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Health‐care associated infections (HAIs) kill about 100,000 people annually; many are preventable. In response, 18 states currently require hospitals to publicly report their infection rates and national reporting is planned. Yet there is limited evidence on the effects of public reporting on HAI rates, and none on what elements of a reporting plan affect its impact on HAI rates. The author aims to review here what little we know, emphasizing his own case study of Pennsylvania.

The paper contains a narrative description of empirical challenges in attributing changes in infection rates to the introduction of public reporting, and the author's own research findings from a case study of Pennsylvania using both infection rates estimated from administrative (billing) data (“inpatient rates”) and public reported rates.

Hospitals, faced with public HAI reporting, may respond both by reducing infection rates and through time‐inconsistent reporting (“gaming”). Both effects are likely to be stronger at hospitals with high reported rates, relative to peers. From 2003‐2008, Pennsylvania inpatient CLABSI rates dropped by 14 per cent, versus a 9 per cent increase in control states. The overall drop comes primarily from hospitals in the highest third of reported rates. Reported CLABSI rates fell much faster, by 40 per cent, from 2005 to 2007. This difference suggests time‐inconsistent reporting.

Much more research is needed before we can have confidence that public reporting affects HAI rates (and for which HAIs), or know how to design an effective reporting scheme. HAI reporting cannot yet be considered to be “evidence based.” National reporting mandates will foreclose the state experiments needed to address these questions.

What little we know about impact of public reporting on HAI rates comes in significant part from the case study of Pennsylvania described in this article.

This study aims to explore the efficacy of government policy directions in mitigating the effects of the COVID-19 pandemic by employing a panel of 22 countries throughout the 2020-second quarter of 2022.

The panel autoregressive distributed lag (ARDL) model is employed to examine this phenomenon and to investigate the long-run effects of government policy decisions on infection and mortality rates from the pandemic.

The study reveals the following key findings: (1) Income support and debt relief facilities and stringent standards of governments are associated with reduced infection and death rates. (2) The response of governments has resulted in decreased mortality rates while simultaneously leading to an unexpected increase in infection rates. (3) Containment and healthcare practices have led to a decrease in infection rates but an increase in mortality rates, presenting another counterintuitive outcome. Despite the expectation that robust government responses would decrease infection rates and that healthcare containment practices would reduce mortality, these results highlight a lack of health equity and the challenge of achieving high vaccination rates across countries.

To effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.

This study concludes by suggesting the importance of implementing economic support in terms of income, and debt relief has played a crucial role in mitigating the spread of COVID-19 infections and reducing fatality rates.

To effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.

This research makes a unique contribution to the existing literature by investigating the impact of government responses on reducing COVID-19 infections and fatalities, specifically focusing on the period before COVID-19 vaccinations became available.

This paper aims to examine the policy and guidance that was issued, either as a direct result of the NHS Plan, or part of a subsequent initiative, surrounding cleaning in the NHS.

A literature review of the Department of Health and related agency web sites was completed. In addition there was a literature review of the relevant academic journals.

There is a growing evidence base on environmental cleaning in the NHS and more specifically the relationship between environmental cleaning and infection control. This paper has examined the contradiction in the evidence in the suspected correlation between infection control and environmental cleaning. However, one thing that does appear to be consistent is that a performance measure based on an observation (visual) assessment is not a sufficient tool to evaluate the environmental cleanliness of a hospital ward.

While the clinical community recognise the contribution of environmental cleaning and the impact on healthcare, more needs to be done to have the relevant studies published in the FM domain. Conversely there also needs to be work done to allow the FM community to have a “voice” in the infection control journals. The literature reviewed suggests that a usable technological solution is required to confirm satisfactory cleaning standards in healthcare facilities.

There is relatively little published work on the importance of cleaning to operations in the NHS, particularly in the FM domain.

