Seafood Consumption Trends among U.S. Consumers: Influences of the COVID-19 Pandemic
Abstract
:1. Introduction
2. Materials and Methods
2.1. Survey Design
2.2. Participants
2.3. Statistical Analysis
3. Results
3.1. Adherence to Seafood Intake Recommendations
3.2. Changes in Seafood Intake during the COVID-19 Pandemic
3.3. Home Seafood Consumption and Online Seafood Shopping
3.4. Types of Seafood Eaten during the Pandemic
4. Discussion
Study Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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U.S. Adults (N = 1200) | ||
---|---|---|
n | % | |
Respondent or Household Member Eats Seafood | 1155 | 96.3% |
Gender | ||
Female | 627 | 52.3% |
Male | 555 | 46.3% |
Non-Binary/Prefer to Self-Describe * | 18 | 1.5% |
Race/Ethnicity | ||
Non-Hispanic White | 816 | 68.0% |
Hispanic | 137 | 11.4% |
Non-Hispanic Black | 132 | 11.0% |
Asian | 42 | 3.5% |
Other | 73 | 6.1% |
Education Level Attained | ||
Less than High School Diploma | 41 | 3.4% |
High School Graduate/GED | 362 | 30.2% |
College, Less than 4-year Degree | 406 | 33.9% |
College, 4-year Degree or Higher | 391 | 32.5% |
Marital Status | ||
Currently Married or Domestic Partnership | 668 | 55.7% |
Not Married | 532 | 44.3% |
Child(ren) in the Household | ||
Child(ren) Under 5 years Reside in the Household | 87 | 7.3% |
Child(ren) Between 5 and 18 years Reside in the Household | 203 | 16.9% |
Prefer Not to Indicate Whether Children Reside in the Household * | 12 | 1.0% |
Household Income Level | ||
$29,999 or less | 250 | 20.8% |
$30,000–$59,999 | 290 | 24.2% |
$60,000–$99,999 | 270 | 22.5% |
$100,000 and above | 271 | 22.6% |
Prefer not to indicate income level | 119 | 9.9% |
Coastal or Shoreline State of Residence | ||
No Coast or Shoreline | 234 | 19.5% |
Atlantic Coast | 232 | 19.3% |
Pacific Coast | 193 | 16.1% |
Gulf Coast | 221 | 18.4% |
Great Lakes Shoreline | 320 | 26.7% |
Estimated Coefficient (Standard Deviation) | Marginal Effects (Standard Deviation) | 95% Confidence Intervals | |||||
---|---|---|---|---|---|---|---|
Income ($10,000) | 0.045 *** (0.008) | 0.005 *** (0.001) | 0.028–0.061 | ||||
Male (1/0) | −0.204 (0.201) | −0.024 (0.023) | −0.599–0.190 | ||||
Married (1/0) | −0.422 *** (0.021) | −0.051 *** (0.004) | −0.463–−0.380 | ||||
Having child(ren) under 5 years (1/0) | 0.273 (0.597) | 0.0352 (0.083) | −0.898–1.444 | ||||
Having child(ren) aged 5–18 years (1/0) | −0.061 (0.265) | −0.007 (0.031) | −0.581–0.459 | ||||
Race (base = non-Hispanic White) | |||||||
Hispanic | 0.628 ** (0.263) | 0.087 ** (0.044) | 0.111–1.144 | ||||
Non-Hispanic Black | 0.919 *** (0.270) | 0.139 *** (0.048) | 0.390–1.448 | ||||
Asian | 0.895 *** (0.078) | 0.141 *** (0.013) | 0.743–1.047 | ||||
Other races | 0.773 (0.581) | 0.117 (0.105) | −0.366–1.911 | ||||
Age cohort (base = Silent Generation) | |||||||
Baby Boomer | −0.699 *** (0.150) | −0.077 *** (0.017) | −0.992–−0.406 | ||||
Generation X | −0.299 * (0.164) | −0.034 * (0.018) | −0.620–0.022 | ||||
Millennial | −0.872 *** (0.115) | −0.091 *** (0.009) | −1.097–−0.648 | ||||
Generation Z | −0.624 (0.544) | −0.061 (0.045) | −1.691–0.442 | ||||
Coastal residency (base = no coast/shoreline) | |||||||
Atlantic Coast | 0.771 *** (0.064) | 0.109 *** (0.012) | 0.646–0.896 | ||||
Pacific Coast | −0.119 ** (0.056) | −0.014 ** (0.007) | −0.228–−0.010 | ||||
Gulf Coast | −0.616 *** (0.221) | −0.064 *** (0.019) | −1.049–−0.183 | ||||
Great Lakes shoreline | 0.467 *** (0.070) | 0.060 *** (0.009) | 0.329–0.605 | ||||
Educational attainment (base = High School Graduate or GED) | |||||||
Less than High School Diploma | 0.146 (0.220) | 0.018 (0.028) | −0.285–0.576 | ||||
College, less than 4-year degree | 0.757 *** (0.087) | 0.101 *** (0.015) | 0.587–0.927 | ||||
College, 4-year degree or higher | 0.811 (0.509) | 0.107 (0.076) | −0.186–1.809 | ||||
Employment (base = not currently employed) | |||||||
Currently employed | 0.466 ** (0.237) | 0.061 * (0.033) | −0.529–1.051 | ||||
Retired | 0.261 (0.403) | 0.031 (0.048) | 0.002–0.930 | ||||
