Containment Policies Research Articles

Decreased community-acquired pneumonia coincided with rising awareness of precautions before governmental containment policy in Japan.

The effectiveness of population-wide compliance to personal precautions (mask-wearing and hand hygiene) in preventing community-acquired pneumonia has been unknown. In Japan, different types of nonpharmaceutical interventions from personal precautions to containment and closure policies (CACPs, e.g. stay-at-home requests) were sequentially introduced from late January to April 2020, allowing for separate analysis of the effects of personal precautions from other more stringent interventions. We quantified the reduction in community-acquired pneumonia hospitalizations and deaths and assessed if it coincided with the timing of increased public awareness of personal precautions before CACPs were implemented. A quasi-experimental interrupted time-series design was applied to non-COVID-19 pneumonia hospitalization and 30-day death data from April 2015 to August 2020 across Japan to identify any trend changes between February and April 2020. We also performed a comparative analysis of pyelonephritis and biliary tract infections to account for possible changes in the baseline medical attendance. These trend changes were then compared with multiple indicators of public awareness and behaviors related to personal precautions, including keyword usage in mass media coverage and sales of masks and hand hygiene products. Hospitalizations and 30-day deaths from non-COVID-19 pneumonia dropped by 24.3% (95% CI 14.8-32.8) and 16.1% (5.5-25.5), respectively, in February 2020, before the implementation of CACPs, whereas pyelonephritis and biliary tract infections did not suggest a detectable change. These changes coincided with increases in indicators related to personal precautions rather than those related to contact behavior changes. Community-acquired pneumonia could be reduced by population-wide compliance to moderate precautionary measures.

Stock market response to Covid-19, containment measures and stabilization policies—The case of Europe

Policymakers imposed constraints on public life in order to contain the Covid-19 pandemic. At the same time, fiscal, monetary and macroprudential policy implemented a large range of expansionary measures to limit the economic consequences of the pandemic and stimulate the recovery. In this paper, we assess the response of the equity market as a high-frequency indicator of economic activity to containment and stabilization policies for 29 European economies. We construct indicators of containment and stabilization policies and estimate a range of panel VAR models. The main results are threefold: First, we find that stock markets are highly responsive to containment and stabilization policies. We show that domestic fiscal policy, macroprudential policy as well as monetary policy support the recovery as reflected in the stock market. Second, expansionary fiscal policy conducted at the European level reduces rather raises stock prices. Third, we estimate the model over subsamples and show that the counter-intuitive stock market response to EU policies is driven by the responses in medium- and high-debt countries. These countries’ stock markets are also particularly susceptible to monetary policy announcements.

Managing disease containment measures during a pandemic

Throughout the current COVID‐19 pandemic, governments have implemented a variety of containment measures, ranging from hoping for herd immunity (which is essentially no containment) to mandating complete lockdown. On the one hand, containment measures reduce lives lost by limiting the disease spread and controlling the load on the healthcare system. On the other hand, such measures slow down economic activity, leading to lost jobs, economic stall, and societal disturbances, such as protests, civil disobedience, and increases in domestic violence. Hence, determining the right set of containment measures is a key social, economic, and political decision for policymakers. In this paper, we provide a model for dynamically managing the level of disease containment measures over the course of a pandemic. We determine the timing and level of containment measures to minimize the impact of a pandemic on economic activity and lives lost, subject to healthcare capacity and stochastic disease evolution dynamics. On the basis of practical evidence, we examine two common classes of containment policies—dynamic and static—and we find that dynamic policies are particularly valuable when the rate of disease spread is low, recovery takes longer, and the healthcare capacity is limited. Our work reveals a fundamental relationship between the structure of Pareto‐efficient containment measures (in terms of lives lost and economic activity) and key disease and economic parameters such as disease infection rate, recovery rate, and healthcare capacity. We also analyze the impact of virus mutation and vaccination on containment decisions.