The COVID-19 pandemic and its related economic meltdown and social unrest severely challenged most countries, their societies, economies, organizations, and individual citizens. Focusing on both more and less successful country-specific initiatives to fight the pandemic and its multitude of related consequences, this chapter explores implications for leadership and effective action at the individual, organizational, and societal levels. As international management scholars and consultants, the authors document actions taken and their wide-ranging consequences in a diverse set of countries, including countries that have been more or less successful in fighting the pandemic, are geographically larger and smaller, are located in each region of the world, are economically advanced and economically developing, and that chose unique strategies versus strategies more similar to those of their neighbors. Cultural influences on leadership, strategy, and outcomes are described for 19 countries. Informed by a cross-cultural lens, the authors explore such urgent questions as: What is most important for leaders, scholars, and organizations to learn from critical, life-threatening, society-encompassing crises and grand challenges? How do leaders build and maintain trust? What types of communication are most effective at various stages of a crisis? How can we accelerate learning processes globally? How does cultural resilience emerge within rapidly changing environments of fear, shifting cultural norms, and profound challenges to core identity and meaning? This chapter invites readers and authors alike to learn from each other and to begin to discover novel and more successful approaches to tackling grand challenges. It is not definitive; we are all still learning.

The authors analyze the effects of political freedom and personal freedom on the spread of COVID-19 in a cross-country study. The authors also investigate how income inequality, urbanization and previous experience with a similar respiratory epidemic/pandemic, such as SARS and MERS, affect the spread of COVID-19.

The authors employ data from 102 countries to examine the relationship of countries' economic and sociopolitical factors, such as political freedom and personal freedom and their COVID-19 infection cases per million population at 120 days, 150 days and 180 days after the reported 10th infection case. The authors also include the log term of real GDP per capita to control for counties' economic development and regional dummies to control for regional-specific effects.

Results of this study show that personal freedom, rather than democracy, has a significant positive effect on countries' COVID-19 infection cases. On the contrary, democracy has a negative impact on the infection rate. The authors also find that socioeconomic factors such as higher income inequality and urbanization rate adversely affect the COVID-19 infection cases. A larger older population is associated with fewer infection cases, holding everything else equal. Previous experiences with the coronavirus crisis affect countries only at the 120 days mark. Real GDP per capita has no significant effect.

The main contribution of this paper is to jointly explore personal freedom, which implies a social framework with more emphasis on self-value and self-realization and political freedom, that is, democracy. The authors show that it is personal freedom, rather than democracy, that contributes to higher COVID-19 infection cases. Democracy, on the other hand, reduces the number of infection cases.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2021-0769

The COVID-19 pandemic educed extraordinary policy responses globally, including in India, to flatten the infection-growth curve. The trajectories of infections, recovery, and deaths vastly differed across Indian states. The purpose of this study is to investigate whether persistent investments by states in critical social sectors, such as health and education, explain their preparedness and hence better management of the pandemic.

This study uses secondary data on the number of infected, recovered and deceased due to COVID-19, along with data on population and income across 302 districts in 11 major states in India. Data on health and education indices are collected at the state-level. Linear regression models that also control for heteroskedasticity are applied.

This study finds that higher investments in health care and education reduce the propensity of the infection spread. Further, states with persistent investments in health care and education exhibit a higher rate of recovery. This study also finds that death rates are significantly lower in states with higher investments in education.

The findings support the conjecture that states that have consistently invested in social sectors benefited from the associated positive externalities during the crisis that helped them manage the pandemic better.

This study will help policymakers understand the underlying social forces critical to the success in the fight against pandemics. Apart from improving preparedness for future pandemics, the evidence provided in the paper may help give better direction and purpose to tax-financed public spending in states where social sector development has hitherto received low priority.

Injecting drug use (IDU) has traditionally been seen as a law enforcement problem and a stain on society. With the emergence of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), however, the discourse on IDU has widened to include crucial public health and human rights concerns. Economic analysis, too, has much to contribute to the policy debate. By examining the costs and benefits of drug use from the perspective of injecting drug users, economic analysis can shed light on the problem of IDU and the transmission of HIV among users. This chapter also presents new results on the economic analysis of needle exchange programmes.

Hepatitis A is a prevalent disease that is largely preventable by vaccine usage. The vaccine for this illness is highly underused in most regions. In an attempt to find the strategies that are most beneficial in regard to quality-adjusted life years (QALYs) and cost in current environments, the purpose of this paper is to conduct cost-effectiveness analyses to investigate vaccination strategies in a more economically developed country (MEDC), generally known as a “developed” area: the USA, and a less economically developed country (LEDC), generally known as a “developing” area: the state of Rio de Janeiro, Brazil.