Religious affiliation (base = no affiliation, atheist, or agnostic) | |||||||
Religion, non-Christian | −0.198 (0.139) | −0.022 (0.014) | −0.471–0.075 | ||||
Religion, Christian | 0.185 (0.186) | 0.022 (0.023) | −0.179–0.550 | ||||
Familiarity with DGA (base = not familiar) | |||||||
Some familiarity | 0.209 *** (0.045) | 0.025 *** (0.006) | 0.121–0.298 | ||||
Very familiar | 0.653 ** (0.274) | 0.094 ** (0.045) | 0.115–1.190 | ||||
Eats seafood for health | 0.164 *** (0.023) | 0.020 *** (0.003) | 0.120–0.208 | ||||
Agrees seafood improves heart health | 0.485 *** (0.165) | 0.054 *** (0.018) | 0.161–0.809 | ||||
Agrees seafood improves blood pressure | 0.462 * (0.253) | 0.056 * (0.031) | −0.034–0.958 | ||||
Constant | −3.353 *** (0.174) | ||||||
State fixed effects | Yes | ||||||
Cluster estimator by coastal vs. non-coastal province | |||||||
No. of observations: 1004 | |||||||
Pseudo-R2: 13.76% | |||||||
*** p < 0.01, ** p < 0.05, * p < 0.1 | |||||||
Actual outcome: meeting DGA (1) or not meeting DGA (0) | |||||||
0 | 1 | ||||||
Predicated outcome: meeting DGA (1) or not meeting DGA (0) | 0 | 803 | 162 | ||||
1 | 80 | 26 | |||||
Correct prediction ratio | 90.94% | 13.84% | 77.40% |
Decreased Seafood Intake | U.S. Residents |
---|---|
n = 151 (13.9%) | |
Usually eat seafood at restaurants | 45.8% |
Seafood was more expensive | 29.4% |
Eating more frequently at home | 29.2% |
Had less money available | 24.7% |
Lack of available seafood | 15.6% |
Difficult to prepare seafood | 10.6% |
Other | 9.0% |
Don’t know how to prepare seafood | 6.1% |
Don’t want seafood in grocery delivery | 4.7% |
Sustainability reasons | 2.6% |
Hosting fewer guests | 1.6% |
Increased Seafood Intake | U.S. Residents |
n = 140 (12.9%) | |
Health reasons | 44.5% |
Seafood is easy to prepare | 40.0% |
Adding variety to diet | 35.1% |
Eating more frequently at home | 32.7% |
Eating more protein | 30.7% |
Eating more often with seafood eaters | 25.6% |
Increased availability of seafood | 19.5% |
Had more money available | 18.4% |
Found a good seafood recipe | 13.0% |
Seafood was less expensive | 12.3% |
Typical meat/poultry was less available | 8.1% |
Ate at restaurants more frequently | 7.4% |
Other | 6.5% |
Reasons for Not Purchasing Seafood to Prepare at Home | U.S. Residents |
---|---|
n = 189 (16.4%) | |
I dislike the preparation | 26.4% |
Other | 21.7% |
I don’t like the taste | 19.9% |
The seafood I want is not affordable | 17.4% |
I don’t like the smell | 15.7% |
I don’t know how to prepare it | 13.1% |
My family doesn’t like the taste | 9.0% |
I don’t know how to select seafood to buy | 5.0% |
The seafood I want is not available where I shop | 4.7% |
My family doesn’t like the smell | 4.6% |
Reasons for Not Including Seafood in Grocery Orders | U.S. Residents |
---|---|
n = 148 | |
I prefer to pick out seafood myself | 66.6% |
Seafood in my order might be spoiled or damaged | 29.5% |
Other reasons | 13.0% |
I personally do not eat seafood | 10.5% |
Reasons for Not Purchasing Meal Kits that Contain Seafood | U.S. Residents |
n = 25 | |
I didn’t like the available seafood options | 29.1% |
Seafood options were not available | 24.0% |
Seafood in my order might be spoiled or damaged | 23.0% |
Other reasons | 20.6% |
I personally do not eat seafood | 6.1% |
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Errickson, L.B.; Jin, Y.; Zemeckis, D.; Hallman, W.K. Seafood Consumption Trends among U.S. Consumers: Influences of the COVID-19 Pandemic. Foods 2024, 13, 2682. https://doi.org/10.3390/foods13172682
Errickson LB, Jin Y, Zemeckis D, Hallman WK. Seafood Consumption Trends among U.S. Consumers: Influences of the COVID-19 Pandemic. Foods. 2024; 13(17):2682. https://doi.org/10.3390/foods13172682
Chicago/Turabian StyleErrickson, Lauren B., Yanhong Jin, Douglas Zemeckis, and William K. Hallman. 2024. "Seafood Consumption Trends among U.S. Consumers: Influences of the COVID-19 Pandemic" Foods 13, no. 17: 2682. https://doi.org/10.3390/foods13172682