| Literature DB >> 33398347 |
Benoît Lamarche1,2, Didier Brassard1,2, Annie Lapointe1, Catherine Laramée1, Michèle Kearney1, Mélina Côté1,2, Ariane Bélanger-Gravel1,3,4, Sophie Desroches1,2, Simone Lemieux1,2, Céline Plante5.
Abstract
BACKGROUND: The impact that the coronavirus disease 2019 (COVID-19)-related early lockdown has had on dietary habits of the population and on food insecurity is unknown.Entities:
Keywords: 24-hour recalls; COVID-19; diet quality; eating habits; prospective study; web-based study
Year: 2021 PMID: 33398347 PMCID: PMC7799255 DOI: 10.1093/ajcn/nqaa363
Source DB: PubMed Journal: Am J Clin Nutr ISSN: 0002-9165 Impact factor: 7.045
Baseline sociodemographic characteristics of all eligible participants in NutriQuébec, those who did not participate, and those who participated, as well as characteristics of participants after calibration weighting[1]
| Variable | All eligible ( | Did not participate ( | Participated ( | Participants ( |
|---|---|---|---|---|
| Sex | ||||
| Female | 2147 (86.1) | 1403 (85.4) | 744 (87.2) | 427 (50.1) |
| Male | 348 (13.9) | 239 (14.6) | 109 (12.8) | 426 (49.9) |
| Age | ||||
| 18–29 y | 357 (14.3) | 263 (16.0) | 94 (11.0) | 154 (18.0) |
| 30–49 y | 881 (35.3) | 660 (40.2) | 221 (25.9) | 272 (31.9) |
| 50–69 y | 1062 (42.6) | 614 (37.4) | 448 (52.5) | 288 (33.8) |
| ≥70 y | 195 (7.8) | 105 (6.4) | 90 (10.6) | 139 (16.2) |
| BMI (kg/m2) | ||||
| Normal weight, <25 | 1291 (51.7) | 847 (51.6) | 444 (52.0) | 383 (45.0) |
| Overweight, 25.0–29.9 | 707 (28.3) | 467 (28.4) | 240 (28.2) | 261 (30.6) |
| Obese, ≥30 | 497 (19.9) | 328 (20.0) | 169 (19.8) | 209 (24.5) |
| Smoking | ||||
| Never | 1608 (64.5) | 1074 (65.4) | 534 (62.6) | 525 (61.6) |
| Former | 759 (30.4) | 475 (28.9) | 284 (33.3) | 280 (32.8) |
| Current | 128 (5.1) | 93 (5.7) | 35 (4.1) | 48 (5.6) |
| Census metropolitan area | ||||
| Quebec | 633 (25.4) | 400 (24.4) | 233 (27.3) | 84 (9.8) |
| Montreal | 978 (39.2) | 636 (38.7) | 342 (40.1) | 431 (50.5) |
| Other | 884 (35.4) | 606 (36.9) | 278 (32.6) | 338 (39.7) |
| Education | ||||
| Trade school, high school, or no diploma | 362 (14.5) | 236 (14.4) | 126 (14.7) | 444 (52.0) |
| CEGEP | 576 (23.1) | 385 (23.5) | 191 (22.4) | 157 (18.4) |
| University | 1558 (62.4) | 1021 (62.2) | 537 (62.9) | 253 (29.6) |
| Household income | ||||
| ≤29,999 $CAD | 195 (7.8) | 137 (8.3) | 58 (6.8) | 82 (9.6) |
| 30,000–59,999 $CAD | 580 (23.2) | 379 (23.1) | 201 (23.6) | 261 (30.6) |
| 60,000–99,999 $CAD | 734 (29.4) | 474 (28.9) | 260 (30.5) | 280 (32.9) |
| ≥100,000 $CAD or more | 985 (39.5) | 652 (39.7) | 333 (39.1) | 230 (26.9) |
| Occupation | ||||
| Full-time worker | 1203 (48.2) | 864 (52.6) | 339 (39.8) | 359 (42.1) |
| Part-time worker | 189 (7.6) | 125 (7.6) | 64 (7.5) | 49 (5.8) |
| Student | 190 (7.6) | 136 (8.3) | 54 (6.4) | 78 (9.2) |
| Retired | 738 (29.6) | 389 (23.7) | 348 (40.8) | 295 (34.6) |
| Other | 175 (7.0) | 128 (7.8) | 47 (5.5) | 71 (8.3) |
| Marital status | ||||
| Married | 803 (32.2) | 500 (30.4) | 303 (35.6) | 292 (34.2) |
| De facto union | 911 (36.5) | 617 (37.6) | 294 (34.5) | 284 (33.3) |
| Widower, separated, divorced | 295 (11.8) | 188 (11.5) | 107 (12.5) | 102 (12.0) |
| Single | 486 (19.5) | 337 (20.5) | 149 (17.5) | 175 (20.5) |
| Use of dietary supplements | ||||
| No | 1229 (49.3) | 861 (52.4) | 368 (43.2) | 398 (46.7) |
| Yes | 1266 (50.7) | 781 (47.6) | 485 (56.8) | 455 (53.3) |
| Risk factors | ||||
| No risk factor | 2018 (80.9) | 1362 (83.0) | 656 (76.9) | 608 (71.3) |
| High cholesterol or high blood pressure | 369 (14.8) | 221 (13.4) | 148 (17.4) | 154 (18.1) |
| High cholesterol and high blood pressure | 108 (4.3) | 59 (3.6) | 49 (5.7) | 90 (10.6) |
| Chronic diseases | ||||
| No | 2154 (86.3) | 1441 (87.8) | 713 (83.6) | 693 (81.3) |
| Yes (cancer, heart disease, stroke, diabetes) | 341 (13.7) | 201 (12.2) | 140 (16.4) | 160 (18.7) |
| Screen time[ | ||||
| <3.0 h/d | 552 (22.1) | 339 (20.6) | 213 (25.0) | 205 (24.1) |
| 3.0–5.5 h/d | 643 (25.8) | 424 (25.8) | 219 (25.7) | 203 (23.8) |
| 5.6–7.5 h/d | 626 (25.1) | 412 (25.1) | 214 (25.1) | 192 (22.5) |
| ≥7.6 h/d | 673 (27.0) | 467 (28.5) | 206 (24.2) | 253 (29.7) |
| Vigorous physical activity[ | ||||
| <0.5 h/wk | 787 (31.6) | 518 (31.5) | 270 (31.6) | 285 (33.4) |
| 0.5–2.0 h/wk | 499 (20.0) | 326 (19.8) | 173 (20.3) | 185 (21.7) |
