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Background: With the emergence of SARS-CoV-2 in late 2019, governments worldwide implemented a multitude of non-pharmaceutical interventions in order to control the spread of the virus. Most countries have implemented measures within the school setting in order to reopen schools or keep them open whilst aiming to contain the spread of SARS-CoV-2. For informed decision-making on implementation, adaptation, or suspension of such measures, it is not only crucial to evaluate their effectiveness with regard to SARS-CoV-2 transmission, but also to assess their unintended consequences., Objectives: To comprehensively identify and map the evidence on the unintended health and societal consequences of school-based measures to prevent and control the spread of SARS-CoV-2. We aimed to generate a descriptive overview of the range of unintended (beneficial or harmful) consequences reported as well as the study designs that were employed to assess these outcomes. This review was designed to complement an existing Cochrane Review on the effectiveness of these measures by synthesising evidence on the implications of the broader system-level implications of school measures beyond their effects on SARS-CoV-2 transmission., Search Methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, four non-health databases, and two COVID-19 reference collections on 26 March 2021, together with reference checking, citation searching, and Google searches., Selection Criteria: We included quantitative (including mathematical modelling), qualitative, and mixed-methods studies of any design that provided evidence on any unintended consequences of measures implemented in the school setting to contain the SARS-CoV-2 pandemic. Studies had to report on at least one unintended consequence, whether beneficial or harmful, of one or more relevant measures, as conceptualised in a logic model.  DATA COLLECTION AND ANALYSIS: We screened the titles/abstracts and subsequently full texts in duplicate, with any discrepancies between review authors resolved through discussion. One review author extracted data for all included studies, with a second review author reviewing the data extraction for accuracy. The evidence was summarised narratively and graphically across four prespecified intervention categories and six prespecified categories of unintended consequences; findings were described as deriving from quantitative, qualitative, or mixed-method studies., Main Results: Eighteen studies met our inclusion criteria. Of these, 13 used quantitative methods (3 experimental/quasi-experimental; 5 observational; 5 modelling); four used qualitative methods; and one used mixed methods. Studies looked at effects in different population groups, mainly in children and teachers. The identified interventions were assigned to four broad categories: 14 studies assessed measures to make contacts safer; four studies looked at measures to reduce contacts; six studies assessed surveillance and response measures; and one study examined multiple measures combined. Studies addressed a wide range of unintended consequences, most of them considered harmful. Eleven studies investigated educational consequences. Seven studies reported on psychosocial outcomes. Three studies each provided information on physical health and health behaviour outcomes beyond COVID-19 and environmental consequences. Two studies reported on socio-economic consequences, and no studies reported on equity and equality consequences., Authors' Conclusions: We identified a heterogeneous evidence base on unintended consequences of measures implemented in the school setting to prevent and control the spread of SARS-CoV-2, and summarised the available study data narratively and graphically. Primary research better focused on specific measures and various unintended outcomes is needed to fill knowledge gaps and give a broader picture of the diverse unintended consequences of school-based measures before a more thorough evidence synthesis is warranted. The most notable lack of evidence we found was regarding psychosocial, equity, and equality outcomes. We also found a lack of research on interventions that aim to reduce the opportunity for contacts. Additionally, study investigators should provide sufficient data on contextual factors and demographics in order to ensure analyses of such are feasible, thus assisting stakeholders in making appropriate, informed decisions for their specific circumstances., (Copyright © 2022 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

Objective: To determine among patients with axial spondyloarthritis (SpA) the factors associated with decreased spinal mobility and to determine whether poor mobility is a predictor of response to anti-tumor necrosis factor (anti-TNF) therapy., Methods: This was a prospective UK cohort study of persons meeting Assessment of Spondylarthritis international Society (ASAS) criteria for axial SpA. At recruitment, clinical and patient-reported factors independently associated with spinal mobility (measured by the Bath Ankylosing Spondylitis Metrology Index [BASMI]) were determined. Among those commencing anti-TNF therapy, factors that were independent predictors of response were determined using ASAS criteria, quality of life, and Ankylosing Spondylitis Disease Activity Score (ASDAS) response criteria., Results: A total of 1,960 participants were eligible; 70% were male, the median age was 48 years (interquartile range [IQR] 37, 59), and the median BASMI score 3.6 (IQR 2.2, 5.3). Factors independently associated with poor spinal mobility were poorer function, meeting radiographic criteria for AS, longer symptom duration, higher levels of inflammation (measured by C-reactive protein level), older age, male sex, not being currently employed, and lower levels of education. For 51% of participants, the measured BASMI score was within 1 of that estimated. Poorer mobility (higher BASMI score) was an independent predictor of not meeting response criteria for ASAS 20% improvement (odds ratio [OR] per increasing score 0.80 [IQR 0.66, 0.98]), ASAS 40% improvement (OR 0.69 [IQR 0.50, 0.95]), and quality of life (measured by the Ankylosing Spondylitis Quality of Life Questionnaire) (β = 0.64 [IQR 0.26, 1.02]), but was not related to meeting ASDAS response criteria., Conclusion: The BASMI score was estimated moderately well by other routinely measured factors in patients with axial SpA and was an independent predictor of response to biologic therapy for some, but not all, commonly used measures. Consensus around its role in disease monitoring and clinical decisions, particularly in the likely context of face-to-face consultations becoming less frequent, remains to be established., (© 2020 American College of Rheumatology.)

