safe food handling and preparation principles and safer
food handling and preparation practices (Altekruse
et al., 1996; McIntosh et al., 1994). Based on these findings, we hypothesized that pathogen awareness is higher
among consumers who have safer food handling and
preparation practices. Two questions in the survey were
used. First, ‘‘Before you begin preparing food, how
often do you wash your hands with soap? Would you
say all of the time, most of the time, some of the time,
or rarely’’ (WASHHANDS). Second, ‘‘In your home,
are hamburgers usually served rare, medium, or welldone’’ (HAMBURGER). Those who said ‘‘medium’’
were further asked whether they meant ‘‘brown all the
way through’’ or ‘‘still have some pink in the middle’’.
We also hypothesized that consumers are more likely
to have heard of the pathogens if they have stopped buying specific kinds of food due to safety concern (STOPBUY), think they themselves or someone in the
household have had suspected foodborne illness (ILLNESS), have one or more health conditions that may
weaken their immunity (HEALTH), are the primary
meal preparers in the household (MEALPREP), are
older, are female (FEMALE), or reside in a household
with young children (CHILD5). These consumers would
have greater awareness of pathogens because they may
pay more attention to food safety.
Finally, we included in the model several demographic characteristics, i.e., race/ethnicity, household
size, education, income, and geographic region. In
particular, we hypothesized that more educated consumers and consumers who come from higher income households have greater access to food safety information and
greater capability to use the information and therefore
406
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
would likely be more aware of the pathogens. We also
hypothesized that awareness of pathogens differs among
geographic regions. Two possible causes of the difference are: (1) the number of outbreaks differ among geographic regions (CDC, 2000b) and (2) the number of
cases of some highly publicized outbreaks also differ in
various parts of the country. For example, cases in the
1993 E. coli O157:H7 outbreak associated with undercooked hamburgers concentrated in four Western US
states (CDC, 1993).
density /(e1, e2, . . ., en; R). The likelihood contribution
for an observation is the n-variate standard normal
probability
Prðy 1 ; . . . ; y n j xÞ
Z ð2y 1 1Þx0 b1 Z
¼
1
Z
ð2y 2 1Þx0 b2
1
ð2y n 1Þx0 bn
/ðe1 ; e2 ; . . . ; en ; Z 0 RZÞden de2 de1 ;
1
ð2Þ
2.4. Statistical model
We used descriptive analysis to characterize the sample (i.e., means). We applied a weighting factor to the
data for the descriptive analysis, using the weighting
procedure described above. The SAS PROC MEANS
procedure was used for the analysis (SAS Institute
Inc., 2001).
To analyze the relationships between awareness of
the four pathogens and the explanatory variables, we
used a multivariate probit econometric technique. Chisquare analysis has been used to investigate awareness
of a single pathogen (Herrmann & Warland, 2000).
McIntosh et al. (1994) applied the ordinary least-squares
econometric technique with the dependent variable defined as the sum of ones (‘‘heard of’’) and zeros (‘‘not
heard of’’) for five pathogens (Salmonella, Campylobacter, E. coli, Listeria, and Clostridium perfringens). Another approach would be to model each pathogen
individually, i.e., using a univariate technique such as
probit analysis for discrete dependent variables. Univariate techniques, however, ignore the potential correlation among the unobserved disturbances in the
awareness, and thus may compromise statistical efficiency. If there are unobserved and unmeasured common factors underlying the different awareness, then
the univariate technique, as used in previous research,
would be more prone to biases caused by neglecting
common factors.
To overcome the shortcoming in univariate techniques, we adopted a multivariate probit econometric
technique in this study. Drawing upon the statistics literature (Ashford & Sowden, 1970), the multivariate probit
econometric model is characterized by a set of n binary
dependent variables yi (with observation subscripts suppressed) such that
yi ¼ 1
¼0
if x0 bi þ ei > 0;
if x0 bi þ ei 6 0; i ¼ 1; 2; . . . ; n;
ð1Þ
where x is a vector of explanatory variables, b1, b2, . . ., bn
are conformable parameter vectors, and random error
terms e1, e2, . . ., en are distributed as multivariate normal
distribution with zero means, unitary variance and an
n · n contemporaneous correlation matrix R = [qij], with
where Z = diag[2y11, . . ., 2yn 1]. Maximum-likelihood estimation was carried out by maximizing the sample likelihood function, which is the product of
probabilities (2) across sample observations. To quantify the marginal effects of explanatory variables, the
awareness probability for each pathogen
Prðy i ¼ 1Þ ¼ Uðx0 bi Þ;
i ¼ 1; 2; . . . ; n;
ð3Þ
can be differentiated, where U(Æ) is the univariate standard normal cumulative distribution function. Because all
explanatory variables are binary, the effects of each variable on the awareness probabilities (3) were calculated
by simulating a finite change in the variable (i.e., from
0 to 1) while holding all other variables at the sample
means.
