| Literature DB >> 34917577 |
Emily J Nicklett1, Kimson E Johnson2,3, Lisa M Troy4, Maitreyi Vartak5, Ann Reiter1.
Abstract
Background: COVID-19 has imposed challenges for older adults to access food, particularly in minority, lower income, and rural communities. However, the impact of COVID-19 on food access, diet quality, and nutrition of diverse older adult populations has not been systematically assessed. Objective: To examine changes in food access, diet quality, and nutritional status among older adults during the COVID-19 pandemic and the potential differential impacts of the COVID-19 pandemic on these nutrition-related outcomes using the framework of the socio-ecological model.Entities:
Keywords: COVID-19; diet quality; food access; food security; nutritional status; older adults
Mesh:
Year: 2021 PMID: 34917577 PMCID: PMC8669368 DOI: 10.3389/fpubh.2021.763994
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Figure 1Literature search strategy: sources and exclusion criteria (published October 1, 2019 to March 1, 2021). CINAHL, PubMed, and Web of Science. Key search terms for capturing food access during COVID-19 included food access (or food security, food insecurity, diet, nutrition), older adults (or older adult, elder, elderly), and COVID-19 (or coronavirus). Above figure adapted from Moher et al. (38).
Characteristics of studies examining food access, diet quality, and nutrition among older adults during COVID-19.
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| 1) Bedock et al. ( | Assess malnutrition in hospitalized patients with COVID-19, investigate links between malnutrition and disease severity at admission, study impact of malnutrition on clinical outcomes | Medicine ward at a university hospital in Paris, France | Cross-sectional, non-experimental design | Nutritional status was defined using Global Leadership Initiative on Malnutrition (GLIM) criteria | The overall prevalence of malnutrition was 42.1%, reaching 66.7% among patients admitted from ICU. No significant association was found between nutritional status and clinical signs of COVID-19. Lower albumin levels were associated with a higher risk of transfer to ICU in adjusted models | Intrapersonal |
| 2) Cicero et al. ( | Evaluate the effect of COVID-related quarantine on smoking and dietary habits | Population-based sample representative of Brisighella, a rural North-Italian village | Longitudinal, non-experimental design | Dietary habits were assessed using the Dietary Quality Index (DQI), a validated tool providing information on the usual food intake of 18 food items, grouped in three food categories | COVID-19-related quarantine might worsen the overall quality of the diet, leading to an increased intake of almost all categories of food. Although trends are mixed, the overall results show a trend toward decreasing diet quality that could flag future health problems | Environmental |
| 3) Giebel et al. ( | Explore the impact of COVID-19 public health restrictions on the lives of older adults | Convenience sample of older adults in Uganda | Qualitative semi-structured interview study; non-experimental design | Diet and food access emerged in the following themes: economic impacts; lack of access to basic necessities; social impacts; and violent reinforcements of public health restrictions | Participants reported reducing food intake, in some cases to one meal a day, due to the economic impact of COVID-19 | Environmental |
| 4) Górnicka et al. ( | Identify patterns of dietary changes during COVID-19 pandemic | Polish residents aged 18 and older | Rapid, large cross-sectional online survey, non-experimental design | Questionnaire assessed “Impact of the COVID-19 Pandemic on the Diet and Lifestyle of adults” (PLifeCOVID-19), which measured patterns associated with dietary change, including Prohealthy, Constant, and Unhealthy | Older adults were significantly less likely to follow the Prohealthy pattern compared to those aged 30 and younger (67% lower in respondents aged 50-59 years and 78% lower in respondents aged 60+. Older adults were significantly more likely to follow the Constant pattern compared to those aged 30 and younger (3 × higher in respondents aged 50-59 and 2.8 × higher in respondents aged 60+). Adherence to the Unhealthy pattern was not significantly associated with age group | Intrapersonal/ |
| 5) Im et al. ( | Determine the nutritional status of COVID-19 patients, particularly as it pertains to immunity | Adults with COVID-19 admitted to Inha University Hospital, South Korea | Cross-sectional, non-experimental design using a control group for 25-hydroyvitamin D3 | Nutrient levels assessed included vitamin B1, B6, B12, vitamin D (25-hydroyvitamin D3), folate, selenium, and zinc | Severe vitamin D deficiency was found in 24% of patients in the COVID-19 group and 7.3% in the control group. A deficiency of vitamin D or selenium may decrease the immune defenses against COVID-19 and cause progression to severe disease | Intrapersonal |
| 6) Li et al. ( | Evaluate the prevalence of malnutrition and its related factors in older patients | COVID-19 patients admitted to Wuhan Tongji Hospital, China | Cross-sectional, non-experimental design | Nutritional status was assessed using the Mini Nutritional Assessment (MNA). Participants were categorized into non-malnutrition (MNA ≥ 24), risk of malnutrition (MNA 17–23.5) and malnutrition groups (MNA score <17) | The prevalence of malnutrition was high: 27.5% were in the group with malnutrition risk and 52.7% were in the malnutrition group. Nutrition support should be strengthened during treatment, especially among those with diabetes mellitus, low calf circumference, or low albumin | Intrapersonal |
