An eerie quantum effect of magnetic fields also applies to gravity | Science News https://www.sciencenews.org/article/quantum-particles-gravity-spacetime-aharonov-bohm-effect
Day: 01/14/2022
Worker surveillance rises as more companies offer remote work options.
Worker surveillance rises as more companies offer remote work options – The Washington Post https://www.washingtonpost.com/technology/2021/09/24/remote-work-from-home-surveillance/?utm_campaign=eng-lal-tech&utm_medium=acq-intl&utm_source=facebook&utm_content=worksurveillance&fbclid=IwAR3Gd20P2bxj-6r8K9yMww-bSQcBhsOXU9p_QbLDA7KIUa4un38hdU-fMyQ
Against accepting age-related sight loss
Our world is built on the assumption of vision, posing challenges for the roughly 2·2 billion individuals with vision impairment. Age is a leading risk factor for many eye diseases, including diabetic retinopathy, glaucoma, and age-related macular degeneration. In 2020, there were approximately 34 million adults aged 50 years and older who were blind and 206 million with moderate to severe vision impairment. As our populations continue to age rapidly, eye health will become increasingly relevant.
Jan 4, 2022, marks World Braille Day and is an opportunity to not only draw attention to the importance of access and inclusion for those using braille but also, more broadly, to the importance of eye health. Although people of all ages with vision impairment and blindness face challenges, older people are more likely to have difficulties with mobility, to be disabled, and to have higher risk of falls and fractures. These limitations not only impede access to health services but also impair wellbeing, quality of life, and autonomy. Driving exemplifies the link between vision impairment and wellbeing because the ability to drive not only permits access to basic needs, health-care services, and support networks, but also provides an important sense of independence that older people are often denied. A 2017 survey by the charity Independent Age reports that 44% of drivers aged 70 years and older in the UK feel that losing the ability to drive would mean losing a part of their identify. Being stripped of the right to drive is especially burdensome for older people living in rural areas because it exacerbates existing access barriers and takes a toll on mental health, with the report showing that 54% of older people in these areas say that they would feel lonelier if no longer able to drive. Vision impairment has also been shown to decrease employment rates and productivity across ages. For older workers, who already face impediments in employment due to health issues, caring responsibilities, and structural ageism, the inability to participate in the workforce is detrimental.
Compounding these challenges, treatment and rehabilitative services for eye care are plagued with access barriers due to poor integration within primary health-care services and inefficient referral pathways. In many low-income and middle-income countries (LMICs), eye-care services are restricted to secondary or tertiary hospitals and, as these are disproportionately located in urban settings, many older people living in rural areas receive delayed diagnoses and treatment. Moreover, eye-care diagnosis and treatment, such as cataract surgery or refractive error assessment, are largely not covered by health insurance in LMICs, and out-of-pocket costs are also evident in high-income countries.
However, low uptake of eye-health services is not entirely attributable to these access barriers. Eye-health literacy remains low, leading to low adherence to interventions and overall poor health outcomes. The 2019 WHO World report on vision shows that older people are often unaware that vision problems are treatable and therefore do not report impairments, driven in part by assumptions that these are a normal part of ageing. Given that 50% of sight loss is preventable, early detection is crucial to ensure timely interventions.
The challenges in eye health are nicely summarised in a study in our current issue, in which a global panel of individuals across various disciplines in eye health, including policy makers and patients, identified key priorities in global eye health, including improving access to and uptake of services, increasing integration between care sectors, and reducing out-of-pocket costs. To care for our ageing populations, eye care must be moved to the forefront of health-care and policy planning. The growing burden of vision impairment must be addressed by integrating eye care and by facilitating access to interventions for older people (eg, voice assistant technology).
On a broader level, the future of eye health, and the care of older people more generally, requires perceptions of ageing to be reframed. The under-reporting of eye conditions in older populations reflects the widespread confounding of age as a risk factor for various conditions and the inevitability of these conditions with age. The dismissal of functional decline as an unavoidable by-product of ageing is rooted in societal ageism, which equates ageing with impairment. This dangerous notion leads older people to accept declining health in silence. We must shed these misperceptions and recognise that, although ageing is inevitable, age-related impairment, including loss of vision, is not.
Risk of serious COVID-19 outcomes among adults with asthma in Scotland: a national incident cohort study
Summary
Background
There is considerable uncertainty over whether adults with asthma should be offered booster vaccines against SARS-CoV-2 and, if so, who should be prioritised for booster vaccination. We were asked by the UK’s Joint Commission on Vaccination and Immunisation to undertake an urgent analysis to identify which adults with asthma were at an increased risk of serious COVID-19 outcomes to inform deliberations on booster COVID-19 vaccines.
Methods
This national incident cohort study was done in all adults in Scotland aged 18 years and older who were included in the linked dataset of Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II). We used data from EAVE II to investigate the risk of COVID-19 hospitalisation and the composite outcome of intensive care unit (ICU) admission or death from COVID-19 among adults with asthma. A Cox proportional hazard model was used to derive adjusted hazard ratios (HRs) and 95% CIs for the association between asthma and COVID-19 hospital admission and ICU admission or death, stratified by markers of history of an asthma attack defined by either oral corticosteroid prescription (prednisolone, prednisone, and dexamethasone) in the 2 years before March 1, 2020, or hospitalisation for asthma before March 1, 2020. Analyses were adjusted for age, sex, socioeconomic status, comorbidity, previous hospitalisation, and vaccine status.
Findings
Between March 1, 2020, and July 27, 2021, 561 279 (12·7%) of 4 421 663 adults in Scotland had clinician-diagnosed-and-recorded-asthma. Among adults with asthma, 39 253 (7·0%) had confirmed SARS-CoV-2 infections, of whom 4828 (12·3%) were admitted to hospital for COVID-19 (among them, an estimated 600 [12·4%] might have been due to nosocomial infections). Adults with asthma were found to be at an increased risk of COVID-19 hospital admission (adjusted HR 1·27, 95% CI 1·23–1·32) compared with those without asthma. When using oral corticosteroid prescribing in the preceding 2 years as a marker for history of an asthma attack, the adjusted HR was 1·54 (95% CI 1·46–1·61) for those with three or more prescribed courses of oral corticosteroids, 1·37 (1·26–1·48) for those with two prescribed courses, 1·30 (1·23–1·37) for those with one prescribed course, and 1·15 (1·11–1·21) for those without any courses, compared with those aged 18 years or older without asthma. Adults with asthma were found to be at an increased risk of COVID-19 ICU admission or death compared with those without asthma (adjusted HR 1·13, 95 % CI 1·05–1·22). The adjusted HR was 1·44 (95% CI 1·31–1·58) for those with three or more prescribed courses of oral corticosteroids, 1·27 (1·09–1·48) for those with two prescribed courses, 1·04 (0·93–1·16) for those with one prescribed course, and 1·06 (0·97–1·17) for those without any course, compared with adults without asthma.
Interpretation
Adults with asthma who have required two or more courses of oral corticosteroids in the previous 2 years or a hospital admission for asthma before March 1, 2020, are at increased risk of both COVID-19 hospitalisation and ICU admission or death. Patients with a recent asthma attack should be considered a priority group for booster COVID-19 vaccines.
Funding
UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Health Data Research UK, and Scottish Government.
Introduction
There is emerging evidence that immunity after COVID-19 vaccination wanes, especially against the Delta variant of concern.1, 2 Many countries have been discussing booster doses to ensure that people remain protected against SARS-CoV-2 as the winter months approach. By Nov 15, 2021, more than 170 million booster doses had been administered worldwide.3 In the USA, booster vaccinations are available for Pfizer-BioNTech vaccine recipients who completed their initial series at least 6 months ago, including those aged 65 years and older, those aged at least 18 years who have underlying medical conditions, those who work in high-risk settings, and those who live in high-risk settings.4 Israel started its booster campaign aimed at those aged over 50 years and has administered over three millions third doses of Pfizer-BioNTech vaccine to date.3 Turkey began administering booster vaccines in July, 2021, to health-care workers and people older than 50 years.5 Similar initiatives are underway in other countries, including Cambodia, Thailand and Uruguay.5Research in contextEvidence before this studyUnderstanding which adults with asthma are at an increased risk of serious COVID-19 outcomes is of critical importance in deliberations on prioritisation of booster vaccines. We searched PubMed for observational studies, with no language restrictions, using the terms “SARS-CoV-2”, “COVID-19”, “hospitalisation”, “hospital admission”, “death”, “adults”, and “asthma”, for studies published between March 1, 2020, and Sept 16, 2021. We found six studies that investigated the association between markers of asthma severity and risk of severe COVID-19 outcomes among adults. Five of these six studies showed that asthma severity, defined using different patterns of inhaled and oral corticosteroid prescribing, were associated with increased risks of serious COVID-19 outcomes. Of these five studies, only one assessed and reported an increased risk of COVID-19 hospital or intensive care unit (ICU) admission in adults with severe asthma, and the remaining four studies found that severe asthma was associated with an increased risk of COVID-19 death. The sixth, smaller study, which investigated a single hospital in England, found no association between markers of asthma severity and COVID-19 deaths, possibly because the study was underpowered. Through the peer review process, we were alerted to an additional study that assessed the risk of severe COVID-19 outcomes in adults with different asthma phenotypes. The authors found an association between markers of asthma severity and severe COVID-19 outcomes, but this was not observed in those with features suggestive of underlying type 2 inflammatory asthma.Added value of this studyWe report on risk factors for severe COVID-19 outcomes in adults with asthma during different waves of the pandemic, taking vaccination status into account. We found that adults with a history of an asthma attack in the preceding 24 months (defined by either two or more oral corticosteroid prescriptions or previous asthma hospitalisation) had an increased risk of COVID-19 hospital admission and the composite outcome of ICU admission or death, when compared with those with no asthma. These increased risks remained after adjusting for age, sex, socioeconomic status, comorbidity, previous non-asthma hospitalisation, and COVID-19 vaccine status. Our study has added UK evidence using nationwide population-level data and quantified the strength of associations across different waves of the pandemic, taking vaccination status into account.Implications of all the available evidenceWe provide national evidence that adults aged 18 years and older with a history of an asthma attack in the preceding 24 months are at an increased risk of COVID-19 hospital admission and ICU admission or death. These findings have been used by the UK Joint Commission on Vaccination and Immunisation to inform policy decisions on which adults with asthma to prioritise for COVID-19 booster vaccination.The UK Joint Commission on Vaccination and Immunisation (JCVI) issued interim advice on COVID-19 booster vaccination on June 30, 2021, indicating that any COVID-19 booster programme should be offered to the most vulnerable people first (ie, those at the greatest risk of serious COVID-19 outcomes). The UK COVID-19 booster vaccine programme started in September, 2021, with the aim of maximising individual protection for the most vulnerable individuals and reducing the potential risk of the UK National Health Service (NHS) surge capacity being breached over the coming winter. The individuals prioritised thus far are care home residents, people aged over 40 years, frontline health and social care workers, clinically extremely vulnerable adults, and those who are immunosuppressed.6There have been several reports indicating that adults with severe asthma might have an increased risk of severe COVID-19 outcomes, namely hospitalisation, intensive care unit (ICU) admission, and death.7, 8, 9, 10, 11, 12 According to the Global Initiative for Asthma (GINA), asthma severity is generally seen as a retrospective assessment of the treatment required to minimise symptoms or exacerbations, whereas asthma control relates to how a patient experiences symptoms and the risk of exacerbations.13 Therefore, asthma control can relate to severity, but might also be affected by adherence, inhaler technique, and exposure to triggers (eg, smoking or allergen exposure).13 However, the existing evidence base showing that adults with asthma are at the highest risk of serious COVID-19 outcomes is difficult to interpret, since there have been no previous studies investigating asthma risk during different waves of the pandemic and taking vaccination status into account. As a result, there is considerable uncertainty over whether adults with asthma should be offered booster vaccines against SARS-CoV-2 and, if so, who should be prioritised for booster vaccination.In response to a request from the UK JCVI, we sought to use our national surveillance platform to investigate the risk of hospitalisation, ICU admission, and death from COVID-19 among adults with markers of history of an asthma attack in the preceding 24 months.
