Demographic, Lifestyle, and Dietary Factors Associated with Red Meat Consumption Among A Cross-Section of New York City Residents
DOI:
https://doi.org/10.22230/ijdrp.2021v3n2a301Keywords:
red meat, nutrition, policyAbstract
Introduction: Red meat reduction policies have become the focus of public policy in New York City (NYC). To inform on who might be impacted most by these policies, we sought to identify factors associated with red meat consumption among NYC residents.
Methods: We studied non-institutionalized adults in the cross-sectional 2013-2014 NYC Health and Nutrition Examination Survey. The outcome was self-reported weekly red meat consumption. We used multivariable linear regression to assess the association of red meat consumption with age, gender, race/ethnicity, US nativity, education, marital status, percentage of ZIP code in poverty, physical activity, smoking, alcohol, restaurant meal consumption, and dietary components (dark-green vegetables, other vegetables, fresh fruit, poultry, fish/shellfish, sugar-sweetened soda (SSS), and sugar-sweetened fruit drink (SSFD)).
Results: Among 1,495 subjects, higher frequency of red meat consumption was associated (b-coefficient; p-value) with younger age (-0.08; p = .03), male gender (0.47; p < .001), and greater weekly consumption of alcohol (0.08; p < .001), poultry (0.16; p < .001), fish/shellfish (0.15; p = .01), SSS (0.14; p < .001), and SSFD (0.06; p = .005). Red meat consumption was also associated with race/ethnicity (p = .002); subjects who identified as Asian had the highest consumption. Percentage of ZIP code in poverty also associated with red meat consumption (p = .003), wherein those in poorest ZIP codes (≥ 30% in
poverty) followed by the richest ZIP codes (<10% in poverty) consumed less red meat than those in ZIP codes with 10-20% and 20-30% in poverty.
Conclusion: Demographic, lifestyle, and dietary factors were associated with red meat consumption frequency in NYC. Public health efforts in NYC should consider these associations and differences from associations in national data when designing and evaluating outcomes from programs targeting reducing red meat consumption in NYC.