The Lancet Public Health

BackgroundDuring the early phase of the Covid-19 pandemic in England, people with pre-existing conditions that put them at severe clinical risk if infected were advised to drastically reduce face-to-face contacts in a policy known as "shielding". The impact of this policy in preventing COVID-19 hospitalisations and deaths has not been evaluated at the national level using transmission-dynamic modelling. MethodsWith the approval of NHS England, we present a retrospective cohort evaluation of the shielding policy, drawing data from electronic health records (EHRs) for 24 million patients in England accessed through the OpenSAFELY platform. The study is from 1 January to 1 December 2020, prior to vaccination and new SARS-CoV-2 variants. We used a dynamic transmission model of SARS-CoV-2 transmission, infection, and hospitalisation, stratified by age and shielding status for the general population (excluding care homes). We estimated transmission rates in the shielding and non-shielding groups using data from the CoMix social contact survey, and fitted the model to hospitalisations and deaths in and outside hospital. FindingsWe found that the risk of hospitalisation was higher for shielding people in all age groups and increased with age. The hospital fatality ratio was similar between shielding and non-shielding people from January to June 2020 and greater in shielding people from July 2020 onward. By comparing the observed epidemic to a counterfactual scenario without shielding, we projected that between 7800 and 10,600 hospitalisations and 2300 to 3500 deaths due to COVID-19 were directly averted by the policy, corresponding to reductions of 25% (24, 28%) and 23% (21, 25%), respectively, in the shielding population in England up to 1 December 2020. Including also the indirect effect in the non-shielding population, we projected between 14,700, and 21,800 hospitalisations and 3700 and 5500 deaths due to COVID-19 were averted by the policy in the total population, each corresponding to reductions of 13% (11, 16%). InterpretationBased on the data and assumptions in this study, the shielding policy reduced pressure on the NHS and severe illness and mortality in clinically-extremely vulnerable shielding patients in England up to 1 December 2020, and, through indirectly-reduced exposure, also in the non-shielding population. Similar policies for other infections could have a comparable public health impact in reducing both mortality and pressure on public health services. FundingMedical Research Foundation, Medical Research Council, National Institute for Health and Care Research, NHS England, The Wellcome Trust.

IntroductionThe PROTECT National Core Study was funded by the UK Health and Safety Executive (HSE) to investigate routes of transmission for SARS-CoV-2 and variation between settings. MethodsA workshop was organised in Oct 2022.We brought together evidence from five published epidemiological studies that compared risks of SARS-CoV-2 infection or COVID-19 mortality by occupation or sector funded by PROTECT relating to three non-overlapping data sets, plus additional unpublished analyses relating to the Omicron period. We extracted descriptive study level data and model results. We investigated risk across four pandemic waves using forest plots for key occupational groups by time-period. ResultsResults were largely consistent across different studies with different expected biases. Healthcare and social care sectors saw elevated risks of SARS-CoV-2 infection and COVID-19 mortality early in the pandemic, but thereafter this declined and varied by specific occupational subgroup. The education sector saw sustained elevated risks of infection after the initial lockdown period with little evidence of elevated mortality. ConclusionsIncreased in risk of infection and mortality were consistently observed for occupations in high risk sectors particularly during the early stage of the pandemic. The education sector showed a different pattern compared to the other high risk sectors, as relative risk of infections remained high in the later phased of the pandemic, although no increased in COVID-19 mortality (compared to low-risk occupations) was observed in this sector in any point during the pandemic.