Methods
Study design
This national incident cohort study was done in all adults in Scotland aged 18 years and older who were included in the linked dataset of Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II). EAVE II is a Scotland-wide COVID-19 surveillance platform that has been used to track and forecast the epidemiology of COVID-19, inform risk stratification assessment, and investigate vaccine effectiveness and safety.1, 14, 15, 16, 17, 18, 19 It comprises national health-care datasets on 5·4 million people (approximately 99% of the Scottish population) deterministically linked through the Community Health Index (CHI) number, which is a unique identifier used in all health-care contacts across NHS Scotland. We used data from EAVE II to describe the demographic profile of adults with asthma who had SARS-CoV-2 infections, COVID-19 hospital admissions, and ICU admissions or deaths. We used the composite outcome of ICU admission or death because there have been concerns about possible rationing of access to ICU beds, particularly in the early phases of the pandemic. We also undertook a national incident cohort analysis to investigate risks of hospitalisation, ICU admission, or deaths in adults with asthma, stratified by markers of history of an asthma attack. The cohort was set up on March 1, 2020 (retrospectively assigned as it was shortly before the first person was admitted to hospital due to COVID-19 in Scotland). All individuals were followed up from March 1, 2020, until the date of death or the end of follow-up (July 27, 2021), whichever came first.Ethics approval was obtained from the National Research Ethics Service Committee, Southeast Scotland 02 (reference number 12/SS/0201). The Public Benefit and Privacy Panel Committee of Public Health Scotland approved the linkage and analysis of the de-identified datasets for this project (1920-0279).
Data sources and procedures
The national datasets linked using CHI number were the Electronic Communication of Surveillance in Scotland (national database for all virology testing), primary care (demographics and clinical history), the Scottish Morbidity Record (which records hospitalisations), National Records of Scotland (which records mortality data), and Prescribing Information System (for prescription data). A data linkage diagram is available in the appendix (p 17).Asthma and other risk groups of interest were measured on March 1, 2020, and defined by the QCOVID risk prediction algorithm, which consists of 30 clinical characteristics (including asthma) identified from primary care records that are known to be associated with an increased risk of serious COVID-19 outcomes in adults (appendix p 1).20 We excluded one risk group that had substantial missing data (ethnicity data were missing for 1 858 385 [42%] of participants; further details are given in the appendix p 3). This resulted in 28 risk groups in addition to asthma being included and analysed as potential confounders (appendix p 3).We also assessed the risk of COVID-19 hospitalisation and ICU admission or death stratified by two markers of history of an asthma attack. First, we used previous oral corticosteroid prescribing (prednisolone, prednisone, and dexamethasone) as a marker of history of an asthma attack in the 2 years before March 1, 2020. Second, we used hospitalisation for asthma before March 1, 2020. This included all hospitalisations with a primary admission diagnosis based on International Classification of Diseases Tenth Revision codes J45 and J46 within 2 years before March 1, 2020.Building on methods that have previously been described in detail,17, 21 we defined individuals who tested positive with real-time RT-PCR as having SARS-CoV-2 infections. We defined a COVID-19 hospital admission as being hospitalised within 14 days following a positive RT-PCR test for SARS-CoV-2, including those who tested positive while being hospitalised, or those who were hospitalised with an admission diagnosis of COVID-19 (appendix p 2). COVID-19 related deaths were all-cause deaths occurring within 28 days after a positive test for SARS-CoV-2 that were registered with National Records Scotland and included death certification, or deaths with COVID-19 on the death certificate as the cause of death.
Statistical analysis
A Cox proportional hazard model was used to derive the hazard ratios (HR) and 95% confidence intervals (CIs) for the association between history of an asthma attack in the preceding 24 months and COVID-19 hospital admission and ICU admission or death. This model, with calendar time as the timescale, eliminates the need to model the underlying temporal trends as these are estimated as the baseline hazard. The Cox model adjusted for a penalised spline of age, sex, socioeconomic status, body-mass index (BMI), number of other risk groups of interest (ie, those identified by the QCOVID algorithm), number of non-asthma related hospitalisations within the 2-year period before March 1, 2020, and vaccine status. Socioeconomic status was determined using the Scottish Index of Multiple Deprivation (SIMD).22 The SIMD classification is based on deprivation quintiles: quintile 1 refers to the most deprived and quintile 5 refers to the most affluent. The SIMD was assigned according to residential postcode. BMI was categorised into less than 18·5 kg/m2, 18·5–24·9 kg/m2, 25·0–29·9 kg/m2, 30·0–34·9 kg/m2, and 35 kg/m2 or greater, and not recorded.20 Adjusted for previous hospitalisation was used as a marker of severity or health-care seeking behaviour. Vaccine status was included in the Cox model as a time-dependent variable with five statuses: no vaccination or before vaccination, within 27 days after first dose, 28 days or more after first dose, within 27 days after second dose, and 28 days or more after second dose. Individuals who had a second vaccine dose within 28 days of their first dose would not have the status 28 days after first dose and would go straight from within 27 days after first dose to within 27 days after second dose at the date of second dose. Post-hoc interaction tests were done between vaccine status and history of asthma attack in the preceding 24 months in the adjusted Cox model. If the interaction tests (likelihood ratio χ2 test) were significant (P<0·01), the interaction was included in the Cox model with vaccination status as an effect modifier. By contrast, if the interaction test was not significant, vaccination status was included in the model as a covariate. All analyses were carried out in adults (≥18 years). Anyone with missing SIMD or BMI data was excluded from the Cox models. We also did a post-hoc investigation to see if the magnitude of the differences between the asthma markers investigated and no asthma varied across the different waves of the pandemic (first wave: March 1, 2020, to July 31, 2020; second wave before vaccination programme started: Aug 1, 2020, to Dec 7, 2020; second wave after the vaccination programme started: Dec 8, 2020, to May 17, 2021; third wave: May 18, 2021, to end of study).The Cox proportional hazards models used sampling weights, which were used to correct for the size of the registered general practice population being greater than the population in Scotland (in part due to individuals who had recently moved). These weights were derived by matching the age and sex numbers in the general practice data to the Scottish population data. This adjustment ensured that the denominators in the tables matched the Scottish population.The models were fit to a dataset with all events and a random sample, without replacement, of 100 individuals per event with sample weights calculated to represent the sampling fraction and thus ensure the correct calculation of the person-years at risk for the whole population. A combined weight (sampling weights from the random sampling procedure and the weights used to correct for the size of Scottish population) was used in the statistical modelling.A sensitivity analysis was carried out using a 1-year look back before March 1, 2020, for the markers of history of an asthma attack. We also conducted a sensitivity analysis only looking at those who tested positive for SARS-CoV-2 and measured the markers of history of an asthma attack at the date of test to see if the risk of severe COVID-19 outcome was higher in those with history of an asthma attack in the preceding 24 months following testing positive. This was to account for adults with prior oral corticosteroids prescribing or previous hospitalisation for asthma after March 1, 2020, but before their SARS-CoV-19 infections. Misdiagnosis of asthma is common in primary care, especially in older patients. We did a post-hoc stratified analysis in those with and without coexisting chronic obstructive pulmonary disease (COPD) and investigated the association between history of an asthma attack in the preceding 24 months and COVID-19 hospitalisation within each stratum. We also did a post-hoc subgroup analysis only including those younger than 50 years to minimise the risk of confounding by COPD and a subgroup analysis reporting on separate ICU admission and mortality outcomes.We followed the strengthening the STROBE checklist23 to guide transparent reporting of this cohort study (appendix pp 4–5). Analyses were done in R version 3.6.1.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report.
Results
4 421 663 adults in the EAVE II linked dataset who were aged 18 years or older on March 1, 2020, were included in the analysis. 561 279 (12·7%) adults had clinician-diagnosed-and-recorded asthma. Among adults with asthma, 39 253 (7·0%) had confirmed SARS-CoV-2 infections, of whom 4828 (12·3%) were admitted to hospital for COVID-19 (among them, an estimated 600 [12·4%] might have been due to nosocomial infections). There were 1600 (4·1%) ICU admissions and 1206 (3·1%) deaths, of which 1186 (98·3%) had COVID-19 recorded as the cause of death on the death certificate. The baseline characteristics for adults with asthma stratified by markers of history of an asthma attack are available in the appendix (pp 6–7).The numbers of adults being tested for SARS-CoV-2, testing positive, and being admitted to hospital with COVID-19 per 100 000 people were higher in adults with asthma (irrespective of history of an asthma attack) compared with those without asthma (table 1). The number of COVID-19 ICU admissions or deaths in adults without a recent asthma attack was similar to those without asthma, but higher in those with a history of an asthma attack in the preceding 2 years. Adults with a history of an asthma attack in the preceding 2 years (defined by either previous oral corticosteroid prescription or asthma hospitalisation) had higher rates of COVID-19 hospitalisation and ICU admission or death compared with those with better-controlled asthma. The absolute rates of COVID-19 hospitalisation in adults with three or more, two, one, or zero prescribed courses of oral corticosteroids were 2375, 1600, 1274, and 642 per 100 000 people, respectively. The absolute rate of COVID-19 hospitalisation was 3290 per 100 000 people in adults with previous hospitalisation for asthma and 835 per 100 000 people in adults without previous hospitalisation for asthma.Table 1Number and rate (per 100 000 people) of being tested, testing positive, COVID-19 hospitalisation, ICU admission, and deaths in adults with asthma, stratified by markers of history of an asthma attack
| Overall number | Number of patients tested | Number testing positive | Number of patients hospitalised with COVID-19 | Number of ICU admissions or deaths* | Number of ICU admissions | Number of deaths | |
|---|---|---|---|---|---|---|---|
| Asthma | |||||||
| No | 3 860 383 | 1 500 203 (38·9%) | 225 052 (5·8%) | 23 861 (0·6%) | 10 693 (0·3%) | 2463 (0·1%) | 8913 (0·2%) |
| Yes | 561 279 | 271 374 (48·3%) | 39 253 (7·0%) | 4828 (0·9%) | 1600 (0·3%) | 516 (0·1%) | 1206 (0·2%) |
| History of an asthma attack (OCS prescription†) | |||||||
| No asthma | 3 680 316 | 1 414 860 (38·4%) | 215 345 (5·9%) | 20 678 (0·6%) | 9193 (0·2%) | 2198 (0·1%) | 7561 (0·2%) |
| Asthma with no courses of OCS | 440 388 | 204 035 (46·3%) | 30 944 (7·0%) | 2827 (0·6%) | 955 (0·2%) | 297 (0·1%) | 732 (0·2%) |
| Asthma with one course of OCS | 142 583 | 71 258 (50·0%) | 9154 (6·4%) | 1817 (1·3%) | 681 (0·5%) | 171 (0·1%) | 568 (0·4%) |
| Asthma with two courses of OCS | 50 880 | 26 240 (51·6%) | 3124 (6·1%) | 814 (1·6%) | 330 (0·6%) | 88 (0·2%) | 268 (0·5%) |
| Asthma with three or more courses of OCS | 107 496 | 55 184 (51·3%) | 5738 (5·3%) | 2553 (2·4%) | 1134 (1·1%) | 225 (0·2%) | 990 (0·9%) |
| History of an asthma attack (previous hospitalisation‡) | |||||||
| No asthma§ | 3 859 720 | 1 499 858 (38·9%) | 225 009 (5·8%) | 23 845 (0·6%) | 10 689 (0·3%) | 2463 (0·1%) | 8909 (0·2%) |
| Asthma without previous hospitalisation | 555 833 | 267 681 (48·2%) | 38 790 (7·0%) | 4643 (0·8%) | 1557 (0·3%) | 498 (0·1%) | 1178 (0·2%) |
| Asthma with previous hospitalisation | 6110 | 4038 (66·1%) | 506 (8·2%) | 201 (3·3%) | 47 (0·3%) | 18 (0·3%) | 32 (0·5%) |
| History of an asthma attack (previous hospitalisation‡ or OCS prescription†) | |||||||
| No asthma§ | 3 680 068 | 1 414 738 (38·4%) | 215 332 (5·9%) | 20 675 (0·6%) | 9192 (0·2%) | 2198 (0·1%) | 7560 (0·2%) |
| Asthma with 0–1 course(s) of OCS and no previous hospitalisation | 580 684 | 273 907 (47·2%) | 39 915 (6·9%) | 4590 (0·8%) | 1625 (0·3%) | 465 (0·1%) | 1292 (0·2%) |
| Asthma with ≥2 courses of OCS or previous hospitalisation | 160 910 | 82 932 (51·5%) | 9058 (5·6%) | 3424 (2·1%) | 1476 (0·9%) | 316 (0·2%) | 1267 (0·8%) |
Data are n or n (%). Denominators of the percentages are those listed in the overall number column. ICU=intensive care unit. OCS=oral corticosteroid.* ICU deaths referred to those who had COVID-19-related ICU admissions or COVID-19-related death with or without previous ICU admissions.† OCS prescriptions for prednisolone, prednisone, and dexamethasone in the 2-year period before March 1, 2020.‡ Hospitalisation for asthma within 2-year period before March 1, 2020.§ The no asthma group under variable asthma was derived using only the general practitioner-recorded diagnosis whereas the no asthma group under the variable history of an asthma attack (OCS prescription) was derived using both general practitioner diagnosis and prescribing records; therefore, patients who never had asthma recorded in their general practice records, but had OCS prescriptions were not included in the second no asthma group, but were included in the first asthma group (using only general practitioner records), which explains the smaller group size.
