The aim of the study was to investigate mental health and conspiracy theory beliefs concerning COVID-19 among health care professionals (HCPs).

During lockdown, an online questionnaire gathered data from 507 HCPs (432 females aged 33.86 ± 8.63 and 75 males aged 39.09 ± 9.54).

A post-stratification method to transform the study sample was used; descriptive statistics were calculated.

Anxiety and probable depression were increased 1.5–2-fold and were higher in females and nurses. Previous history of depression was the main risk factor. The rates of believing in conspiracy theories concerning the COVID-19 were alarming with the majority of individuals (especially females) following some theory to at least some extend.

The current paper reports high rates of depression, distress and suicidal thoughts in the HCPs during the lockdown, with a high prevalence of beliefs in conspiracy theories. Female gender and previous history of depression acted as risk factors, while the belief in conspiracy theories might act as a protective factor. The results should be considered with caution due to the nature of the data (online survey on a self-selected but stratified sample).

Economic models play a central role in the decision-making process of the National Institute for Health and Care Excellence (NICE). Inadequate validation methods allow for errors to be included in economic models. These errors may alter the final recommendations and have a significant impact on outcomes for stakeholders.

To describe the patterns of technical errors found in NICE submissions and to provide an insight into the validation exercises carried out by the companies prior to submission.

All forty-one single technology appraisals (STAs) completed in 2017 by NICE were reviewed and all were on medicines. The frequency of errors and information on their type, magnitude, and impact was extracted from publicly available NICE documentation along with the details of model validation methods used.

Two STAs (5 percent) had no reported errors, nineteen (46 percent) had between one and four errors, sixteen (39 percent) had between five and nine errors, and four (10 percent) had more than ten errors. The most common errors were transcription errors (29 percent), logic errors (29 percent), and computational errors (25 percent). All STAs went through at least one type of validation. Moreover, errors that were notable enough were reported in the final appraisal document (FAD) in eight (20 percent) of the STAs assessed but each of these eight STAs received positive recommendations.

Technical errors are common in the economic models submitted to NICE. Some errors were considered important enough to be reported in the FAD. Improvements are needed in the model development process to ensure technical errors are kept to a minimum.

To investigate the impact of the uncertainty stemming from products with European conditional marketing authorization (CMA) or authorization in exceptional circumstances (AEC) on the National Institute for Health and Care Excellence's (NICE) recommendations.

Products which received CMA/AEC by European Medicines Agency (EMA) up to 1 December 2016 were identified and matched with corresponding NICE decisions issued by August 2017, the status of which was then traced to August 2019. We assessed whether the conversion of CMA to full marketing authorization triggered a review of a NICE decision. The odds of a recommendation carrying a commercial arrangement for products with and without CMA/AEC were calculated.

Fifty-four products were granted CMA/AEC by EMA. NICE conducted thirty evaluations of products with CMA/AEC. Twelve products were recommended by NICE by August 2017 and fourteen by August 2019. All recommendations had an associated commercial arrangement. The odds of carrying a commercial arrangement were higher for products with CMA/AEC compared to those with full authorization. Conversions from conditional to full authorization among products not recommended by NICE did not trigger an appraisal review.

Uncertainty, stemming from the lack of robust clinical data of products authorized with CMA/AEC, has a substantial impact on HTA recommendations, frequently requiring risk mitigation mechanisms such as commercial and data collection arrangements. Further analyses should be conducted to assess whether the benefits of early access strategies outweigh the risks for patients and the healthcare system.