Food Protection Trends - January/February 2014 - (Page 27)
(Pennsylvania), or 24th (Rhode Island) restaurant from the
list, arranged by county name, was selected to be included in
the sample. Only restaurants and delis not associated with a gas
station, grocery store, or caterer were included in the sample.
Some states reported violation summaries, while others
included detailed health inspection reports. The most recent
routine inspection report from each establishment was
selected for analysis. Dates of inspections ranged from June
2009 to February 2012.
Data collection and consolidation
State health inspection forms included different violations
(i.e., Oklahoma notes 51 different violations, Pennsylvania
208, and Rhode Island 54), with inconsistent wording. The
original wording was maintained for categorizing and further
analyses of the data. Before combining the Food Code
violations into predetermined categories, each violation
was recorded, based on each state's inspection form. The
Microsoft Access database with a separate file for each state.
Food Code violations were evaluated and assigned to
predetermined violation categories (i.e., behavioral versus
non-behavioral, critical versus non-critical). Oklahoma and
Rhode Island inspection reports included instances in which
the descriptions implied both behavioral and non-behavioral
violations. For example, one violation related to handwashing
stated, "Handwash facilities adequate/Accessible with
soap and towels." Even though employees have no control
over adequate/inadequate handwashing facilities (nonbehavioral), employees can ensure that soap and paper
towels are properly stocked (behavioral). Another example,
related to sinks' stated, "Proper number of sinks: mop sinks,
dishwashing sinks, food preparation sinks, used for intended
purposes." An establishment may be cited for this code,
based on inadequate facilities (non-behavioral) or improper
use of sinks (behavioral). Because these cases cannot be
verified as behavioral or non-behavioral violations, they
were categorized as both. Therefore, the sum of behavioral
and non-behavioral code violations slightly exceeds the total
inspection items for both Oklahoma and Rhode Island.
Critical versus non-critical violations were determined
on the basis of each state's inspection reports, because
violations were not consistently reported from state to state.
If the state's inspection report indicated certain violations as
critical, we classified them as critical even though the other
two states may not consider them critical.
After each code violation was classified into one of 30
violation categories and as behavioral/non-behavioral, and
critical/non-critical, the data from all schools and restaurants
were recoded, using the following Microsoft Excel equation:
Ncat#, beh = Ncode1 x Beh(0 or 1) + Ncode2 x Beh(0 or 1) + Ncode3 x
Beh(0 or 1) + . . .
where Ncat#, beh is the number of behavioral code violations in
category #; Ncode# is the number of code violations within the
category #; and Beh(0 or 1) is the behavioral violation factor when 0
is assigned for non-behavioral and 1 for behavioral code violations. A
similar equation was used to calculate total critical violations:
Ncat#, Crit = Ncode1 x Crit(0 or 1) + Ncode2 x Crit(0 or 1) + Ncode3 x
Crit(0 or 1) + . . .
where Ncat#, Crit is the number of critical code violations in
category # and Crit (0 or 1) is the critical violation factor when
0 is assigned for non-critical 1 for critical code violations.
The number of behavioral, non-behavioral, critical, and noncritical violations in each food code violation category for each
establishment was calculated using Microsoft Excel formulas.
After each state's data were recoded into consistent
categories and the numbers of behavioral, non-behavioral,
critical, and non-critical violations were calculated, the
data from all three states were compiled into one Microsoft
Excel file, which was then converted to an SPSS format for
further analysis. The final dataset included both restaurant
and school inspection reports, with the number of behavioral
and non-behavioral violations identified within each food
code violation category. Data analysis was performed with 2,624
restaurant inspection reports and 2,511 school inspection reports.
Data analyses
SPSS for Windows (version 19.0, 2011) was used for data
analysis. Descriptive statistics, with frequencies, means, and
standard deviations were calculated to summarize the data.
Cross-tabulation with chi-square analysis was conducted to
compare the distribution of the data between restaurants
and schools. Differences in the numbers of code violations
between restaurants and schools were compared by use of
independent sample t-tests for each category within the
behavioral and non-behavioral violation classifications.
Finally, the likelihood of behavioral, non-behavioral, critical,
and non-critical violations in restaurants compared with
schools was calculated by use of odds ratios. Before odds
ratio analysis, data were recoded as1 if there were any
violations within each classification and as 0 if there were no
violations. Statistical significance was determined at P < 0.01.
RESULTS AND DISCUSSION
verall, restaurants had more total violations (4.75 ±
4.51) than schools (1.99 ± 1.98)(P < 0.001). In fact,
schools had fewer violations in every category (behavioral,
non-behavioral, critical, and non-critical) than restaurants,
albeit the most highly significant difference (t value) was
observed for behavioral violations (Table 1).
In general, the number of food code violations in this
study revealed that food safety risks were fewer and might
be less severe for school foodservice establishments than for
commercial restaurants, possibly because food preparation in
school foodservice is less complex, with fewer items prepared
than in restaurants. Many school foodservice operations use
a large amount of pre-prepared food products to help contain
operational costs (1), which also reduces personnel requirements.
These factors may lessen the number of violations in schools.
O
foodprotection.org
Food Protection Trends
27
http://www.foodprotection.org
Table of Contents for the Digital Edition of Food Protection Trends - January/February 2014
Food Protection Trends - January/February 2014
Contents
Economically Motivated Adulteration of Honey: Quality Control Vulnerabilities in the International Honey Market
Efficacy of Quaternary Ammonium Compounds on Different Conveyor Chips Contaminated with Poultry Rinsate
What Implications Does a Baseline of Self-efficacy of Food Safety in Adolescent Populations Have for Future Food Safety Education Interventions?
Food Safety Risks in Restaurants and School Foodservice Establishments: Health Inspection Reports
INOFOOD 2013 Conference & Expo
The 3rd Asia Pacific International Conference on Food Safety
China International Food Safety & Quality Conference (CIFSQ) + Expo
Dubai International Food Safety Conference (DIFSC)
Beyond the Bio
FSMA: Article 7
PDG
Industry Products
Coming Events
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