Can wage changes solve the labour crisis in the National Health Service?

This study aimed to examine the healthcare labour demand and supply elasticity regarding wage in the National Health Service (NHS) in England amid a labour crisis. A simultaneous error-correction regression analysis was conducted using secondary data from the NHS and Office for National Statistics from 2009 Q3 to 2022 Q1. Findings indicate both labour demand and supply of HCHS doctors in the NHS are highly inelastic with respect to real wages, with only a 0.

Individuals with long-term illness, disability or infirmity are more likely to smoke than healthy controls: An instrumental variable analysis.

Despite the prevalence of smoking cessation programs and public health campaigns, individuals with long-term illness, disability, or infirmity have been found to smoke more often than those without such conditions, leading to worsening health. However, the available literature has mainly focused on the association between long-term illness and smoking, which might suffer from the possible bidirectional influence, while few studies have examined the potential causal effect of long-term illness on smoking. This gap in knowledge can be addressed using an instrumental variable analysis that uses a third variable as an instrument between the endogenous independent and dependent variables and allows the identification of the direction of causality under the discussed assumptions.

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US.

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest.