COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults

Abstract

Objectives: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data.

Design: A cross-sectional study.

Setting: AncestryDNA customers in the USA who consented to research.

Participants: The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test.

Results: We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study.

Conclusions: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

Keywords: COVID-19; Epidemiology; Public health.

Figure 1

Susceptibility and severity association cohort definitions. The susceptibility cohort for association analyses and risk models (short-dashed boxes) was comprised of a subset of the individuals who reported taking a nasopharyngeal swab test and receiving a positive or negative result. The severity cohort for the hospitalisation association analyses (long-dashed boxes) was comprised of those who reported receiving a positive test result. They were further subdivided into those who reported hospitalisation and those who did not (either directly or inferred, see the Methods section). The severity cohort for the critical case association analyses (dash-dotted boxes) was also comprised of those who reported receiving a positive test result. They were further subdivided into those who reported meeting the criteria for a critical case and those who did not (either directly or inferred, see the Methods section).

Figure 2

Susceptibility (positive test result) ORs and 95% CIs estimated from simple (‘unadjusted models’, grey) and multiple (‘adjusted models’, black) logistic regression with adjustment for other risk factors. Open circles indicate not significant (p>0.05) after accounting for multiple hypothesis tests using Bonferroni correction. Age, sex, genetic ancestry and obesity ORs were estimated in relation to the reference variables indicated. Exposure, health and symptom ORs were each estimated separately as binary variables. Symptom ORs were estimated as binary variables among symptomatic testers only (see the Methods section). Risk factor adjustments for susceptibility include: sex, age and at least one known COVID-19 exposure. Where applicable, individual adjustment variables were omitted to avoid duplicate adjustment (see the Methods section). BMI, body mass index.

Figure 3

Severity (hospitalisation) ORs and 95% CIs estimated from simple (‘unadjusted models’, grey) and multiple (‘adjusted models’, black) logistic regression with adjustment for other risk factors. Open circles indicate not significant (p>0.05) after accounting for multiple hypothesis tests using Bonferroni correction. Age, sex, genetic ancestry and obesity ORs were estimated in relation to the reference variables indicated. Exposure, health and symptom ORs were each estimated separately as binary variables. Symptom ORs were estimated as binary variables among symptomatic testers only (see the Methods section). Risk factor adjustments for severity include: sex, age, obesity (binarised if BMI ≥30) and underlying health conditions (Y/N if any). Where applicable, individual adjustment variables were omitted to avoid duplicate adjustment (see the Methods section). See online supplemental figure 1 for critical case severity ORs. BMI, body mass index.

Figure 4

Comparison of susceptibility-adjusted ORs (horizontal axis) and severity-adjusted ORs (vertical axis) for symptoms in figures 2 and 3. Severity aORs are for hospitalisation. Note that aORs for susceptibility and severity are adjusted differently according to descriptions in figures 2 and 3 captions. The aORs are plotted on a log scale for visibility. Shortness of breath is the strongest indicator of increased severity, while change in taste or smell is the strongest indicator for testing positive for COVID-19 among symptomatic individuals (see the Methods section). Refer to online supplemental figure 2 for demographic, health condition and exposure aORs. aORs, adjusted ORs.

Figure 5

Performance of risk models on independent holdout data. (A) Receiver operating characteristic (ROC) curves for susceptibility models to predict COVID-19 cases among testers reporting a result (positive or negative). (B) Area under the curve (AUC) for the four susceptibility models in (A), stratified by cohort. ‘All’ represents everyone in (A). (C) ROC curves for severity models to predict either hospitalisation (red) or critical illness progression (black) among COVID-19 cases. (D) Area under the curve (AUC) for the two severity models in (B), stratified by cohort. ‘All’ represents everyone in (C). Refer to the Methods section as well as online supplemental figure 3 and online supplemental tables 12, 17–21, 24 and 25 for additional model performance data and model risk factor information. Dem+Exp, model based on demographics and exposures only; Dem+Exp+Symp, model based on demographics, exposures and symptoms; HWF Exp+Symp, model called ‘How We Feel’ based on nearly identical self-reported symptoms and self-reported exposures; HWF Symp, model called ‘How We Feel’ based on nearly identical self-reported symptoms.