Search

Due to the COVID-19 pandemic, the pressure on healthcare systems has been tremendous and it is having a huge economic impact. The objective of this paper is to carry out a systematic literature review of the publications that have analysed the costs derived from COVID-19 from the patient's perspective. Not only is this study aimed at measuring the impact of COVID-19 in economic terms, but also in qualitative terms, analysing, for instance, the types of costs (direct and indirect) and their variations depending on the geographical area under study. Searches were conducted in PubMed, Cochrane Library, Web of Science and Scopus. The time frame for the analysis was from the start of the pandemic until 9th December 2021. A total of 322 papers were found. The number of articles assessed for eligibility was 32, and after applying the exclusion criteria, 13 papers were included in the qualitative synthesis. Most of the studies analysed only direct costs (69.23%) and were focused on Asia (61.54%), where the larger indirect costs, as well as the greater total costs, were found. However, the higher costs per patient and year were shown for the United States. This investigation showed the importance of COVID-19 in national, regional and local budgets. More studies are to be developed in Europe due to both the existing differences in the health care systems and financing by country and the difference in the incidence of COVID-19 by country and wave., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However, interpretation of resistance from genotypic data poses a major challenge., Methods: As an alternative to current interpretation systems, we have developed artificial neural network (ANN) models to predict virological response to combination therapy from HIV genotype and other clinical information., Results: ANN models trained with genotype, baseline viral load and time to follow-up viral load (1154 treatment change episodes from multiple clinics), produced predictions of virological response that were highly significantly correlated with actual responses (r2 = 0.53; P < 0.00001) using independent test data from clinics that contributed training data. Augmented models, trained with the additional variables of baseline CD4+ T-cell count and four treatment history variables, were more accurate, explaining 69% of the variance in virological response. Models trained with the full input dataset, but only those data involving highly active antiretroviral therapy (three or more full-dose antiretroviral drugs in combination), performed at an intermediate level, explaining 61% of the variance. The augmented models performed less well when tested with data from unfamiliar clinics that had not contributed data to the training dataset, explaining 46% of the variance in response., Conclusion: These data indicate that ANN models can be quite accurate predictors of virological response to HIV therapy even for patients from unfamiliar clinics. ANN models therefore warrant further development as a potential tool to aid treatment selection.