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Nursing Home Resident Weight Loss During Coronavirus Disease 2019 Restrictions

Published:August 28, 2020DOI:https://doi.org/10.1016/j.jamda.2020.08.032
      To the Editor:
      To mitigate spread of the 2019 novel coronavirus (SARS-CoV-2), a Chicago area nursing home ceased all nonessential visitors on March 14, 2020 and replaced group meals with in-room delivery beginning March 23 following Center for Medicare and Medicaid Services and Department of Public Health guidelines.
      Services CfMaM
      Guidance for Infection Control and Prevention of Coronavirus Disease 2019 in Nursing Homes.
      Residents requiring assistance ate with a nursing assistant at staggered times. Intervals between meals were uneven, resulting in reduced appetite and consumption when meals were closer together than usual. Group activities ceased and residents were encouraged to stay in their rooms, eliminating a common source of physical activity. This restriction also reduced mealtime conversation and social interactions among residents that are known to support consumption during meals.
      • Palese A.
      • Bressan V.
      • Kasa T.
      • et al.
      Interventions maintaining eating independence in nursing home residents: A multicentre qualitative study.
      Finally, family visits ceased so residents did not receive outside food. This study evaluated the effects of these restrictions on nursing home residents' weight. We conducted a secondary data analysis of nursing home resident care plan weights from a single 240-bed nonprofit nursing home located in a suburban area, using all residents with at least 1 weight measurement per month from December 2019 through April 2020. For residents with multiple weight measurements in a month, we calculated an average value for the month. We defined a clinically significant weight change episode as ≥5% within a 30-day period or ≥10% within a 180-day period based on the definition in the Minimum Data Set.
      Services Centers for Medicare and Medicaid Services
      Long-Term Care Facility Resident Assessment Instrument 3.0, User's Manual.
      A binary covariate was created to represent whether weights were recorded before (December, January, February) or after (April) implementing restrictions. March weights were excluded because some were made before and others after implementing nursing home restrictions. Weight measurements were nested within persons so a mixed model was estimated to account for dependencies between those observations.
      • Snijders T.A.B.
      • Bosker R.J.
      Multilevel Aanalysis: An Introduction to Basic and Advanced Multilevel Modeling.
      This model included a fixed effect and random slope for the binary restrictions variable (within-person), and fixed effects for the time trend (within-person), age and sex (between-person), and unstructured residual covariances. Analyses were conducted in SPSS v 26 (IBM, Armonk, NY). The average age of the sample (n = 166) was 86.9 years (range = 61‒102 years) and 67.5% were female; 60.8% of residents had a cognitive impairment diagnosis and 52.2% had a depression or anxiety diagnosis. Mean December weight in pounds was 156.75 ± 42.05, January weight was 156.23 ± 41.10, February weight was 156.00 ± 41.52, March weight was 154.77 ± 41.20, and April weight was 151.82 ± 40.23. From February to April, 67% (n = 111) of residents lost weight, and 23% (n = 39) lost over 5% body weight. From December to April, 11% (n = 18) lost >10% body weight [vs 2% (n = 3) who gained >10% weight]. Most of the weight variability over a short-time period in this diverse sample was between people rather than within people (intraclass coefficient = .98). Coefficients from the mixed model are shown in Table 1. Older adults weighed 3.68 lb less after implementing nursing home restrictions than they averaged in the 3 months before restrictions before. This model adjusted for a linear trend of time from December through April (which was not statistically significant by itself), age-related weight differences of –1.92 lb/year, and sex differences. Men weighed an average of 23.7 lb more than women. The random effect for the nursing home restrictions variable indicated that responses varied significantly between people. Significant weight loss occurred among nursing home residents in the month following implementation of restrictions on visitors and group dining due to COVID-19. Social distancing slows disease spread
      • Courtemanche C.
      • Garuccio J.
      • Le A.
      • et al.
      Strong social distancing measures in the United States reduced the COVID-19 growth rate.
      but can also have unintended consequences and adverse physical health effects that impact vulnerable older adults. A comprehensive social distancing strategy should include countermeasures for those unintended consequences. Nursing homes must be diligent about weight monitoring and enriching care plans with nutritional and physical activity interventions to preserve nonlean and lean body mass. Other measures to address adequate food intake include training and engaging all staff (including administrators and activities staff) on meal assistance and engaging family caregivers and volunteers to assist; however, Centers for Medicare and Medicaid Services guidelines prohibit nonnursing staff from assisting with feeding and all family caregivers and volunteers were restricted from entering the nursing home during the COVID pandemic. Given the current strict regulations in the nursing home industry, policy changes that allow some flexibility for nursing home operators in emergent situations to utilize creative strategies such as repurposing larger spaces for physical distant group dining or permitting private-duty caregivers may assist with feeding and ultimately, maintain resident health and well-being. Limitations of this study include the single site for data collection and the lack of randomization to restrictions. Research is needed to identify other unintended consequences of social distancing and evaluate the efficacy of countermeasures to protect the well-being of nursing home residents.
      Table 1Multilevel Model Coefficients for Predictors of Weight Loss among Nursing Home Residents
      ParametersEstimateSE95% CITest StatisticP
      Fixed effects
       Intercept151.683.52144.72, 158.6443.06.000
       Age−1.920.33‒2.58, −1.26−5.74.000
       Sex23.746.9010.11, 37.383.44.001
       Mo−0.240.26‒0.76, 0.29−0.89.37
       NH restrictions−3.681.00‒5.65, −1.71−3.68.00
      Variance components
       Intercept (1)1393.94164.181106.60, 1755.898.49.000
       NH restrictions (2)27.326.8116.76, 44.544.01.000
       Covariance (1, 2)−58.5723.90‒106.41, −11.73−2.45.014
       Residual20.911.7217.80, 24.5612.19.000
      CI, confidence interval; SE, standard error.
      Age was centered around the mean of 86.91 years. Sex was coded as 0 (women) and 1 (men). Month was coded as 0 (December), 1 (January), 2 (February), and 4 (April). NH restrictions were coded as 0 (December/January/February) or 1 (April). Test statistics for fixed effects and variance components were t-tests and Wald z, respectively.
      NH restrictions are the time periods where nursing home restrictions were implemented.

      Supplementary Data

      Supplementary data related to this article can be found online at https://doi.org/10.1016/j.jamda.2020.08.032.

      Supplementary Data

      References

        • Services CfMaM
        Guidance for Infection Control and Prevention of Coronavirus Disease 2019 in Nursing Homes.
        Centers for Medicare and Medicaid Services, Baltimore, MD2020
        • Palese A.
        • Bressan V.
        • Kasa T.
        • et al.
        Interventions maintaining eating independence in nursing home residents: A multicentre qualitative study.
        BMC Geriatr. 2018; 18: 292
        • Services Centers for Medicare and Medicaid Services
        Long-Term Care Facility Resident Assessment Instrument 3.0, User's Manual.
        Centers for Medicare and Medicaid Services, Baltimore, MD2018
        • Snijders T.A.B.
        • Bosker R.J.
        Multilevel Aanalysis: An Introduction to Basic and Advanced Multilevel Modeling.
        2nd ed. Sage Publishers, Thousand Oaks, CA2012
        • Courtemanche C.
        • Garuccio J.
        • Le A.
        • et al.
        Strong social distancing measures in the United States reduced the COVID-19 growth rate.
        Health Affairs. 2020; 39 (Project Hope)