We estimated the multivariate probit model by programming the log-likelihood function in GAUSS (Aptech, 1997). Numerical maximization of the loglikelihood was accomplished with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm (Luenberger,
1984). The current application to four pathogens involved a four-variate (one for each pathogen) standard
normal probability for each observation, which was
evaluated with a simulation technique known as the
GHK probability simulator (Hajivassiliou, 1994). The
asymptotic standard errors of the maximum-likelihood
estimates were calculated by inverting the outer products of the numerical gradients (Berndt, Hall, Hall, &
Hausman, 1974).
Some of the behavioral variables may be endogenous,
causing simultaneous-equation bias in the statistical estimates. Potential endogeneity of WASHHANDS, HAMBURGER and STOPBUY was investigated by
estimating a simultaneous-equation probit model for
each of these pathogens, one at a time, in which significance of the error correlation would suggest presence
of endogeneity (Rivers & Vuong, 1988). The hypothesis
of exogeneity cannot be rejected for STOPBUY in any
equation. However, there is some evidence of endogeneity for WASHHANDS in the Campylobacter (p-value < 0.01) and Listeria (p-value < 0.01) equations, and
for HAMBURGER in the Listeria (p-value = 0.01)
and E. Coli (p-value = 0.05) equations, respectively.
Estimation of the model without the WASHHANDS
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
and HAMBURGER variables, however, produces very
similar results, in terms of signs and significance of
explanatory variables, as those reported here. Results
of this alternative specification and simultaneous-equation estimates for endogeneity tests are available upon
request from the authors.
As in more traditional regression models, multicollinearity among the explanatory variables can lead to
imprecise parameter estimates (Kleinbaum, Kupper, &
Muller, 1988). To explore potential multicollinearity
among the explanatory variables, we calculated the variance inflation factor (VIF) for each explanatory variable. The VIFs range from <0.01 to 2.75, which do not
reach conventional thresholds (10 or higher) used in
the regression diagnosis literature (Kleinbaum et al.,
1988). Hence, multicollinearity does not appear to be a
problem in the current analysis.
3. Results
The majority of US consumers said they had heard of
Salmonella (94%) and E. coli (90%) as a problem in food
(Table 2). In contrast, about one third of them were
aware of Listeria (32%) and only 7% were aware of
Campylobacter. About 17% of US consumers thought
homes were where food safety problems were most likely
to occur. Sixty-two percent thought it was very common
or somewhat common for people in the United States to
become sick because of the way food was handled or
prepared in their homes. About half of US consumers
considered food contamination by micro-organisms a
very serious or serious food safety problem. They perceived themselves to be somewhat likely to get sick from
four unsafe food handling practices. Sixty-seven percent
of the consumers perceived at least a moderate likelihood of getting sick from unsafe food handling practices
(see VULNERABLE defined above and in Table 2).
Most consumers claimed they always washed their
hands before preparing food, and the hamburgers
served at their homes were usually thoroughly cooked
(i.e., well-done or brown all the way through). About
one quarter (23%) of consumers had stopped buying
specific foods because of food safety concern. As to
awareness of other food safety issues, while a large portion of consumers (69%) said they had heard or read
about possible health problems related to mercury in
some fish, much fewer consumers said they had heard
or read about possible health problems of eating sprouts
(15%) or about health problems of drinking unpasteurized juice (29%). One in three consumers thought they
themselves or someone in the household had been sick
from eating spoiled or unsafe food; 12% of the consumers said they had one or more health conditions, including liver disease, diabetes, reduced gastric acidity, HIV,
AIDS, a weakened immune system, or were under chem-
407
otherapy or radiation therapy. Fifty-five percent of consumers were main meal preparers in their households.
We estimated the multivariate probit model and, for
comparison, a univariate probit model for each of the
four pathogens. The univariate probit models together
can be viewed as a restricted version of the multivariate
probit with all off-diagonal error correlations set to
zeros (i.e., qij = 0 for i > j). Based on the log-likelihood
values of the multivariate and univariate probit models,
a likelihood ratio test (Engle, 1984) suggested joint significance of the error correlations (v2 = 97.01, d.f. = 6,
p-value < 0.001), justifying estimation of the multivariate probit vis-à-vis univariate probit models. This test
result is consistent with the significance of the error correlation coefficients between Listeria and Campylobacter
(0.43), and between E. coli and Salmonella (0.59),
respectively. A comparison of the multivariate and univariate probit results, available from the authors upon
request, however, suggests qualitatively similar effects
of explanatory variables, in terms of signs and significance levels, between the two models. The rest of the
analysis is based on the multivariate probit estimates.
Table 3 shows the estimated relationships between
pathogen awareness and explanatory variables. As typical in cross-sectional analysis, the pseudo R-squareds
(Wooldridge, 2002, p. 463) for the probit equations are
fairly low, ranging from 0.07 for Campylobacter to
0.14 for Salmonella. However, for binary-choice models,
it is more important to examine the predictive power,
defined as the percentage of times the predicted 0–1 outcome matches the actual 0–1 outcome. Specifically, de^,
note the maximum-likelihood estimator of bi as b
i
then the outcome of yi is predicted to be unity when
^ Þ > 0:5 and zero when Uðx0 b
^ Þ 6 0:5 (Wooldridge,
Uðx0 b
i
i
2002, p. 465). The predicted powers of the estimated
models are fairly high, ranging from 67.75% correct predictions for Listeria to 96.46% for Salmonella. In addition, based on results from a randomly selected 80%
sub-sample, the model performs well in out-of-sample
prediction (with the remaining 20% of the sample), with
predicted powers ranging from 66.56% correct predictions for Listeria to 94.98% for Salmonella.