VTE Student Burnout from (Covid)-19 Pandemic: Exploring Student and Family Risk and Protective Factors

VTE Student Burnout from (Covid)-19 Pandemic: Exploring Student and Family Risk and Protective Factors

COVID-19 Containment Measures—a Step Back for Walking Mobility? A 2-Year, 60-Country Analysis of the Apple Mobility Data

COVID-19 containment measures curb viral spread but may hamper walking mobility. As a low daily step count is associated with increased noncommunicable diseases and mortality, assessing the relationship between pandemic responses and walking mobility can help trade-off public health measures. We investigated the association between containment stringency and walking mobility across 60 countries in the period between January 21, 2020 and January 21, 2022 and modeled how this could impact mortality hazard. Walking mobility was measured through the Apple Mobility Trends, containment measures stringency index through the Oxford COVID-19 response tracker (which considers local policies on closures, healthcare, and economy), and meteorological data by National Oceanic and Atmospheric Administration weather stations. Walking mobility was regressed over stringency in a mixed-effect model with weather variables as covariates. The impact of stringency on all-cause mortality due to reduced mobility was modeled based on regression results, prepandemic walking mobility, and the association between step count and all-cause mortality hazard. Across the 60 countries, the average stringency was 55 (9) (mean [SD]) out of 100. Stringency was negatively associated with walking mobility; a log-linear model fitted data better than a linear one, with a regression coefficient for stringency on ln (walking mobility) (95% confidence interval) of -1.201 × 10-2 (-1.221 × 10-2 to -1.183 × 10-2). Increasing stringency, thus decreasing walking mobility, nonlinearly incremented the modeled all-cause mortality hazard by up to ∼40%. In this study, walking mobility was negatively associated with containment measures stringency; the relationship between stringency, mobility, and the subsequent impact on health outcomes may be nonlinear. These findings can help in balancing pandemic containment policies.

COVID‐19 Policy Interventions and Fertility Dynamics in the Context of Pre‐Pandemic Welfare Support

AbstractThis paper focuses on nonpharmaceutical interventions (NPIs) to explain fertility dynamics during the pandemic, while considering countries’ institutional context. We argue that containment policies disrupted people's lives and increased their uncertainty more in countries with weak welfare support systems, while health‐related and economic support NPIs mitigated such disruptions much more there, as they were less expected by citizens. We estimate monthly “excess” crude birth rates (CBRs) and find that countries with low public support—Southern Europe, East Asia, and Eastern Europe—experienced larger decreases and less of a rebound in CBRs than countries with histories of high public spending—Western, Central, and Northern Europe. However, in low support countries, NPIs are much more strongly associated with excess CBRs—containment NPIs more negatively and health and economic support NPIs more positively—with the exception of the one‐month lag of containment NPIs, for which the opposite holds. When putting these coefficients into broader perspective, our findings suggest that the actual implementation of all NPIs taken together mitigated fertility declines. This is especially the case for low public support countries, whereas one might have seen a birth decline even in high support countries if the NPIs were not implemented.

Goals and methods of managed entry agreements – can we get what we want?

ObjectiveWe aimed to examine managed entry agreement (MEA) methods and their relation to nationwide drug reimbursement policy goals in the context of the Hungarian health care system. MethodFirst, a targeted literature review was performed that identified the most relevant MEA methods and drug reimbursement goals. Then, by using the findings of the review, a survey was developed: the first part of the survey aimed to ensure that interviewees have similar understanding of different types of MEAs and related policy goals; in the second part the interviewees were asked to prioritize drug reimbursement goals and indicate their perception about the impact of MEA techniques on the reimbursement policy goals. ResultsThree groups of MEA techniques were identified: cap type, clawback type and rebates or free treatment doses. Six policy goals were identified: cost containment, reduction of uncertainty of effectiveness, sustainable finance, patient access improvement, cost-effectiveness improvement, handling administrative burden. None of the MEA techniques showed clear advantage over all others. Cost containment, improvement of patient access and reduction of uncertainty around effectiveness were ranked as most important drug reimbursement goals. Cap type agreements were deemed to uniformly support the policy goals of cost containment and sustainable financing. Clawback type agreements strongly support cost containment policies. Rebates and free treatment dose(s) methods had positive impact on four policy goals. ConclusionThe implementation of managed entry agreements shall be considered and motivated by pre-defined drug reimbursement policy goals. During the application of MEAs, setting priorities and making trade-offs between different policy goals are inevitable.