| >2.0 h/wk | 1209 (48.5) | 799 (48.7) | 410 (48.1) | 383 (44.9) |
| HEI-2015,[ | 67.0 ± 15.2 | 66.4 ± 16.2 | 68.3 ± 12.9 | 64.4 ± 15.9 |
1Data are specific to the HEI-2015 outcome. Weighted data: calibration based on recent provincial sociodemographic characteristics (sex, age, census metropolitan area, and education). CAD, Canadian dollars; CEGEP, Collège d'Enseignement Général et Professionnel; HEI-2015, Healthy Eating Index 2015; NCI, National Cancer Institute.
2Quartiles of screen time.
3Tertiles of vigorous physical activity.
4Values are means ± SDs. These means are not calibrated for within-individual random errors using the NCI method.
FIGURE 1Changein HEI-2015 and in individual components of the HEI-2015 in adults (n = 853) from the province of Quebec during the COVID-19–related early lockdown compared with baseline values. Data are presented as absolute scores and subscores (least-square means, left panel) and as mean change estimates (least-square means, right panel) with 95% CIs. Mixed models adjusted for sex, age, and census metropolitan areas, occupation, marital status, education, smoking, alcohol intake, cannabis use, supplement use, vigorous physical activity, screen time, presence of chronic diseases, and presence of high blood cholesterol, and/or hypertension to address confounding and selection bias. Analyses were also calibrated for sociodemographic characteristics (see Methods). Data on the HEI-2015 and its components were modeled for within-individual random errors related to dietary intake assessment using 24-h recalls based on the NCI method (see Methods). COVID-19, coronavirus disease 2019; HEI-2015, Healthy Eating Index 2015; NCI, National Cancer Institute.
FIGURE 2Exploratory analyses of change in the HEI-2015 during COVID-19 among various subgroups of the Quebec adult population (n = 853). Data are presented as mean change estimates (95% CIs) in each subgroup between the COVID-19 early lockdown (April–May 2020) and baseline (June 2019–March 2020). These are a posteriori descriptive analyses and therefore changes within each subgroup must be interpreted with caution. Each model was adjusted using calibration weights and included the terms reflecting the multiplicative interaction (P-interaction) between time point and the sociodemographic variable of interest. Because of their exploratory nature, data presented are unadjusted for covariables used in the analysis of the main outcome. This is why mean absolute HEI-2015 values are slightly higher than data shown in Figure 1. CEGEP is a technical college institution specific to the Quebec educational system. CEGEP, Collège d'Enseignement Général et Professionnel; COVID-19, coronavirus disease 2019; HEI-2015, Healthy Eating Index 2015; CMA, Census metropolitan area.
FIGURE 3Proportion of meals consumed outside of home prior to (June 2019–March 2020) and during (April–May 2020) the COVID-19 early lockdown in adults from the province of Quebec (n = 853). The ratio of eating-out occasions was calculated as the population mean number of meals eaten outside of home divided by the population mean total number of meals, using data from all 24-h recalls among all individuals at each time point in the study (see Methods). Ratios were weighted using the calibration weights for sociodemographic characteristics, but analyses were not further adjusted because of their descriptive and hypothesis-generating nature. COVID-19, coronavirus disease 2019.
FIGURE 4Prevalence of food insecurity in adults (n = 922) from the province of Quebec before and during the COVID-19 early lockdown (left panel) and difference between the 2 time points, presented as prevalence ratios (right panel). Food insecurity includes participants in the moderately food-insecure and severely food-insecure categories of the definition proposed by Statistics Canada (Supplemental Methods 1). In the first model (crude) data are unadjusted. In the second model (calibration) data are weighted for sociodemographic characteristics (sex, age, census metropolitan area, and education) to increase the generalizability of the results to the Quebec adult target population. The third model (calibration + nonresponse) further adjusts for the probability of participation in the substudy, hence addressing participation bias (see Methods and Supplemental Methods 1 for details). COVID-19, coronavirus disease 2019.