BackgroundTwice-weekly lateral flow device (LFD) testing was introduced for routine asymptomatic testing of healthcare workers (HCWs) in the National Health Service (NHS) in England in November 2020, with the primary aim of reducing nosocomial infections among staff and patients and a secondary aim of reducing absenteeism among HCWs. Here, we describe the burdens of HCW absenteeism and nosocomial infections in NHS acute trusts and the reported testing intensity of LFDs and associated costs from October 2020 to March 2022 and assess the impact of LFD testing on reducing these burdens. Methods and FindingsWe collected 16 million LFD testing results (total cost GBP 1.64 billion) reported in NHS acute trusts through Englands Pillar 1 and 2 testing programmes from 1 October 2020 to 30 March 2022. We estimated the prevalence of nosocomial COVID-19 infections in NHS acute trusts using data from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC). Testing data were linked with nosocomial infections and full-time equivalent (FTE) days lost by trust for NHS acute trusts. We used a mixed-effects linear model to examine the association between FTE days lost and LFD test coverage. The relationship between weekly prevalence of nosocomial infections and LFD test coverage in the previous week was modelled using logistic regression weighted by the number of new COVID-19 cases reported in the ISARIC dataset for that week. We adjusted both models for community prevalence of COVID-19 infections, average income deprivation score, prevalence of variants of concern and LFD test positivity. FTE days lost among HCWs varied considerably by trust type, staff group, geographical location of trusts, and progress of the pandemic in England. Increased LFD test coverage was associated with decreases in FTE days lost due to COVID-19 from November 2020 to July 2021, with no association observed from August 2021 to March 2022. Higher community prevalence levels were associated with significant increases in FTE days lost due to COVID-19 in all periods except the pre-vaccination period (last two months of 2020). The model predicted that changes in testing levels (50-150%) would have resulted in modest changes in FTE days lost due to COVID-19 for all time periods. We identified 3,794 nosocomial infections (if patients developed COVID-19 symptoms 7 days or more after their hospital admission) among 106,377 hospitalised COVID-19 patients in 136 NHS acute trusts. The proportion of nosocomial infections among new weekly cases in hospitalised patients was negatively associated with reported LFD testing levels. The strength of the association varied over time and was estimated to be highest during the Omicron period, although no effect of testing on HCW absenteeism was found. The observed HCW testing/reporting was estimated to be associated with a 16.8% (95% confidence interval 8.2%, 18.8%) reduction in nosocomial infections compared with a hypothetical testing scenario at 25% of actual levels, translating to a cost saving per quality-adjusted life-year (QALY) gained of GBP 18,500-46,400. ConclusionsLFD testing was an impactful public health intervention for reducing HCW absenteeism and nosocomial infections in NHS acute trusts and was cost effective in preventing nosocomial infections. Author SummaryO_ST_ABSWhy was this study done?C_ST_ABSO_LIIn any pandemic response, mass diagnostic testing plays a key role. C_LIO_LIWe sought to evaluate the burdens of healthcare worker absenteeism and nosocomial infections in NHS acute trusts, the reported testing intensity using lateral flow devices (LFDs) and associated costs, and the impact of LFD testing on reducing these burdens. C_LI What did the researchers do and find?O_LIWe collected 16 million LFD testing results and full-time equivalent (FTE) days lost due to COVID-19, obtained from healthcare workers (HCWs) in NHS acute trusts in England between 1 October 2020 and 30 March 2022. C_LIO_LIWe estimated the number of nosocomial COVID-19 infections in NHS acute trusts using data from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC). C_LIO_LITesting data were linked with nosocomial infections and FTE days lost due to COVID-19 by trust for NHS acute trusts. C_LIO_LIWe used a mixed-effects linear model to examine the association between FTE days lost due to COVID-19 and LFD test coverage and applied a logistic regression to assess the association between nosocomial infections and LFD test coverage. C_LIO_LIWe found that LFD testing in the healthcare setting was an impactful public health intervention. C_LIO_LILFD testing reduced HCW absenteeism and nosocomial infections in NHS acute trusts; it was also cost effective in preventing nosocomial infections. C_LI What do these findings mean?O_LIOur analysis of the available data indicated that testing HCWs had varying impacts (on both nosocomial infections and HCW FTE days lost due to COVID-19) throughout the pandemic, possibly influenced by external factors such as community prevalence and vaccination. C_LIO_LIIn any future pandemic, HCW testing interventions should incorporate collection of and/or timely access to relevant data, including HCW absenteeism, routine test results, community prevalence, and hospitalisation and mortality data. C_LIO_LIThe lessons learnt from this study could be used by relevant authorities to support the real-time assessment of any testing service and adjustment of the testing regimen; they could also be used to help develop more targeted and agile testing systems, which operationally would require the ability to turn mass testing off and on as an epidemic progressed. C_LI

ImportanceDue to high community transmission of the Omicron variant, healthcare workers (HCWs) have been increasingly reporting household exposures to confirmed COVID-19 cases. Quebec (Canada) provincial guidelines required to quarantine these HCWs. Facing the risk of staffing shortages, our hospital decided to allow them to work. ObjectiveTo evaluate the risk for HCWs, who were household contacts, to become positive for COVID-19 by RT-PCR and evaluate the risk of nosocomial COVID-19 transmission. DesignCohort of HCWs with a history of household exposure to a confirmed case of COVID-19. SettingCHU Sainte-Justine, a tertiary care mother and child center in Montreal (QC) Canada ParticipantsConsecutive HCWs who contacted OHS between December 20, 2021 and January 17, 2022 for a history of household exposure to COVID-19. ExposureConfirmed case of COVID-19 in the household Main outcome and measuresThe main outcome was a positive RT-PCR for SARS-CoV-2. Outbreaks and nosocomial cases were identified through daily analysis of COVID-19 cases, by sector and part of the usual Infection Prevention and Control surveillance process. ResultsOverall, 237 of 475 (50%) HCWs who declared a known household contact with a confirmed COVID-19 case remained negative. Of those who became positive, 196 (82.4%) were positive upon initial testing and were quarantined. Only 42 (15%) of 279 HCWs who were allowed to work became positive, a median of 4 days after the initial test. The absence of symptoms at initial evaluation (OR 3.8, 95% CI 2.5-5.7) and having received a third vaccine dose more than 7 days before (OR 1.88, 95% CI 1.3 - 2.8) were associated with an increased odds of remaining negative. There was no outbreak among HCWs and no nosocomial transmission to patients from a HCW that was allowed to work, while a known household contact. Conclusion and relevanceMeasures taken to protect the health care environment from COVID-19 must be cautiously balanced with the risk of staffing shortage. Allowing vaccinated asymptomatic HCWs who are known household contacts of confirmed COVID-19 cases to work is likely a safe alternative, when staff shortage is anticipated.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSTo track uptake of workplace SARS-CoV-2 testing programs using publicly-available data (e.g., press releases), supplementing findings from employer surveys. MethodsWe tracked testing programs reported by 1,159 Canadian and 1,081 international employers across sectors from March 1, 2020 to March 31, 2021. We analyzed trends in uptake of testing programs, including over time and by workplace setting. Results9.5% (n=110) of Canadian employers and 24.6% (n=266) of international employers tracked reported testing. The prevalence of reported testing programs was less than 20% in some settings associated with high risk of transmission including retail and customer-facing environments, and indoor and mixed blue collar workplaces. ConclusionsPublicly-available data suggest that fewer employers are testing than indicated by surveys. Workplace safety in high-risk workplaces could be further improved by implementing testing strategies that deploy both screening and diagnostic tests.