The Effects of Seoul’s Greenbelt on Travel Mode Share in Its Inside and Outside: A Spatial Panel Econometric Analysis

The primary purpose of urban growth management policies is to control urban sprawl and induce the diversification of travel mode share. Hence, how the greenbelt, as an urban containment policy, affects travel mode share is one of the important issues in understanding the efficacy of urban growth management. To analyze it for the Seoul Metropolitan Area (SMA), which has designated its greenbelt for more than forty-five years, this study implemented the spatial Durbin panel regression for the years from 2002 through 2016. Results showed that the greenbelt increased the use of public transportation and decreased car use, but it tended not to increase the use of nonmotorized modes of transportation such as bicycling and walking. There also turned out to be U-shaped (convex) relationships both between population density and car use and between the former and bicycling. Meanwhile, an inverted U-shaped relationship was observed between population density and public transportation mode, which was statistically significant. Based on these results, it can be said that a public transportation system outside of the greenbelt is needed for increasing its accessibility. To promote the use of public and nonmotorized transportation, it will also be important to determine the appropriate urban density.

The macroeconomics of pandemics around the world: Lives versus livelihoods revisited

The COVID-19 pandemic led governments around the world to impose unprecedented restrictions on economic activity. Were these restrictions equally justified in poorer countries with fewer demographic risk factors and less ability to weather economic shocks? We develop and estimate a fully specified model of the macroeconomy with epidemiological dynamics, incorporating subsistence constraints in consumption and allowing preferences over “lives versus livelihoods” to vary with income. Poorer countries’ demography pushes them unambiguously toward laxer policies. But because both infected and susceptible agents near the subsistence constraint will remain economically active in the face of infection risk and even to some extent under government containment policies, optimal policy in poorer countries pushes in the opposite direction. Moreover, for reasonable income-elasticities of the value of a statistical life, the model can fully rationalize equally strict or stricter policies in poorer countries.

502 The effects of removing the requirement for prior reimbursement approval on anticoagulant use in Ireland: A cross sectional study

Abstract Introduction Prior approval for reimbursement is a cost containment policy, aimed at reducing demand (1). It has been used temporarily in the Irish Healthcare system to deal with the initial financial challenges posed by the shift in use towards the Direct Oral Anticoagulants (DOACs) (apixaban, rivaroxaban, edoxaban and dabigatran) from warfarin. Improved safety of DOACs use was a secondary aim of this policy. The use of prior approval policies is increasing in Ireland but studies assessing their effectiveness are limited. Aim To examine the effect of removing the cost containment policy of prior approval for reimbursement of apixaban and rivaroxaban using a national pharmacy claims database. Methods The Health Service Executive-Primary Care Reimbursement Service database in Ireland was used in this cross-sectional study. The prescribing frequencies and associated costs of the oral anticoagulants; ((OACs) apixaban, rivaroxaban, and warfarin) listed in the top 100 most frequently prescribed drugs on the Community Drug Schemes (CDS), between 2018 and 2021 were examined. Levothyroxine was used as a negative control to detect any external deviances in the prescribing data records. Interrupted time series Poisson regression was used to assess the impact of removing the approval requirement of apixaban in September 2019 followed by the other DOACs in November 2020. Results The prescribing frequency of OACs increased by almost 20% during the study period. Figure1 shows the prescribing frequencies of apixaban , rivaroxaban , warfarin and levothyroxine on each of the three community drug schemes – General medical scheme (GMS), Drug payments scheme (DPS) and Long term illness scheme (LTI) during the study period.Timelines at September 2019 and November 2020 show the removal of prior approval for apixaban and all DOACs respectively. This study showed there were significant differences in the proportion of OACs prescribed among the CDS. A statistically significant decreased use of apixaban (<1%, p<0.05) occurred when prior approval was removed for all DOACs. Conclusion The removal of prior approval for reimbursement of DOACs in Ireland had a minimal impact on the prescribing frequency trends of the OACs. Future use of these potentially useful policies by healthcare systems requires careful consideration of drug type, approval criteria and length of time the policy remains in place.