Adults with asthma were found to be at an increased risk of COVID-19 hospital admission (adjusted HR 1·27, 95% CI 1·23–1·32) compared with those without asthma. When using oral corticosteroid prescribing in the preceding 2 years as a marker for history of an asthma attack, the adjusted HR was 1·54 (95% CI 1·46–1·61) for those with three or more prescribed courses of oral corticosteroids, 1·37 (1·26–1·48) for those with two prescribed courses, 1·30 (1·23–1·37) for those with one prescribed course, and 1·15 (1·11–1·21) for those without any courses, compared with those aged 18 years or older without asthma (table 2). 1 858 385 (42%) of the cohort had missing ethnicity so this variable was not adjusted for in the Cox model. 43 467 (1%) of the cohort had missing BMI and 42 132 (1%) of the cohort had missing SIMD and were excluded from the adjusted Cox model. Vaccination was slightly less effective in reducing COVID-19 hospitalisation in those with a history of an asthma attack as measured by oral corticosteroid use in the preceding 2 years compared with those without asthma (Pinteraction=0·0020). For those without asthma, the adjusted HR of at 28 days or more after the second dose of vaccine versus unvaccinated individuals was 0·15 (95% CI 0·14–0·17), whereas for those with three or more courses of oral corticosteroids the adjusted HR was 0·23 (0·19–0·28; figure; appendix p 8). The difference in COVID-19 hospitalisations between those with a history of asthma attack as measured by oral corticosteroid use and those without asthma was larger in the third wave of the COVID-19 pandemic (adjusted HR for three or more courses of oral corticosteroids vs no asthma 2·05, 95% CI 1·75–2·39 compared with 1·56, 1·40–1·74 in the first wave of the COVID-19 pandemic; appendix p 9).Table 2HRs for COVID-19 hospitalisation, ICU admissions, or deaths for those with different markers of history of an asthma attack and those with no asthma in adults
| COVID-19 hospitalisation | COVID-19 ICU admissions or deaths* | |||
|---|---|---|---|---|
| Number of events | Adjusted HR (95% CI) | Number of events | Adjusted HR (95% CI) | |
| Previous prescribed OCS as a marker of history of an asthma attack | ||||
| No asthma | 20 678 | 1 (ref) | 9193 | 1 (ref) |
| Asthma with no course of OCS | 2827 | 1·15 (1·11–1·21) | 955 | 1·06 (0·97–1·17) |
| Asthma with one course of OCS | 1817 | 1·30 (1·23–1·37) | 681 | 1·04 (0·93–1·16) |
| Asthma with two courses of OCS | 814 | 1·37 (1·26–1·48) | 330 | 1·27 (1·09–1·48) |
| Asthma with three or more courses of OCS | 2553 | 1·54 (1·46–1·61) | 1134 | 1·44 (1·31–1·58) |
| Previous hospitalisation for asthma as a marker of history of an asthma attack | ||||
| No asthma | 23 845 | 1 (ref) | 10 689 | 1 (ref) |
| Asthma without previous hospitalisation | 4643 | 1·24 (1·20–1·29) | 1557 | 1·11 (1·03–1·19) |
| Asthma with previous hospitalisation | 201 | 3·01 (2·59–3·49) | 47 | 2·24 (1·56–3·20) |
HRs were derived using Cox proportional hazard models adjusted for age, sex, socioeconomic status, body-mass index, number of risk groups of interest, number of non-asthma-related hospitalisations within the 2-year period before March 1, 2020, and vaccine status. HR=hazard ratio. ICU=intensive care unit. OCS=oral corticosteroid.* ICU admissions or deaths referred to those who had COVID-19-related ICU admissions or COVID-19-related death with or without previous ICU admissions.
When using previous hospitalisation for asthma as the marker of history of an asthma attack, the adjusted HR was 3·01 (95% CI 2·59–3·49) for those with hospitalisation for asthma in the previous 2 years and 1·24 (1·20–1·29) for those without hospitalisation for asthma in the previous 2 years compared with those aged 18 years and older without asthma (table 2).Adults with asthma were found to be at an increased risk of COVID-19 ICU admission or death compared with those without asthma (adjusted HR 1·13, 95 % CI 1·05–1·22). The adjusted HR was 1·44 (95% CI 1·31–1·58) for those with three or more prescribed courses of oral corticosteroids, 1·27 (1·09–1·48) for those with two prescribed courses, 1·04 (0·93–1·16) for those with one prescribed course, and 1·06 (0·97–1·17) for those without any course, compared with adults without asthma (table 2). The adjusted HR was 2·24 (95% CI 1·56–3·20) for those with previous hospitalisation for asthma and 1·11 (1·03–1·19) for those with no previous hospitalisation for asthma compared with those aged 18 years and older without asthma (table 2). Separate analyses for ICU admission and death are shown in the appendix (p 10).The sensitivity analyses using 1-year retrospective data up to March 1, 2020, for the two markers of history of an asthma attack also yielded similar results (appendix p 11). Sensitivity analyses focusing on those who tested positive for COVID-19 and measuring the markers of history of an asthma attack at the date of test showed similar results (appendix p 12). For the group of people without COPD, asthma had a stronger effect on COVID-19 hospitalisation compared with the group of people with COPD (appendix p 13). Similarly, for people younger than 50 years, asthma had a stronger effect on severe COVID-19 outcomes (ICU admission or death) compared with the general population (appendix p 14). The univariable analysis for all the risk factors, including COPD, is available in the appendix (p 15).
Discussion
We found that adults with a prescription of two or more courses of oral corticosteroids or asthma hospitalisation in the preceding 2 years are at an increased risk of both COVID-19 hospitalisation and ICU admission or death compared with those without asthma. This would translate into 160 910 adults with asthma aged 18 years or older who have received two or more courses of oral corticosteroids or previous hospitalisation for asthma in Scotland during the study period who might be prioritised for COVID-19 vaccines, which when scaled-up to the UK would equate to around 1 930 920 adults (assuming the same prevalence of severe asthma in the other UK nations).24 If we restricted our analysis to only those who received two or more courses of oral corticosteroids in the preceding 2 years, this would translate into around 158 000 adults in Scotland, which is similar to the number (around 160 000) if we used both markers of history of an asthma attack (previous asthma hospitalisation or two or more courses of oral corticosteroids in the preceding 2 years). There might be some protection against severe COVID-19 in those who did not have a recent asthma attack, but our findings were not significant.Our study has several strengths. We developed a national linked dataset and have created a platform that allowed rapid access to and analysis of data from routinely collected national electronic health record data. Therefore, this study is less susceptible to recall or misclassification bias than are studies that rely on primary data collection. The use of a large population aided study power, facilitating estimation of HRs in different markers of history of an asthma attack and different outcomes. The study is likely to have excellent generalisability across the UK and potentially across other countries with similar demographics and health systems. Finally, we have been able to show that the associations found were similar across different phases of the pandemic (and hence across variants in circulation).Our study has several limitations. There were relatively small absolute numbers of people with previous asthma hospitalisations, so these data should be interpreted with care. However, our results were broadly consistent across different measures of previous asthma attack. We were unable to assess the association between asthma severity or control, as defined by GINA, and COVID-19-related risks. We included 28 risk groups that were defined by the QCOVID prediction algorithm,21 but we might have missed some important risk groups. Adults with a history of an asthma attack in the preceding 2 years had an increased rate of being tested compared with those with no recent asthma or without asthma. This might be because they could be more likely to be admitted to hospital and therefore more likely to have routine SARS-CoV-2 testing and screening in hospital than those with no recent asthma attack or without asthma. This could partly explain why there was little difference between the waves in effect estimates for asthma. There might also have been different health-care seeking behaviours among adults with a history of asthma attack, which might have resulted in increased chances of being tested for SARS-CoV-2. Although our Cox models were adjusted for potential confounders, unmeasured confounders could still have influenced our estimates. Our analysis did not include some potentially important confounders (such as ethnicity) because of the lack of reliable recording of this variable within Scottish electronic health records, with the consequence that residual confounding remains a possibility. Prescribing of oral corticosteroids was in people with a history of asthma, so our assumption is that these steroids were given for an asthma attack. However, we cannot be sure that this was the case. The indication for treatment and length of the prescription would have been helpful in this respect, but these data were unfortunately unavailable within our dataset.Similar findings have been reported elsewhere.7, 8, 9, 10, 11, 12 Specifically, five studies found an increased risk of COVID-19 death and two studies found an increased risk of COVID-19 hospital or ICU admission in adults with severe asthma.7, 8, 9, 10, 11, 12 No association was observed between severe asthma and COVID-19 deaths in one study, which could have been because of the small study sample size, which only included data from a single English hospital.25 Particularly high risks of COVID-19 hospital admission, ICU admission, and death have been reported in patients with asthma using high doses of inhaled corticosteroids and those with recent oral corticosteroid use for asthma (in the past 1–2 years).7, 8, 9, 12 Our study has contributed to UK evidence using nationwide population-level data and quantified the strength of associations between history of an asthma attack in the preceding 2 years and markers of severe COVID-19 outcomes across different waves of the COVID-19 pandemic, accounting for vaccination status. Our analysis shows that these findings remained robust, regardless of the wave of the pandemic, public behaviour, changes in clinical management, and vaccination policy.Building on this work, it is important to characterise in more detail the markers of history of an asthma attack for severe COVID-19 outcomes in adults and to investigate underlying mechanisms that predispose such adults to these increased risks. This analysis underscores the importance of maintaining good asthma control and careful monitoring of adults with history of an asthma attack if they develop SARS-CoV-2 infection. The finding that two vaccination doses were effective in reducing the risk of serious COVID-19 outcomes in those with a previous recent asthma attack, but less so than in those without asthma, underscores the need for additional vaccine doses in this subsection of the population with asthma. With booster vaccines being administered or planned internationally and nationally, together with other public health surveillance data, policy makers will be able to use data from our study to inform decisions on booster vaccination priorities among adults with asthma.In conclusion, we provide national evidence that adults with two or more courses of oral corticosteroids or asthma admission in the preceding 2 years were associated with an increased risk of COVID-19 hospital admission and ICU admission or death in Scotland. The findings from this linkage of multiple data sources have helped inform UK policy deliberations on vaccine boosters for adults with asthma.
The effect of maternal SARS-CoV-2 infection timing on birth outcomes: a retrospective multicentre cohort study
Summary
Background
The impact of maternal SARS-CoV-2 infection remains unclear. In this study, we evaluated the risk of maternal SARS-CoV-2 infection on birth outcomes and how this is modulated by the pregnancy trimester in which the infection occurs. We also developed models to predict gestational age at delivery for people following a SARS-CoV-2 infection during pregnancy.
Methods
We did a retrospective cohort study of the impact of maternal SARS-CoV-2 infection on birth outcomes. We used clinical data from Providence St Joseph Health electronic health records for pregnant people who delivered in the USA at the Providence, Swedish, or Kadlec sites in Alaska, California, Montana, Oregon, or Washington. The SARS-CoV-2 positive cohort included people who had a positive SARS-CoV-2 PCR-based test during pregnancy, subdivided by trimester of infection. No one in this cohort had been vaccinated for COVID-19 at time of infection. The SARS-CoV-2 negative cohort were people with at least one negative SARS-CoV-2 PCR-based test and no positive tests during pregnancy. Cohorts were matched on common covariates impacting birth outcomes, and univariate and multivariate analysis were done to investigate risk factors and predict outcomes. The primary outcome was gestational age at delivery with annotation of preterm birth classification. We trained multiple supervised learning models on 24 features of the SARS-CoV-2 positive cohort to evaluate performance and feature importance for each model and discuss the impact of SARS-CoV-2 infection on gestational age at delivery.