Based on estimated marginal effects, awareness of
Salmonella is more likely among consumers who perceive homes are where food safety problems are most
likely to occur, who perceive it is very common or somewhat common that people get sick from food handled or
prepared at home, who consider pathogen contamination as a very serious or serious food safety problem,
who perceive higher risk from unsafe food handling
practices, or who always wash hands with soap before
food preparation. Those who have heard of possible
health problems related to drinking unpasteurized juices
or related to mercury in some fish are also more likely to
be aware of Salmonella. Consumers from homes that
usually serve thoroughly cooked (well-done or brown
408
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
Table 3
Multivariate probit estimates of consumer awareness
Salmonella
Coefficient
CONSTANT
Risk perceptions
FROMHOME
HOMERISK
GERMRISK
VULNERABLE
Food safety practices
WASHHANDS
HAMBURGER
STOPBUY
Campylobacter
Marg. effect
INCOME3
INCOME4
HHSIZE2
HHSIZE3
HHSIZE4
CHILD5
FEMALE
COLLEGE
BLACK
Coefficient
E. coli
Marg. effect
1.444***
(0.154)
0.303*
(0.163)
0.184**
(0.085)
0.232**
(0.097)
0.166*
(0.100)
0.010**
(0.004)
0.008**
(0.004)
0.010**
(0.004)
0.007*
(0.004)
0.136
(0.109)
0.051
(0.096)
0.114
(0.086)
0.044
(0.098)
0.015
(0.013)
0.005
(0.010)
0.012
(0.009)
0.004
(0.010)
0.398***
(0.105)
0.215
(0.148)
0.142
(0.113)
0.021***
(0.007)
0.008*
(0.004)
0.006
(0.006)
0.130
(0.105)
0.170
(0.113)
0.147
(0.092)
0.013
(0.010)
0.016*
(0.010)
0.016
(0.011)
0.453***
(0.095)
0.220***
(0.086)
0.190*
(0.114)
0.061***
(0.016)
0.025**
(0.011)
0.019*
(0.011)
Foodborne illness and other diseases
ILLNESS
0.130
0.005
(0.103)
(0.004)
HEALTH
0.199
0.010
(0.123)
(0.007)
Demographics
INCOME2
Marg. effect
2.823***
(0.300)
0.253
(0.262)
Awareness of other food safety issues
SPROUTS
0.130
0.005
(0.223)
(0.008)
JUICES
0.348***
0.013***
(0.140)
(0.005)
MERCURY
0.743***
0.044***
(0.106)
(0.009)
Meal preparation
MEALPREP
Coefficient
Listeria
0.059
(0.064)
0.030
(0.045)
0.104**
(0.046)
0.083*
(0.051)
0.043
(0.053)
0.041
(0.062)
0.011
(0.054)
Coefficient
Marg.
effect
0.117
(0.292)
0.018
(0.020)
0.009
(0.014)
0.031**
(0.014)
0.025*
(0.015)
0.013
(0.016)
0.012
(0.019)
0.003
(0.016)
0.603***
(0.068)
0.289***
(0.052)
0.181***
(0.053)
0.206***
(0.027)
0.091***
(0.017)
0.053***
(0.015)
0.083
(0.080)
0.160***
(0.060)
0.036
(0.062)
0.152***
(0.061)
0.015
(0.015)
0.029**
(0.014)
0.006
(0.011)
0.028**
(0.014)
0.002
(0.065)
0.190**
(0.081)
0.129*
(0.069)
0.000
(0.012)
0.037*
(0.020)
0.024*
(0.014)
0.250**
(0.127)
0.322***
(0.091)
0.593***
(0.076)
0.040
(0.025)
0.053**
(0.022)
0.122***
(0.034)
0.011
(0.092)
0.092
(0.148)
0.001
(0.009)
0.009
(0.014)
0.127***
(0.049)
0.014
(0.068)
0.039***
(0.016)
0.004
(0.021)
0.091
(0.072)
0.039
(0.078)
0.016
(0.013)
0.007
(0.015)
0.034
(0.096)
0.001
(0.004)
0.003
(0.093)
0.000
(0.010)
0.081*
(0.049)
0.024*
(0.015)
0.009
(0.064)
0.002
(0.011)
0.324***
(0.111)
0.554***
(0.135)
0.197
(0.215)
0.069
(0.184)
0.090
(0.192)
0.341*
(0.204)
0.366***
(0.140)
0.464***
(0.112)
0.181
(0.120)
0.616***
(0.143)
0.013***
(0.005)
0.019***
(0.005)
0.007
(0.007)
0.003
(0.007)
0.004
(0.008)
0.018
(0.013)
0.012***
(0.004)
0.020***
(0.005)
0.008
(0.005)
0.042***
(0.013)
0.220
(0.157)
0.235
(0.166)
0.312
(0.207)
0.007
(0.198)
0.039
(0.196)
0.014
(0.219)
0.050
(0.120)
0.160*
(0.096)
0.545***
(0.097)
0.197
(0.139)
0.023
(0.017)
0.026
(0.020)
0.040
(0.031)
0.001
(0.020)
0.004
(0.020)
0.001
(0.023)
0.005
(0.013)
0.016
(0.010)
0.057***
(0.011)