In 2017, the Acting U. S. Secretary of Health and Human Services declared the "opioid crisis" a nation-wide health emergency. However, the crisiss geography was not nation-wide. Many counties and towns had no overdose deaths, whereas others were home to hundreds. According to many influential research reports and news stories, geographic variation in overdose deaths was due to geographic variation in opioid prescription rates and/or geographic variation in socioeconomic factors, such as unemployment. Our goal was to test the degree to which prescription rates and socioeconomic correlates of income inequality predicted overdose deaths in the 1055 U.S. Midwest ("Heartland") counties over the years 2006 to 2020. We used multilevel regression models to gauge the predictive strength of overdose rates and six socioeconomic measures that are correlated with income inequality. There were significant state-level and county-level differences. Intergenerational income mobility was the strongest predictor of overdose deaths, with regression coefficients that averaged about twice as large as the coefficients for opioid prescription rates. Every year, counties with greater upward intergenerational income mobility had lower overdose death rates. Social capital had the second largest regression coefficients, albeit by a small margin. Counties are the smallest demographic unit for which drug overdose rates are available; the results of this study link growing income inequality and drug overdose deaths at the county level.

PurposeHealthcare workers (HCWs) play a critical role in responding to the COVID-19 pandemic. Early in the pandemic, urban centres were hit hardest globally; rural areas gradually became more impacted. We compared COVID-19 infection and vaccine uptake in HCWs living in urban versus rural locations within, and between, two health authorities in British Columbia (BC), Canada. We also analyzed the impact of a vaccine mandate for HCWs. MethodsWe tracked laboratory-confirmed SARS-CoV-2 infections, positivity rates, and vaccine uptake in 29,021 HCWs in Interior Health (IH) and 24,634 HCWs in Vancouver Coastal Health (VCH), by occupation, age, and home location, comparing to the general population in that region. We then evaluated the impact of infection rates as well as the mandate on vaccination uptake. ResultsBy October 27, 2021, the date that unvaccinated HCWs were prohibited from providing healthcare, only 1.6% in VCH yet 6.5% in IH remained unvaccinated. Rural workers in both areas had significantly higher unvaccinated rates compared with urban dwellers. Over 1,800 workers, comprising 6.4% of rural HCWs and 3.3% of urban HCWs, remained unvaccinated and set to be terminated from their employment. While the mandate prompted a significant increase in second doses, the impact on the unvaccinated was less clear. ConclusionsAs rural areas often suffer from under-staffing, loss of HCWs could have serious impacts on healthcare provision as well as on the livelihoods of unvaccinated HCWs. Greater efforts are needed to understand how to better address the drivers of rural-related vaccine hesitancy as the pandemic continues.

BackgroundBetween 1979 and 2016, US overdose death rates rose in a smooth fashion, described by Jalal and Burke using an exponential growth curve that fit observed data nearly perfectly. Fluctuations above this curve have subsequently been seen during shocks related to drug supply and the COVID-19 pandemic. However, large-magnitude dips below the curve have never been demonstrated. Given that overdose mortality began sharply falling during 2023-2024, we assess updated overdose trends against the Jalal-Burke curve. MethodsWe examined US overdose deaths from the National Vital Statistics System between January 1979-December 2024. We recreated the Jalal-Burke curve, fitting an exponential growth curve to overdose rates from 1979 to 2016, linearly projecting through 2024, with 95% confidence intervals. We also examined trends by specific substance involvement. ResultsAfter precipitously surpassing exponential growth predictions in 2020-2023, overdose deaths decreased sharply from approximately 32 per 100,000 in 2021-2023 to 23.7 in 2024, falling below the lower bound of Jalal-Burke curve (24.98 per 100,000) for the first time since 2001. These decreases reflected declining illicit fentanyl-involved deaths (with and without stimulants); however, deaths involving stimulants without fentanyl, and those involving xylazine, represent an increasing share of deaths in 2024. ConclusionsRather than simply representing a return to the Jalal-Burke exponential growth curve, recent decreases in overdose deaths represent the first significant, large-magnitude deviation below exponential growth projections. These trends represent a very positive development. However, challenges in the US drug crisis are shifting, requiring a tailored public health response.

BackgroundWorkers differ in their risk of SARS-CoV-2 infection according to their occupation, but the direct contribution of occupation to this relationship is unclear. This study aimed to investigate how infection risk differed across occupational groups in England and Wales up to April 2022, after adjustment for potential confounding and stratification by pandemic phase. MethodsData from 15,190 employed/self-employed participants in the Virus Watch prospective cohort study were used to generate risk ratios for virologically- or serologically-confirmed SARS-CoV-2 infection using robust Poisson regression, adjusting for socio-demographic and health-related factors and non-work public activities. We calculated attributable fractions (AF) amongst the exposed for belonging to each occupational group based on adjusted risk ratios (aRR). FindingsIncreased risk was seen in nurses (aRR=1.44, 1.25-1.65; AF=30%, 20-39%), doctors (aRR=1.33, 1.08-1.65; AF=25%, 7-39%), carers (1.45, 1.19-1.76; AF=31%, 16-43%), primary school teachers (aRR=1.67, 1.42-1.96; AF=40%, 30-49%), secondary school teachers (aRR=1.48, 1.26-1.72; AF=32%, 21-42%), and teaching support occupations (aRR=1.42, 1.23-1.64; AF=29%, 18-39%) compared to office-based professional occupations. Differential risk was apparent in the earlier phases (Feb 2020 - May 2021) and attenuated later (June - October 2021) for most groups, although teachers and teaching support workers demonstrated persistently elevated risk across waves. InterpretationOccupational differentials in SARS-CoV-2 infection risk vary over time and are robust to adjustment for socio-demographic, health-related, and non-workplace activity-related potential confounders. Direct investigation into workplace factors underlying elevated risk and how these change over time is needed to inform occupational health interventions.

