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ORIGINAL ARTICLES
Epidemiology Biostatistics and Public Health - 2014, Volume 11, Number 3
TWO-YEAR ANALYSIS OF RESTAURANT HEALTH GRADES IN NEW YORK CITY
Using Logistic Regression to Model
New York City Restaurant Grades Over a
Two-Year Period
David W. Nadler (1)
BACKGROUND: The New York City Department of Health and Mental Hygiene restaurants to verify
how they comply with regulations which dictate how restaurants must handle and store food, control
vectors, and employ personal hygiene among its employees. A grade is then provided which must
be posted in the restaurant’s front window to inform potential customers of their record. This study
explored if there are significant differences between a restaurant earning the highest grade and the
type of restaurant.
METHODS: A cross sectional study design has been used to conduct measurements on the restaurants’
health department grades over a two-year period. The subjects have their restaurant grading data
publicly available through the New York City Open Data repository.
The study population was composed of restaurants in the five boroughs of New York City that had at
least one inspection in calendar year 2011 or 2012.
RESULTS: The eight restaurant types showed a significant Pearson’s chi-square statistic with
restaurant score (X2 = 28,000, p < 0.001). A one-way ANOVA was used to test for differences between
borough and restaurant score and the effect of borough on score was significant, F (4, 138908) =
36.46, p < 0.001.
Citywide, all of the restaurant types except Italian had significant crude odd ratios for the prediction
of the highest grade. All of the restaurant types except American-style restaurants showed significant
odds ratios. Logistic regression further showed that Caribbean, Chinese, Italian, Japanese, Latin,
Mexican and Pizzerias had lower odds of receiving the highest grade when using American-style
restaurants as the reference.
CONCLUSIONS: This study suggested that there are associations between receiving the highest
inspection grade and restaurant type in New York City. Public and environmental health agencies
and professionals can use this study as a roadmap for building upon their own restaurant inspection
programs. Identifying who the lower performers are and the specific reasons as to why they are can be
used to help protect the public, particularly in the lowering of foodborne illness cases in a community.
Key words: Restaurants, Public Health, Inspections, Regression
(1) New York Institute of Technology Corresponding author: David W. Nadler, New York
Institute of Technology, Northern Boulevard, PO Box 8000,
Old Westbury, NY 11568. Tel. 800-345-6948.
Email: dwnadler@gmail.com
doi: 10.2427/9442
Published as Online First on September 15, 2014
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TWO-YEAR ANALYSIS OF RESTAURANT HEALTH GRADES IN NEW YORK CITY
INTRODUCTION
The New York City Department of
Health and Mental Hygiene (DOH) “conducts
unannounced inspection of restaurants at least
one time per year” [1]. Health inspectors look
at how a restaurant complies with regulations
which dictate how restaurants must handle
food, store food, control vectors, and employ
personal hygiene among its employees.
Depending upon the number and severity
of violations revealed during an inspection,
the restaurant may receive a letter grade
which must be posted in its front window.
This transparency easily allows for potential
customers to see how a restaurant fares before
entering and consuming a meal.
DOH has three scales of violations: public
health hazard, critical violation and general
violation [2]. Public health hazards may include
a restaurant not being able to keep food at
a certain temperature and warrants at least
seven points. Critical violations may include
a restaurant not washing food that is served
raw and would account for at least five points.
The general violations, which are at least two
points, include not washing utensils before use.
A restaurant receiving no more than
thirteen points may receive a Grade A. Grade
B may be issued if the inspection point total
ranges from fourteen to twenty-seven points.
Finally, a Grade C may be given to a restaurant
if there are twenty-eight or more points scored.
Typical public health studies on restaurants
have focused on the prediction of foodborne
illnesses [3,4]. Data such as type of violation
and number of employees have been used
to predict foodborne illness in a community.
This study explored if there are significant
differences between a restaurant earning a
Grade A from DOH and the type of restaurant.