Background: In response to the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the impact of coronavirus disease 2019 (COVID-19), governments have implemented a variety of measures to control the spread of the virus and the associated disease. Among these, have been measures to control the pandemic in primary and secondary school settings., Objectives: To assess the effectiveness of measures implemented in the school setting to safely reopen schools, or keep schools open, or both, during the COVID-19 pandemic, with particular focus on the different types of measures implemented in school settings and the outcomes used to measure their impacts on transmission-related outcomes, healthcare utilisation outcomes, other health outcomes as well as societal, economic, and ecological outcomes.  SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and the Educational Resources Information Center, as well as COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO COVID-19 Global literature on coronavirus disease (indexing preprints) on 9 December 2020. We conducted backward-citation searches with existing reviews., Selection Criteria: We considered experimental (i.e. randomised controlled trials; RCTs), quasi-experimental, observational and modelling studies assessing the effects of measures implemented in the school setting to safely reopen schools, or keep schools open, or both, during the COVID-19 pandemic. Outcome categories were (i) transmission-related outcomes (e.g. number or proportion of cases); (ii) healthcare utilisation outcomes (e.g. number or proportion of hospitalisations); (iii) other health outcomes (e.g. physical, social and mental health); and (iv) societal, economic and ecological outcomes (e.g. costs, human resources and education). We considered studies that included any population at risk of becoming infected with SARS-CoV-2 and/or developing COVID-19 disease including students, teachers, other school staff, or members of the wider community.  DATA COLLECTION AND ANALYSIS: Two review authors independently screened titles, abstracts and full texts. One review author extracted data and critically appraised each study. One additional review author validated the extracted data. To critically appraise included studies, we used the ROBINS-I tool for quasi-experimental and observational studies, the QUADAS-2 tool for observational screening studies, and a bespoke tool for modelling studies. We synthesised findings narratively. Three review authors made an initial assessment of the certainty of evidence with GRADE, and several review authors discussed and agreed on the ratings., Main Results: We included 38 unique studies in the analysis, comprising 33 modelling studies, three observational studies, one quasi-experimental and one experimental study with modelling components. Measures fell into four broad categories: (i) measures reducing the opportunity for contacts; (ii) measures making contacts safer; (iii) surveillance and response measures; and (iv) multicomponent measures. As comparators, we encountered the operation of schools with no measures in place, less intense measures in place, single versus multicomponent measures in place, or closure of schools. Across all intervention categories and all study designs, very low- to low-certainty evidence ratings limit our confidence in the findings. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the model structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to deviations from intended interventions or missing data. Across all categories, few studies reported on implementation or described how measures were implemented. Where we describe effects as 'positive', the direction of the point estimate of the effect favours the intervention(s); 'negative' effects do not favour the intervention.  We found 23 modelling studies assessing measures reducing the opportunity for contacts (i.e. alternating attendance, reduced class size). Most of these studies assessed transmission and healthcare utilisation outcomes, and all of these studies showed a reduction in transmission (e.g. a reduction in the number or proportion of cases, reproduction number) and healthcare utilisation (i.e. fewer hospitalisations) and mixed or negative effects on societal, economic and ecological outcomes (i.e. fewer number of days spent in school). We identified 11 modelling studies and two observational studies assessing measures making contacts safer (i.e. mask wearing, cleaning, handwashing, ventilation). Five studies assessed the impact of combined measures to make contacts safer. They assessed transmission-related, healthcare utilisation, other health, and societal, economic and ecological outcomes. Most of these studies showed a reduction in transmission, and a reduction in hospitalisations; however, studies showed mixed or negative effects on societal, economic and ecological outcomes (i.e. fewer number of days spent in school). We identified 13 modelling studies and one observational study assessing surveillance and response measures, including testing and isolation, and symptomatic screening and isolation. Twelve studies focused on mass testing and isolation measures, while two looked specifically at symptom-based screening and isolation. Outcomes included transmission, healthcare utilisation, other health, and societal, economic and ecological outcomes. Most of these studies showed effects in favour of the intervention in terms of reductions in transmission and hospitalisations, however some showed mixed or negative effects on societal, economic and ecological outcomes (e.g. fewer number of days spent in school). We found three studies that reported outcomes relating to multicomponent measures, where it was not possible to disaggregate the effects of each individual intervention, including one modelling, one observational and one quasi-experimental study. These studies employed interventions, such as physical distancing, modification of school activities, testing, and exemption of high-risk students, using measures such as hand hygiene and mask wearing. Most of these studies showed a reduction in transmission, however some showed mixed or no effects.   