This study used a dynamic transmission model for comparative effectiveness analyses. The model ran two different scenarios. The two regions studied have different policies and strategies for Hepatitis A vaccination currently, and also used different strategies in 2009. In the USA, a universal vaccination policy was modeled, along with a scenario in which it was removed. In Rio de Janeiro, a no vaccination policy was modeled, along with a scenario in which a universal vaccination policy was effected.

The comparison of resulting incremental cost-effectiveness ratio values to accepted threshold values showed universal vaccination to be cost-effective in both the USA and Rio de Janeiro as compared to no vaccination. When episode and vaccination costs and vaccination efficacy were varied, this still remained true. Universal vaccination was found to result in lower incidence of Hepatitis A in both the USA and Rio de Janeiro. Over the twenty-year time horizon, universal vaccination is projected to prevent 506,945 cases of symptomatic Hepatitis A in the USA and 42,318 cases of Hepatitis A in Rio de Janeiro. Other benefits include a projected increase in cumulative QALYs through the use of universal vaccination.

This analysis showed universal vaccination to be cost-effective as compared to no vaccination, and portions of the study’s approach had not previously been applied in tandem to investigate Hepatitis A interventions. The results may help foster higher compliance rates for Hepatitis A vaccination and even greater per-person economic benefits of universal vaccination, particularly in the USA. The purpose of this study is also to encourage elevated levels of surveillance on age of infection in developing regions and consistent reevaluation utilizing dynamic transmission models in both the USA and Brazil, as well as other rapidly developing regions, in order to prevent future epidemics and costs associated with the disease.

This paper aims to analyze the convergence of the testing and positivity rates for the COVID-19 pandemic focusing on Germany and Italy.

The authors employ the two-regime threshold autoregressive (TAR) panel unit root test by using the weekly data in the period of 2020:03-2021:04.

Following finding out that the testing and positivity rates are nonlinear, the authors determine that the transition country between the two regimes is Italy for the testing rates and Germany for the positivity rates. Their findings support the partial convergence for the testing rates for Germany and Italy. On the other hand, the authors could not find any convergence for the positivity rates of these two countries.

This paper contributes to academic literature in several ways. Firstly, to the best of their knowledge, this paper is the first study that analyzes the convergence of testing and positivity data. This paper further focuses on two Euro-Area countries which have suffered significantly from the COVID-19 pandemic. In addition, the authors employ the two-regime threshold autoregressive (TAR) panel unit root proposed by Beyaert and Camacho (2008) in their empirical analysis. This recent panel data methodology aims to test real convergence in a nonlinear framework by incorporating the threshold model, panel data unit root test and the calculation of critical values by bootstrap simulation.

There was limited study available on successful intervention for central-line-associated bloodstream infection (CLABSI) done at nonintensive care unit (ICU) and resources-limited setting. The objective of this study was to design, implement and evaluate a strategy to reduce CLABSI rate in non-ICU settings at general medical wards of Hospital Tuanku Ja'afar Seremban.

Preinterventional study was conducted in one-month period of January 2019, followed by intervention period from February to March 2019. Postintervention study was conducted from April to July 2019. The CLABSI rates were compared between pre and postintervention periods. A multifaceted intervention bundle was implemented, which comprised (1) educational program for healthcare workers, (2) weekly audit and feedback and (3) implementation of central line bundle of care.

There was a significant overall reduction of CLABSI rate between preintervention and postintervention period [incidence rate ratio (IRR) of 0.06 (95 percent CI, 0.01–0.33; P = 0.001)].

CLABSI rates were reduced by a multifaceted intervention bundle, even in non-ICU and resource-limited setting. This includes a preinterventional study to identify the risk factors followed by a local adaption of the recommended care bundles. This study recommends resources-limited hospitals to design a strategy that is suitable for their own local setting to reduce CLABSI.

This study demonstrated the feasibility of a multifaceted intervention bundle that was locally adapted with an evidence-based approach to reduce CLABSI rate in non-ICU and resource-limited setting.