METHODS
Study Design
A cross sectional study design has
been used to conduct measurements on the
restaurants’ health department grades over a
two-year period [5,6]. The subjects, made up
of a representative sample of the population
at large, have their restaurant grading data
publicly available. A cross sectional design
was most feasible for this study because it is
appropriate for measuring the outcomes and
the other variables of interest at any given
point in time [7]. Organizing the data of those
with and without the highest grade earned
(“A”), grouped by cuisine type, has allowed
for the prediction of the odds ratios of certain
restaurants receiving a Grade A from DOH.
This study relied on 547107 database
records made available by DOH. The main
association that was studied was between
the type of cuisine served at a restaurant
(predictor) and a Grade A (outcome). Certain
criteria were used to eliminate a number of
records from this study, including the years
of restaurant inspections, missing data, and
types of restaurants based on cuisine that had
less than 3% of the total number of recorded
inspections.
Study Population
The study population was composed of
restaurants in the five boroughs of New York
City that had at least one inspection in calendar
year 2011 or 2012. Restaurants identified as
“American” represented the greatest frequency
of those facilities that were inspected (23.7%).
Of the remaining types of restaurants, those
that accounted for at least 3% of the total
inspections were used in the sample population.
These restaurant types have been identified as
“Caribbean,” “Chinese,” “Italian,” “Japanese,”
“Latin,” “Mexican,” and “Pizzeria.” Furthermore,
grading data that was not scored as “A,” “B,”
or “C” was not used in data analysis. Table 1
presents the study population by cuisine type
and grade.
Sample Size and Data Collection
Raw data is available through the City
of New York’s “Open Data” program. Over
540 000 records were stored in the file for
restaurant inspections and grades. Records
meeting the following requirements were
eligible for study inclusion: 1) inspection date
in either calendar year 2011 or 2012, 2)
restaurant type had at least 3% of the City’s
total inspections, 3) a grade of “A,” B,” or “C”
was received, and 4) one of the five boroughs
of New York City was recorded. Records that
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Epidemiology Biostatistics and Public Health - 2014, Volume 11, Number 3
TWO-YEAR ANALYSIS OF RESTAURANT HEALTH GRADES IN NEW YORK CITY
did not meet all of these requirements were
excluded from analysis. In total, 132 002 data
records have been used in this study. The
sample population is presented in Table 1.
RESULTS
Bivariate Analysis
The eight restaurants used as an
independent variable showed a significant
Pearson’s chi-square statistic with restaurant
score in the bivariate analysis (X2 = 28,000,
p < 0.001). A one-way ANOVA was used
to test for differences between the types of
restaurants their grade. The effect of restaurant
type on score was significant, F (7, 84464)
= 172.17, p < 0.001. Bonferroni analysis was
significant between American restaurants
and Caribbean, Chinese, Japanese, Latin and
Mexican restaurants (p < 0.001); between
Caribbean restaurants and Chinese, Italian,
Latin restaurants and Pizzerias (p < 0.001);
between Chinese restaurants and Italian,
Japanese, Mexican restaurants and Pizzerias
(p < 0.001); between Italian restaurants and
Japanese, Latin and Mexican restaurants (p <
0.001); between Japanese restaurants and Latin
restaurants and Pizzerias (p < 0.001); between
Latin restaurants and Mexican restaurants and
Pizzerias (p < 0.001); and between Mexican
restaurants and Pizzerias (p < 0.001).
A one-way ANOVA was used to test for
differences between borough and restaurant
score. The effect of borough on score was
significant, F (4, 138908) = 36.46, p < 0.001.
Besides the insignificant difference in restaurant
scores between those in Queens and Brooklyn,
there was a significant difference between
every other borough-borough comparison
(i.e., Manhattan-Bronx, Queens-Staten Island,
Brooklyn-Bronx, and so forth).
Logistic Regression
The results of listwise logistic regression
are presented in Table 2. Grades of A were
coded as a 1 and Grades B and C were
coded as 0 to get a dichotomous outcome [8].
