| Literature DB >> 32517210 |
Ai Zhao1,2, Zhongyu Li3, Yalei Ke4, Shanshan Huo4, Yidi Ma4, Yumei Zhang4, Jian Zhang4, Zhongxia Ren4.
Abstract
COVID-19, a Public Health Emergency of International Concern, has imposed enormous challenges on the health system, economy, and food supply and has substantially modified people's lifestyles. This study aimed to (1) explore the dietary diversity during the lockdown time in China and (2) examine factors associated with dietary diversity including socio-economic characteristics, sources for food and food purchases, and specific dietary behaviors responding to COVID-19 and isolation. A cross-sectional questionnaire-based survey was conducted online in March 2020. Multi-stage sampling was used to recruit participants living in Hubei Province and other parts of China. Dietary diversity was assessed using the Household Dietary Diversity Score (HDDS) and clustering analysis was used to categorize people with different propensities of methods for purchasing or obtaining foods. Logistic regression was used to model the associations among HDDS, participants' characteristics, approaches to purchase or obtain food, and behaviors adopted to cope with COVID-19.Entities:
Keywords: COVID-19; dietary behaviors; household dietary diversity score
Mesh:
Substances:
Year: 2020 PMID: 32517210 PMCID: PMC7352896 DOI: 10.3390/nu12061699
Source DB: PubMed Journal: Nutrients ISSN: 2072-6643 Impact factor: 5.717
Figure 1Geographical distribution of participants in the study. The color of the map indicates the cumulative number of confirmed cases in each province by the end of 31 March, according to the report from the Chinese Disease and Control Center [13]. Bubble size in the bubble plot represents the sample size of every investigation point.
Food groups consumption in the last 24 h (n, %).
| Food Groups | Consumed | Not-Consumed |
|---|---|---|
| Cereals | 1896 (97.8) | 42 (2.2) |
| Tubers and roots | 1549 (79.9) | 389 (20.1) |
| Vegetables | 1913 (98.7) | 225 (1.3) |
| Fruits | 1823 (94.1) | 115 (5.9) |
| Meat, poultry and offal | 1547 (79.8) | 391 (20.2) |
| Fish and other seafood | 1110 (57.2) | 828 (42.7) |
| Eggs | 1801 (92.9) | 137 (7.1) |
| Pulses, legumes, and nuts | 1360 (70.1) | 578 (29.8) |
| Dairy products | 1545 (79.7) | 393 (20.3) |
| Oils and fats | 1817 (93.7) | 121 (6.2) |
| Sugar and honey | 1345 (69.4) | 593 (30.6) |
| Miscellaneous a | 1103 (56.9) | 835 (43.1) |
a Miscellaneous foods include condiments and most processed foods like snacks and beverages.
Household Dietary Diversity Score (HDDS) among participants with different socio-demographic characteristics.
