Monkeypox Outbreak 2022, from a Rare Disease to Global Health Emergence: Implications for Travellers.

Monkeypox (mpox), a zoonotic disease caused by the monkeypox virus (MPXV), poses a significant public health threat with the potential for global dissemination beyond its endemic regions in Central and West Africa. This study explores the multifaceted aspects of monkeypox, covering its epidemiology, genomics, travel-related spread, mass gathering implications, and economic consequences. Epidemiologically, mpox exhibits distinct patterns, with variations in age and gender susceptibility. Severe cases can arise in immunocompromised individuals, underscoring the importance of understanding the factors contributing to its transmission. Genomic analysis of MPXV highlights its evolutionary relationship with the variola virus and vaccinia virus. Different MPXV clades exhibit varying levels of virulence and transmission potential, with Clade I associated with higher mortality rates. Moreover, the role of recombination in MPXV evolution remains a subject of interest, with implications for understanding its genetic diversity. Travel and mass gatherings play a pivotal role in the spread of monkeypox. The ease of international travel and increasing globalization have led to outbreaks beyond African borders. The economic ramifications of mpox outbreaks extend beyond public health. Direct treatment costs, productivity losses, and resource-intensive control efforts can strain healthcare systems and economies. While vaccination and mitigation strategies have proven effective, the cost-effectiveness of routine vaccination in non-endemic countries remains a subject of debate. This study emphasizes the role of travel, mass gatherings, and genomics in its spread and underscores the economic impacts on affected regions. Enhancing surveillance, vaccination strategies, and public health measures are essential in controlling this emerging infectious disease.

A Narrative Review of Health Status and Healthcare Delivery in the Oil and Gas Industry: Impacts on Employees, Employers, and Local Communities.

Workers in the oil and gas industry are exposed to numerous health risks, ranging from poor health behaviours to the possibility of life-threatening injuries. Determining the most appropriate models of healthcare for the oil and gas industry is difficult, as strategies must be acceptable to multiple stakeholders, including employees, employers, and local communities. The purpose of this review was to broadly explore the health status and needs of workers in the oil and gas industry and healthcare delivery models relating to primary care and emergency responses. Database searches of PubMed, EMBASE, CINAHL, PsycINFO, and Scopus were conducted, as well as grey literature searches of Google, Google Scholar, and the International Association of Oil and Gas Producers website. Resource-sector workers, particularly those in 'fly-in fly-out' roles, are susceptible to poor health behaviours and a higher prevalence of mental health concerns than the general population. Evidence is generally supportive of organisation-led behaviour change and mental health-related interventions. Deficiencies in primary care received while on-site may lead workers to inappropriately use local health services. For the provision of emergency medical care, telehealth and telemedicine lead to favourable outcomes by improving patient health status and satisfaction and reducing the frequency of medical evacuations.

Forecasting emergency department waiting time using a state space representation.

The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the quality and quantity of waiting time information presented to patients. To improve both aspects, we propose a set of state space models with flexible error structures to forecast ED waiting time for low acuity patients. Our approach utilizes a Bayesian framework to generate uncertainties associated with the forecasts. We find that the state-space models with flexible error structures significantly improve forecast accuracy of ED waiting time compared to the benchmark, which is the rolling average model. Specifically, incorporating time-varying and correlated error terms reduces the root mean squared errors of the benchmark by 10%. Furthermore, treating zero-recorded waiting times as unobserved values improves forecast performance. Our proposed model has the ability to provide patient-centric waiting time information. By offering more accurate and informative waiting time information, our model can help patients make better informed decisions and ultimately enhance their ED experience.

Travel-Related Monkeypox Outbreaks in the Era of COVID-19 Pandemic: Are We Prepared?

Several neglected infectious pathogens, such as the monkeypox virus (MPXV), have re-emerged in the last few decades, becoming a global health burden. Despite the incipient vaccine against MPXV infection, the global incidence of travel-related outbreaks continues to rise. About 472 confirmed cases have been reported in 27 countries as of 31 May 2022, the largest recorded number of cases outside Africa since the disease was discovered in the early 1970s.

Hospitalisations related to lower respiratory tract infections in Northern Queensland.

OBJECTIVE: To investigate the admission characteristics and hospital outcomes for patients admitted with lower respiratory tract infections (LRTI) in Northern Queensland. METHODS: We perform a retrospective analysis of the data covering an 11-year period, 2006-2016. Length of hospital stay (LOS) is modelled by negative binomial regression and heterogeneous effects are checked using interaction terms. RESULTS: A total of 11,726 patients were admitted due to LRTI; 2,430 (20.9%) were of Indigenous descent. We found higher hospitalisations due to LRTI for Indigenous than non-Indigenous patients, with a disproportionate increase in hospitalisations occurring during winter. The LOS for Indigenous patients was higher by 2.5 days [95%CI: -0.15; 5.05] than for non-Indigenous patients. The average marginal effect of 17.5 [95%CI: 15.3; 29.7] implies that the LOS for a patient, who was admitted to ICU, was higher by 17.5 days. CONCLUSIONS: We highlighted the increased burden of LRTIs experienced by Indigenous populations, with this information potentially being useful for enhancing community-level policy making. Implications for public health: Future guidelines can use these results to make recommendations for preventative measures in Indigenous communities. Improvements in engagement and partnership with Indigenous communities and consumers can help increase healthcare uptake and reduce the burden of respiratory diseases.