0.018
(0.011)
0.142**
(0.067)
0.283***
(0.071)
0.354***
(0.097)
0.016
(0.105)
0.171
(0.106)
0.290***
(0.112)
0.179***
(0.065)
0.053
(0.051)
0.303***
(0.046)
0.372***
(0.067)
0.043**
(0.021)
0.089***
(0.023)
0.117***
(0.035)
0.005
(0.032)
0.051*
(0.031)
0.081***
(0.030)
0.056***
(0.021)
0.016
(0.016)
0.091***
(0.014)
0.100***
(0.017)
0.375***
(0.075)
0.493***
(0.087)
0.354***
(0.138)
0.030
(0.124)
0.141
(0.122)
0.235*
(0.131)
0.167**
(0.079)
0.227***
(0.067)
0.247***
(0.069)
0.426***
(0.077)
0.064***
(0.023)
0.079***
(0.029)
0.052**
(0.026)
0.005
(0.023)
0.026
(0.024)
0.046
(0.030)
0.028**
(0.014)
0.041**
(0.018)
0.044**
(0.021)
0.092***
(0.030)
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
409
Table 3 (continued)
Salmonella
HISPANIC
OTHER RACE
AGE1829
AGE3049
MWEST
WEST
NEAST
% predicted
Pseudo R2
Campylobacter
Listeria
E. coli
Coefficient
Marg. effect
Coefficient
Marg. effect
Coefficient
Marg. effect
Coefficient
Marg.
effect
1.057***
(0.142)
0.660**
(0.285)
0.175
(0.151)
0.384***
(0.132)
0.144
(0.131)
0.037
(0.118)
0.220
(0.148)
96.46
0.137
0.108***
(0.024)
0.052
(0.036)
0.007
(0.005)
0.015***
(0.005)
0.006
(0.005)
0.002
(0.005)
0.008*
(0.005)
0.199
(0.185)
0.581***
(0.144)
0.115
(0.126)
0.062
(0.102)
0.088
(0.107)
0.249**
(0.123)
0.024
(0.115)
90.68
0.070
0.018
(0.015)
0.091***
(0.032)
0.013
(0.015)
0.006
(0.011)
0.009
(0.010)
0.023**
(0.010)
0.002
(0.012)
0.070
(0.081)
0.264***
(0.106)
0.045
(0.072)
0.164***
(0.055)
0.070
(0.058)
0.376***
(0.069)
0.007
(0.060)
67.75
0.109
0.021
(0.023)
0.087**
(0.037)
0.013
(0.021)
0.050***
(0.017)
0.021
(0.018)
0.103***
(0.018)
0.002
(0.018)
0.775***
(0.102)
0.385**
(0.167)
0.116
(0.090)
0.134*
(0.078)
0.008
(0.077)
0.040
(0.086)
0.197**
(0.089)
93.38
0.085
0.195***
(0.043)
0.085**
(0.039)
0.020
(0.016)
0.024
(0.015)
0.001
(0.014)
0.007
(0.016)
0.032**
(0.017)
Note: Log-likelihood = 3452.268. Asymptotic standard errors in parentheses. Levels of statistical significance: *** = 1%, ** = 5%, * = 10%.
all the way through) hamburgers are less likely to be
aware of Salmonella (though the probability difference
is negligible, 0.008). As for Campylobacter, consumers
are more likely to have heard of it if thoroughly-cooked
hamburgers are usually served in their homes. In addition, awareness of health problems related to eating
sprouts, drinking unpasteurized juices, or mercury in
some fish is also associated with a larger probability of
having heard of Campylobacter. Likewise, the same
awareness is associated with the probability of having
heard of Listeria. Meanwhile, those who perceive pathogen contamination as a very serious or serious food
safety problem, who perceive higher likelihood of getting sick from unsafe practices, who have household
member(s) with sickness possibly caused by eating
spoiled or unsafe food, or who are the main meal preparers in their households are also more likely to be
aware of the pathogen. Having heard of the pathogen
E. coli is more likely among those who think it is very
common or somewhat common for people in the United
States to become sick because of the way food is handled
or prepared in their homes, who perceive higher likelihood of getting sick from unsafe food handling
practices, who are from a household where thoroughly-cooked hamburgers are usually served, or who
have heard about possible health problems related to
drinking unpasteurized juices or related to mercury in
some fish. But awareness of E. coli is less likely among
consumers who have stopped buying specific kinds of
food due to safety concern.
