Emerging from the other end: Key measures for a successful COVID-19 lockdown exit strategy and the potential contribution of pharmacists.
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SUBMITTER: Dawoud D
PROVIDER: S-EPMC7219380 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
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Emerging from the other end: Key measures for a successful COVID-19 lockdown exit strategy and the potential contribution of pharmacists.
Research in social & administrative pharmacy : RSAP 20200513 1
As the world edges towards relaxing the lockdown measures taken to control the spread of the novel coronavirus SARS-CoV-2 (COVID-19), governments have started putting in place a variety of measures to avoid a second peak in the number of infections. The implementation of and adherence to such measures will be key components of any successful lockdown exit strategy. Ranging from expanded testing and widespread use of technology to building the public's trust in the post COVID-19 world, there is a ...[more]
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Project description:After the national lockdown imposed on March 11, 2020, the Italian government has gradually resumed the suspended economic and social activities since May 4, while maintaining the closure of schools until September 14. We use a model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission to estimate the health impact of different exit strategies. The strategy adopted in Italy kept the reproduction number Rt at values close to one until the end of September, with marginal regional differences. Based on the estimated postlockdown transmissibility, reopening of workplaces in selected industrial activities might have had a minor impact on the transmissibility. Reopening educational levels in May up to secondary schools might have influenced SARS-CoV-2 transmissibility only marginally; however, including high schools might have resulted in a marked increase of the disease burden. Earlier reopening would have resulted in disproportionately higher hospitalization incidence. Given community contacts in September, we project a large second wave associated with school reopening in the fall.
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Project description:BackgroundAlternative payment models are common for both primary care providers and pharmacies. These models rely on quality measures to determine reimbursement, and pharmacists and primary care providers can contribute to performance on a similar set of medication-related measures. Therefore, payers need to decide which provider to incentivize for which measures when both are included in alternative payment models.ObjectivesTo explore the relative contribution of pharmacies and primary care group practices to a range of quality measures.MethodsThis retrospective cross-sectional study used Medicare Part A, B, and D claims for a 20% random sample of Medicare beneficiaries for 2014-2016. Eight quality measures were selected from the Merit-based Incentive Payment System and Medicare Part D Stars Ratings. Measures included medication adherence measures, appropriate prescribing measures such as high-risk medication use in the elderly, statin use in persons with diabetes (SUPD), and others. The residual intraclass correlation coefficient (RICC) was used to estimate the contribution of pharmacists and primary care providers to measure variation. To estimate the relative contribution across provider types, the pharmacy RICC was divided by the group practice RICC to yield a RICC ratio.ResultsDue to varying measure eligibility requirements, the number of patients per measure ranged from 179,430 to 2,226,129. Across all measures, the RICC values were low, ranging from 0.013 for SUPD to 0.145 for adult sinusitis. Adherence measures had the highest RICC ratios (1.15-1.44), and the annual influenza vaccination measure had the lowest (0.56).Discussion and conclusionsThe relative contributions of pharmacists and primary care providers vary across quality measures. As payers design payment models with measures to which pharmacists and primary care providers can contribute, the RICC ratio may be useful in aligning incentives to the providers with the greatest relative contributions. Additional research is needed to validate this method and extend it to additional sets of providers.
| S-EPMC9418985 | biostudies-literature
Project description:As of mid-2020, eradicating COVID-19 seems not to be an option, at least in the short term. The challenge for policy makers consists of implementing a suitable approach to contain the outbreak and limit extra deaths without exhausting healthcare forces while mitigating the impact on the country's economy and on individuals' well-being. To better describe the trade-off between the economic, societal and public health dimensions, we developed an integrated bioeconomic optimization approach. We built a discrete age-structured model considering three main populations (youth, adults and seniors) and 8 socio-professional characteristics for the adults. Fifteen lockdown exit strategies were simulated for several options: abrupt or progressive (4 or 8 weeks) lockdown lift followed by total definitive transitory final unlocking. Three values of transmission rate (Tr) were considered to represent individuals' barrier gesture compliance. Optimization under constraint to find the best combination of scenarios and options was performed on the minimal total cost for production losses due to contracted activities and hospitalization in the short and mid-term, with 3 criteria: mortality, person-days locked and hospital saturation. The results clearly show little difference between the scenarios based on the economic impact or the 3 criteria. This means that policy makers should focus on individuals' behaviors (represented by the Tr value) more than on trying to optimize the lockdown strategy (defining who is unlocked and who is locked). For a given Tr, the choices of scenarios permit the management of the hospital saturation level with regard to both its intensity and its duration, which remains a key point for public health. The results highlight the need for behavioral or experimental economics to address COVID-19 issues through a better understanding of individual behavior motivations and the identification of ways to improve biosecurity compliance.
