Food Protection Trends - January/February 2020 - 19

preparers include gender, relationship status, age, ethnicity,
formal education, and employment status. For gender, this
paper defined an indicator for male at-home meal preparers.
For relationship status, this paper defined an indicator for
married at-home meal preparers. For age, this paper defined
an indicator for those age 18 to 34; an indicator for those
age 35 to 59; and, an indicator for those age 60 and over. For
ethnicity, this paper defined an indicator for Hispanic athome meal preparers, defined as a respondent who is either
Spanish, Hispanic, or Latino (2). For formal education, this
paper defined an indicator for at-home meal preparers with
at most a high school diploma; an indicator for those with
at most an associate's degree; and, an indicator for those
with at least a bachelor's degree. For employment status, this
paper defined an indicator for employed and an indicator for
unemployed, and an indicator for not in the labor force.
Available categories to describe the households of at-home
meal preparers consisted of household size, geographic
location and poverty status. For household size, this paper
defined the total number of people in the home. For
geographic location, this paper defined an indicator for athome meal preparers living in the Midwest-defined as living
in Iowa, Illinois, Indiana, Kansas, Michigan, Minnesota,
Missouri, North Dakota, Nebraska, Ohio, South Dakota,
or Wisconsin (2). For poverty status, this paper defined an
indicator for household incomes exceeding 185% of the
federal poverty line, adjusted for household size.
Time use data are available on time spent food shopping, preparing meals, and eating. The ATUS-EHM asks
about how many minutes in the last 24 hours you spent
grocery shopping; how many minutes in the last 24 hours
you spent preparing food, including cleanup; and, how
many minutes in the last 24 hours you spent eating as a
primary activity, which includes eating and drinking, eating and drinking not elsewhere classified, and eating and
drinking as part of a job (12).
The ATUS-EHM asked respondents what type of store
they used for the majority of their groceries. Answer choices
were grocery store, supercenter, warehouse club, drugstore
or convenience store, or some other place. To describe
food store preferences, this study defined an indicator for
grocery store; an indicator for supercenter or warehouse
club; and an indicator for drugstore, convenience store or
some other place. The ATUS-EHM also asked respondents to
characterize their household's food sufficiency in the past 30
days as enough food to eat, sometimes not enough to eat, or
often not enough to eat. To describe food insufficiency, this
study defined households as food insufficient if respondents
answered either sometimes not having enough to eat or often
not having enough to eat.
Populations at risk of foodborne illness include those who
are immunocompromised, children, and the elderly. While
data on the extent to which at-home meal preparers were
immunocompromised is not available in the ATUS-EHM,

respondents were asked to rank their physical health on a
scale of poor, fair, good, very good, or excellent. This paper
defined an indicator for the situation in which an at-home
meal preparer was in poor physical health. For the presence
of children, the ATUS-EHM asks about the presence of
children in the household, either children under the age
of 18 who may or may not be related to the respondent, or
biological, children, step children or adopted children who
reside in the respondent's home or in another home. This
paper defined an indicator for the presence of least one
child in the household. For the presence of elderly, the CPS
recorded the age of each person in the household. This paper
defined an indicator for at least one person who was not the
at-home meal preparer being age 62 or over.
Regression framework
To explain food thermometer use by at-home meal
preparers, this study needs to address the confounding
influence that multiple variables, such as demographics, time
use, and employment, may have on the decision to whether
or not to use a food thermometer. To model this decisionmaking process, this study assumes that an at-home meal
preparer will use a food thermometer if the utility from
use, V1+ε1, exceeds the utility from nonuse, V0+ε0, where
Vj is the deterministic component of utility and εj is the
random component of utility and j indicates an at-home meal
preparer's binary choice. The probability of observing an athome meal preparer choosing to use a food thermometer is
equal to the probability that the utility from use exceeds the
utility from nonuse:
(1) Pr(j = 1) = Pr(V1 + ε1 > V0 + ε0 ).
This probability simplifies to:
(2) Pr(j = 1) = F(V1 - V0),
where F is the cumulative density function of ε0 - ε1.
Assuming ε0 and ε1 are independent and type 1 extreme
value distributed, and setting V1 - V0 equal to xTβ where x
is a column vector of observable characteristics and β is a
column vector of parameters, then the probability that an
at-home meal preparer uses a food thermometer reduces to
the logit model:
exp(xTβ)
(3) Pr(j = 1) =
.
1+exp(xTβ)
Parameters were estimated in Stata® 14 (19) for the subsample of at-home meal preparers who reported preparing
any meals with meat, poultry, or seafood in the previous
week. For clarity, this study refers to this group as "at-home
meal preparers" throughout the following discussion of
regression results. The resulting logit regression estimates and
standard errors were then used to estimate average marginal

January/February Food Protection Trends

Food Protection Trends - January/February 2020

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