Findings
Between March 5, 2020, and July 4, 2021, 73 666 pregnant people delivered, 18 335 of whom had at least one SARS-CoV-2 test during pregnancy before Feb 14, 2021. We observed 882 people infected with SARS-CoV-2 during their pregnancy (first trimester n=85; second trimester n=226; and third trimester n=571) and 19 769 people who have never tested positive for SARS-CoV-2 and received at least one negative SARS-CoV-2 test during their pregnancy. SARS-CoV-2 infection indicated an increased risk of preterm delivery (p<0·05) and stillbirth (p<0·05), accounted for primarily by first and second trimester SARS-CoV-2 infections. Gestational age at SARS-CoV-2 infection was correlated with gestational age at delivery (p<0·01) and had the greatest impact on predicting gestational age at delivery. The people in this study had mild or moderate SARS-CoV-2 infections and acute COVID-19 severity was not correlated with gestational age at delivery (p=0·31).
Interpretation
These results suggest that pregnant people would benefit from increased monitoring and enhanced prenatal care after first or second trimester SARS-CoV-2 infection, regardless of acute COVID-19 severity.
Funding
US National Institutes of Health.
Introduction
There is a great need to understand the effects of maternal SARS-CoV-2 infection on birth outcomes. Multiple studies have reported increased preterm birth, but not stillbirth, following SARS-CoV-2 infection, with higher rates among symptomatic versus asymptomatic people.1, 2, 3 Additionally, SARS-CoV-2 infection has been associated with increased rates of pre-eclampsia.2 Thus far, studies have been done either with small study populations and greater clinical detail, or with large populations but limited detail (insurance claims or aggregate reporting). It is also well known that minority and low-income communities are disproportionately impacted by COVID-19,4, 5 and many risk factors associated with poor birth outcomes are elevated in these populations, which could account for some of the disparity in birth outcomes for pregnant people infected with SARS-CoV-2.6 To the best of our knowledge, as of November, 2021, no primary study has examined the impact of the timing of maternal SARS-CoV-2 infection on birth outcomes in a large, geographically distributed cohort, with analysis across a broad set of biomedical and contextual variables.Throughout pregnancy, there are crucial periods during which there is a greater impact of in utero shocks on fetal development.7 Fetuses are most vulnerable to maternal stress during the fifth and sixth month of pregnancy, resulting in higher rates of preterm birth, low birthweight, and small for gestational age (SGA) than exposure during other periods of pregnancy.7 Likewise, the effect of maternal influenza infection on birth outcomes depends on the timing of exposure.8 Influenza exposure during pregnancy is associated with increased infant and neonatal mortality during the first trimester, decreased birthweight during the second trimester, and increased preterm birth and decreased birthweight during the third trimester. Thus, we expect a difference in birth outcomes based on the gestational age at time of maternal SARS-CoV-2 infection.Research in contextEvidence before this studyWe searched PubMed for the keywords “SARS-CoV-2” or “COVID-19” combined with “preterm birth” or “trimester,” including all articles published before March 21, 2021. No language restrictions were applied. We found several studies that examined the impact of maternal SARS-CoV-2 infection on pregnancy outcomes using electronic health records (EHRs) or insurance claims. However, these studies were limited by small sample size or narrow scope of the data. Also, most studies did not differentiate between the timing of preterm birth, which has clinically significant implications in terms of neonatal survival, care, and potential lifelong health problems. To our knowledge, as of November, 2021, there has not yet been any primary study that accounts for the trimester of maternal SARS-CoV-2 infection, despite the awareness of the crucial periods during pregnancy when there exists a greater potential for in utero shocks on fetal development.Added value of this studyWe did a retrospective cohort study using EHRs from hospitals and clinics across five states in the USA. We compared pregnancy outcomes of unvaccinated people with a positive SARS-CoV-2 test during pregnancy to a matched control cohort with negative SARS-CoV-2 test results. The positive cohort was substratified by trimester of infection. To our knowledge, our study is the first to modulate birth outcomes by the trimester of maternal infection. Additionally, using propensity score matching, we controlled for several confounding variables known to be associated with negative pregnancy outcomes, which were enriched in the SARS-CoV-2 positive cohort. We evaluated outcomes using delivery events, including preterm birth classifications. There was an increase in preterm birth and stillbirth following SARS-CoV-2 infection, primarily driven by increases following first or second trimester maternal infection. We built supervised learning models that predict gestational age at delivery using demographics, maternal comorbidities, information related to SARS-CoV-2 infection, and fetal characteristics. The single greatest predictor of gestational age at delivery is gestational age at infection, with earlier age at infection associated with earlier age at delivery. There was no correlation between severity of COVID-19 and gestational age at delivery.Implications of all the available evidenceThese results suggest that pregnant people are at increased risk of preterm birth following SARS-CoV-2 infection regardless of COVID-19 severity. We recommend enhanced prenatal care and increased monitoring for pregnant people following a SARS-CoV-2 infection. Due to increased risk of maternal–fetal health of SARS-CoV-2 infection, we propose prioritisation of vaccination of pregnant people in areas where vaccine distribution is scarce.In this study, we investigated the impact of maternal SARS-CoV-2 infection at each trimester of pregnancy on birth outcomes (preterm birth, stillbirth, birthweight, and SGA) in an unvaccinated population, adjusting for common confounding factors. We also developed models to predict gestational age at delivery for people following a SARS-CoV-2 infection during pregnancy. We evaluate performance and feature importance for each model and discuss the impact of SARS-CoV-2 infection on gestational age at delivery. This research investigates the risk of negative birth outcomes for people exposed to SARS-CoV-2 infection during pregnancy.
Methods
Study setting and participants
In this retrospective cohort study, we used clinical data from Providence St Joseph Health (PSJH) electronic health records for pregnant people (aged 18 years and older and younger than 45 years) who delivered in the USA at the Providence, Swedish, or Kadlec sites in Alaska, California, Montana, Oregon, and Washington between March 5, 2020, and July 4, 2021 (appendix pp 1–2). PSJH is an integrated health-care system that has strong continuity of care across inpatient and outpatient settings; the US organisation’s 51 hospitals, 1085 clinics, and 120 000 caregivers collaborate to provide health and social services across seven states. We provide the COVID-19 seroprevalence rates for each of the five states in the study and the USA at the national level during the Centre for Disease Control and Prevention’s monitoring period containing the date Feb 14, 2021 (appendix p 12).9The SARS-CoV-2 positive cohort included people who had a positive SARS-CoV-2 PCR-based test during pregnancy, subdivided by trimester of infection (appendix pp 1–2). No patients in the SARS-CoV-2 positive cohort were vaccinated for COVID-19 at the time of SARS-CoV-2 infection. The SARS-CoV-2 negative cohort were people with at least one negative PCR-based SARS-CoV-2 test and no positive tests during pregnancy. The cohort was limited to women with singleton pregnancies who delivered after 140 days’ gestational age (20 weeks), who had either commercial or state-provided Medicaid insurance. At the time of this study, PSJH did not routinely scan pregnant people for active SARS-CoV-2 infection or antibody presence during prenatal care or at delivery (appendix p 3). Due to scarce testing availability at the beginning of the pandemic, testing was restricted to the sickest patients. As testing became more accessible, PSJH began testing anyone with COVID-19 symptoms and screening patients 2 days before admission for a planned procedure, including scheduled caesarean delivery or labour induction. The date after which broader testing began was site specific.To account for covariates associated with preterm birth, propensity score matching was done to generate a SARS-CoV-2 negative matched control cohort.6, 10 The unsupervised learning model nearest neighbours with replacement (k=1) was done to match across ten common covariates using the Python library sklearn (version 0.22.1; appendix pp 13–14).11 For each SARS-CoV-2 positive pregnant patient, this identifies the single most similar SARS-CoV-2 negative pregnant person across these ten variables. For people who were missing information in the pregravid body-mass index (BMI) field, a value was imputed using the median of the pregravid BMI reported for other people in their cohort (either the cohort of 882 people who had positive SARS-CoV-2 test results or the cohort of 17 453 people in the SARS-CoV-2 negative cohort before matching). Absence of data for race, ethnicity, fetal sex, parity, and delivery method was encoded as −1. This resulted in a control cohort of 889 people that had improved representation of the matched covariates to the SARS-CoV-2 positive cohort.All procedures were reviewed and approved by the Institutional Review Board at the PSJH through expedited review (study number STUDY2020000196). Consent was waived because disclosure of protected health information for the study was determined to involve no more than a minimal risk to the privacy of individuals.
Outcomes
The primary outcome evaluated in this study was gestational age at delivery with annotation of preterm birth classification. Secondary outcomes assessed included stillbirth, birthweight, fetal growth percentile, SGA, and rates of common pregnancy-related conditions.
Statistical analysis
The composition of each cohort’s demographics, comorbidities, and birth characteristics was represented as proportions or median along with the IQR (table 1). A Fisher’s exact test (for categorical data) or a Mann-Whitney U test (for continuous data) were used to evaluate differences between cohorts, using the R stats (version 3.6.3) and Python scipy (version 1.4.1) packages, respectively (appendix p 13). Comorbidities were identified by patient diagnosis codes, using SNOMED-CT (version 20200901; appendix p 15). Stillbirth is defined as pregnancy loss at 20 or more weeks of gestation. Term birth is defined as birth from 37 or more weeks of gestation; late preterm birth as less than 37 weeks and 34 or more weeks of gestation; moderate preterm birth as less than 34 weeks and 32 or more weeks of gestation; very preterm birth as less than 32 weeks and 28 or more weeks of gestation; and extremely preterm birth as less than 28 weeks of gestation.Table 1Demographic and birth characteristics of people with SARS-CoV-2 test results during pregnancy
| SARS-CoV-2 positive (n=882) | SARS-CoV-2 negative (n=17 453) | SARS-CoV-2 negative matched (n=889) | ||
|---|---|---|---|---|
| Demographics | ||||
| Maternal age at birth, years | 27·6 (9·4) | 31·5 (8·2) | 27·9 (9·0) | |
| Pregravid body-mass index, kg/m2 | 28·2 (8·6) | 25·8 (9·0) | 28·4 (2·1) | |
| Race | ||||
| Total number known | 849 | 16 920 | 869 | |
| American Indian or Alaska Native | 20 (2·4%) | 232 (1·4%) | 16 (1·8%) | |
| Asian | 29 (3·4%) | 1571 (9·3%) | 38 (4·4%) | |
| Black | 46 (5·4%) | 708 (4·2%) | 45 (5·2%) | |
| Multiracial | 9 (1·1%) | 185 (1·1%) | 9 (1·0%) | |
| Native Hawaiian or Pacific Islander | 19 (2·2%) | 215 (1·3%) | 17 (2·0%) | |
| Other* | 306 (36·0%) | 2851 (16·8%) | 295 (33·9%) | |
| White | 420 (49·5%) | 11 158 (65·9%) | 449 (51·7%) | |
| Ethnicity | ||||
| Total number known | 863 | 16 898 | 873 | |
| Hispanic or Latino | 453 (52·5%) | 3607 (21·3%) | 449 (51·4%) | |
| Not Hispanic or Latino | 410 (47·5%) | 13 291 (78·7%) | 424 (48·6%) | |
| Insurance | ||||
| Total number known | 882 | 17 453 | 889 | |
| Medicaid | 661 (74·9%) | 8191 (46·9%) | 668 (75·1%) | |
| Private | 221 (25·1%) | 9262 (53·1%) | 221 (24·9%) | |
| Smoker | 79 (9·0%) | 1903 (10·9%) | 86 (9·7%) | |
| Illicit drug user | 93 (10·5%) | 2146 (12·3%) | 93 (10·5%) | |
| Preterm history | 44 (5·0%) | 695 (4·0%) | 43 (4·8%) | |
| Parity | ||||
| Total number known | 874 | 17 156 | 882 | |
| Nulliparity | 426 (48·7%) | 10 288 (60·0%) | 439 (49·8%) | |
| Low multiparity | 413 (47·3%) | 6522 (38·0%) | 410 (46·5%) | |
| Grand multipara | 35 (4·0%) | 346 (2·0%) | 33 (3·7%) | |
| Gravidity | ||||
| Total number known | 874 | 17 156 | 882 | |
| Nulligravidity | 239 (27·3%) | 5757 (33·6%) | 239 (27·1%) | |
| Low multigravidity | 568 (65·0%) | 10 451 (60·9%) | 583 (66·1%) | |
| Grand multigravidity | 67 (7·7%) | 948 (5·5%) | 60 (6·8%) | |
| Educational attainment | ||||
| Total number known | 40 | 567 | 45 | |
| Less than high school | 8 (20·0%) | 53 (9·3%) | 7 (15·6%) | |
| High school | 26 (65·0%) | 229 (40·4%) | 23 (51·1%) | |
| Undergraduate degree | 5 (12·5%) | 203 (35·8%) | 14 (31·1%) | |
| Graduate degree | 1 (2·5%) | 82 (14·5%) | 1 (2·2%) | |
| Comorbidities | ||||
| Chronic diabetes | 87 (9·9%) | 1542 (8·8%) | 101 (11·3%) | |
| Chronic hypertension | 13 (1·5%) | 448 (2·6%) | 23 (2·6%) | |
| Gestational diabetes | 79 (9·0%) | 1404 (8·0%) | 89 (10·0%) | |
| Gestational hypertension | 52 (5·9%) | 1078 (6·2%) | 63 (7·1%) | |
| Preeclampsia | 41 (4·6%) | 41 (0·2%) | 41 (4·6%) | |
| Severe pre-eclampsia | 3 (0·3%) | 23 (0·1%) | 0 (0·0%) | |
| Birth characteristics | ||||
| Fetal sex | ||||
| Total number known | 828 | 16 840 | 841 | |
| Female | 396 (47·8%) | 8246 (49·0%) | 409 (48·6%) | |
| Male | 432 (52·2%) | 8594 (51·0%) | 432 (51·4%) | |
| Mode of delivery | ||||
| Total number known | 834 | 16 869 | 842 | |
| Caesarean section | 265 (31·8%) | 6235 (37·0%) | 270 (32·1%) | |
| Vaginal | 569 (68·2%) | 10 634 (63·0%) | 572 (67·9%) |
Median (IQR) of the maternal age at birth (years) and pregravid body-mass index for people with maternal SARS-CoV-2 infection during pregnancy (n=882), with no SARS-CoV-2 infection and at least one negative SARS-CoV-2 test during pregnancy (n=17 453), and propensity score matched negative control (n=889). Distribution of race, ethnicity, insurance type, parity, gravidity educational attainment, fetal sex, and mode of delivery. Number of people who smoke, use illicit drugs, have previously delivered prematurely, or have common pregnancy-related comorbidities.* Other is a category used in electronic health records that might be patient-reported or selected by health-care staff.