| S-EPMC7705347 | biostudies-literature
Project description:In recent years, Cannabis use/misuse for treating pregnancy-related symptoms and other chronic conditions has increased among pregnant women, favored by decriminalization and/or legalization of its recreational uses in addition to its easy accessibility. However, there is evidence that prenatal Cannabis exposure might have adverse consequences on pregnancy progression and a deleterious impact on proper neurodevelopmental trajectories in the offspring. Maternal Cannabis use could interfere with the complex and finely controlled role performed by the endocannabinoid system in reproductive physiology, impairing multiple gestational processes from blastocyst implantation to parturition, with long-lasting intergenerational effects. In this review, we discuss current clinical and preclinical evidence regarding the role of endocannabinoids in development, function, and immunity of the maternal–fetal interface, focusing on the impact of Cannabis constituents on each of these gestational processes. We also discuss the intrinsic limitations of the available studies and the future perspectives in this challenging research field.
| S-EPMC10048990 | biostudies-literature
Project description:Background: During the coronavirus disease 2019 (COVID-19) lockdown, contact clustering in social bubbles may allow extending contacts beyond the household at minimal additional risk and hence has been considered as part of modified lockdown policy or a gradual lockdown exit strategy. We estimated the impact of such strategies on epidemic and mortality risk using the UK as a case study. Methods: We used an individual based model for a synthetic population similar to the UK, stratified into transmission risks from the community, within the household and from other households in the same social bubble. The base case considers a situation where non-essential shops and schools are closed, the secondary household attack rate is 20% and the initial reproduction number is 0.8. We simulate social bubble strategies (where two households form an exclusive pair) for households including children, for single occupancy households, and for all households. We test the sensitivity of results to a range of alternative model assumptions and parameters. Results: Clustering contacts outside the household into exclusive bubbles is an effective strategy of increasing contacts while limiting the associated increase in epidemic risk. In the base case, social bubbles reduced fatalities by 42% compared to an unclustered increase of contacts. We find that if all households were to form social bubbles the reproduction number would likely increase to above the epidemic threshold of R=1. Strategies allowing households with young children or single occupancy households to form social bubbles increased the reproduction number by less than 11%. The corresponding increase in mortality is proportional to the increase in the epidemic risk but is focussed in older adults irrespective of inclusion in social bubbles. Conclusions: If managed appropriately, social bubbles can be an effective way of extending contacts beyond the household while limiting the increase in epidemic risk.
| S-EPMC7871360 | biostudies-literature
Project description:ObjectiveTo analyse the impact and repercussions of the surge in healthcare demand in response to the COVID-19 pandemic, assess the potential effectiveness of various infection/disease control measures, and make projections on the best approach to exit from the current lockdown.DesignA four-compartment model was constructed for SARS-CoV-2 infection based on the Wuhan data and validated with data collected in Italy, the UK, and the US. The model captures the effectiveness of various disease suppression measures in three modifiable factors: (a) the per capita contact rate (β) that can be lowered by means of social distancing, (b) infection probability upon contacting infectious individuals that can be lowered by wearing facemasks, personal hygiene, etc., and (c) the population of infectious individuals in contact with the susceptible population, which can be lowered by quarantine. The model was used to make projections on the best approach to exit from the current lockdown.ResultsThe model was applied to evaluate the epidemiological data and hospital burden in Italy, the UK, and the US. The control measures were identified as the key drivers for the observed epidemiological data through sensitivity analyses. Analysing the different lockdown exit strategies showed that a lockdown exit strategy with a combination of social separation/general facemask use may work, but this needs to be supported by intense monitoring which would allow re-introduction/tightening of the control measures if the number of new infected subjects increases again.Conclusions and relevanceGovernments should act early in a swift and decisive manner for containment policies. Any lockdown exit will need to be monitored closely, with regards to the potential of lockdown reimplementation. This mathematical model provides a framework for major pandemics in the future.
