Biomedical Engineering Reference
In-Depth Information
homes may lead to sanitation challenges in that they may serve as a breeding ground
for pests and/or rodents. Pothukuchi et al. investigated why a report of higher preva-
lence of food safety violations in stores in poorer neighborhoods and with higher
populations of African Americans might exist. Similar to the more recent FMI
report, they suggested that poor infrastructure, crime, and employee turnover likely
all contributed to challenges for small retailers (Pothukuchi and Mohamed
2008
). In
addition to time and money, Yapp et al. identifi ed potential barriers to food safety
compliance to include lack of trust in food safety regulations and compliance, as
well lack of motivation, knowledge, and understanding of food safety legislation
(Yapp and Fairman
2004
). These fi ndings would all imply the likelihood that small
independent markets would have more critical and non-critical code violations.
Kwon et al. found this to be true for ethnic restaurants in Kansas, with signifi cantly
more violations reported in ethnic restaurants for categories including time and tem-
perature controls, physical facility maintenance, protection from contamination,
and demonstrated knowledge (Kwon et al.
2010
).
Geographic Information Systems (GIS) technology has been used extensively to
map the access different populations have to different types of retail food outlets
(Charreire et al.
2010
). Darcey and Quinlan used GIS technology to map critical
health code violations (CHV) in retail facilities across a range of population
demographics in Philadelphia, Pennsylvania. Data regarding CHV over a 3 year
period was obtained from publically available health inspection data. Overall, it was
found that food service facilities in higher poverty areas had a greater number of
facilities with at least one CHV and had more frequent inspections than facilities in
areas with lower poverty (Table
11.3
). Additionally, CHV rates in census tracts with
Table 11.3
Distribution of zero/nonzero critical health code violation (CHV) establishments,
average CHV, and days between inspections by poverty category
Distribution of food service
facilities w/Zero CHV rate
Critical health code
violation
s
Days between
inspections
>0 CHV per
inspection
(% of area
vendors)
Average
days
between
inspection
Neighborhood
poverty (
n
= # of
census tracts)
Zero CHV per
inspection (% of
area vendors)
Total
vendors
(N)
Average
CHV per
inspection
Total
vendors
(N)
1. Low (
n
= 85)
689 (46 %)
809 (54.0 %)
1,498
0.93
a
1,039
241.2
b
2. Low-medium
(
n
= 95)
1,497 (51.7 %)
c
1,396 (48.3 %)
2,893
0.73
2,154
247.6
b
3. Medium
(
n
= 80)
1,079 (44.7 %)
1,334 (55.3 %)
2,413
0.75
1,825
207.2
4. High- medium
(
n
= 67)
996 (44.5 %)
1,241 (55.5 %)
2,237
0.72
1,703
204.1
5. High (
n
= 41) 788 (43.3 %)
c
1,030 (56.7 %) 1,818 0.77 1,444 214.4
a
Average CHV per inspection signifi cantly greater for low poverty (high income) category
(
p
< 0.001)
b
Average days between inspections were signifi cantly greater for the two lowest poverty (high
income) categories (
p
< 0.001) when compared to all other categories
c
The second lowest poverty category had the greatest number of facilities with zero CHVs and the