Do access, quality and cost of general practice affect emergency department use?

Limited access, poor experience, and high out-of-pocket (OOP) costs of primary care services may lead to avoidable emergency department (ED) presentations. But, the evidence has been limited with most of the studies using surveys conducted in EDs. Using detailed health survey data of Australian women linked to multiple administrative datasets, we extend the literature by estimating the effects of access, costs, and experience of general practice (GP) services on the probability of ED attendance while accounting for a large set of health and socioeconomic covariates. Our findings suggest that improvements in access to primary care services can significantly reduce the demand for low acuity ED presentations. We also show that the impact of increased accessibility of GP services is expected to be the highest for socioeconomic vulnerable populations and patients whose access is the poorest. This evidence can be useful for the design of targeted policies aimed at improving access to doctors in particular areas that are socioeconomically disadvantaged and where medical skill shortages are significant. However, policies aimed at reduction in primary care OOP costs or improvement in the perception of GP quality are less likely to be effective in reducing the number of non-urgent ED presentations.

Does High Public Trust Amplify Compliance with Stringent COVID-19 Government Health Guidelines? A Multi-country Analysis Using Data from 102,627 Individuals.

PURPOSE: To examine how public trust mediates the people's adherence to levels of stringent government health policies and to establish if these effects vary across the political regimes. METHODS: This study utilizes data from two large-scale surveys: the global behaviors and perceptions at the onset of COVID-19 pandemic and the Oxford COVID-19 Government Response Tracker (OxCGRT). Linear regression models were used to estimate the effects of public trust and strictness of restriction measures on people's compliance level. The model accounted for individual and daily variations in country-level stringency of preventative measures. Differences in the dynamics between public trust, the stringent level of government health guidelines and policy compliance were also examined among countries based on political regimes. RESULTS: We find strong evidence of the increase in compliance due to the imposition of stricter government restrictions. The examination of heterogeneous effects suggests that high public trust in government and the perception of its truthfulness double the impact of policy restrictions on public compliance. Among political regimes, higher levels of public trust significantly increase the predicted compliance as stringency level rises in authoritarian and democratic countries. CONCLUSION: This study highlights the importance of public trust in government and its institutions during public health emergencies such as the COVID-19 pandemic. Our results are relevant and help understand why governments need to address the risks of non-compliance among low trusting individuals to achieve the success of the containment policies.

Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study.

BACKGROUND: The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide. METHODS: Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country. RESULTS: We found significant negative association between disease arrival time and number of cases imported from Italy (r = -0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre. CONCLUSION: We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.

Predicting waiting time to treatment for emergency department patients.

BACKGROUND: The current systems of reporting waiting time to patients in public emergency departments (EDs) has largely relied on rolling average or median estimators which have limited accuracy. This study proposes to use machine learning (ML) algorithms that significantly improve waiting time forecasts. METHODS: By implementing ML algorithms and using a large set of queueing and service flow variables, we provide evidence of the improvement in waiting time predictions for low acuity ED patients assigned to the waiting room. In addition to the mean squared prediction error (MSPE) and mean absolute prediction error (MAPE), we advocate to use the percentage of underpredicted observations. The use of ML algorithms is motivated by their advantages in exploring data connections in flexible ways, identifying relevant predictors, and preventing overfitting of the data. We also use quantile regression to generate time forecasts which may better address the patient's asymmetric perception of underpredicted and overpredicted ED waiting times. RESULTS: Using queueing and service flow variables together with information on diurnal fluctuations, ML models outperform the best rolling average by over 20 % with respect to MSPE and quantile regression reduces the number of patients with large underpredicted waiting times by 42 %. CONCLUSION: We find robust evidence that the proposed estimators generate more accurate ED waiting time predictions than the rolling average. We also show that to increase the predictive accuracy further, a hospital ED may decide to provide predictions to patients registered only during the daytime when the ED operates at full capacity, thus translating to more predictive service rates and the demand for treatments.

Role of modelling in COVID-19 policy development.

Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Early projections of international spread influenced travel restrictions and border closures. Model projections based on the virus's infectiousness demonstrated its pandemic potential, which guided the global response to and prepared countries for increases in hospitalisations and deaths. Tracking the impact of distancing and movement policies and behaviour changes has been critical in evaluating these decisions. Models have provided insights into the epidemiological differences between higher and lower income countries, as well as vulnerable population groups within countries to help design fit-for-purpose policies. Economic evaluation and policies have combined epidemic models and traditional economic models to address the economic consequences of COVID-19, which have informed policy calls for easing restrictions. Social contact and mobility models have allowed evaluation of the pathways to safely relax mobility restrictions and distancing measures. Finally, models can consider future end-game scenarios, including how suppression can be achieved and the impact of different vaccination strategies.

Economic Consequences of the COVID-19 Outbreak: the Need for Epidemic Preparedness.

COVID-19 is not only a global pandemic and public health crisis; it has also severely affected the global economy and financial markets. Significant reductions in income, a rise in unemployment, and disruptions in the transportation, service, and manufacturing industries are among the consequences of the disease mitigation measures that have been implemented in many countries. It has become clear that most governments in the world underestimated the risks of rapid COVID-19 spread and were mostly reactive in their crisis response. As disease outbreaks are not likely to disappear in the near future, proactive international actions are required to not only save lives but also protect economic prosperity.