| S-EPMC7313816 | biostudies-literature
Project description:BackgroundInfectious outbreaks, most recently coronavirus disease 2019 (COVID-19), have required pervasive public health strategies, termed lockdown measures, including quarantine, social distancing, and closure of workplaces and educational establishments. Although evidence analysing immediate effects is expanding, repercussions following lockdown measures remain poorly understood. This systematic review aims to analyse biopsychosocial consequences after lockdown measures end according to short, medium, and long-term impacts.MethodsPubMed, Ovid MEDLINE, Embase, PsycInfo, Web of Science, and Scopus databases were searched from inception to January 12, 2021. Reference lists were manually reviewed. Eligible studies analysed biopsychosocial functioning after lockdown measures secondary to recent infectious outbreaks ended. Lockdown measures were defined as quarantine, isolation, workplace or educational closures, social or physical distancing, and national or local closure of public institutions deemed non-essential. Studies exclusively researching outcomes during lockdown measures, examined infectious participants, or analysed lockdown measures not pertaining to an infectious outbreak were excluded. Two independent reviewers extracted data and assessed bias with a third resolving discrepancies. Data was extracted from published reports with further information requested from authors where necessary. The mixed methods appraisal tool assessed study quality, languages were restricted to English, German, Italian, and French and narrative synthesis was applied.ResultsOf 5149 identified studies, 40 were eligible for inclusion. Psychological distress, economic repercussions, social, biological, and behavioural ramifications were observed. Short to medium-term effects comprised reactions relating to early trauma processing whereas medium to long-term repercussions manifested in maladaptive behaviours and mental health deterioration. Increased alcohol intake, stigmatisation, and economic effects were also identified consequences. High-risk groups included health care workers, children, elderly, inpatients, those with pre-existing psychiatric diagnoses, and socially isolated individuals.ConclusionsSupporting vulnerable groups and offering education, workplace modifications, financial, and social assistance may mitigate negative repercussions. Establishing a rapid and comprehensive evidence base appraising the efficacy of such interventions and identifying areas for development is essential. This review was limited by study heterogeneity and lack of randomisation in available literature. Given the unprecedented nature and progression of COVID-19, the relevance of previous outcomes remains uncertain.Protocol registrationPROSPERO registration CRD42020181134.
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Project description:BackgroundWith at least 94 countries undergoing or exiting lockdowns for contact suppression to control the COVID-19 outbreak, sustainable and public health-driven exit strategies are required. Here we explore the impact of lockdown and exit strategies in Singapore for immediate planning.MethodsWe use an agent-based model to examine the impacts of epidemic control over 480 days. A limited control baseline of case isolation and household member quarantining is used. We measure the impact of lockdown duration and start date on final infection attack sizes. We then apply a 3-month gradual exit strategy, immediately re-opening schools and easing workplace distancing measures, and compare this to long-term social distancing measures.FindingsAt baseline, we estimated 815 400 total infections (21.6% of the population). Early lockdown at 5 weeks with no exit strategy averted 18 500 (2.27% of baseline averted), 21 300 (2.61%) and 22 400 (2.75%) infections for 6, 8 and 9-week lockdown durations. Using the exit strategy averted a corresponding 114 700, 121 700 and 126 000 total cases, representing 12.07-13.06% of the total epidemic size under baseline. This diminishes to 9 900-11 300 for a late 8-week start time. Long-term social distancing at 6 and 8-week durations are viable but less effective.InterpretationGradual release exit strategies are critical to maintain epidemic suppression under a new normal. We present final infection attack sizes assuming the ongoing importation of cases, which require preparation for a potential second epidemic wave due to ongoing epidemics elsewhere.FundingSingapore Ministry of Health, Singapore Population Health Improvement Centre.
| S-EPMC7395828 | biostudies-literature
Project description:The widespread lockdowns imposed in many countries at the beginning of the COVID-19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible-exposed-infected-removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individual's risk of needing intensive care unit (ICU) treatment if infected. Using COVID-19-related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest risk population months earlier while consistently abiding by ICU capacity restrictions. Exit policies without risk predictions, meanwhile, would considerably exceed ICU capacity or require the isolation of a substantial portion of population for over a year in order to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects. Our work indicates that predictive modeling based on machine learning and artificial intelligence could bring significant value to managing pandemics. Such a strategy, however, requires governments to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This includes health data policies to train predictive models and apply them to all residents, as well as policies for targeted resource allocation to maintain strict isolation for high-risk individuals.
| S-EPMC9115407 | biostudies-literature