Awareness of a pathogen is also associated with
demographic characteristics. Consumers with at least
some college education are more likely to have heard
of any one of the four pathogens, except for Salmonella,
than those with less education. Female consumers are
more aware of Salmonella or E. coli than males. Awareness of Salmonella, Listeria, or E. coli is higher among
those who have one or more children younger than
5 years old in their households or who come from households with annual income above $20,000. Consumers
age 30 to 49 have higher awareness of Salmonella or Listeria than older consumers (50 and above). Compared to
White consumers, Black, Hispanic, and other-race consumers are generally less aware of the pathogens, particularly Salmonella and E. coli. Northeastern consumers
are more aware of Salmonella and E. coli and Western
consumers less aware of Campylobacter or Listeria, than
South consumers.
4. Discussion and conclusion
Similar to findings by Altekruse et al. (1996), our results suggest that self-reported awareness of Salmonella
is associated with safer self-reported hand washing practice. Specifically, we found the relationship in hand
washing before meal preparation while they found the
relationship in hand washing after handling raw meat.
Hence, it appears that safer hand washing behavior is
a consistent predictor of Salmonella awareness. Furthermore, this study finds that awareness of Campylobacter
or E. coli is associated with serving thoroughly-cooked
hamburgers, while McIntosh et al. (1994) reported that
a higher number of five bacteria Texas consumers reported having heard of was associated with reported
preference toward more cooked hamburger patties. We
find that serving thoroughly-cooked hamburgers is associated with a statistically significant but practically insignificant decrease in the probability of having heard of
Salmonella. By comparison, Altekruse et al. (1996)
410
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
found that the awareness of Salmonella was not associated with the degree of doneness in hamburgers served.
Therefore, despite that pathogen awareness is different
from safe practices and higher awareness does not necessarily mean better practices, the totality of available
evidence suggests that raising pathogen awareness can
be a potentially useful approach to advocating safer
food handling and preparation practices.
As shown in this and previous research, pathogen
awareness can have no or negative, albeit negligible,
relationship with how hamburgers are served. Hence,
the evidence also seems to suggest that higher awareness
of pathogens does not necessarily translate into safer
practices when a practice may affect the taste of a food.
This phenomenon appears plausible since taste often
plays a more important role in food preferences than
food safety. Therefore, pathogen awareness could be
less useful in promoting safer cooking practices than
safer hand washing practices. It is noteworthy that the
co-existence of both positive and negative as well as
both significant and insignificant awareness-behavior
relationships may also be attributable to the fact that
all existing research is based on self-reports rather than
objectively verifiable measures.
As expected, awareness of pathogens, except Campylobacter, is in general higher among those who perceive
homes, rather than other places, are where food safety
problems are most likely to occur, it is very or somewhat common for people in the United States to become sick because of how food is handled or
prepared in their homes, microorganism contamination
is a very serious or serious food safety problem, or
higher vulnerability to unsafe practices. In addition,
pathogen awareness is positively related to awareness
of potentially-risky foods and substances (sprouts,
unpasteurized juices, and mercury). Therefore, the data
appear to suggest that those who pay more attention to
food safety information and are also more motivated to
learn about food safety are more likely to be aware of
the pathogens. We caution, however, that the almost
uniform pattern of relationships between pathogen
awareness and awareness of potentially-risky foods
and substances, both based on self-reports, may be vulnerable to the ‘‘social desirability bias’’ (Maccoby &
Maccoby, 1954). In other words, some survey respondents might have given answers that were believed to be
intelligent or desirable.
Compared to previous studies, our findings suggest
two things about the prevalence of pathogen awareness.
First, more US consumers said they had heard of Salmonella, E. coli, and Listeria in 2001 than in the early
1990s. As high as 94% consumers had heard of Salmonella in 2001, while the corresponding figure for 1993
and 1991 was approximately 80% (Altekruse et al.,
1996; McIntosh et al., 1994). The awareness of E. coli
was 90% in 2001, much higher than the 30% reported
in McIntosh et al. (1994). Differences also exist in the
awareness of Listeria among this study (32%), McIntosh
et al. (1994) (21%), and Herrmann and Warland (2000)
(52%). Note, however, Herrmann and Warland (2000)
used a mail rather than a telephone survey and asked
a different question (‘‘are you concerned about Listeria
bacteria, or is that something you never have heard
of’’). Thus, the notably different results in Herrmann
and Warland (2000) could be attributable to differences
in the mode of data collection, question wording, and
time. Second, the percentage ranking in awareness
among the four pathogens remained relatively stable between the early 1990s and 2001. Salmonella had consistently been the most widely heard pathogen, E. coli
second, Listeria next, and Campylobacter least.
