Differences in Intakes of Select Nutrients by Urbanization Level in the United States Population 2 Years and Older, NHANES 2013–2018

Abstract

BackgroundDifferences in nutrient intakes by urbanization level in the Unites States is not well understood. ObjectiveDescribe, by urbanization level: 1) intake of protein, fiber, percent of energy from added sugars (AS) and saturated fat (SF), calcium, iron, potassium, sodium, and vitamin D; 2) the percent of the population meeting nutrient recommendations. MethodsTwenty-four-hour dietary recalls from 23,107 participants aged 2 y and over from the 2013–2018 National Health and Nutrition Examination Surveys were analyzed. Usual intakes were estimated, and linear regression models adjusted for age, sex, race and Hispanic origin, and whether family income met the 130% threshold examined intake differences by urbanization levels—large urban areas (LUA), small to medium metro areas (SMMA), and rural areas (RA). ResultsA small percentage of the population met the nutrient recommendations, except for protein (92.8%) and iron (70.5%). A higher percentage of the population met recommendations than SMMA and RA for fiber (11.8% compared with 8.1% and 5.3%, P < 0.001), AS (40.2% compared with 33.4% and 31.3%, P < 0.001), SF (26.8% compared with 18.2% and 20.1%, P < 0.001), and potassium (31.5% compared with 25.5% and 22.0%, P < 0.001). Mean protein intake were also higher in LUA than RA (80.0 g compared with 77.7 g, P = 0.003) and fiber intake higher in LUA than SMMA (16.5 g compared with 15.9 g, P = 0.01) and RA (16.5 g compared with 15.2 g, P = 0.001). In addition, contributions to energy intake were lower in LUA than SMMA for AS (11.3% compared with 12.0%, P < 0.001) and SF (11.5% compared with 11.7%, P < 0.001), and for LUA than RA for AS (11.3% compared with 12.9%, P < 0.001) and SF (11.5% compared with 11.8%, P < 0.001). ConclusionsRA had some markers of poorer diet quality—lower protein and fiber intake and higher AS intake—compared with LUA, and these differences persisted in adjusted regression models. These results may inform public health efforts to address health disparities by urbanization levels in the Unites States.