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.

Ethiopian Healthcare Workers' Experiences During the COVID-19 Pandemic (preprint)

Background: The COVID-19 pandemic has caused widespread health and socioeconomic disruptions around the world. Understanding the impact that this crisis has had on health workers and the delivery of routine health care services within countries provides evidence on pandemic preparedness and response. Here, we conduct an investigation into these factors for the Ethiopian context. Methods and findings: We conducted an online cross-sectional survey with Ethiopian health care professionals between August 27 and October 10, 2020 via existing research networks. The variables of interest were confidence in COVID-19 related knowledge, training and experience, the adoption of precautionary health practices, risk perceptions, and respondent concerns. The majority of surveyed health care professionals in Ethiopia reported seeing fewer patients than usual during the COVID-19 crisis, gaps in pandemic training, inadequate access to personal protective equipment (PPE) and barriers to accessing COVID-19 testing. Most health care professionals were also deeply concerned and worried about their own COVID-19 risks and the likelihood that they would transmit the disease to others. Conclusions: Our study findings point to a possible reduction in routine health care services during the COVID-19 pandemic and gaps in pandemic preparedness in Ethiopia. The ministry of health and other stakeholders should work towards improving access to PPE and testing, and identify approaches to ensure that essential healthcare provision (such as immunizations) is not disrupted during crises akin to the COVID-19 outbreak.

Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries (preprint)

Background: Countries have adopted different approaches, at different times, to reduce the transmission of coronavirus disease 2019 (COVID-19). Cross-country comparison could indicate the relative efficacy of these approaches. We assess various non-pharmaceutical interventions (NPIs) over time, comparing the effects of self-imposed (i.e. voluntary) behavior change and of changes enforced via official regulations, by statistically examining their impacts on subsequent death rates in 13 European countries. Methods and findings: We examine two types of NPI: the introduction of government-enforced closure policies over time; and self-imposed alteration of individual behaviors in response to awareness of the epidemic, in the period prior to regulations. Our proxy for the latter is Google mobility data, which captures voluntary behavior change when disease salience is sufficiently high. The primary outcome variable is the rate of change in COVID-19 fatalities per day, 16-20 days after interventions take place. Linear multivariate regression analysis is used to evaluate impacts. Voluntarily reduced mobility, occurring prior to government policies, decreases the percent change in deaths per day by 9.2 percentage points (95% CI 4.5-14.0 pp). Government closure policies decrease the percent change in deaths per day by 14.0 percentage points (95% CI 10.8-17.2 pp). Disaggregating government policies, the most beneficial are intercity travel restrictions, cancelling public events, and closing non-essential workplaces. Other sub-components, such as closing schools and imposing stay-at-home rules, show smaller and statistically insignificant impacts. Conclusions: This study shows that NPIs have substantially reduced fatalities arising from COVID-19. Importantly, the effect of voluntary behavior change is of the same order of magnitude as government-mandated regulations. These findings, including the substantial variation across dimensions of closure, have implications for the phased withdrawal of government policies as the epidemic recedes, and for the possible reimposition of regulations if a second wave occurs, especially given the substantial economic and human welfare consequences of maintaining lockdowns.