extensive protective measures, SARS-CoV-2 widely circulates within healthcare facilities, posing a significant risk to both patients and healthcare workers. Several control strategies have been proposed; however, the global efficacy of local measures implemented at the ward level may depend on hospital-level organizational factors. We aimed at better understanding the role of between-ward interactions on nosocomial outbreaks and their control in a multiward psychiatric hospital in Western France. We built a stochastic compartmental transmission model of SARS-CoV-2 in the 24-wards hospital, accounting for the various infection states among patients and staff, and between-ward connections resulting from staff sharing. We first evaluated the potential of hospital-wide diffusion of local outbreaks, depending on the ward they started in. We then assessed control strategies, including a screening area upon patient admission, an isolation ward for COVID-19 positive patients and changes in staff schedules to limit between-ward mixing. Much larger and more frequent outbreaks occurred when the index case originated in one of the most connected wards with up to four times more transmissions when compared to the more isolated ones. The number of wards where infection spreads was brought down by up to 53 % after reducing staff sharing. Finally, we found that setting up an isolation ward reduced the number of transmissions by up to 70 %, while adding a screening area before admission seemed ineffective. Significance StatementHospital acquired COVID-19 poses a major problem to many countries. Despite extensive protective measures, transmission within hospitals still occurs regularly and threatens those essential to the fight against the pandemic while putting patients at risk. Using a stochastic compartmental model, we simulate the spread of SARS-CoV-2 in a multi-ward hospital, assessing the effect of different scenarios and infection control strategies. The novelty of our method resides in the consideration of staff sharing data to better reflect the field reality. Our results highlight the poor efficiency of implementing a screening area before hospital admission, while the setting up of an isolation ward dedicated to COVID-19 patients and the restriction of healthcare workers movements between wards significantly reduce epidemic spread.

BackgroundFit notes ("sick notes") are issued by general practitioners (GPs) when a person cant work for health reasons and is an indication of the public health and economic burden for people recovering from COVID-19. MethodsWith NHS England approval, we used routine clinical data from >24 million patients to compare fit note incidence in people 18-64 years with and without evidence of COVID-19 in 2020, 2021 and 2022. We fit Cox regression models to estimate adjusted hazard ratios, overall and by time post-diagnosis and within demographic subgroups. ResultsWe identified 365,421, 1,206,555 and 1,321,313 people with evidence of COVID-19 in 2020, 2021 and 2022. The fit note rate was 4.88 per 100 person-months (95%CI 4.83-4.93) in 2020, 2.66 (95%CI 2.64-2.67) in 2021, and 1.73 (95%CI 1.72-1.73) in 2022. Compared with the age, sex and region matched general population, the hazard ratio (HR) adjusted for demographics and clinical characteristics over the follow-up period was 4.07 (95%CI 4.02-4.12) in 2020 decreasing to 1.57 (95%CI 1.56-1.58) in 2022. The HR was highest in the first 30 days post-diagnosis in all years. ConclusionsDespite likely underestimation of the fit note rate, we identified a considerable increase among people with COVID-19, even in an era when most people are vaccinated. Most fit notes are associated with the acute phase of the disease, but the increased risk several months post-diagnosis provides further evidence of the long-term impact. Evidence before this studyWe searched Pubmed from 1 March 2020 to 30 June 2023 using the following search terms: ("COVID-19" OR "SARS-CoV-2" OR "coronavirus") AND ("United Kingdom" OR "England" OR "Britain" OR "Scotland" OR "Wales") AND ("fit note" OR "sick note" OR "sick leave" OR "sickness absence"). We also searched the reference list of relevant articles. We included both peer-reviewed research studies and grey literature that quantified receipt of fit notes or sick leave during the COVID-19 pandemic. We found two peer-reviewed studies and one briefing by an independent think tank. A study of 959,356 National Health Service (NHS) employees in England quantified receipt of non-COVID-19 related fit notes during the first wave of the pandemic. They found that the overall fit note rate was lower in 2020 compared with 2019. However, increases in the number of people receiving fit notes were observed for respiratory, infectious disease, and mental health conditions. The second study of 15,931 domiciliary care workers in Wales between Mar 2020 and Nov 2021 found that 15% had been issued a fit note over the study period. Fit notes were more common among women, people [≥]45 years, and those with comorbidities. The briefing found that the percentage of sickness absence days taken by NHS employees was higher in 2022 (5.6%) compared with 2019 (4.3%), with a particular increase in absences due to mental health and infectious diseases. In 2022, 18% of sickness absence days were attributable to COVID-19. Added value of this studyThis study is the first to quantify changes in fit note rate since the start of the COVID-19 pandemic among people with a reported SARS-CoV-2 infection and how this compares with the general population in the UK. We found that people with evidence of SARS-CoV-2 infection had a higher fit note rate than the general population, even after adjusting for demographics and clinical characteristics. While this increased risk was greatest in 2020 (hazard ratio [HR] = 4.07, 95%CI 4.02-4.12), it continued to a lesser extent even into 2022 (HR = 1.57, 95%CI 1.56-1.58). The fit note rate was greatest in the first 30 days post-diagnosis, suggesting that most sick leave is associated with the acute phase. In subgroup analyses, the groups with the greatest relative increased risk changed over the years. People aged 18-24 years had a larger relative increased risk of fit notes (as measured by HR) in 2022 than 2021, when compared with the general population in each year. Additionally, while in 2020 and 2021 the HR increased along with lessening deprivation, this effect dissipated in 2022. In contrast, people hospitalised with COVID-19 were less likely to be issued a fit note than the pneumonia cohort, suggesting the long-term effects may be similar to comparable severe respiratory infections cases resulting in hospitalisation. Implications of all the available evidenceWhile we have likely underestimated the fit note rate due to overcounting of people in the workforce and misclassification of COVID-19 status, we still identified a substantial increased risk of receiving a fit note in people with COVID-19 compared with the general population over all years, even after adjusting for demographics and a wide range of clinical characteristics. The increased risk persisted into 2022, in an era where most people are vaccinated and the severity of COVID-19 illness is lessened. Given the high infection rates still occurring, these findings provide evidence for a substantial impact of COVID-19 on productivity and further evidence of the long-term impacts of COVID-19.

BackgroundUnderstanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour can help to protect the vulnerable and guide equity-driven interventions. Using COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from October 2020 to March 2022, we investigated the relationship between sociodemographic factors and testing behaviours in England. MethodsWe used mass testing data for lateral flow device (LFD; data for 290 million tests performed and reported) and polymerase chain reaction (PCR) (data for 107 million tests performed and returned from the laboratory) tests made available for the general public, provided by date, self-reported age and ethnicity at lower tier local authority (LTLA) level. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. Using confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability by PCR by sociodemographic groups. We also estimated the daily incidence allowing us to determine the fraction of cases captured by the testing programme. FindingsFrom March 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per-capita than those in the least deprived areas (Median ratio [Inter quartile range, IQR]: 0{middle dot}50 [0{middle dot}44, 0{middle dot}54]). During October 2020 - June 2021, PCR testing patterns were in the opposite direction (Median ratio [IQR]: 1{middle dot}8 [1{middle dot}7, 1{middle dot}9]). Infection prevalences in Asian or Asian British communities were considerably higher than those of other ethnic groups during the Alpha and Omicron BA.1 waves. Our estimates indicate that the England COVID-19 testing program detected 26% - 40% of all cases (including asymptomatic cases) over the study period with no consistent differences by deprivation levels or ethnic groups. PCR testing biases were generally higher than for LFDs, which was in line with the general policy of symptomatic and asymptomatic use of these tests. During the invasion phases of the Delta and Omicron variants of concern, the PCR testing bias in the most deprived populations was roughly double (ratio: 2{middle dot}2 and 2{middle dot}7 respectively) that in the least. We also determined that ethnic minorities and older individuals were less likely to use confirmatory PCR tests through most of the pandemic and that there was possibly a longer delay in reporting a positive LFD test in the Black populations. InterpretationDifferences in testing behaviours across sociodemographic groups may be reflective of the relatively higher costs of self-isolation to vulnerable populations, differences in test accessibility, digital literacy, and differing perception about the utility of tests and risks posed by infection. Our work shows how mass testing data can be used in conjunction with surveillance surveys to identify gaps in the uptake of public health interventions at fine scale levels and by sociodemographic groups. It provides a framework for monitoring local interventions and yields valuable lessons for policy makers in ensuring an equitable response to future pandemics. FundingUK Health Security Agency.

BackgroundAccess to medications for opioid use disorder (MOUD) in the U.S. is highly restricted. In March 2020, to reduce transmission of COVID-19, SAMHSA issued emergency regulations allowing up to two weeks of take-home doses for most patients. ObjectivesWe evaluated the benefits and unintended consequences of these new regulations expanding take-home eligibility to inform MOUD policy post-pandemic MethodsWe conducted a mixed-methods evaluation of an opioid treatment program in San Francisco caring for a diverse, low-income urban population. We assessed clinic-level intake, retention, and take-home prescribing; individual-level acute care utilization and mortality; and patient/provider perceptions of benefits, harms and challenges of the new regulations. ResultsClinic volume, intake and retention were largely unchanged after implementation of the new regulations, though the average monthly proportion of individuals receiving take-homes significantly increased from 31% to 47% (p<0.001). Among 506 established patients ([&ge;]90 days of care), the 10-month mortality was 2.7% among those who never received take-homes versus 3.2% among those newly started (p=0.79) and 0.8% among those with increases in take-homes (p=0.24). Individuals who never received take-homes had higher rates of emergency department visits (47.0%) and hospitalizations (19.7%) versus those with new starts (ED visits 29.2%, p<0.001; hospitalizations 14.3%, p=0.19) or increases in take-homes (ED visits 17.5%, p<0.001; hospitalizations 10.0%, p=0.02). Both patients and providers reported increased treatment flexibility, leading to increased engagement and stabilization. ConclusionsGiven the benefit and lack of appreciable harms, policymakers should consider extending expanded MOUD take-home eligibility after COVID-19, with careful monitoring for unintended outcomes.

