Bayesian estimation of the effect of health inequality in disease detection

Spatial equity and factors associated with intensive care unit bed allocation in China

BACKGROUND

At present, unequal allocation of medical resources represents a major problem for medical service management in China and many other countries. Equity of intensive care unit (ICU) bed allocation is essential for timely and equitable access to medical care for critically ill patients. This study analysed the equity of ICU bed allocation in 31 provincial regions in China, and the associated factors, to provide a theoretical basis for improvement in the allocation of ICU beds.

METHODS

The equity of ICU bed allocation was investigated in 31 provincial regions in China in 2021. The Gini coefficient combined with Lorenz curves were used to analyse the current status of ICU bed allocation by both population and service area. The spatial heterogeneity and aggregation of ICU bed density were analysed using the Global Moran's index. The spatial distribution pattern was visualized via LISA maps using the Local Moran's index. Three grey correlation models were constructed to assess the key factors influencing ICU bed density. Finally, robustness analysis was performed to test the reliability of the results.

RESULTS

The allocation of ICU beds in China was highly inequitable by service area (Gini = 0.68) and showed better balance by population distribution (Gini = 0.14). The distribution of ICU beds by service area was highly spatially clustered (Global Moran's I = 0.22). The bed utilization rate exhibited the strongest association with ICU bed density by population. Registered nurses per 10,000 square kilometres was the strongest factor affecting ICU bed density by service area.

CONCLUSIONS

The allocation of ICU beds by population is better than by service area; the allocation by service area is less equitable in China. These findings emphasise the need to implement better measures to reduce ICU bed equity differences between regions and balance and coordinate medical resources. Service area size, bed utilization, the number of registered nurses and other key factors should be considered when performing regional health planning for ICU bed supply. This will increase the equitable access to critical medical services for all populations.

Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020-2022 pandemic

The gap between the reported and actual COVID-19 infection cases has been an issue of concern. Here, we present Bayesian hierarchical spatiotemporal disease mapping models for state-level predictions of COVID-19 infection risks and (under)reporting rates among people aged 65 and above during the first two years of the pandemic in the United States. With prior elicitation based on recent prevalence studies, the study suggests that the median state-level reporting rate of COVID-19 infection was 90% (interquartile range: [78%, 96%]). Our study uncovers spatiotemporal variations and dynamics in state-level infection risks and (under)reporting rates, suggesting time-varying associations between higher population density, higher percentage of minorities, and higher percentage of vaccination and increased risks of COVID-19 infection, as well as an association between more easily accessible tests and higher reporting rates. With sensitivity analyses, we highlight the impact and importance of incorporating covariates information and objective prior references for evaluating the issue of underreporting.

JAGS model specification for spatiotemporal epidemiological modelling

Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (BUGS) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of BUGS to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (JAGS) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling.

Global Mapping and Visualization Analysis of One Health Knowledge in the COVID-19 Context

Globally, the COVID-19 pandemic had a significant impact on the health, social, and economic systems, triggering lasting damage and exposing the complexity of the problem beyond just being a health emergency. This crisis has highlighted the need for a comprehensive and collaborative strategy to successfully counter infectious diseases and other global challenges. With the COVID-19 pandemic pushing One Health to the forefront of global health and sustainable development agendas, this concept has emerged as a potential approach for addressing these challenges. In the context of COVID-19, this study investigates global knowledge about One Health by examining its state, significant contributions, and future directions. It seeks to offer an integrated framework of insights guiding the development of well-informed decisions. A comprehensive search using the Scopus database was conducted, employing specific terms related to One Health and COVID-19. VOSviewer 1.6.19 software was used to generate network visualization maps. Countries' research output was adjusted based on their gross domestic product (GDP) and population size. The study identified a total of 527 publications. The United States led with 134 documents (25.4%), but India topped the adjusted ranking. One Health journal stood as the most common outlet for disseminating knowledge (49 documents; 9.3%), while Centers for Disease Control and Prevention (CDC), the United States emerged as the most prolific institution (13 documents; 2.5%). Key topics were related to the virus transmission mechanisms, climate change impacts, antimicrobial resistance, ecosystem health, preparedness, collaboration, community engagement, and developing of efficient surveillance systems. The study emphasizes how critical it is to capitalize on the present momentum of COVID-19 to advance One Health concepts. Integrating social and environmental sciences, and a variety of professions for better interaction and collaboration is crucial. Additionally, increased funding for developing countries, and legislative empowerment are vital to advance One Health and boost disease prevention.

Spatiotemporal Bayesian estimation of the number of under-reported COVID-19 cases in Victoria Australia

Having an estimate of the number of under-reported cases is crucial in determining the true burden of a disease. In the COVID-19 pandemic, there is a great need to quantify the true disease burden by capturing the true incidence rate to establish appropriate measures and strategies to combat the disease. This study investigates the under-reporting of COVID-19 cases in Victoria, Australia, during the third wave of the pandemic as a result of variation in geographic area and time. It is aimed to determine potential under-reported areas and generate the true picture of the disease in terms of the number of cases. A two-tiered Bayesian hierarchical model approach is employed to estimate the true incidence and detection rates through Bayesian model averaging. The proposed model goes beyond testing inequality across areas by looking into other covariates such as weather, vaccination rates, and access to vaccination and testing centres, including interactions and variations between space and time. This model aims for parsimony yet allows a broader range of scope to capture the underlying dynamic of the reported COVID-19 cases. Moreover, it is a data-driven, flexible, and generalisable model to a global context such as cross-country estimation and across time points under strict pandemic conditions.