Records with missing data were not regressed
by the software for the listwise method. The
independent variable was regressed against
the outcome variable one at a time, providing
the crude odds ratios. The adjusted odds ratios
were recorded after every independent variable
was used in the same regression.
The grade outcome of a restaurant
inspection, either as a “Grade A” or “not a
Grade A”, was logistically regressed against the
eight restaurant types. The logistic regression
models were done citywide and individually
for each of the five boroughs of New York
City. The 2x2 table for logistic regression in
this study is represented in Figure 1. The odds
ratio for a restaurant type receiving a Grade A
is calculated as [9]:
Grade
Cuisine Type a B C ToTal
American 32 400 13 443 7 404 53 247
Caribbean 3 261 1 924 1 194 6 379
Chinese 10 948 8 887 4 961 24 796
Italian 6 005 3 023 1 605 10 633
Japanese 3 416 2 449 1 290 7 155
Latin 4 861 3 649 2 419 10 929
Mexican 3 507 2 238 1 386 7 131
Pizzeria 6 836 3 376 1 520 11 732
Total 71 234 38 989 21 779 132 002
TaBle 1
sample populaTion from nyC open daTa
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TWO-YEAR ANALYSIS OF RESTAURANT HEALTH GRADES IN NEW YORK CITY
Citywide, all of the restaurant types except
Italian had significant crude odd ratios for the
prediction of a Grade A. When adjusted, all but
American restaurants showed significant odds
ratios. Figures 2 and 3 present the citywide
odds ratios by restaurant type and by borough.
In Manhattan, six of the restaurant types
showed significant odds ratios for receiving a
Grade A. The odds ratios are interpreted as the
odds of a specific restaurant type receiving a
Grade A compared to all other restaurant types
receiving a Grade A divided by the restaurant
type not receiving a Grade A compared to all
other restaurant types not getting a Grade A.
The logistic regression model was significant
(X2 = 1393, p < 0.001, n = 55770), Chinese
(OR = 0.33, 95% CI = 0.30 – 0.35), Italian (OR
= 0.78, 95% CI = 0.73 – 0.84), Japanese (OR =
0.51, 95% CI = 0.47 – 0.55), Latin (OR = 0.47,
95% CI = 0.43 – 0.52), Mexican (OR = 0.71, 95%
CI = 0.64 – 0.79) and Pizzerias (OR = 0.77, 95%
CI = 0.71 – 0.84).
In The Bronx, seven of the restaurant types
showed significant odds ratios for receiving a
Grade A. The logistic regression model was
significant (X2 = 438, p < 0.001, n = 13952),
Caribbean (OR = 0.72, 95% CI = 0.61 – 0.84),
Chinese (OR = 0.51, 95% CI = 0.46 – 0.57),
Italian (OR = 0.54, 95% CI = 0.44 – 0.65),
Japanese (OR = 0.27, 95% CI = 0.19 – 0.39),
Latin (OR = 0.41, 95% CI = 0.37 – 0.46),
Mexican (OR = 0.41, 95% CI = 0.34 – 0.48) and
Pizzerias (OR = 0.70, 95% CI = 0.61 – 0.80).
In Brooklyn, seven of the restaurant types
showed significant odds ratios for receiving a
Grade A. The logistic regression model was
significant (X2 = 509, p < 0.001, n = 33879),
American (OR = 0.91, 95% CI = 0.85 – 0.96),
Caribbean (OR = 0.59, 95% CI = 0.54 – 0.65),
Chinese (OR = 0.54, 95% CI = 0.51 – 0.58),
Italian (OR = 0.80, 95% CI = 0.71 – 0.90),
Japanese (OR = 0.71, 95% CI = 0.62 – 0.81),
Latin (OR = 0.56, 95% CI = 0.50 – 0.62), and
Mexican (OR = 0.57, 95% CI = 0.51 – 0.63).