ImportanceThe SARS-CoV-2 pandemic portends a significant increase in health care use related to post-acute COVID sequelae, but the magnitude is not known. ObjectiveTo assess the burden of post-acute health care use after a positive versus negative polymerase chain reaction (PCR) test for SARS-CoV-2. Design, Setting, and ParticipantsRetrospective cohort study of community-dwelling adults January 1, 2020 to March 31, 2021 in Ontario, Canada, using linked population-based health data. Follow-up began 56 days after PCR testing. ExposuresIndividuals with a positive SARS-CoV-2 PCR test were matched 1:1 to individuals who tested negative based on hospitalization, test date, public health unit, sex, and a propensity score of socio-demographic and clinical characteristics. Main Outcomes and MeasuresThe health care utilization rate was the number of outpatient clinical encounters, homecare encounters, emergency department visits, days hospitalized, and days in long-term care per person-year. Mean health care utilization for test-positive versus negative individuals was compared using negative binomial regression, and rates at 95th and 99th percentiles were compared. Outcomes were also stratified by sex. ResultsAmong 530,232 unique, matched individuals, mean age was 44 years (sd 17), 51% were female, and 0.6% had received [&ge;]1 COVID-19 vaccine dose. The mean rate of health care utilization was 11% higher in test-positive individuals (RR 1.11, 95% confidence interval [CI] 1.10-1.13). At the 95th percentile, test-positive individuals had 2.1 (95% CI 1.5-2.6) more health care encounters per person-year, and at the 99th percentile 71.9 (95% CI 57.6-83.2) more health care encounters per person-year. At the 95th percentile, test-positive women had 3.8 (95% CI 2.8-4.8) more health care encounters per person-year while there was no difference for men. At the 99th percentile, test-positive women had 76.7 (95% CI 56.3-89.6) more encounters per person-year, compared to 37.6 (95% CI 16.7-64.3) per person-year for men. Conclusions and RelevancePost-acute health care utilization after a positive SARS-CoV-2 PCR test is significantly higher compared to matched test-negative individuals. Given the number of infections worldwide, this translates to a tremendous increase in use of health care resources. Stakeholders can use these findings to prepare for health care demand associated with long COVID. Key PointsO_ST_ABSQuestionC_ST_ABSHow does the burden of health care use [&ge;]56 days after a positive SARS-CoV-2 polymerase chain reaction (PCR) test compare to matched individuals who tested negative? FindingsAfter accounting for multiple factors, the mean burden of post-acute health care use was 11% higher among those who tested positive, with higher rates of outpatient encounters, days hospitalized, and days in long-term care. Rates of homecare use were higher for test-positive women but lower for men. For perspective, for every day in January 2022 with 100,000 or more infections, this translates to an estimated 72,000 additional post-acute health care encounters per year for the 1% of people who experienced the most severe complications of SARS-CoV-2; among those in the top 50% of health care use, this translates to 245,000 additional health care encounters per year. This increase will occur in the context of an ongoing pandemic and, in many health care systems, a depleted workforce and backlogs of care. Unless addressed, this increase is likely to exacerbate existing health inequities. MeaningGiven the large number of people infected, stakeholders can use these findings to plan for health care use associated with long COVID.

BackgroundIn March 2020, the government of Scotland identified people deemed clinically extremely vulnerable to COVID due to their pre-existing health conditions. These people were advised to strictly self-isolate (shield) at the start of the pandemic, except for necessary healthcare. We examined who was identified as clinically extremely vulnerable, how their healthcare changed during isolation, and whether this process exacerbated healthcare inequalities. MethodsWe linked those on the shielding register in NHS Grampian, a health authority in Scotland, to healthcare records from 2015-2020. We described the source of identification, demographics, and clinical history of the cohort. We measured changes in out-patient, in-patient, and emergency healthcare during isolation in the shielding population and compared to the general non-shielding population. ResultsThe register included 16,092 people (3% of the population), clinically vulnerable primarily due to a respiratory disease, immunosuppression, or cancer. Among them, 42% were not identified by national healthcare record screening but added ad hoc, with these additions including more children and fewer economically-deprived. During isolation, all forms of healthcare use decreased (25%-46%), with larger decreases in scheduled care than in emergency care. However, people shielding had better maintained scheduled care compared to the non-shielding general population: out-patient visits decreased 35% vs 49%; in-patient visits decreased 46% vs 81%. Notably, there was substantial variation in whose scheduled care was maintained during isolation: younger people and those with cancer had significantly higher visit rates, but there was no difference between sexes or socioeconomic levels. ConclusionsHealthcare changed dramatically for the clinically extremely vulnerable population during the pandemic. The increased reliance on emergency care while isolating indicates that continuity of care for existing conditions was not optimal. However, compared to the general population, there was success in maintaining scheduled care, particularly in young people and those with cancer. We suggest that integrating demographic and primary care data would improve identification of the clinically vulnerable and could aid prioritising their care.