As the majority of studies included in the review were modelling studies, there was a lack of empirical, real-world data, which meant that there were very little data on the actual implementation of interventions., Authors' Conclusions: Our review suggests that a broad range of measures implemented in the school setting can have positive impacts on the transmission of SARS-CoV-2, and on healthcare utilisation outcomes related to COVID-19. The certainty of the evidence for most intervention-outcome combinations is very low, and the true effects of these measures are likely to be substantially different from those reported here. Measures implemented in the school setting may limit the number or proportion of cases and deaths, and may delay the progression of the pandemic. However, they may also lead to negative unintended consequences, such as fewer days spent in school (beyond those intended by the intervention). Further, most studies assessed the effects of a combination of interventions, which could not be disentangled to estimate their specific effects. Studies assessing measures to reduce contacts and to make contacts safer consistently predicted positive effects on transmission and healthcare utilisation, but may reduce the number of days students spent at school. Studies assessing surveillance and response measures predicted reductions in hospitalisations and school days missed due to infection or quarantine, however, there was mixed evidence on resources needed for surveillance. Evidence on multicomponent measures was mixed, mostly due to comparators. The magnitude of effects depends on multiple factors. New studies published since the original search date might heavily influence the overall conclusions and interpretation of findings for this review., (Copyright © 2022 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

Background: Starting in late 2019, COVID-19, caused by the novel coronavirus SARS-CoV-2, spread around the world. Long-term care facilities are at particularly high risk of outbreaks, and the burden of morbidity and mortality is very high among residents living in these facilities., Objectives: To assess the effects of non-pharmacological measures implemented in long-term care facilities to prevent or reduce the transmission of SARS-CoV-2 infection among residents, staff, and visitors., Search Methods: On 22 January 2021, we searched the Cochrane COVID-19 Study Register, WHO COVID-19 Global literature on coronavirus disease, Web of Science, and CINAHL. We also conducted backward citation searches of existing reviews., Selection Criteria: We considered experimental, quasi-experimental, observational and modelling studies that assessed the effects of the measures implemented in long-term care facilities to protect residents and staff against SARS-CoV-2 infection. Primary outcomes were infections, hospitalisations and deaths due to COVID-19, contaminations of and outbreaks in long-term care facilities, and adverse health effects., Data Collection and Analysis: Two review authors independently screened titles, abstracts and full texts. One review author performed data extractions, risk of bias assessments and quality appraisals, and at least one other author checked their accuracy. Risk of bias and quality assessments were conducted using the ROBINS-I tool for cohort and interrupted-time-series studies, the Joanna Briggs Institute (JBI) checklist for case-control studies, and a bespoke tool for modelling studies. We synthesised findings narratively, focusing on the direction of effect. One review author assessed certainty of evidence with GRADE, with the author team critically discussing the ratings., Main Results: We included 11 observational studies and 11 modelling studies in the analysis. All studies were conducted in high-income countries. Most studies compared outcomes in long-term care facilities that implemented the measures with predicted or observed control scenarios without the measure (but often with baseline infection control measures also in place). Several modelling studies assessed additional comparator scenarios, such as comparing higher with lower rates of testing. There were serious concerns regarding risk of bias in almost all observational studies and major or critical concerns regarding the quality of many modelling studies. Most observational studies did not adequately control for confounding. Many modelling studies used inappropriate assumptions about the structure and input parameters of the models, and failed to adequately assess uncertainty. Overall, we identified five intervention domains, each including a number of specific measures. Entry regulation measures (4 observational studies; 4 modelling studies) Self-confinement of staff with residents may reduce the number of infections, probability of facility contamination, and number of deaths. Quarantine for new admissions may reduce the number of infections. Testing of new admissions and intensified testing of residents and of staff after holidays may reduce the number of infections, but the evidence is very uncertain. The evidence is very uncertain regarding whether restricting admissions of new residents reduces the number of infections, but the measure may reduce the probability of facility contamination. Visiting restrictions may reduce the number of infections and deaths. Furthermore, it may increase the probability of facility contamination, but the evidence is very uncertain. It is very uncertain how visiting restrictions may adversely affect the mental health of residents. Contact-regulating and transmission-reducing measures (6 observational studies; 2 modelling studies) Barrier nursing may increase the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent cleaning and environmental hygiene measures may reduce the number of infections, but the evidence is very uncertain. It is unclear how contact reduction measures affect the probability of outbreaks. These measures may reduce the number of infections, but the evidence is very uncertain. Personal hygiene measures may reduce the probability of outbreaks, but the evidence is very uncertain.  