The availability heuristic is a possible explanation for
the differences over time in the awareness of Salmonella,
E. coli, and Listeria and the consistent pattern of order
ranking in the awareness. The availability heuristic, a
simplified judgmental rule, posits that, in assessing the
frequency of a risk, individuals often base their decisions
on how easy it is to recall the risk and how recent occurrences of the risk are (Tversky & Kahneman, 1982,
Chapter 1). Food safety incidents, especially those
affecting many consumers and those resulting in deaths,
are often reported by the media (IFICF, 2002; OllingerSynder & Matthews, 1994). Many consumers receive
information about food safety through mass media such
as newspapers, magazines, and television (ADA/ConAgra, 2000a). Hence, it is possible that, when asked
whether they have heard of a pathogen, consumers
would report awareness if the pathogen name is readily
available in the memory and there are recent events related to the pathogen. The numbers of outbreaks associated with Salmonella, E. coli, and Listeria were all higher
in 2001 than in 1992 (CDC, 2004). There had been several recent large and highly publicized foodborne illness
outbreaks since the early 1990s, such as the 1993 E. coli
O157:H7 outbreak related to undercooked hamburgers
and in which four children died (CDC, 1993). In addition, the number of voluntary recalls of foods due to
potential risk of pathogen contamination was also higher in 2000 than in early 1990s. For example, the number
of recalls doubled from 1994 to 2000 for FSIS-inspected
meat and poultry products due to Listeria contamination (USDA-FSIS, 1994, 2000).
Availability heuristic may also explain why Salmonella is most heard of, E. coli second, Listeria next,
and Campylobacter least. Epidemiological data show
that, from 1993 to 2000, the annual numbers of outbreaks and cases are highest for Salmonella, second for
E. coli, third for Campylobacter, and lowest for Listeria
(CDC, 2000a; CDC, 2004). In addition, the awareness of
Listeria could have been higher than that of Campylobacter because the death rate is higher among listeriosis
victims than among campylobacteriosis victims.
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
Thus, our data suggest that self-reported awareness of
the four pathogens is strongly influenced by the prevalence and visibility of foodborne illnesses associated with
these pathogens. This finding, though reasonable, raises
the possibility that the level of awareness varies according
to what and when publicized foodborne illnesses occur.
Insofar as such incidents are unpredictable and most
foodborne illness cases go unreported (CAST, 1994), it
would be useful to reach out to consumers with pathogen
information and make the information widely and constantly available as higher awareness may be associated
with safer food handling and preparation practices.
Our findings suggest that awareness of different pathogens is related to college education and higher household income. This may be because less-educated or
low-income consumers have less access to food safety
information and less capacity to use the information.
This may also suggest that less-educated or lower-income consumers may have put themselves under higher
risk of foodborne illness due to lack of basic knowledge
of microbial food safety. How to increase the awareness
of these consumers therefore calls for more attention in
food safety education. On the other hand, one cannot
rule out the possibility that socially desirable responses
may have prevented the disclosure of more genuine relationships between pathogen awareness and education
and income in this as well as other survey-based studies.
Two other demographic subgroups appear to have
less awareness of pathogens—males and non-White consumers. The finding that males are less likely to have
heard of Salmonella or E. coli is not surprising as previous food safety studies have repeatedly shown that male
consumers, in general, are less interested than females in
food safety issues and less likely to handle or prepare
food safely (Adu-Nyako, Kunda, & Ralston, 2003;
Albrecht, 1995; Altekruse et al., 1996; Klontz, Timbo,
Fein, & Levy, 1995). But the finding does signal a potential area that deserves more attention in food safety education. Since these pathogens are major sources of
foodborne illness and male consumers handle a lot of
food preparation and grilling, especially in the summer
(ADA/ConAgra, 2000b), increasing male consumers
awareness can help reduce foodborne illness. NonWhite consumers are less likely to report awareness of
pathogens, especially Salmonella and E. Coli. While
the cause of this difference is worth investigation, the
effectiveness of food safety education may benefit from
paying special attention to non-White consumers. This
is particularly important to Hispanics as they are now
the largest minority in the United States and their number is expected to grow rapidly (Pew Hispanic Center,
2002), yet they may be more likely to engage in risky
food consumption practices than other race/ethnic
groups (FDA/USDA, 1999).
The age variation indicates that middle-age consumers, 30–49 years old, are more aware of Salmonella and
411
Listeria than others. Meanwhile, there is a significant
association between Salmonella, Listeria, and E. coli
awareness and the presence of young children. Taken together, these findings suggest that many consumers who
have just started a family or are raising children have
some knowledge of microbial food safety. Since these
consumers may be preparing foods for multiple household members, including young children, their food handling and preparation practices can affect not only their
own health but also the health of other household members. Thus, it is important for food safety education to
maintain or even increase food safety knowledge among
these consumers, including awareness of foodborne
pathogens.
In conclusion, this study finds that there are noticeable variations in US consumers awareness of four major
foodborne pathogens. The prevalence of awareness also
changed between 2001 and the early 1990s. The variations and change appear to be related to the number
and severity of illnesses associated with these pathogens.
