Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life: an application to settings in Europe, Asia, Africa, and South America (preprint)

Long-term COVID-19 complications are a globally pervasive threat, but their plausible social drivers are often not prioritized. Here, we use data from a multinational consortium to quantify the relative contributions of social and clinical factors to differences in quality of life among participants experiencing long COVID and measure the extent to which social variables impacts can be attributed to clinical intermediates, across diverse contexts. In addition to age, neuropsychological and rheumatological comorbidities, educational attainment, employment status, and female sex were identified as important predictors of long COVID-associated quality of life days (long COVID QALDs). Furthermore, a great majority of their impacts on long COVID QALDs could not be tied to key long COVID-predicting comorbidities, such as asthma, diabetes, hypertension, psychological disorder, and obesity. In Norway, 90% (95% CI: 77%, 100%) of the effect of belonging to the highest versus lowest educational attainment quintile was not attributed to intermediate comorbidity impacts. The same was true for 86% (73%, 100%) of the protective effects of full-time employment versus all other employment status categories (excluding retirement) in the UK and 74% (46%,100%) of the protective effects of full-time employment versus all other employment status categories in a cohort of four middle-income countries (MIC). Of the effects of female sex on long COVID QALDs in Norway, UK, and the MIC cohort, 77% (46%,100%), 73% (52%, 94%), and 84% (62%, 100%) were unexplained by the clinical mediators, respectively. Our findings highlight that socio-economic proxies and sex may be as predictive of long COVID QALDs as commonly emphasized comorbidities and that broader structural determinants likely drive their impacts. Importantly, we outline a multi-method, adaptable causal machine learning approach for evaluating the isolated contributions of social disparities to long COVID quality of life experiences.

Immune Correlates Analysis of the PREVENT-19 COVID-19 Vaccine Efficacy Clinical Trial (preprint)

In the randomized, placebo-controlled PREVENT-19 phase 3 trial conducted in the U.S. and Mexico of the NVX-CoV2373 adjuvanted, recombinant spike protein nanoparticle vaccine, anti-spike binding IgG concentration (spike IgG) and pseudovirus 50% neutralizing antibody titer (nAb ID50) measured two weeks after two doses were assessed as correlates of risk and as correlates of protection against PCR-confirmed symptomatic SARS-CoV-2 infection (COVID-19). These immune correlates analyses were conducted in the U.S. cohort of baseline SARS-CoV-2 negative per-protocol participants using a case-cohort design that measured the antibody markers from all 12 vaccine recipient breakthrough COVID-19 cases starting 7 days post antibody measurement and from 639 vaccine recipient non-cases (Mexico was excluded due to zero breakthrough cases with the efficacy data cut-off date April 19, 2021). In vaccine recipients, the baseline risk factor-adjusted hazard ratio of COVID-19 was 0.36 (95% CI: 0.20, 0.63), p<0.001 (adjusted p-0.005) per 10-fold increase in IgG spike concentration and 0.39 (0.19, 0.82), p=0.013 (adjusted p=0.030) per 10-fold increase in nAb ID50 titer. At spike IgG concentration 100, 1000, and 6934 binding antibody units/ml (100 is the 3rd percentile, 6934 is the 97.5th percentile), vaccine efficacy to reduce the probability of acquiring COVID-19 at 59 days post marker measurement was 65.5% (95% CI: 23.0%, 90.8%), 87.7% (77.7%, 94.4%), and 94.8% (88.0%, 97.9%), respectively. At nAb ID50 titers of 50, 100, 1000, and 7230 IU50/ml (50 is the 5th percentile, 7230 the 97.5th percentile), these estimates were 75.7% (49.8%, 93.2%), 81.7% (66.3%, 93.2%), 92.8% (85.1%, 97.4%) and 96.8% (88.3%, 99.3%). The same two antibody markers were assessed as immune correlates via the same study design and statistical analysis in the mRNA-1273 phase 3 COVE trial (except in COVE the markers were measured four weeks post dose two). Spike IgG levels were slightly lower and nAb ID50 titers slightly higher after NVX-CoV2373 than after mRNA-1273 vaccination. The strength of the nAb ID50 correlate was similar between the trials, whereas the spike IgG antibodies appeared to correlate more strongly with NVX-CoV2373 in PREVENT-19, as quantified by the hazard ratio and the degree of change in vaccine efficacy across antibody levels. However, the relatively few breakthrough cases in PREVENT-19 limited the ability to infer a stronger correlate. The conclusion is that both markers were consistent correlates of protection for the two vaccines, supporting potential cross-vaccine platform applications of these markers for guiding decisions about vaccine approval and use.

Immune Correlates Analysis of a Single Ad26.COV2.S Dose in the ENSEMBLE COVID-19 Vaccine Efficacy Clinical Trial (preprint)

Anti-spike IgG binding antibody, anti-receptor binding domain IgG antibody, and pseudovirus neutralizing antibody measurements four weeks post-vaccination were assessed as correlates of risk of moderate to severe-critical COVID-19 outcomes through 83 days post-vaccination and as correlates of protection following a single dose of Ad26.COV2.S COVID-19 vaccine in the placebo-controlled phase of ENSEMBLE, an international, randomized efficacy trial. Each marker had evidence as a correlate of risk and of protection, with strongest evidence for 50% inhibitory dilution (ID50) neutralizing antibody titer. The outcome hazard ratio was 0.49 (95% confidence interval 0.29, 0.81; p=0.006) per 10-fold increase in ID50; vaccine efficacy was 60% (43, 72%) at nonquantifiable ID50 (< 2.7 IU50/ml) and rose to 89% (78, 96%) at ID50 = 96.3 IU50/ml. Comparison of the vaccine efficacy by ID50 titer curves for ENSEMBLE-US, the COVE trial of the mRNA-1273 vaccine, and the COV002-UK trial of the AZD1222 vaccine supported consistency of the ID50 titer correlate of protection across trials and vaccine types.

Causal survival analysis under competing risks using longitudinal modified treatment policies (preprint)

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, numerical, or continuous exposures measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to right-censoring and competing risks. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as $\sqrt{n}$-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.