Mask and personal protective equipment usage may reduce the number of infections, the probability of outbreaks, and the number of deaths, but the evidence is very uncertain. Cohorting residents and staff may reduce the number of infections, although evidence is very uncertain. Multicomponent contact -regulating and transmission -reducing measures may reduce the probability of outbreaks, but the evidence is very uncertain. Surveillance measures (2 observational studies; 6 modelling studies) Routine testing of residents and staff independent of symptoms may reduce the number of infections. It may reduce the probability of outbreaks, but the evidence is very uncertain. Evidence from one observational study suggests that the measure may reduce, while the evidence from one modelling study suggests that it probably reduces hospitalisations. The measure may reduce the number of deaths among residents, but the evidence on deaths among staff is unclear.  Symptom-based surveillance testing may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Outbreak control measures (4 observational studies; 3 modelling studies) Separating infected and non-infected residents or staff caring for them may reduce the number of infections. The measure may reduce the probability of outbreaks and may reduce the number of deaths, but the evidence for the latter is very uncertain. Isolation of cases may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent measures (2 observational studies; 1 modelling study) A combination of multiple infection-control measures, including various combinations of the above categories, may reduce the number of infections and may reduce the number of deaths, but the evidence for the latter is very uncertain., Authors' Conclusions: This review provides a comprehensive framework and synthesis of a range of non-pharmacological measures implemented in long-term care facilities. These may prevent SARS-CoV-2 infections and their consequences. However, the certainty of evidence is predominantly low to very low, due to the limited availability of evidence and the design and quality of available studies. Therefore, true effects may be substantially different from those reported here. Overall, more studies producing stronger evidence on the effects of non-pharmacological measures are needed, especially in low- and middle-income countries and on possible unintended consequences of these measures. Future research should explore the reasons behind the paucity of evidence to guide pandemic research priority setting in the future., (Copyright © 2021 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

Background: In late 2019, the first cases of coronavirus disease 2019 (COVID-19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers., Objectives: To assess the effectiveness of international travel-related control measures during the COVID-19 pandemic on infectious disease transmission and screening-related outcomes., Search Methods: We searched MEDLINE, Embase and COVID-19-specific databases, including the Cochrane COVID-19 Study Register and the WHO Global Database on COVID-19 Research to 13 November 2020., Selection Criteria: We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across international borders during the COVID-19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID-19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome., Data Collection and Analysis: Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements., Main Results: Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission. Most studies compared travel-related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects. Below we outline the results for each intervention category by illustrating the findings from selected outcomes. Travel restrictions reducing or stopping cross-border travel (31 modelling studies) The studies assessed cases avoided and shift in epidemic development. We found very low-certainty evidence for a reduction in COVID-19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect. There was very low-certainty evidence that cross-border travel controls can slow the spread of COVID-19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure. Screening at borders (13 modelling studies; 13 observational studies) Screening measures covered symptom/exposure-based screening or test-based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely. For symptom/exposure-based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate-certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low-certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low-certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low-certainty evidence), although all but one study observed this proportion to be less than 54%. For test-based screening, one modelling study provided very low-certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low-certainty evidence). Quarantine (12 modelling studies) The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low- to low-certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low-certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance. Quarantine and screening at borders (7 modelling studies; 4 observational studies) The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low- to low-certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low-certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine., Authors' Conclusions: With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross-border travel and quarantine of travellers, there is a lack of 'real-world' evidence. The certainty of the evidence for most travel-related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure-based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure-based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel-related control measures from a societal perspective., (Copyright © 2021 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)