ImportanceNurses work-related stress and sickness absence are high. The consequences of sickness absence are severe for health systems efficiency and productivity. ObjectiveTo measure the association between nurse staffing configurations and sickness absence in hospital ward nursing teams. DesignRetrospective case-control study using hospital routinely collected data SettingFour general acute care hospitals in England Participants3,583,586 shifts worked or missed due to sickness absence by 18,674 registered nurses (RN) and nursing assistant (NA) staff working in 116 hospital units. ExposureNursing team skill-mix; temporary staffing hours; understaffing; proportion of long shifts (12+ hours) worked; full-time/part-time work status in the previous 7 days. Main outcomeEpisodes of sickness absence, defined as a sequence of sickness days with no intervening days of work. ResultsThere were 43,097 sickness episodes. In our reduced parsimonious model, being exposed to a skill mix that was richer in RNs was associated with lower RN sickness absence (OR= 0.98; 95% CI = 0.96-0.99). For each 10% increase in proportion of hours worked as long shifts worked in the previous 7 days odds of sickness were increased by 2% (OR = 1.02; 95% CI = 1.02- 1.03) for RNs. Part-time work for RNs was associated with higher sickness absence (OR = 1.09; 95% CI = 1.04 - 1. 15). When RN staffing over the previous week was below average, the odds of sickness absence for NAs increased by 2% for every 10% increase in understaffing across the period (OR = 1.02; 95% CI = 1.01 - 1.03). For RNs there was a significant interaction between part-time work and RN understaffing, whereby short staffing in the previous week increased sickness absence for full time staff but not among those working part time. NA understaffing was not associated with sickness absence for any staffing group. Conclusions and RelevanceWorking long shifts and working on understaffed wards increases the risk of sickness absence in nursing teams. Adverse working conditions for nurses, already known to pose a risk to patient safety, may also create risks for nurses and the possibility of further exacerbating staff shortages. Key pointsO_ST_ABSQuestionC_ST_ABSWhat is the association between variation in nurse staffing configurations and nurses sickness absence? FindingsRegistered Nurse (RN) understaffing in the preceding 7 days was associated with sickness absence for Nursing Support (NS) staff, but for RNs the association was only seen when working full time. Exposure to shifts with a skill-mix richer in RNs, to higher bank hours and working lower proportions of 12+ h shifts in the preceding 7 days was a protective factor of RN sickness absence. MeaningTo support nurses health and health systems productivity and efficiency, investing in avoiding RN understaffing may be warranted.

BackgroundSocio-economic inequalities in COVID-19 case rates have been noted worldwide. Previous studieshave compared case rates over set phases. There has been no analysis of how inequalities in cases changed overtime and were shaped by national mitigation strategies (e.g. lock downs). This paper provides the first analysis of the evolution of area-level inequalities in COVID-19 cases by deprivation levels in the first wave of the pandemic (January to July 2020) in England - with a focus on the effects of the first national lockdown (March - July 2020). MethodsWeekly case rates per Middle Super Output Area (MSOA, n=4412) in England from 2020-03-15 to 2020-07-04 were obtained, and characteristics of local epidemics were calculated, e.g. the highest case rate per area. Simple linear and logistic regression analyses were employed to assess the association of these metrics with index of multiple deprivation (IMD). Local authority-level (n=309) cases were used similarly in a sensitivity analysis, as these data were available daily and extended further back in time. The impact of lockdown was assessed by comparing the cumulative case rate in the most deprived 20% of MSOAs to the least deprived 20%, for the periods before the lockdown, and by the end of lockdown. FindingsLess deprived areas began recording COVID-19 cases earlier than more deprived areas and were more likely to have peaked by March 2020. More deprived areas case rates grew faster and peaked higher than less deprived areas. During the first national lockdown in the UK, the relative excess in case rates in the most deprived areas increased to 130% of that of the least deprived ones. InterpretationThe pattern of disease spread in England confirm the hypothesis that initial cases of a novel infectious disease are likely to occur in more affluent communities, but more deprived areas will overtake them once national mitigation strategies begin, and bear the brunt of the total case load. The strict first national lockdown served to increase case rate inequalities in England. FundingThis work was supported by a grant from The Health Foundation (Ref: 2211473), who took no part in the design, analysis or writing of this study. Research in Context Evidence before this studyThe magnitude and distribution of deprivation-related inequalities in COVID-19 cases have been reported for England and many other countries, however, none have yet investigated the initial evolution of these inequalities, nor the effects of the first national lockdown. Added value of this studyWe leverage the benefits of two separate datasets of COVID-19 case counts to investigate the initiation and evolution in inequalities in disease burden by deprivation. We found that cases were first recorded in less deprived areas before rising faster in more deprived areas. The first national lockdown led to an increase in these geographical inequalities. Implications of all the available evidenceNational lockdowns are an important tool in the armoury of pandemic control, but their timing and duration must be carefully decided and be locally specific. Because case rate inequalities were already present before lockdown in England, movement restrictions served to further increase them. Summary Box Section 1: What is already known on this subjectGeographical inequalities in COVID-19 case rates have been noted worldwide, and in England. However, how these inequalities were affected by policy responses - such as national lockdowns - has yet to be investigated. Section 2: What this study addsWe examined geographical inequalities in COVID-19 case rates by deprivation during the first English lock down (March - July, 2020). We find that cases were first reported in the less deprived areas of England, but this pattern quickly reversed and large excesses of cases occurred in the most deprived areas during the first national lockdown. Case rates in more deprived areas also rose more sharply, peaked higher, and then dropped faster than in less deprived areas. Inequality in cumulative case rates grew over the lockdown, increasing inequalities in disease burden.

