People with HIV Have Higher Risk of COVID-19 Diagnosis but Similar Outcomes than the General Population

Background We investigated the effect of HIV on COVID-19 outcomes with attention to selection bias due to differential testing and to comorbidity burden. Methods Retrospective cohort analysis using four hierarchical outcomes: positive SARS-CoV-2 test, COVID-19 hospitalization, intensive care unit (ICU) admission, and hospital mortality. The effect of HIV status was assessed using traditional covariate-adjusted, inverse probability weighted (IPW) analysis based on covariate distributions for testing bias (testing IPWs), HIV infection status (HIV IPWs), and combined models. Among PWH, we evaluated whether CD4 count and HIV plasma viral load (pVL) discriminated between those who did or did not develop study outcomes using receiver operating characteristic analysis. Results Between March and November 2020, 63,319 people were receiving primary care services at UCSD, of whom 4,017 were people living with HIV (PWH). PWH had 2.1 times the odds of a positive SARS-CoV-2 test compared to those without HIV after weighting for potential testing bias, comorbidity burden, and HIV-IPW (95% CI 1.6-2.8). Relative to persons without HIV, PWH did not have an increased rate of COVID-19 hospitalization after controlling for comorbidities and testing bias [adjusted incidence rate ratio (aIRR): 0.5, 95% CI: 0.1-1.4]. PWH had neither a different rate of ICU admission (aIRR:1.08, 95% CI; 0.31-3.80) nor in-hospital death (aIRR:0.92, 95% CI; 0.08-10.94) in any examined model. Neither CD4 count nor pVL predicted any of the hierarchical outcomes among PWH. Conclusions PWH have a higher risk of COVID-19 diagnosis but similar outcomes compared to those without HIV.

How to remove the testing bias in CoV-2 statistics

BACKGROUNDPublic health measures and private behaviour are based on reported numbers of SARS-CoV-2 infections. Some argue that testing influences the confirmed number of infections.OBJECTIVES/METHODSDo time series on reported infections and the number of tests allow one to draw conclusions about actual infection numbers? A SIR model is presented where the true numbers of susceptible, infectious and removed individuals are unobserved. Testing is also modelled.RESULTSOfficial confirmed infection numbers are likely to be biased and cannot be compared over time. The bias occurs because of different reasons for testing (e.g. by symptoms, representative or testing travellers). The paper illustrates the bias and works out the effect of the number of tests on the number of reported cases. The paper also shows that the positive rate (the ratio of positive tests to the total number of tests) is uninformative in the presence of non-representative testing.CONCLUSIONSA severity index for epidemics is proposed that is comparable over time. This index is based on Covid-19 cases and can be obtained if the reason for testing is known.

COVID-19 infections among Healthcare Workers and Transmission within Households

ImportanceProtecting healthcare workers (HCWs) from COVID-19 is a priority to maintain a safe and functioning healthcare system. The risk of transmitting COVID-19 to family members is a source of stress for many.ObjectiveTo describe and compare HCW and non-HCW COVID-19 cases in Ontario, Canada, as well as the frequency of COVID-19 among HCWs’ household members.Design, Setting, and ParticipantsUsing reportable disease data at Public Health Ontario which captures all COVID-19 cases in Ontario, Canada, we conducted a population-based cross-sectional study comparing demographic, exposure, and clinical variables between HCWs and non-HCWs with COVID-19 as of 14 May 2020. We calculated rates of infections over time and determined the frequency of within household transmissions using natural language processing based on residential address.Exposures and OutcomesWe contrasted age, gender, comorbidities, clinical presentation (including asymptomatic and presymptomatic), exposure histories including nosocomial transmission, and clinical outcomes between HCWs and non-HCWs with confirmed COVID-19.ResultsThere were 4,230 (17.5%) HCW COVID-19 cases in Ontario, of whom 20.2% were nurses, 2.3% were physicians, and the remaining 77.4% other specialties. HCWs were more likely to be between 30-60 years of age and female. HCWs were more likely to present asymptomatically (8.1% versus 7.0%, p=0.010) or with atypical symptoms (17.8% versus 10.5%, p<0.001). The mortality among HCWs was 0.2% compared to 10.5% of non-HCWs. HCWs commonly had exposures to a confirmed case or outbreak (74.1%), however only 3.1% were confirmed to be nosocomial. The rate of new infections was 5.5 times higher in HCWs than non-HCWs, but mirrored the epidemic curve. We identified 391 (9.8%) probable secondary household transmissions and 143 (3.6%) acquisitions. Children < 19 years comprised 14.6% of secondary cases compared to only 4.2% of the primary cases.Conclusions and RelevanceHCWs represent a disproportionate number of COVID-19 cases in Ontario but with low confirmed numbers of nosocomial transmission. The data support substantial testing bias and under-ascertainment of general population cases. Protecting HCWs through appropriate personal protective equipment and physical distancing from colleagues is paramount.Key PointsQuestionWhat are the differences between healthcare workers and non-healthcare workers with COVID-19?FindingsIn this population-based cross-sectional study there were 4,230 healthcare workers comprising 17.5% of COVID-19 cases. Healthcare workers were diagnosed with COVID-19 at a rate 5.5 times higher than the general population with 0.8% of all healthcare workers, compared to 0.1% of non-healthcare workers.MeaningHigh healthcare worker COVID-19 burden highlights the importance of physical distancing from colleagues, appropriate personal protective equipment, as well as likely substantial testing bias and under-ascertainment of COVID-19 in the general population.