Associations between the stringency of COVID-19 containment policies and health service disruptions in 10 countries

BackgroundDisruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020.MethodsUsing routine health information systems and administrative data from 10 countries (Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand) we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors.FindingsChronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point (95% CI -3.9, -2.7) reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1 (95% CI -19.6, 8.7) and 10.7 (95% CI -12.7, -8.7) percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model.ConclusionsAlthough containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.

Stochastic disease spreading and containment policies under state-dependent probabilities

We analyze the role of disease containment policy in the form of treatment in a stochastic economic-epidemiological framework in which the probability of the occurrence of random shocks is state-dependent, namely it is related to the level of disease prevalence. Random shocks are associated with the diffusion of a new strain of the disease which affects both the number of infectives and the growth rate of infection, and the probability of such shocks realization may be either increasing or decreasing in the number of infectives. We determine the optimal policy and the steady state of such a stochastic framework, which is characterized by an invariant measure supported on strictly positive prevalence levels, suggesting that complete eradication is never a possible long run outcome where instead endemicity will prevail. Our results show that: (i) independently of the features of the state-dependent probabilities, treatment allows to shift leftward the support of the invariant measure; and (ii) the features of the state-dependent probabilities affect the shape and spread of the distribution of disease prevalence over its support, allowing for a steady state outcome characterized by a distribution alternatively highly concentrated over low prevalence levels or more spread out over a larger range of prevalence (possibly higher) levels.

How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden

As COVID-19 shows a heterogeneous spreading process globally, investigating factors associated with COVID-19 spreading among different countries will provide information for containment strategy and medical service decisions. A significant challenge for analyzing how these factors impact COVID-19 transmission is assessing key epidemiological parameters and how they change under different containment strategies across different nations. This paper builds a COVID-19 spread simulation model to estimate the core COVID-19 epidemiological parameters. Then, the correlation between these core COVID-19 epidemiological parameters and the times of publicly announced interventions is analyzed, including three typical countries, China (strictly containment), the USA (moderately control), and Sweden (loose control). Results show that the recovery rate leads to a distinct COVID-19 transmission process in the three countries, as all three countries finally have similar and close to zero spreading rates in the third period of COVID-19 transmission. Then, an epidemic fundamental diagram between COVID-19 “active infections” and “current patients” is discovered, which could plan a country's COVID-19 medical capacity and containment strategies when combined with the COVID-19 spreading simulation model. Based on that, the hypothetical policies are proved effectively, which will give support for future infectious diseases.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

BackgroundCOVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.MethodsWe present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.ResultsRegression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.ConclusionsThis article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Genome Assembly and Population Sequencing Reveal Three Populations and Signatures of Insecticide Resistance of Tuta absoluta in Latin America.

Tuta absoluta is one of the largest threats to tomato agriculture worldwide. Native to South America, it has rapidly spread throughout Europe, Africa, and Asia over the past two decades. To understand how T. absoluta has been so successful and to improve containment strategies, high-quality genomic resources and an understanding of population history are critical. Here, we describe a highly contiguous annotated genome assembly, as well as a genome-wide population analysis of samples collected across Latin America. The new genome assembly has an L50 of 17 with only 132 contigs. Based on hundreds of thousands of single nucleotide polymorphisms, we detect three major population clusters in Latin America with some evidence of admixture along the Andes Mountain range. Based on coalescent simulations, we find these clusters diverged from each other tens of thousands of generations ago prior to domestication of tomatoes. We further identify several genomic loci with patterns consistent with positive selection and that are related to insecticide resistance, immunity, and metabolism. This data will further future research toward genetic control strategies and inform future containment policies.