Fetal growth percentile was calculated using the WHO Fetal Growth Charts based on gestational age and weight.12 A moving points average, which creates a series of averages across different subsets of the full dataset, was used to calculate weight by percentile at gestational age. The fetal growth percentile was then calculated by interpolation. SGA is defined as the babies in the bottom 10th percentile of fetal growth at birth.
Predictive models
To evaluate which variables are most predictive of gestational age at delivery, we trained multiple supervised learning models on 24 features of the SARS-CoV-2 positive cohort (n=882; appendix p 16). SARS-CoV-2 encounters were only observed for people who engaged with the health-care system. These features included patient demographics, fetal characteristics, comorbidities, and treatment following SARS-CoV-2 infection before delivery (including encounters, diagnoses, medications, and COVID-19 severity score). COVID-19 severity was defined as the patient’s maximum score on the WHO Ordinal Scale.13Models were generated using the Python package sklearn with default settings for linear regression, ridge regression, gradient boosting regression, and random forest.11 The models were trained on 80% of the data, with 20% of the data held out for performance testing of the final model. Performance was evaluated on the test set for accuracy within 1 week of the actual gestational age at delivery, the coefficient of determinant (R2 value), and root mean square deviation. Models were also evaluated by plotting the predicted versus the observed gestational age at delivery and calculating the trendline and R2 value. Gini importance and Shapley additive explanations (SHAP) were applied to understand each feature’s marginal contribution and influence on model prediction using the Python libraries sklearn and SHAP (version 0.37.0), respectively.1, 14 Both these approaches evaluate the degree of influence of the feature on the sample’s outcome, which provides interpretation of the machine learning models. Additionally, the variance captured by each non-binary feature was individually evaluated via R2 value, and the correlation of each feature with gestational age at delivery was evaluated via Pearson’s correlation using the Python scipy package. Predictive models were reported following the TRIPOD guidelines15 (appendix pp 18–19).
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Between March 5, 2020, and July 4, 2021, 73 666 people delivered, 18 335 (24·9%) of whom had at least one SARS-CoV-2 test during pregnancy (appendix pp 1–2). We observed 882 people infected with SARS-CoV-2 during their pregnancy (first trimester: n=85; second trimester: n=226; and third trimester: n=571) and 19 769 people who have never tested positive for SARS-CoV-2 and received at least one negative SARS-CoV-2 test during their pregnancy (table 1). Compared with the SARS-CoV-2 negative cohort, patients in the SARS-CoV-2 positive cohort were more likely to have the following characteristics: Hispanic ethnicity (p<0·0001), race other than Asian or White (p<0·001), lower age (p<0·0001), higher BMI (p<0·0001), higher parity (p<0·0001), higher gravidity (p<0·01), Medicaid insurance (p<0·001), and lower educational attainment (85% attained a high school degree or less, p<0·01; table 1; appendix p 13). To account for these covariates, which are known to be associated with negative birth outcomes, propensity score matching was used to create a control SARS-CoV-2 negative cohort of 889 people (table 1; appendix pp 13–14).Patients with SARS-CoV-2 infection during the first and second trimester of pregnancy had decreased gestational age at delivery (first trimester p<0·05, second trimester p<0·05; table 2). There was an increase in preterm delivery across all trimesters of pregnancy infections (all pregnancies p<0·05) with the most pronounced increase occurring following first trimester infection (figure 1; first trimester p=0·22, second trimester, p=0·22, and third trimester p=0·06). Pregnant patients with SARS-CoV-2 infection gave birth to infants with lower birthweight (first trimester p<0·05, second trimester p=0·07, third trimester p<0·05, all pregnancies p<0·01); however, lower fetal growth percentile at delivery was observed only following third trimester exposure (first trimester p=0·29, second trimester p=0·25, third trimester p<0·05, all pregnancies p<0·05; table 2; appendix p 4). There were higher rates of SGA among infants born to mothers who had a SARS-CoV-2 positive test during pregnancy (p<0·05), with the most pronounced increase following third trimester infection (table 2; appendix p 4). Additionally, people with SARS-CoV-2 infection during pregnancy had no increased risk of common pregnancy disorders including gestational diabetes, gestational hypertension, and pre-eclampsia (table 2; appendix p 17).Table 2Birth outcomes of people with SARS-CoV-2 infection during their first, second, or third trimester
| First trimester (n=85) | Second trimester (n=226) | Third trimester (n=571) | Total (n=882) | Matched negative control (n=889) | |||||
|---|---|---|---|---|---|---|---|---|---|
| N or median | p value | N or median | p value | N or median | p value | N or median | p value | N or median | |
| Stillbirth* | 1 (1·2%) | 0·05 | 5 (2·2%) | 0·06 | 1 (0·2%) | 0·36 | 7 (0·8%) | <0·05 | 1 (0·1%) |
| Small for gestational age*† | 9 (10·6%) | 0·79 | 29 (12·8%) | 0·37 | 78 (13·7%) | 0·08 | 117 (13·3%) | <0·05 | 91 (10·2%) |
| Fetal growth percentile‡ | 49 (49·2) | 0·29 | 47 (49·4) | 0·25 | 45 (48·6) | <0·05 | 46 (50·0) | <0·05 | 48 (51·0) |
| Gestational days‡ | 273 (13·0) | <0·05 | 273 (16·0) | <0·05 | 275 (13·0) | 0·38 | 274 (14·0) | 0·12 | 274 (12·0) |
| Birthweight, oz‡ | 117 (24·4) | <0·05 | 117 (23·9) | 0·07 | 118 (23·5) | <0·05 | 117 (23·7) | <0·01 | 119 (21·1) |
Data are n (%) or median (IQR), unless stated otherwise. Stillbirth and small for gestational age rates for people with maternal SARS-CoV-2 infections during their first, second, or third trimester compared with the matched negative control group. Median (IQR) of the fetal growth percentile at delivery, gestational age at delivery, and weight at delivery.* Categorical variables: Fisher’s exact test.† Percentages reported are calculated from the total of infants whose birthweight at delivery was recorded.‡ Continuous variables: Mann-Whitney U test.
Multivariable predictive modeling was deployed to evaluate the ability of demographic data, comorbidities, health following SARS-CoV-2 infection, and fetal characteristics features to predict the outcome gestational age at delivery. Supervised machine learning models predicting gestational age at delivery were trained on 24 features with model performance evaluated on the held-out test dataset (appendix pp 16–17). The highest performing model was random forest, which accurately predicted gestational age at delivery within a week 58% of the time, and the model accounted for 28% of the data variance (appendix pp 5, 17). The most important feature of the random forest model was gestational age at infection (figure 2; appendix pp 6–7). Earlier gestational age at infection is associated with earlier gestational age at delivery (figure 3). The second most important feature in the model is the number of unique active ingredients in medications prescribed following SARS-CoV-2 infection. Earlier gestational age at delivery was associated with higher numbers of unique active ingredients in medications prescribed (figure 3). The second highest performing model was the gradient boosting regression model, which accurately predicted gestational age at delivery within a week 56% of the time and accounted for 28% of the data variance (appendix pp 8, 17). The most important feature in this model was also gestational age at infection (appendix pp 9–11).
Gestational age at infection and the count of unique active ingredients in medications accounted for 1·2% and 5·6%, respectively, of the variance in gestational days at delivery alone (figure 3). People who delivered prematurely tended to be infected with SARS-CoV-2 earlier in pregnancy, but there are people who had early infections who went on to deliver at term. Similarly, patients with higher numbers of unique active ingredients in medications prescribed following SARS-CoV-2 infection tended to deliver prematurely. However, some people with no medications delivered prematurely and some with dozens of medications prescribed delivered at term. There was a slight uptick in the number of SARS-CoV-2 encounters among people who had extremely and very preterm birth, but the people with the highest numbers of SARS-CoV-2 encounters had late preterm or term deliveries.Gestational age at infection (r=0·11, p<0·01), preterm history (r=0·17, p<0·0001), commercial insurance (r=0·09, p<0·01), and number of cord vessels (r=0·15, p<0·0001) were associated with higher gestational age at delivery (table 3). Count of unique medication active ingredients (r=–0·21, p<0·0001), maternal age (r=–0·10, p<0·01), gestational hypertension status (r=–0·24, p<0·0001), gestational diabetes status (r=–0·07, p<0·05), chronic hypertension status (r=–0·11, p<0·01), chronic diabetes status (r=–0·09, p<0·05), and unique diagnoses count (r=–0·08, p<0·05) were associated with lower gestational age at delivery (table 3).Table 3Contribution of features towards the predicted gestational age
| Pearson’s correlation | p value | |
|---|---|---|
| Demographic | ||
| Maternal age, years | −0·1 | <0·01 |
| Pregravid body-mass index, kg/m2 | −0·05 | 0·17 |
| Race | −0·05 | 0·15 |
| Ethnicity | −0·03 | 0·42 |
| Insurance | 0·09 | <0·01 |
| Parity | −0·03 | 0·35 |
| Gravidity | −0·03 | 0·35 |
| Preterm history | 0·17 | <0·0001 |
| Smoker | 0·01 | 0·85 |
| Illicit drug user | −0·01 | 0·83 |
| Comorbidities | ||
| Chronic diabetes status | −0·09 | <0·05 |
| Chronic hypertension status | −0·11 | <0·01 |
| Gestational diabetes status | −0·07 | <0·05 |
| Gestational hypertension status | −0·24 | <0·0001 |
| Health following SARS-CoV-2 infection | ||
| Max COVID-19 severity index | 0·03 | 0·31 |
| Gestational age at maternal SARS-CoV-2 infection | 0·11 | <0·01 |
| Max patient class | 0·05 | 0·13 |
| Number of SARS-CoV-2 encounters | 7·5 × 10−4 | 0·98 |
| Max oxygen device | −0·04 | 0·18 |
| Unique medication active ingredients | −0·21 | 0·0001 |
| Total number of diagnoses | −0·07 | 0·05 |
| Unique number of diagnoses | −0·08 | 0·05 |
| Fetal characteristics | ||
| Fetal sex | 0·05 | 0·10 |
| Delivery cord vessels | 0·15 | <0·0001 |
Pearson’s correlation coefficient and the corresponding p values for the 24 features (two-tailed) used in the predictive models.