In Queens, six of the restaurant types
showed significant odds ratios for receiving a
Grade A. The logistic regression model was
significant (X2 = 368, p < 0.001, n = 31385),
American (OR = 1.09, 95% CI = 1.02 – 1.16),
Caribbean (OR = 0.78, 95% CI = 0.70 – 0.88),
Chinese (OR = 0.63, 95% CI = 0.58 – 0.67), Latin
(OR = 0.65, 95% CI = 0.59 – 0.70), and Mexican
(OR = 0.73, 95% CI = 0.65 – 0.83).
In Staten Island, three of the restaurant types
showed significant odds ratios for receiving
a Grade A. The logistic regression model was
significant (X2 = 92, p < 0.001, n = 4615),
American (OR = 0.80, 95% CI = 0.68 – 0.93),
Caribbean (OR = 0.41, 95% CI = 0.18 – 0.94),
VariaBle Crude or 95% Ci adjusTed or 95% Ci
American 1.23 1.20 – 1.26 0.99 0.95 – 1.01
Caribbean 0.80 0.75 – 0.85 0.67 0.63 – 0.72
Chinese 0.55 0.53 – 0.57 0.50 0.48 – 0.51
Italian 0.99 0.94 – 1.04 0.84 0.79 – 0.88
Japanese 0.70 0.66 – 0.74 0.59 0.56 – 0.63
Latin 0.62 0.59 – 0.65 0.54 0.51 – 0.56
Mexican 0.75 0.70 – 0.79 0.63 0.60 – 0.67
Pizzeria 1.08 1.03 – 1.14 0.91 0.86 – 0.95
TaBle 2
resulTs of indiVidual and lisTwise loGisTiC reGressions, CiTywide
fiGure 1
2x2 maTrix for odds raTio deVelopmenT.
Grade a noT a Grade a
Restaurant A B
Other Restaurants C D
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TWO-YEAR ANALYSIS OF RESTAURANT HEALTH GRADES IN NEW YORK CITY
Chinese (OR = 0.42, 95% CI = 0.34 – 0.52),
Japanese (OR = 0.67, 95% CI = 0.49 – 0.91),
Mexican (OR = 0.68, 95% CI = 0.50 – 0.93), and
Pizzerias (OR = 0.71, 95% CI = 0.55 – 0.92).
Using the coefficients from the logistic
regression of the selected model using the logit
command in Stata 12, the fitted equation for the
citywide prediction of a Grade A in the eight
studied restaurant types is:
logit(p) = 0.28 - 0,14X1 - 0.39X2 - 0.70X3 -
0.18X4 - 0.52X5 - 0.62X6 - 0.46X7 - 0.10X9, where
0.28 = constant,
X1 = American,
X2 = Caribbean,
X3 = Chinese,
X4 = Italian,
X5 = Japanese,
X6 = Latin,
X7 = Mexican,
X8 = Pizzerias.
fiGure 2
odds raTios By resTauranT Type
fiGure 3
odds raTios By BorouGh
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TWO-YEAR ANALYSIS OF RESTAURANT HEALTH GRADES IN NEW YORK CITY
DISCUSSION
Implications for Practice
There are some important outcomes in this
study for the prediction of a Grade A rating for
restaurants in New York City. One outcome
of this study is the prediction of the odds
for a particular style of restaurant receiving a
Grade A inspection. An exhaustive literature
review did not reveal similar outcomes or
studies. Analogous studies typically analyzed
associations between restaurant violations and
the likelihood of foodborne illnesses. Future
studies may refer to the database for the
development of a model on restaurant grades,
cuisines and the types of violations cited.
This study can be used by health
departments across the United States to help
them streamline restaurant inspections. It has
been shown in this study that there are
disparities between different restaurant types
and the likelihood of the highest grade granted
compared to other restaurant types in the
region. Assuming that each health department
inspects all restaurants in their jurisdiction
with the same frequency, how can they use
these presented results? One answer would
be to develop a strategy that would focus on
why certain restaurant types fare better than
others. Italian restaurants in Staten Island have
much higher odds of getting a Grade A than
Japanese restaurants in The Bronx. This has
been shown to be statistically significant. It
would appear that a logical step would be for
DOH to focus on how the Japanese restaurants
in The Bronx could increase their odds of
receiving the highest rating possible. Solutions
should focus on discussions between the local
inspection agencies and the statistically poorer
performers to develop a roadmap to success.