BackgroundThe annual winter season poses substantial challenges to the National Health Service (NHS) in England. Hospitalisation and mortality increase during winter, especially in people with multiple long-term conditions (MLTC or multimorbidity). We aimed to describe which combinations of long-term conditions (LTC) are associated with a higher risk of hospitalisation and death during winter amongst adults in England. MethodsIn this population-based study, we used linked primary and secondary care data from the General Practice Extraction Service Data for Pandemic Planning (GDPPR) database, Hospital Episode Statistics, and Office for National Statistics death registry. We included individuals aged [&ge;]18 years and alive on 1st December 2021 and used overdispersed Poisson models to estimate the incidence rate ratios of all-cause hospitalisations and deaths associated with the combinations of MLTCs - compared to those with no LTC - during the winter season (1 December 2021 to 31 March 2022). FindingsComplete data were available for 48,253,125 adults, of which 15 million (31.2%) had MLTC. Hospitalisation per 1000 person-years was higher in individuals with MLTCs, and varied by combination, e.g.: 96, 1643, and 1552 in individuals with no LTC, cancer+chronic kidney disease (CKD)+cardiovascular disease (CVD)+type 2 diabetes mellitus, and cancer+CKD+CVD+osteoarthritis, respectively. Incidence of death (per 1000 person-years) was 345 in individuals with cancer+CKD+CVD+dementia and 1 with no LTC. CVD+dementia appeared in all the top five MLTC combinations by death and was associated with a substantially higher rate of death than many 3-, 4- and 5-disease combinations. InterpretationRisks of hospitalisation and death vary by combinations of MLTCs and are substantially higher in those with vs. without any LTCs. We have highlighted high-risk combinations for prioritisation and preventive action by policymakers to help manage the challenges imposed by winter pressures on the NHS. FundingNational Institute for Health and Care Research (NIHR) through Health Data Research UK rapid funding call for the research activity "Data Science to inform NHS compound winter pressure policy response" (grant number: HDRUK2022.0313) Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, from inception to April 2023, for published population-based studies examining MLTC combinations in cohorts of adults aged 18 years and over. The search terms were "multimorbidity" or multiple-long-term conditions alongside "groups" or "combinations". We found no previous studies examining MLTC in relation to death or hospitalisation during the winter season. Added value of this studyWe have identified distinct combinations of LTCs and estimated the associated risk of hospitalisation and deaths during the winter season using the whole-population primary and secondary care data in England. Implications of all the available evidenceUnderstanding which combinations of MLTCs are associated with the highest risk of hospitalisation and death allows clinicians and policymakers to prioritise resources for preventative measures, such as vaccination to those that will benefit most during winter seasons.

BackgroundUse of masks and respirators for prevention of respiratory infectious disease transmission is not new, but has proven controversial, and even politically polarizing during the SARS-CoV-2 pandemic. In the Canadian province of Ontario, mask mandates were introduced by the 34 regional health authorities in an irregular fashion from June to September 2020, creating a quasi-experiment that can be used to evaluate impact of community mask mandates. Ontario SARS-CoV-2 case counts were strongly biased by testing focussed on long-term care facilities and healthcare workers. We developed a simple regression-based test-adjustment method that allowed us to adjust cases for undertesting by age and gender. We used this test- adjusted time series to evaluate mask mandate effectiveness. MethodsWe evaluated the effect of masking using count-based regression models that allowed adjustment for age, sex, public health region and time trends with either reported (unadjusted) cases, or testing-adjusted case counts, as dependent variables. Mask mandates were assumed to take effect in the week after their introduction. Model based estimates of effectiveness were used to estimate the fraction of SARS- CoV-2 cases, severe outcomes, and costs, averted by mask mandates. ResultsModels that used unadjusted cases as dependent variable identified protective effects of masking (effectiveness 15-42%), though effectiveness was variably statistically significant, depending on model choice. Mask effectiveness in models predicting test-adjusted case counts was substantially higher, ranging from 49% (44- 53%) to 73% (48-86%) depending on model choice. Effectiveness was greater in women than men (P = 0.016), and in urban health units as compared to rural units (P < 0.001). The prevented fraction associated with mask mandates was 46% (41-51%), averting approximately 290,000 clinical cases, averting 3008 deaths and loss of 29,038 QALY. Costs averted represented $CDN 610 million in economic wealth. ConclusionsLack of adjustment for SARS-CoV-2 undertesting in younger individuals and males generated biased estimates of infection risk and obscures the impact of public health preventive measures. After adjustment for under-testing, the effectiveness of mask mandates emerges as substantial, and robust regardless of model choice. Mask mandates saved substantial numbers of lives, and prevented economic costs, during the SARS-CoV-2 pandemic in Ontario, Canada.

BackgroundSince the emergence of SARS-CoV-2, health care workers (HCWs) have been on the front line in caring for COVID-19 patients. Better knowledge of risk factors for SARS-CoV-2 infection is crucial for the prevention of disease among this population. MethodsWe conducted a seroprevalence survey among HCWs in a French university hospital after the first wave (May-June 2020), based on a validated lateral flow immuno-assay test (LFIAT) for SARS-CoV-2. Demographic characteristics as well as data on the working characteristics of COVID-19 and non-COVID-19 wards and 23 care activities were systematically recorded. The effectiveness of protective equipment was also estimated, based on self-declaration of mask use. SARS-CoV-2 IgG status was modelled by multiple imputations approach, accounting for the performance of the test and data on serum validation ELISA immunoassay. FindingsAmong the 3,234 enrolled HCWs, the prevalence of SARS-CoV-2 IgG was 3.8%. Contact with relatives or HCWs who developed COVID-19 were risk factors for SARS-CoV-2 infection, but not contact with COVID-19 patients. In multivariate analyses, suboptimal use of protective equipment during naso-pharyngeal sampling, patient mobilisation, clinical and eye examination was associated with SARS-CoV-2 infection. In addition, patients washing and dressing and aerosol-generating procedures were risk factors for SARS-CoV-2 infection with or without self-declared appropriate use of protective equipment. InterpretationMain routes of transmission of SARS-CoV-2 IgG among HCWs were i) contact with relatives or HCWs with COVID-19, ii) close or prolonged contact with patients, iii) aerosol-generating procedures.