- Finally, the COVID-19 severity score was not correlated with gestational age at delivery (p=0·31; table 3; appendix p 12), although this cohort only had patients who had mild or moderate acute COVID-19, and no patients who required mechanical ventilation. There was no correlation between gestational age at delivery and the number of SARS-CoV-2 encounters (p=0·98), need for supplemental oxygen (p=0·18; WHO COVID-19 severity score 4–5), or maximum patient class (p=0·13; outpatient, emergency or urgent care, or inpatient; table 3; appendix p 12). Also, there was no correlation between COVID-19 severity and the number of days before delivery SARS-CoV-2 infection occurs (p=0·25; appendix p 3). Altogether, people who delivered the earliest had mild SARS-CoV-2 infection (WHO COVID-19 severity ≤3) and early gestational age at infection. 35 (4·0%) of 882 pregnant people with SARS-CoV-2 infection received anticoagulation medication (heparin or enoxaparin) as treatment for COVID-19 (defined as received medication 2 weeks before or 4 weeks following a positive SARS-CoV-2 test). For these people, 34 (97·1%) received prophylactic doses and one received a therapeutic dose. Ten (28·6%) second trimester and 25 (71·4%) third trimester COVID-19 infections were observed.
Discussion
In this study, we examined the impact of maternal SARS-CoV-2 infection on birth outcomes modulated by gestational age, which resulted in the following key findings. First, more negative birth outcomes were observed when infections occurred earlier in gestation, including increased risk for preterm birth and stillbirth. Second, there were increased rates of SGA infants born to people who had a positive SARS-CoV-2 test result during pregnancy, suggesting that preterm delivery is induced via a mechanism that could impact fetal growth. Third, there appears to be two distinct populations of pregnant people: a subset with a negative correlation between the time of maternal SARS-CoV-2 infection and gestational age at delivery and a second population appearing to be unaffected. Fourth, the difference between mild and moderate severity of the SARS-CoV-2 infection does not appear to play a part in whether a pregnancy is likely to be negatively affected. Taken together, these findings suggest that SARS-CoV-2 infection early in pregnancy is an important risk factor that should be monitored in health systems.The biggest predictor of gestational age at delivery is gestational age at maternal SARS-CoV-2 infection followed by the number of unique medication active ingredients prescribed following a SARS-CoV-2 infection. Notably, there appeared to be two subpopulations when comparing the gestational age at delivery versus gestational age at SARS-CoV-2 infection. One subpopulation shows a positive correlation, but for most individuals, the timing of infection did not show a direct association with gestational age at delivery (figure 3). This finding suggests there could be some event, or class of events, that happen during some pregnancies and not others, triggering the chance of negative effects on outcomes. Surprisingly, there was no correlation between gestational age at delivery and COVID-19 severity or the number of SARS-CoV-2 encounters, suggesting that the mechanism behind the effect of SARS-CoV-2 on preterm birth might differ from those that drive severity. Pregnant people with preterm birth tended to have mild SARS-CoV-2 infections and limited numbers of encounters during an active SARS-CoV-2 infection. Further studies are needed to investigate the mechanisms of how SARS-CoV-2 infection affects preterm birth and stillbirth.Previous studies reported more severe birth outcomes among symptomatic than asymptomatic people.1, 2 However, these studies did not account for trimester of infection. Cohorts might have been skewed towards people infected during their third trimester due to the state of testing early in the pandemic and studies’ earlier reporting, which would mean a low number of observations of first or second trimester SARS-CoV-2 infection. We showed that gestational timing of infection is the most important factor of birth outcome. Further, COVID-19 disproportionately impacts low-income and minority communities, resulting in many covariates known to increase the risk of preterm birth and other negative birth outcomes.16 There were significant differences in the demographics, comorbidities, and birth characteristics of people in the SARS-CoV-2 positive and SARS-CoV-2 negative cohorts, including ethnicity, race, and Medicaid insurance status. Because these variables have previously been associated with differences in birth outcomes in the USA, we control for these cofactors using propensity score matching.A potential mechanism that could account for worse outcomes with SARS-CoV-2 infections earlier in gestation is increased levels of angiotensin converting enzyme 2 (ACE2) in the placenta earlier in gestation.17 The SARS-CoV-2 spike protein interacts with ACE2 for entry into human cells,18, 19, 20 and ACE2 placental levels are dependent on gestational age, with the highest levels observed early in gestation and near undetectable levels observed near term.17, 20 This difference implicates an increased risk of SARS-CoV-2 infecting the placenta via ACE2 binding. Vertical SARS-CoV-2 transmission has been reported, but remains a rare mode of infection.21, 22 However, an intrauterine infection can lead to ACE2-expressing neutrophils and monocytes (macrophage) to invade the placenta, which could result in increased risk for fetal distress.17 This is a potential mechanism by which increased stress occurs on the placenta during SARS-CoV-2 infection in a gestational age dependent manner. The histological features of SARS-CoV-2 infected placenta remain ill-defined due to scarce placenta histopathological reports. However, given that patients with COVID-19 have an increased risk of thromboembolic events, additional investigations into placental thrombosis, thromboembolic events, and anticoagulation in pregnant patients following maternal SARS-CoV-2 infection are justified.22 Only 35 people in the SARS-CoV-2 positive cohort received anticoagulants as a treatment for COVID-19 in this study. Because few people in this study received anticoagulation during acute COVID-19 infection, it is not possible to do a rigorous analysis on the association between this treatment and birth outcomes. This remains an important area for future investigation. Future studies examining the impact of SARS-CoV-2 infection on placenta health are needed to provide insight into the mechanism by which maternal SARS-CoV-2 infection might promote negative pregnancy outcomes.The trend of increased rates of preterm birth and stillbirth among women who had a first or second trimester SARS-CoV-2 infection is concerning. Extremely preterm birth is associated with lower fetal survival and lifelong health problems, including neuropsychiatric impairment and elevated levels of major comorbidities.23, 24, 25 Furthermore, extremely preterm infants require substantial resources for care, with costs nearly 100 times that of care for full-term infant in the first 6 months of life.26 In addition to increased risk of negative pregnancy outcomes following SARS-CoV-2 infection, pregnant people have higher infection rates and are more likely to develop severe COVID-19·1, 2, 25, 27 Taken together, both maternal and fetal health are at increased risk following maternal SARS-CoV-2 infection. It might, therefore, be prudent to consider pregnant people as a prioritised population for SARS-CoV-2 vaccination in areas where vaccine dissemination is scarce. There is also evidence that vaccine antibodies can pass through the umbilical cord to the fetus in utero, meaning that maternal vaccination can have the additional benefit of protecting the subsequent neonate.28, 29The results suggest that additional monitoring of birth outcomes following first or second trimester maternal SARS-CoV-2 infection is warranted. The pregnant people who tested positive for SARS-CoV-2 in this study were unvaccinated against COVID-19; therefore, it would be interesting to examine in future studies whether vaccination helps to prevent negative birth outcomes in breakthrough cases. Also, modifiable features that are predictive of premature delivery can suggest hypotheses for clinical interventions, and additional features can be added to improve model accuracy and explore additional potential risk factors. The impact of in utero SARS-CoV-2 exposure on neonatal outcomes, including meeting developmental milestones, also needs further investigation. Finally, the impact of COVID-19 on subsequent pregnancy outcomes should be studied by examining the birth outcomes of non-pregnant women who had COVID-19 and later went on to become pregnant. Such studies are needed to assess the long-term impact of COVID-19 and the effect of post-acute sequelae of SARS-CoV-2 on pregnancy.Here we examine outcomes of pregnancies that reached at least 20 weeks of gestation. It is possible that first trimester SARS-CoV-2 infection increases the risk of miscarriage, an outcome that we do not evaluate in this study. People in our study cohort had mild or moderate SARS-CoV-2 infections, so our findings might not extrapolate to a pregnant person with severe COVID-19, which remains rare in pregnant people. We have identified features that are predictive of gestational age at delivery, but they are not necessarily risk factors for premature delivery. Furthermore, this study was done at PSJH without validation at an independent health-care system. However, concerns regarding generalisability of this study are mitigated by the size and diversity of PSJH, which serves patients at 51 hospitals and 1085 clinics across five western US states.Additionally, this study focused on birth outcomes and does not account for neonatal health. Thus far, there have been no serious outcomes observed among infants exposed to SARS-CoV-2 as neonates.22 However, it is too early to observe the impact of in utero SARS-CoV-2 exposure on subsequent neonate and early development. Previously, in utero exposure to viruses have been shown to increase later risk of developing autism or other neuropsychiatric diseases.30 Furthermore, in utero exposure to infection is associated with the development of subsequent autoimmune diseases, including asthma and type 1 diabetes.31, 32 Therefore, it is important to follow children exposed to SARS-CoV-2 in utero to determine if this leads to increase risk of the development of long-term outcomes, especially neuropsychiatric or autoimmune diseases.This cohort study found that first and second trimester maternal SARS-CoV-2 infection was a risk factor for preterm birth and stillbirth. The greatest predictor of gestational age at delivery following a maternal SARS-CoV-2 infection was the gestational age at infection. In this cohort of patients with mild or moderate COVID-19, there was no correlation between COVID-19 severity and gestational age at delivery. These findings suggest that increased monitoring and enhanced prenatal care could be appropriate for pregnant people who have had a SARS-CoV-2 infection during the first or second trimester of pregnancy, regardless of infection severity.
Fourth dose of COVID-19 vaccines in Israel
On Jan 2, 2022, Israel’s prime minister Naftali Bennett announced that the country would offer a fourth dose of the COVID-19 vaccine to health-care workers and people older than 60 years. A fourth dose has already been approved for Israelis in immunocompromised groups. Israel has recently seen a surge in SARS-CoV-2 infections. Hospitalisations are also increasing, though overall numbers remain low.Around two-thirds of Israelis have received two doses of the COVID-19 vaccine. 80% of the eligible population have received two doses plus a booster jab, including 90% of individuals over the age of 60 years. Israel began vaccinating 5–11-year-olds in November, 2021. The efficiency of the early vaccination campaign, which had delivered two doses to over half the Israeli population by April, 2021, meant that Israel was well-placed to observe how immunity waned over time. When the delta variant of SARS-CoV-2 hit the country, it became apparent that individuals who had been vaccinated more than 6 months previously were not adequately protected against severe disease. In July, 2021, Israel began rolling out the world’s first booster campaign against COVID-19.
After the emergence of the omicron variant in November, 2021, Israel moved quickly to restrict international travel. It established a rigorous network of contact tracing targeting the new variant. “The first phase of our response to omicron was containment; the aim was to delay local transmission for as long as possible”, explains Ran Balicer, chair of Israel’s National Experts Advisory Panel on COVID-19. He reckons that the measures gained Israel several valuable weeks in which to prepare for the inevitable spike in cases.“We have now moved from a containment phase to a more generalised approach of mitigation”, Balicer told The Lancet Respiratory Medicine. “We know it is going to be very difficult to control the spread of the omicron variant, so we are concentrating on ensuring that the population at the highest risk of adverse outcomes is protected.” Jonathan Sterne, professor of medical statistics and epidemiology at the University of Bristol (Bristol, UK) noted that there is good evidence that the third dose of the COVID-19 vaccine reduces short-term rates of clinical events. Whether a fourth dose will perform similarly remains to be seen. “Fourth doses are mainly being done on a precautionary basis; we have very little evidence of their effect either from studies of immune function or from observational studies of clinical events”, said Sterne.