The study also finds that the awareness is associated
with food safety perceptions, awareness of potentially
risky foods and substances, food safety related behaviors, and demographics. Thus, increasing consumer
awareness of major foodborne pathogens is a potentially
useful way to help promote safer food handling practices, especially those that do not affect food tastes, such
as hand washing. In addition, foodborne illnesses may
be reduced by raising consumer motivation to acquire
and use food safety information and by targeting consumer food safety education toward certain subgroups
such as males, non-White, less-educated, and lower-income consumers.
References
ADA/ConAgra, American Dietetic Association/Foundation and the
ConAgra Foundation. (2000a). Home Food Safety Benchmark
Survey. Available at <http://www.homefoodsafety.org> (accessed
28 January 2004).
ADA/ConAgra (2000b). Online survey reveals consumers need the
recipe for safe summer grilling. Available at <http://www.homefoodsafety.org> (accessed 28 January 2004).
Adu-Nyako, K., Kunda, D., & Ralston, K. L. (2003). Safe food
handling labels and consumer behavior in the Southern U.S. In:
AAEA Annual Meetings, Montreal, Canada, July 27–30.
Albrecht, J. A. (1995). Food safety knowledge and practices of
consumers in the U.S.A. Journal of Consumer Studies and Home
Economics, 19, 119–134.
Altekruse, S. F., Street, D. A., Fein, S. B., & Levy, A. S. (1996).
Consumer knowledge of foodborne microbial hazards and
food-handling practices. Journal of Food Protection, 59,
287–294.
AAPOR, American Association for Public Opinion Research. (2004).
Standard definitions: final dispositions of case codes and outcome
rates for surveys. Available at <http://www.aapor.org/pdfs/standarddefs2004.pdf> (accessed 10 April 2004).
Aptech (1997). GAUSS mathematical and statistical system, vol. II,
Command reference. Maple Value, WA: Aptech Systems, Inc.
412
C.-T.J. Lin et al. / Food Quality and Preference 16 (2005) 401–412
Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis.
Biometrics, 26, 535–546.
Berndt, E. K., Hall, B. H., Hall, R. E., & Hausman, J. A. (1974).
Estimation and inference in nonlinear structural models. Annals of
Economic and Social Measurement, 3, 653–665.
CAST, Council for Agricultural Science and Technology. (1994).
Foodborne pathogens: risks and consequences. Task Force Report
No. 122, Washington, DC and Ames, IA.
CDC, US Department of Health and Human Services, Centers for
Disease Control and Prevention (1993). Update: multistate outbreak of Escherichia coli O157:H7 infections from hamburgers—
Western United States, 1992–1993. Morbidity and Mortality
Weekly Report, 42, 258–263.
CDC (2000a). CDC surveillance summaries, March 17. Morbidity and
Mortality Weekly Report, 49(No. SS-1).
CDC (2000b). Multistate outbreak of listeriosis—United States, 2000.
Morbidity and Mortality Weekly Report, 49(50), 1129–1130.
CDC (2002). Outbreak of Salmonella serotype kottbus infections
associated with eating alfalfa sprouts—Arizona, California, Colorado, and New Mexico, February–April 2001. Morbidity and
Mortality Weekly Report, 51(1), 7–9.
CDC (2004). U.S. Foodborne Disease Outbreaks. Available at <http://
www2.cdc.gov/ncidod/foodborne/fbsearch.asp> (accessed 14 January 2004).
Doyle, M. P., Ruoff, K. L., Pierson, M., Weinberg, W., Soule, B., &
Michaels, B. S. (2000). Reducing transmission of infectious agents
in the home, Part I: source of infection. Dairy, Food and
Environmental Sanitation, 20, 330–337.
Engle, R. F. (1984). Wald, likelihood ratio, and Lagrange multiplier
tests in econometrics. In Z. Griliches & M. D. Intriligator (Eds.).
Handbook of econometrics (vol. 2, pp. 775–826). Amsterdam:
North-Holland.
FDA-CFSAN, US Food and Drug Administration, Center for Food
Safety and Applied Nutrition. (1998). What consumers need to
know about juice safety. Available at <http://vm.cfsan.fda.gov/
~dms/juicsafe.html> (accessed 14 January 2004).
FDA-CFSAN (2002). 2001 Food Safety Survey. Unpublished data.
College Park, MD.
FDA-CFSAN (2003a). Bad bug book. Available at <http://
www.cfsan.fda.gov/~mow/intro.html> (accessed 26 June 2003).
FDA-CFSAN (2003b). Food safety A to Z reference guide. Available at
<http://www.cfsan.fda.gov/~dms/a2z-e.html> (accessed 26 June
2003).
FDA-CFSAN (2003c). 2001 Food code. Available at <http://
www.cfsan.fda.gov/~dms/fc01-pre.html> (accessed 26 June 2003).
FDA-CFSAN (2003d). FDA news: risk assessment reinforces that
keeping ready-to-eat foods cold may be the key to reducing listeriosis.
Available at <http://www.fda.gov/bbs/topics/NEWS/2003/NEW
00963.html> (accessed 14 January 2004).
FDA/USDA, US Food and Drug Administration, Center for Food
Safety and Applied Nutrition and US Department of Agriculture,
Food Safety and Inspection Service. (1999). Research findings
related to safe cooking. Available at <http://www.foodsafety.gov/
~fsg/f99rsrch.html> (accessed 2 April 2004).
