Reducing COVID-19 hospitalization risk through behavior change

  1. Mary L. Adams
  2. David L. Katz
  3. Joseph Grandpre
  4. Douglas Shenson

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Abstract

Our objective was to determine strategies that could potentially reduce the risk of hospitalizations from COVID-19 due to underlying conditions. We used data (N=444,649) from the 2017 Behavioral Risk Factor Surveillance System to identify potentially modifiable risk factors associated with reporting any of the underlying conditions (cardiovascular disease, asthma, chronic obstructive pulmonary disease, diabetes, hypertension or obesity) found to increase risk of US hospitalizations for COVID-19. Risk factors included lifetime smoking, sedentary lifestyle, and inadequate fruit and vegetable consumption. Multiple logistic regression in Stata produced adjusted odds ratios (AORs) used to estimate population attributable-risk (PAR) in Excel. PARs for the 3 risk factors ranged from 12.4% for inactivity to 15.6% for diet for a combined PAR of 36.3%, implying that total elimination of these 3 risk factors could potentially reduce underlying conditions as much as 36%. This suggests that reducing COVID-19 hospitalizations might be a measurable and feasible US goal for the coronavirus pandemic. The simple lifestyle changes of increasing physical activity and fruit and vegetable consumption could reduce obesity, a key underlying condition and risk factor for 4 others. Reducing obesity and inactivity may also boost immunity. With uncertainly around how long the pandemic might last, other proposed strategies include wearing face masks when social distancing is not feasible, and addressing the special issues for nursing home residents. Such actions have the potential to lessen the impact of COVID-19 in the short term along with providing long term health benefits regarding chronic conditions.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.21.20159350: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Reliability and validity of the BRFSS have been found to be moderate to high for many survey measures.
    BRFSS
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: There are several limitations to this study including variability in reliability and validity of the self-reported data.10 Validity was usually high when compared to medical records but obesity and current tobacco use showed some differences between self-reports and physical measures.10 It is possible that additional conditions will be found to be associated with COVID-19 hospitalizations and death, some of which might not be available on the BRFSS. PAR estimates assume there is a causal relationship and this may not be true for the SDOH measures. Persons who ever smoked were grouped together with no differentiation for the length of time smoked or how long it might have been since they quit which may result in imprecision in the accuracy of the attributable-risk estimates for smoking. The generalizability of these results is unknown but there is no reason to believe it would not be good. Another study limitation is that only non-institutionalized adults were surveyed so the 1.3 million adults 23 in nursing homes who may be more likely to have the outcomes studied were excluded. Household adults who are physically or mentally unable to respond to a survey are also excluded which may omit some potential respondents with these underlying conditions.26 The effect of excluding persons unable to respond to a telephone survey implies that adults in this current study may be less affected by their outcomes than other adults in the community.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.21.20159350v1 on medRxiv