Time for Africa to future-proof, starting with COVID-19
Persisting inequities are slowing progress towards global and national targets for the control of HIV, tuberculosis, and malaria. The same is true for the COVID-19 pandemic, with wealthier countries continuing to monopolise the global share of vaccines. Africahas been disproportionately affected by this vaccine nationalism. A WHO target of achieving full primary immunisation in 40% of the global population by the end of December, 2021, was reached in only seven African countries, with just under 9% of people on the continent being fully vaccinated by the end of 2021. The target of 70% global coverage of primary series immunisation by June, 2022, is a pipe dream. The inequity in COVID-19 vaccine distribution is prolonging the pandemic, facilitating the emergence of new variants with potential for immune evasion, increased disease severity, and global spread. This has been witnessed with the discovery of the delta variant (B.1.617.2) of concern in India and of omicron (B.1.1.529) in southern Africa. In the context of the omicron variant, WHO is urging countries to focus on the most relevant outcomes of severe disease and death and to support “urgent and broad access to current COVID-19 vaccines for priority populations worldwide”. Yet, world leaders refuse to learn from experience or heed the warnings and recommendations of experts.COVID-19 vaccine inequity has often featured in our pages over the past year. In our June, 2021, Editorial we described how equitable vaccine distribution was being undermined by over-purchasing of vaccine doses by wealthier nations—a problem that remains with prioritisation of booster programmes (the subject of our September, 2021, Editorial). Many countries are, shamefully, failing to live up to commitments made to COVAX and other vaccine-sharing schemes. According to WHO, Africa will need more than 900 million vaccine doses to fully vaccinate 40% of its population, yet as of Jan 10 it had procured only 492 million. Whereas we previously reported limited production capacity as being an impediment to global vaccine availability, that can no longer be considered the case: 26 different COVID-19 vaccines were in use as of Jan 6, and 41 billion doses are predicted to be available in 2022.
Source: Lancet
Omicron Variant Shouldn’t Be Categorized as ‘Mild”
Although the highly contagious Omicron variant appears to be less severe than the Delta variant, it shouldn’t be categorized as “mild,” the WHO said on Thursday.
“Just like previous variants, Omicron is hospitalizing people and it is killing people,” Tedros Adhanom Ghebreyesus, the director-general of the WHO, said during a COVID-19 news briefing.
“In fact, the tsunami of cases is so huge and quick, that it is overwhelming health systems around the world,” he said.
Hospitals are becoming overcrowded and understaffed, which leads to deaths not only from COVID-19 but also other diseases and injuries where patients can’t receive timely care, he said.
The highest number of worldwide COVID-19 cases was reported last week, he said, adding that the WHO knows “for certain” that the count was an underestimate.
“Reported numbers do not reflect the backlog of testing around the holidays, the number of positive self-tests not registered, and burdened surveillance systems that miss cases around the world,” he said.
So far, fewer patients need to be hospitalized due to the Omicron variant, but with the large number of people contracting COVID-19, hospitalization numbers are increasing. At Northwell Health in New York, about 10% of patients are ending up in intensive care, as compared with 25% to 35% in previous surges, according to The Wall Street Journal. Fully vaccinated patients are staying for an average of four days, and unvaccinated patients are staying for almost two weeks.
WHO officials encouraged global leaders to support campaigns to vaccinate 70% of people in every country by the middle of 2022 and ensure that reliable tests and breakthrough treatments are available in all countries. At the current pace of vaccine rollouts, 109 countries would miss the goal of fully vaccinating 70% of their populations by the beginning of July.
“Vaccine inequity is a killer of people and jobs, and it undermines a global economic recovery,” Tedros said. “Alpha, Beta, Delta, Gamma and Omicron reflect that in part because of low vaccination rates, we’ve created the perfect conditions for the emergence of virus variants.”
Some countries are beginning to vaccinate people with a fourth dose while others haven’t received enough regular supply to fully vaccinate their health care workers and most vulnerable groups, he added. In fact, 36 countries haven’t reached the 10% vaccination mark, and among the most severe patients worldwide, 80% are unvaccinated.
“Booster after booster in a small number of countries will not end a pandemic while billions remain completely unprotected,” he said.
Speculations that the Omicron variant might be the last outbreak of the pandemic are “wishful thinking,” Michael Ryan, MD, the WHO emergencies chief, told reporters during the briefing.
“There still is a lot of energy in this virus,” he said.
Maria Van Kerkhove, the WHO’s technical lead on COVID-19, backed up Ryan’s thoughts.
“I think it’s very unlikely that Omicron will be the last variant that you will hear us discussing,” she said.
One Man’s Psychedelic Journey to Confront His Cancer
Pradeep Bansal considered the five capsules he was about to swallow. Together they made up a 25 milligram dose of a substance that, in another setting, could have landed him in federal prison.
The substance was psilocybin, the active ingredient in magic mushrooms. To be more exact, it was a synthetic form of psilocybin called COMP360, made to pharmaceutical standards by a company called COMPASS Pathways. He was taking it as part of an FDA-approved clinical study on mental health therapy for people with cancer.
Bansal, a New York gastroenterologist, was far more comfortable giving medical treatment than receiving it. But he was getting used to it.
He had already been through surgery and a number of other treatments to address the physical aspects of his cancer. The psilocybin was to address the mental aspects — the crushing anxiety and depression that had stuck with him after his diagnosis.
Bansal did not arrive at this moment lightly.
“I was extremely skeptical going into this process,” says Bansal, who during a long medical career had looked with distrust and even disdain at alternative therapies.
“I don’t have much patience for holistic medicine, homeopathy, acupuncture, or alternative medicines with claims of spiritual upliftment or altered states of mind.”
But Bansal had done his homework on psilocybin and was impressed.
People with late-stage cancer and other serious health conditions who got psilocybin-assisted psychotherapy had “significant decreases” in anxiety and depression as long as 12 months after the treatment, according to studies published in 2011, 2014, and 2016.
One study from Johns Hopkins University tracked the effects of a single guided dose of psilocybin in terminal cancer patients with anxiety and depression. More than 80% had a “significant decrease” in symptoms — even 6 months after treatment — with more than 60% of the group remaining in the normal mood range.
For the study Bansal joined, there had been weeks of screening and consultation and preparation in a strictly controlled scientific trial.
And yet even with all that he had learned, even with his psychiatrist-guide by his side, he was afraid. Afraid of what he might experience under the powerful effects of psilocybin. And afraid that this was all a misguided waste of time — that his mental angst would still be there when it was all over.
He knew that psilocybin, like other psychedelic substances, could take you on a “trip” — could remove you, at least for a time, from normal conscious experience.
Maybe he would feel “funny,” he thought. Maybe he would have some hallucinations. But how would that change the reality of his cancer? How would it lift the black dread and anxiety he felt about his future?
Stuck in a Dark Place
Bansal had first noticed blood in his urine — a lot of it — in September 2019.
Two months later, doctors diagnosed cancer in his right kidney. He would need surgery to remove the kidney and surrounding lymph nodes (an operation called radical nephrectomy).
It was a shock, says Bansal. But the diagnosis and the surgery happened so quickly that he hardly had time to think. And treatment results seemed good. The cancer was only in stage I and the CT scans showed no signs of cancer after surgery.
“We were so relieved. Everyone was so happy,” Bansal says. “They didn’t even give me chemotherapy after surgery because it seemed so early.”
But a routine scan in June 2020 revealed more cancer in his lung. Within a couple of months, it was in his bladder too.
“It was devastating,” Bansal says. “I went from thinking I was healthy again to stage IV cancer.”
As doctors scheduled surgery to remove part of his lung, Bansal started on painful immunotherapy (BCG therapy) for his bladder.
At this point, from a psychological standpoint, Bansal was reeling. As a doctor, he knew all too well the meaning of stage IV cancer.
With two adult children and a grandchild on the way, Bansal had been looking forward to retirement with his wife of almost 40 years. “Suddenly, I wasn’t sure I was going to last that long,” Bansal recalls.
“I was in a very dark place. I was very anxious, very depressed from lack of sleep.”
He saw a therapist about his cancer diagnosis and maintained his regular meditation practice at home. He hired a personal trainer and tried to focus on any good news that he got about his treatment.
Those things helped, but not enough.
The basic facts were inescapable. His cancer might end everything. He couldn’t stop thinking about it. And then he couldn’t stop thinking about how he couldn’t stop thinking about it.
If the worst happened, he didn’t want to spend his last days in a state of such relentless existential angst. And it wasn’t just for himself. He wanted to be strong and mentally present for his family and his loved ones and his patients.
As he searched for something to ease his mental anguish, Bansal recalled some psychedelic research on end-of-life anxiety and depression that he’d read about in Michael Pollan’s 2018 book on psychedelics, How to Change Your Mind.
The studies were small and the research was new, but Bansal was impressed enough with the results to take a chance. He called a lead researcher of one of the studies, a fellow New York doctor, and eventually found himself accepted into a new study.
Starting the Journey
By the time Bansal arrived at the Bill Richards Center for Healing at the Aquilino Cancer Center in Rockville, MD, he had already been through weeks of screening.
The main requirements for the study were a cancer diagnosis and a measurable level of depression. But study participants also had to be physically fit enough to handle the medication, and psychologically free from a personal or family history of psychosis or schizophrenia. (The study also required participants to slowly wean themselves from medications like SSRIs for depression or anti-anxiety medications under the strict supervision of a qualified doctor.)
Bansal’s week of treatment began almost immediately on arrival at Aquilino. Everything was carefully choreographed but not rushed. From Monday through Wednesday, doctors followed his physical health with exams, ECGs, and blood work. And most importantly, they began to prepare him for the “dosing session” on Thursday when he would take the psilocybin.
This is the careful crafting of “set and setting” stressed in so many psychedelic therapies. “Set” refers to your mindset going into the drug experience. “Setting” is the space and people around you when the drug sends you into an altered state of consciousness.
Bansal met several times with at least three therapists in the days leading up to his dosing. He attended 4-plus hours of therapist-led group sessions with other people who would get a dosing on the same day. Together, they talked about what to expect during the experience and what to do in the face of fear or panic.
He connected with a therapist who would be his personal guide. Bansal’s therapist was a military psychiatrist with over 30 years’ experience.
“He was there with me from day 1, and so we established a relationship,” Bansal says.
“He asked me a lot of personal background history — you know, my religious convictions, aspirations, all those things.”
“Trust and let go,” was a kind of mantra for the treatment repeated by his guide and other doctors.
For Bansal, a doctor and scientist accustomed to using hard facts rather than touchy-feely slogans to navigate the care of patients, it was an adjustment, to say the least.
But he did his best to set aside his doubts and embrace the journey he was about to take.
The Day of the Trip
Thursday morning finally arrived. The setting of the dosing room was warm and welcoming, more like a cozy home study than a hospital room.
This matters more than you might think. First, because it’s important that you feel safe, open, and comfortable enough to let go and enter into a therapeutic process. But also because though rare, it’s possible — especially with psilocybin — for people to lose track of where they are and what they’re doing and put themselves or others in danger.
The dose, 25 milligrams, had been carefully calibrated to induce a psychedelic experience sufficient for therapy. Much less than that, say 10 milligrams, isn’t enough for most people to enter this state. A double dose, 50 milligrams, though not physically unsafe, may leave you too incoherent to have the useful insights key to therapeutic value.
A doctor, the lead investigator of the study, brought the five capsules into the room in an intricately carved crucible with a small ceremonial cup that held the water with which to take it.
“It was very solemn,” Bansal says. “He sat down with me in a very calming way.”
The doctor said: “Don’t worry about it. Just trust and let go.”
And that’s just what he did.
Bansal swallowed the capsules and lay down. The doctor quietly left the room so that Bansal and his psychiatrist guide could begin their session together.
Special eye shades kept him in the pitch dark whether his eyes were open or closed. Headphones streamed a curated musical playlist – much of it Western classical like Strauss, Bach, Mozart, and Beethoven — but also modern electronica and other music from cultures around the globe.
Bansal would remain here, with his therapist-guide by his side, in largely this same position, for the next 7-and-a-half hours.
It took about 45 minutes for the medication to kick in.
The Investigator
The doctor who brought the capsules into the dosing room was Manish Agrawal, MD, co-director of clinical research at the Aquilino Cancer Center and lead investigator of the study.
Agrawal trained at the National Cancer Institute and practiced for many years as an oncologist before developing an interest in psychedelic therapies. It was his work with cancer patients that drew him to psychedelics in the first place.
He had seen too many of his patients mentally wrecked by a cancer diagnosis, and he often felt helpless to comfort them.