| HDDS (Mean ± SD) b |
| |||
|---|---|---|---|---|
| Gender | Male | 665 (34.3) | 9.68 ± 2.61 | 0.275 |
| Female | 1273 (65.7) | 9.72 ± 2.04 | ||
| Age group(y) | 18–45 | 1620 (83.6) | 9.65 ± 2.10 | <0.001 |
| >45 | 318 (16.4) | 9.97 ± 1.89 | ||
| Education level | Senior high school or under | 219 (11.3) | 9.60 ± 2.23 | 0.121 |
| Bachelor degree | 1464 (75.5) | 9.76 ± 2.03 | ||
| Master degree or above | 255 (13.2) | 9.49 ± 2.08 | ||
| Family annual income (Chinese yuan) | <30 thousands | 206 (10.6) | 8.98 ± 2.43 | <0.001 a |
| 30–100 thousands | 690 (35.6) | 9.69 ± 2.02 | ||
| 100–300 thousands | 750 (38.7) | 9.80 ± 1.97 | ||
| >300 thousands | 292 (15.1) | 10.02 ± 2.06 | ||
| Family size (people living in the same household during isolation) | <3 | 979 (50.5) | 9.73 ± 1.97 | 0.887 |
| 3–5 | 760 (39.2) | 9.68 ± 2.09 | ||
| >5 | 197 (10.2) | 9.68 ± 2.41 | ||
| Geographic Region | Urban | 414(21.4) | 9.78 ± 2.04 | 0.003 |
| Rural | 1524(78.6) | 9.44 ± 2.15 | ||
| Pregnant or lactating women in the household | Yes | 44 (2.3) | 9.80 ± 2.37 | 0.770 |
| No | 1894 (97.7) | 9.70 ± 2.06 | ||
| <5 y children in the household | Yes | 325 (16.8) | 9.86 ± 2.12 | 0.143 |
| No | 1613 (83.2) | 9.67 ± 2.05 | ||
| >65 y elders in the household | Yes | 805 (41.5) | 9.70 ± 2.06 | 0.262 |
| No | 1133 (58.5) | 9.66 ± 2.03 |
ap for trend. b HDDS = Household Dietary Diversity Score. y = year
Figure 2Participants’ choices of approaches to obtain or purchase foods during the isolation period (%).
Odds of high HDDS comparing participants differ in geographic regions, approaches to obtain and purchase foods, frequency of outdoor activity, and dietary behaviors to cope with COVID-19.
| OR (95% CI) | ORadjust1 (95% CI) a | |||
|---|---|---|---|---|
| Participants characteristics | ||||
| Geographic regions by case number | <500case/province b | 987 (50.9) | 1 | 1 |
| >500case/province b | 862 (44.5) | 0.84 (0.70, 1.01) | 0.79 (0.65, 0.96) | |
| Hubei | 89 (4.6) | 0.58 (0.38, 0.90) | 0.60 (0.39, 0.93) | |
| Status during isolation | Self-isolation | 1254 (64.7) | 1 | |
| Working outside | 684 (35.3) | 1.04 (0.86, 1.25) | 0.95 (0.78, 1.15) | |
| Total of going out times | 0–2×/week | 401 (20.7) | 1 | 1 |
| 3–4×/week | 916 (47.3) | 1.13 (0.89, 1.43) | 1.06 (0.84, 1.35) | |
| ≥5×/week | 621 (32.0) | 1.01 (0.78, 1.30) | 0.91 (0.70, 1.78) | |
| Frequencies of going out for food purchase | 0–2×/week | 417 (21.5) | 1 | 1 |
| 3–4×/week | 1125 (58.0) | 1.20 (0.96, 1.51) | 1.15 (0.92, 1.45) | |
| ≥5×/week | 396 (20.4) | 1.06 (0.80, 1.40) | 0.97 (0.74, 1.29) | |
| Food purchasing behaviors c | ||||
| Cluster1 | 320 (16.5) | 1 | 1 | |
| Cluster2 | 752 (38.8) | 1.03 (0.79, 1.34) | 1.03(0.79, 1.35) | |
| Cluster3 | 866 (44.7) | 1.12 (0.87, 1.45) | 1.11(0.86, 1.44) | |
| Dietary behaviors in COVID-19 d | ||||
| Reported dietary behaviors to cope with COVID-19 | No | 126 (62.7) | 1 | 1 |
| Yes | 722 (37.2) | 1.27 (1.05, 1.53) | 1.23 (1.02, 1.45) | |
a Adjusting for age, family average annual income, and geographic region (rural or urban). b All provinces in Mainland China except Hubei. c Based on k-means clustering analysis, participants were clustered into three clusters. People in cluster 1 mainly depended on in-person grocery shopping to purchase food; people in cluster 2 depended on both in-person grocery shopping and in-house storage; and people in cluster 3 depended mostly on online food ordering and delivery services. d Certain dietary behaviors include increased intake of vitamin C, probiotics, other dietary supplements, alcohol andvinegar with the intention to cope with COVID-19.