Specific violation types would need to be
analyzed in a future study to help advance the
underperformers. This was beyond the scope
of this study.
This study also showed disparities
between the five boroughs of New York City.
Each borough is its own county, so health
departments which may oversee more than one
county can develop a protocol for 1) finding
how restaurants fare from county-to-county
and 2) where they would need to focus their
resources to help restaurants get better grades.
This may be more practical at the state level.
Limitations
Sample Size. The size of the sample
population was large enough for the number of
variables, power and effect size chosen for this
study. Furthermore, the methodology chosen
was the correct technique for this type of study.
The restaurant type variable was not a diversely
recorded one; the American style restaurant
accounted for approximately 40 % of the data
in the study.
Lack of Prior Research. Previous studies
had more focus on the prediction of foodborne
illnesses from restaurant data. Petran et al.
[3] developed a predictive tool for the risk
of norovirus, Salmonella and Clostridium
perfringens outbreaks. Other studies have used
restaurant grades are interpreted by the public
and health inspectors [10]. Results of this study
cannot be compared directly to similar studies.
Longitudinal Effects. This study did not
account for the period before 2011 and after
2012 within the study population. Accounting
for this type of effect would be better suited for
a long-term study.
Missing Variables. A number of other
variables could have been collected to help test
the hypotheses that were posed in this study.
Sato [11] used a number of additional variables
were included such as type of ownership
(corporation or franchise), number of years
under current ownership, number of menu
items, hours of operation, number of food
handlers employed, and number of customers
served daily.
CONCLUSIONS
This study suggested that there are
associations between receiving a Grade A
inspection and restaurant type. American-style
restaurants, when compared to seven other
ethnicity-specific restaurants, did not have
significant odds of receiving a Grade A. Five
restaurant types had significant odds ratio
less than 0.85 of receiving a Grade A from
inspections across New York City as a whole.
Several other restaurant styles, such as “Indian,”
“Coffee Shops,” and “Thai” were not included
in this study due to lower inspection data in
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TWO-YEAR ANALYSIS OF RESTAURANT HEALTH GRADES IN NEW YORK CITY
comparison to the ones used. A future study
should incorporate all types of restaurants and
use “American” restaurants as the reference
group. Odds ratios of the ones presented in this
study would differ when all restaurant types
are used in the prediction modeling.
Public and environmental health agencies
and professionals can use this study as a
roadmap for building upon their own restaurant
inspection programs. Identifying who the lower
performers are and the specific reasons as
to why they are can be used to help protect
the public, particularly in the lowering of
foodborne illness cases in a community.
A novel equation was developed based
upon those variables used in the statistical
analyses. In its current state, this equation
should be used as one to be built upon as a
method for predicting a Grade A inspection.
This study was conducted because there
is no other research that appears in peer-
reviewed literature that poses the same
questions. Several peer-reviewed journals were
searched for restaurant grading and cuisine
type, particularly from 2009 through present.
Most models using restaurant data were for the
prediction of foodborne illnesses, not grades
given by public health inspectors. The results
of this study should be used as a building block
for the enhancement of this field of research.
References
[1] New York City Department of Health and Mental
Hygiene. Restaurant inspections. Available from
http://www.nyc.gov/html/doh/html/services/restau-
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[2] New York City Department of Health and Mental
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http://www.nyc.gov/html/doh/downloads/pdf/rii/
how-we-score-grade.pdf. [Accessed 2012]
[3] Petran R, White B, Hedberg C. Using a theoreti-
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[5] Berger ML. Health care cost, quality, and outcomes:
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[7] Friis RH, Sellers TA. Epidemiology for public
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[8] Lunt, M. Modeling binary outcomes. Available from
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[10] Dundes L, Rajapaksa S. Scores and grades: A sam-
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[11] Sato, HD. Risk assessment model to predict food-
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