“You take care of the physical aspects of the cancer, right? You talk about side effects and recommend another scan to look for recurrence.”
“But what about the psychological effects?”
They can be very serious and too often go ignored, says Agrawal. Your plans for the future suddenly become moot. You may be concerned about your ability to work or worried about the pain and suffering and financial strain that might be ahead for both you and your family. And to top it all off, you’re staring into the face of your own mortality.
So it’s no wonder, says Agrawal, that many people develop clinical levels of anxiety and depression after a cancer diagnosis.
Like Bansal, Agrawal had been impressed by early studies on psilocybin-assisted therapies for end-of-life anxiety and depression. He had tried other approaches — support groups, one-on-one therapy, religious counselors, psychiatrist-prescribed medication — but he was never really happy with the results.
To Agrawal, psilocybin-assisted therapy was the first thing that looked like it could really make a difference.
And so after his psychedelic certification at the California Institute of Integral Studies (CIIS), Agrawal was determined to change his approach.
Pre-publication results for Agrawal’s study show half of all participants no longer had clinical depression 8 weeks after a single dose of psilocybin and accompanying therapy. And about 80% of the people studied had their depression scores drop by at least 50%. (The trial measured depression with the Montgomery-Asberg Depression Rating Scale, or MADRS.)
The result was The Bill Richards Center for Healing at Aquilino Cancer Center, built specifically to study psychedelic-assisted therapies for psychological distress in people with cancer. The mission of the center is to help develop safe, FDA-approved psychedelic therapies for the mental health of cancer patients, and, once approved, provide a state-of-the-art facility and staff to administer those treatments.
A Trip Into the Unknown
Back in the dosing room, Bansal was starting to feel the effects of the medication. As the psilocybin kicked in, spectacular images swirled.
“It was as if a million stained glass windows had suddenly come to life and were dancing in front of my vision,” Bansal says.
There were moving landscapes and intricate swirling patterns and massive stages in the sky where he saw orchestras playing the music he was hearing.
Bansal saw himself being crushed by a huge machine and buried, dead, in the Earth. He died and returned to life several times, glided over the top of New York City with the skyscrapers just below him, and took in the vision of the entire universe.
“I saw this expanse of the sky that was limitless. And there was this prehistoric reptile creature that spanned galaxies in the sky ahead of me who was dying. I said, ‘My God, the universe is dying,’ but then after a few moments, the universe came to life again in a burst of stars exploding.”
All the while, Bansal says, he was well aware that it was simply his mind creating these images, thoughts, and ideas. He knew he was in a safe room wearing eyeshades and headphones.
And yet, he says, it felt true. “The images and feelings are so powerful that you cannot help but believe they are in some way a part of reality.”
“At one point, I saw this giant Ferris wheel coming towards me and it was full of giant crabs, clicking and clacking their pincers. And my brain told me, ‘That’s my cancer!'”
— Pradeep Bansal, MD
“At one point, I saw this giant Ferris wheel coming towards me and it was full of giant crabs, clicking and clacking their pincers. And my brain told me, ‘That’s my cancer!'”
Bansal was terrified. But he and his therapist had arranged a system of signals before the session. “If I was feeling afraid, I would hold his hand and if I had other issues, I would raise my hand. If I was feeling good, I would give him a thumbs up.”
Bansal reached out to his therapist and grasped his hand. “I said, ‘My cancer is coming at me!'”
His therapist was clear about what to do: Stand firm and walk toward it.
“That’s what they tell you: If you see anything frightening, you face it. And that’s the whole point of this exercise. And so, I stood and walked forward, and it just blew off in a puff of smoke.”
A State of Peace
Around 3 hours into the experience, Bansal started to feel an immense sense of peace, happiness, and even comfort.
“I felt like I was watching a movie or a multidimensional slideshow. I was also a part of the movie. I felt like I could tell my mind what I wanted to see, and it would show it to me. It’s almost like you can mold your own visions. It was mystical.”
After about 8 hours, as the effects of the drug wore off, Bansal removed his eyeshades and headphones. He was completely drained.
“Even though I was lying down on my back for 7 hours, I felt like I had been run over by a truck. I was exhausted beyond belief physically and mentally.”
This was partly due to the fact that he hadn’t eaten much during the session. But mostly, says Bansal, it was due to the searing emotional intensity of the experience.
After the Journey
It’s hard to put into words, says Bansal, what this treatment has done for his life. He feels as if he has stumbled onto something very precious that had been right in front of him all along. He wrote of his change in perspective almost obsessively in his journal in the days and weeks after treatment. One passage reads:
“It seems that as time is passing on, I’m becoming more relaxed and hopeful, more calm, and at peace. Family has become even more important to me now. Money, politics, material gains, alcohol, seem less important.”
And yet there was nothing “easy” about the experience. In fact, in some ways the experience demanded more from him.
“I feel I need to be more compassionate and considerate — less irritable and angry, more understanding of others’ needs. I feel I need to be a better human being, a better patient, a better father, and a better doctor for my patients.”
The experience, he says, gave him something far more important than mere ease. It gave him a sense of meaning.
“How many sorrows in the universe? My cancer is nothing. Life does not end with the end of life. What was will be again. Eternally.”
— from Pradeep Bansal’s journal
From his journal:
“I died, and I was reborn. If I survived this, then I can face anything and anybody in the cosmic scheme. I can become part of it.
“How many sorrows in the universe? My cancer is nothing. Life does not end with the end of life. What was will be again. Eternally.”
That’s not an unusual response, according to the namesake of The Bill Richards Center for Healing. Richards, PhD, has worked in the world of psychedelic-assisted psychotherapy since 1963.
A psychologist with decades of experience, Richards and his colleagues figure that, with few possible exceptions, he has helped treat more people with psychedelic therapies than anyone alive in Western medicine today. At Aquilino, he works directly with patients and oversees the therapy protocol that goes along with the psilocybin dosing sessions.
“It’s inspiring,” Richards says.
“You meet someone who’s very depressed and scared and isolating from family and having all kinds of physical complaints. And a few days later, you talk to the same person and they have a whole new lease on life.”
And the positive effects can extend deep into the family system, he says.
After psilocybin treatment, says Richards, the person with cancer can become a kind of social worker for the family. They’re often far better able to talk about death and loss and even money and family issues than their loved ones. It’s not uncommon after treatment to see the resolution of years-old resentments or grievances that have dogged a family for many years.
Plus, says Richards, the cancer patient often ends up as a kind model to other family members for how to approach death. “They can demonstrate how to live fully — right to the last breath — which is a real gift because those relatives and loved ones have to die someday too, you know.”
At 80 years old, Richards is still in active practice and hopes to spend the rest of his days working with people in end-of-life care.
After the Experience
Psychedelic-assisted therapy does not end with the dosing session. Integration sessions, where you discuss what happened during the dosing session, are a key part of most treatments.
The goal is to help participants absorb and “integrate” their experience. It typically happens over two or more sessions of 60 to 90 minutes with a therapist. In some cases, the therapist may invite a significant other to join in the integration process.
Agrawal’s trial at the Bill Richards center added something new: group therapy. Not only did Bansal meet with his therapist, he also met with a group of three other people in the trial who had their dosing the same day.
The point, says Agrawal, is to try and determine the effect of the group on the therapy. After their private dosing sessions, they come back together to discuss their experiences.
“After the psilocybin, they feel like they’ve been to war together,” Agrawal says. “There is this profound openness and connection. They feel able to share things with each other that they wouldn’t with other people.”
It will take some time to figure out how the group affects the overall outcome, but Bansal thinks it was integral to the success of his treatment.
In fact, he continues to meet regularly with his therapy group, even though it’s long since past the requirements of the study.
Pradeep 2.0
Bansal still has tough days with his cancer. Recently, immunotherapy treatment for his bladder caused side effects — pain, bleeding, fever, and chills — for most of the night. He felt like he was “passing razor blades” when he peed.
“And yet it was somehow OK,” he says. “It was only pain.”
“It’s as if there is a part of me that is watching myself objectively, going through the painful process of treatments saying, ‘It’s all right. I will be with you through this journey, through this experience. Don’t worry.'”
Months after taking that one dose, Bansal still calls it as “the single most powerful experience of my life.”
The change in his mental outlook, Bansal says, was profound, particularly in regard to his cancer.
“I understood that I still had cancer and that it could kill me in a few weeks, or months, or years. But my perspective had shifted.”
Bansal was as surprised as anyone.
“Had somebody told me going into this that I would come out a transformed being or a person with a completely different perspective on life, I would never have believed it.”
He even named his new outlook. “I call it Pradeep 2.0.”
Are Calorie Counts on Restaurant Menus Making Meals Healthier?
Was your New Year’s resolution to eat healthier? Try ordering a recently added menu item at your favorite chain restaurant, instead of a long-time favorite.
That’s because newer dishes served by large restaurant chains tend to contain fewer calories now that menus must list the calorie content of all items.
New research finds that menu items introduced after calorie labeling went into effect in 2018 contained about 25% fewer calories on average compared to dishes introduced before labeling.
“The nationwide rollout of these calorie labels appeared to prompt restaurants to introduce lower-calorie items to their menus,” said lead scientist Anna Grummon, a research fellow in nutrition at the Harvard T.H. Chan School of Public Health.
A provision of the U.S. Affordable Care Act requires that restaurant chains with 20 or more U.S. locations must post the calorie content of prepared foods on menus alongside the item’s price, researchers said in background notes.
Prior research has found that people eating out have cut their calories slightly — 4% to 6% on average — in response to the new labeling, according to the paper.
But it hasn’t been clear how the restaurant industry responded to the labeling requirement, in terms of shaking up their menus and offering lower-calorie options.
For this study, Grummon and her colleagues analyzed the calories of more than 35,300 menu items sold at 59 large chain restaurants in the United States between 2012 and 2019. The chains included such names as Qdoba, Chipotle, Burger King, IHOP, Dunkin Donuts and KFC, she said.https://4c785464a669c6bb44c9987ec86ad259.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html
The researchers found that restaurants didn’t change their formula for existing menu items in the face of calorie labeling. Dishes that had been on the menu beforehand had the same calorie content going forward, according to the study published Dec. 30 in JAMA Network Open.
That didn’t surprise Connie Diekman, a food and nutrition consultant in St. Louis and former president of the Academy of Nutrition and Dietetics. She said there’s little pressure on restaurants to change the content and the flavor of their perpetually popular offerings.
“Much of that, in my mind, is due to the fact that people are familiar with the quick-serve or fast-food restaurants,” Diekman said. “They know what they like. They trust what they order. Therefore, they’re happy with what’s there and they’re going to buy it no matter what the calorie label says.”
But new dishes offered after menu labeling went into effect tended to contain an average 113 fewer calories, or about 25% less, than the calories of foods introduced before the requirement, the researchers reported.
“That suggests the labeling law is potentially leading to consumers having more lower-calorie options,” Grummon said.
All told, the findings are encouraging to Diekman, who sees it as restaurants slowly guiding their patrons toward a healthier diet.
“They’re going to slowly introduce it to the consumer,” Diekman said of healthier food options. “That’s exciting, because they’re not trying to force it down people’s throats. They’re going to walk with the consumer and help them change.
“Behavior change is a process. It’s not an overhaul. They haven’t jumped in and tried to scare the consumer, but at the same time they’ve recognized their opportunity and their responsibility,” she continued.
Grummon believes that many folks eating out are using the calorie information.
“I do think that the transparency provided by the calorie labels is really helpful to consumers,” Grummon said. “These labels are giving consumers information about foods they might want to order that was not easy to access before the law. Folks can decide how they want to use that information to meet their health goals.”
People who want to eat out in a healthier way should review a restaurant’s menu online beforehand and find a handful of items that look yummy and are lower-calorie, Diekman said. That way, you’ll be aware of the more nutritious options available to you when you place your order.
You should look, in particular, for menu items marked “new item” or “seasonal option,” since this study indicates those are more likely to be lower in calories, Diekman said.
“View it as a process. I’m going to try it today, see how I like it, and then I’ll decide if the next time I want to try another new item or go back to my favorite,” Diekman said.
She added that it’s fine not to overthink the calorie content of your restaurant meal if you dine out infrequently.
Once in a while, it’s OK to choose a higher-calorie item than you would normally eat, Diekman said. “The problem, of course, is when we do this every single day,” she said.
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