Spatial networks and the spread of COVID-19: results and policy implications from Germany

Spatial networks are known to be informative about the spatiotemporal transmission dynamics of COVID-19. Using district-level panel data from Germany that cover the first 22 weeks of 2020, we show that mobility, commuter and social networks all predict the spatiotemporal propagation of the epidemic. The main innovation of our approach is that it incorporates the whole network and updated information on case numbers across districts over time. We find that when disease incidence increases in network neighbouring regions, case numbers in the home district surge one week later. The magnitude of these network transmission effects is comparable to within-district transmission, illustrating the importance of networks as drivers of local disease dynamics. After the introduction of containment policies in mid-March, network transmission intensity drops substantially. Our analysis suggests that this reduction is primarily due to a change in quality—not quantity—of interregional movements. This implies that blanket mobility restrictions are not a prerequisite for containing the interregional spread of COVID-19.

An analytical study of the economic impact of the COVID pandemic and response policies: the case of the United States of America

The study aims at analyzing the impact of the COVID-19 pandemic on economy, and the response policies applied accordingly to reduce its effects, commencing with the international/global framework, and finishing with the real situation in the United States of America, through a context analysis of the impact and the containment policies, using a descriptive analytical approach, and within the time framework of the of events. The study concluded that a coordinated global effort must be made to support countries that currently do not have sufficient fiscal ground to finance the economic and social policy. For the United States, it is important to engage in a significant spending and investing in key areas of the economy, in order to reduceimpacts on the supply chains and the business sector

Polarized Politics: Protest Against COVID-19 Containment Policies in the USA

Abstract Protest against containment policies in U.S. states is fueled by two drivers: the stringency of containment policies and the partisan control over the governorship and legislatures in each state. In our analysis of the period from March 2020 to March 2022, we find more protest events in states fully controlled by Democrats than in Republican controlled states both in a sample consisting of all states and a balanced sample in which we constrain observations to those red and blue states with on average similarly stringent containment policies. Protest was therefore politicized, and we find that partisanship exerts a roughly equal substantive effect on the number of protest events as the stringency of containment policies. If we assume no direct effect of partisanship on protest but allow for causal heterogeneity along partisan lines in the effect of containment policies, we find that the same increase in the stringency of policies evokes a stronger protest response in blue states than in red states.

Local containment policies and countrywide spread of COVID-19 in the United States: an epidemiologic analysis

ABSTRACT We analyse the spatial diffusion of new COVID-19 cases and the countrywide impact of state-specific containment policies during the early months of the COVID-19 pandemic in the United States. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states’ lockdown policies on infections in neighbouring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county-level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. We then develop a spatial version of the epidemiological susceptible–infected–recovered (SIR) model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighbouring states. We incorporate lockdown policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous US states and DC. Our results suggest that had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run.

Assessing the impact of local context and priorities regarding domestic disease outbreaks and imported risk on early pandemic response: Cross-continental comparisons.

Containment and closure policies are effective measures used in the early stages of a highly transmissible global pandemic such as COVID-19 to mitigate the spread and reduce transmissions. However, these policies can have negative impacts on the economy and personal freedom. Governments must carefully consider the necessity of increasing their stringency. Local contexts and priorities regarding domestic disease outbreaks and the risk of imported cases from other countries may vary among different countries, and could influence the decision to increase containment measures. Thus, this study aimed to differentiate the impacts of these affecting factors on the stringency of governmental containment measures through cross-continental comparisons. This study utilized a zero/one inflated beta (ZOIB) regression model to investigate how domestic epidemic, imported risk, and local context affect government responses to a pandemic. We used a country's weekly confirmed case and death numbers as a measure of its domestic threat. The imported risk was measured using a combination of weekly new cases in each country and the air passenger traffic between countries. The findings indicate that domestic case numbers are a primary concern for governments when deciding to increase policy stringency. Countries with higher development levels tend to implement stricter policies as they can better handle the negative impacts. Additionally, there is an interaction between case numbers and development level, with countries at the second or third highest development level focusing more on domestic outbreaks than imported risks, while those at the highest level have similar concerns for both. We concluded that most countries adjust policies' stringency majorly based on the variation of domestic case number rather than the other pandemic factors and the countries with a high development level tend to implement strict policies since their socio-economical condition could afford such policies. These insights can aid policymakers in improving containment and closure policies for future pandemics.