| 7) Niles et al. ( | Assess food insecurity, food access, coping strategies, and suggested potential interventions among food secure, consistently food insecure, and newly food insecure respondents | Convenience sample using Limesurvey in Vermont, USA | Cross-sectional, non-experimental design | United States Department of Agriculture six-item validated food security module to measure food insecurity before and since COVID-19 | Overall, there was a nearly one-third increase in household food insecurity, with 35.5% of food insecure households classified as newly food insecure. Two-thirds of food insecure households eat less since COVID-19. Age was not significantly associated with food insecurity in this study | Interpersonal/ |
| 8) Otaki et al. ( | Examine the relationship between dietary variety and frailty during COVID-19 restrictions on outings | The study was conducted among Japanese women | Cross-sectional, non-experimental design | A dietary variety score (ranging from 0 to 10 points) was used to assess the food group intake | Diet was correlated with frailty in older adults living in the community during the period of restriction on outings due to COVID-19 | Intrapersonal/ |
| 9) Pironi et al. ( | Evaluate the prevalence of malnutrition and provided nutritional therapy | Clinical audit on adult patients hospitalized for COVID-19 in Bologna, Italy | One-day clinical audit of nutritional status and nutritional therapy | Assess malnutrition risk and diagnosis of malnutrition using modified Nutritional Risk Screening 2002 tool (NRS-2002) and modified Global Leadership Initiative on Malnutrition (GLIM) criteria | Most patients were at nutritional risk and one-half of them were malnourished. The frequency of nutritional risk, malnutrition, and decreased hospital diet intake differed by intensity group setting. Patient energy and protein intakes were at the lowest limit or below the recommended amounts, indicating the need for actions to improve nutritional care practice | Environmental |
| 10) Ruiz-Roso et al. ( | Examine the impact of the COVID-19 lockdown on nutrition and exercise habits | Patients with type 2 diabetes from the University Hospital La Princesa in Madrid, Spain | Cross-sectional, non-experimental design including network mapping and analyses | A food frequency questionnaire (FFQ), food craving questionnaire-state (FCQ-S) and food craving questionnaire-trait (FCQ-T) were used | Increases in vegetable, sugary food, and snack consumption were found. An association between levels of food cravings and snack consumption was also found | Intrapersonal/ |
| 11) Shinohara et al. ( | Clarify association between frailty and changes in lifestyle | Community-dwelling older adults residing in Takasaki City, Japan, helped regularly by volunteers | Cross-sectional, non-experimental design | As part of the Questionnaire for Change of Life (QCL), participants were asked about changes in meal size in the past 6 months during the pandemic | Meal size decreased significantly among older adults with frailty (compared to those without frailty) during the COVID-19 pandemic in Japan | Intrapersonal/ |
| 12) Visser et al. ( | Examine the self-reported impact of COVID-19 pandemic on nutrition and physical activity behaviors in Dutch older adults | Longitudinal cohort study of community-dwelling older adults in Amsterdam, the Netherlands | Longitudinal, non-experimental design | Frequency of perceived changes in nutrition behaviors during the past weeks due to COVID-19 (difficulty obtaining groceries, skipping warm meals, eating less than normal, eating too little or losing weight, and snacking more | An impact on nutritional behavior predisposing to overnutrition (e.g., snacking more) was reported by 20-32% and undernutrition (e.g., skipping warm meals) was reported by 7–15% of participants. COVID-19 appears to have a negative impact on nutrition and physical activity behavior of many older adults | Intrapersonal/ |
| 13) Zhao et al. ( | Identify nutritional risk and examine association with mortality risk among COVID-19 patients | West Campus of Union Hospital in Wuhan, China | Retrospective, observational study | Nutritional risk was assessed using Nutritional Risk Screening 2002 (NRS), including nutritional status (based on weight loss, BMI, and food intake) and disease severity. An NRS total score of >3 was considered “at risk.” Other nutritional biomarkers were assessed | Most patients, especially critically ill patients, had significant changes in nutrition-related parameters. Critically ill patients had significantly higher NRS scores, which were correlated with nutrition-related markers. Only 24% of at risk patients received nutritional support. Most severely and critically ill patients with COVID-19 are at nutritional risk. Patients with higher nutrition risk had worse outcomes and required nutrition therapy | Intrapersonal/ |
Intrapersonal factors refer to factors that impact individual access to resources (e.g., demographics, financial, mental health, physical health, and functional status). Interpersonal factors refer to factors that require informal assistance or connections with other people (e.g., emotional and financial support). Environmental factors refer to factors caused by related to an individual's surrounding (e.g., institutional, community and social structures, program availability, and policies to increase access).