Hajivassiliou, V. A. (1994). Classical estimation methods for LDV
models using simulation. In R. F. Engle & D. L. McFadden (Eds.).
Handbook of econometrics (vol. 4, pp. 2383–2441). Amsterdam:
North-Holland.
Herrmann, R. O., & Warland, R. H. (2000). Awareness of an
unfamiliar food safety hazard: Listeria 1999. Consumer Interests
Annual, 46, 1–6.
IFICF, International Food Information Council Foundation. (2002).
Food for Thought IV. Conducted by Center for Media and Public
Affairs, Washington, DC.
Kleinbaum, D. G., Kupper, L. L., & Muller, K. E. (1988). Applied
regression analysis and other multivariable methods (2nd ed.).
Boston, MA: PWS-Kent.
Klontz, K. C., Timbo, B., Fein, S., & Levy, A. (1995). Prevalence of
selected food consumption and preparation behaviors associated
with increased risks of food-borne disease. Journal of Food
Protection, 58, 927–930.
Luenberger, D. G. (1984). Linear and nonlinear programming. Reading,
MA: Addison-Wesley.
Maccoby, E., & Maccoby, N. (1954). The interview: a tool of social
science. In G. Lindzey (Ed.). Handbook of Social Psychology (vol.
1, pp. 449–487). Cambridge, MA: Addison-Wesley.
Marketing Systems Group (2004). An introduction to GENESYS
sampling systems. Available at <http://www.m-s-g.com/genesys/
intro/introgen.htm> (accessed 8 January 2004).
McIntosh, W. A., Christensen, L. B., & Acuff, G. R. (1994).
Perceptions of risks of eating undercooked meat and willingness
to change cooking practices. Appetite, 22, 83–96.
Mead, P., Slutsker, L., Dietz, V., McCaig, L., Bresee, J., Shapiro, C.,
Griffin, P., & Tauxe, R. (1999). Food-related illness and death in
the United States. Emerging Infectious Diseases, 5 (No. 5).
Available at <http://www.cdc.gov/ncidod/eid/vol5no5/mead.htm>
(accessed 26 June 2003).
Medeiros, L. C., Hillers, V. N., Kendall, P. A., & Mason, A. (2001).
Food safety education: what should we be teaching to consumers?
Journal of Nutrition Education, 33, 108–113.
Ollinger-Synder, P., & Matthews, M. E. (1994). Food safety
issues: press reports heighten consumer awareness of microbiological safety. Dairy, Food and Environmental Sanitation, 14,
580–589.
Pew Hispanic Center (2002). U.S.-born Hispanics increasingly drive
population developments, Washington, D.C. Available at <http://
www.pewhispanic.org/site/docs/pdf/demography_pdf_version.pdf>
(accessed 28 January 2004).
Redmond, E. C., & Griffith, C. J. (2003). Consumer food handling in
the home: a review of food safety studies. Journal of Food
Protection, 66, 130–161.
Rivers, D., & Vuong, Q. H. (1988). Limited information estimators
and exogeneity tests for simultaneous probit models. Journal of
Econometrics, 39, 347–366.
SAS Institute Inc. (2001). SAS software, Version 8.2. Cary, NC.
Tversky, A., & Kahneman, D. (1982). Judgment under uncertainty:
heuristics and biases. In D. Kahneman, P. Slovic, & A. Tversky
(Eds.), Judgment under uncertainty: Heuristics and biases
(pp. 3–20). Cambridge: Cambridge University Press.
US Census (2001). Annual demographic survey, March supplement.
Available at <http://ferret.bls.census.gov/macro/032001/hhinc/
new01_000.htm> (accessed 14 January 2004).
USDA-ERS, US Department of Agriculture, Economic Research
Service. (2000). Economics of foodborne diseases. Available at
<http://www.ers.usda.gov/briefing/foodbornedisease/> (accessed 26
June 2003).
USDA-FSIS, US Department of Agriculture, Food Safety and
Inspection Service. (1994). 1994 Recall cases. Available at <http://
www.fsis.usda.gov/OA/recalls/recdb/rec1994.htm> (accessed 5
April 2004).
USDA-FSIS (2000). 2000 Recall cases. Available at <http://www.
fsis.usda.gov/OA/recalls/rec_summ.htm> (accessible 26 January
2004).
USDHHS. (US Department of Health and Human Services). (1999).
Consumers advised of risks associated with raw sprouts. HHS
News, P99–13. Available from <http://www.cfsan.fda.gov/~lrd/
hhssprts.html> (accessed 15 January 2004).
USDHHS. (2001). FDA publishes final rule to increase safety of fruit
and vegetable juices. HHS News, P01–03. Available from <http://
www.cfsan.fda.gov/~lrd/hhsjuic4.html> (accessed 15 January
2004).
Wooldridge, J. M. (2002). Econometric analysis of cross section and
panel data. Cambridge, MA: MIT Press.