Background Outbreaks of infectious diseases generate outbreaks of scientific evidence. In 2016 epidemics of Zika virus emerged, and in 2020, a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic of coronavirus disease 2019 (COVID-19). We compared patterns of scientific publications for the two infections to analyse the evolution of the evidence. Methods We annotated publications on Zika virus and SARS-CoV-2 that we collected using living evidence databases according to study design. We used descriptive statistics to categorise and compare study designs over time. Results We found 2286 publications about Zika virus in 2016 and 21,990 about SARS-CoV-2 up to 24 May 2020, of which we analysed a random sample of 5294 (24%). For both infections, there were more epidemiological than laboratory science studies. Amongst epidemiological studies for both infections, case reports, case series and cross-sectional studies emerged first, cohort and case-control studies were published later. Trials were the last to emerge. The number of preprints was much higher for SARS-CoV-2 than for Zika virus. Conclusions Similarities in the overall pattern of publications might be generalizable, whereas differences are compatible with differences in the characteristics of a disease. Understanding how evidence accumulates during disease outbreaks helps us understand which types of public health questions we can answer and when.
Aim: The current study estimates the societal impact of early intensified treatment compared with initial monotherapy with subsequent treatment intensification in newly diagnosed adults with type 2 diabetes mellitus in Mexico. Methods: An individual patient-level simulation and a static cohort model were employed to simulate the treatment pathway and the probability of experiencing complications of diabetes. The avoided number of events was translated into avoided productivity losses, which were monetized using wages. Results: Patients on early intensified treatment experienced approximately 13,000 fewer complication events over 10 years. This was translated into a societal impact of $54 million (USD). Conclusion: Early treatment intensification is likely to be of particular benefit to health outcomes and productivity losses.
SummaryBackgroundOutbreaks of infectious diseases generate outbreaks of scientific evidence. In 2016 epidemics of Zika virus emerged, largely in Latin America and the Caribbean. In 2020, a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic of coronavirus disease 2019 (COVID-19). We compared patterns of scientific publications for the two infections over time.MethodsWe used living systematic review methods to search for and annotate publications according to study design. For Zika virus, a review team performed the tasks for publications in 2016. For SARS-CoV-2, a crowd of 25 volunteer scientists performed the tasks for publications up to May 24, 2020. We used descriptive statistics to categorise and compare study designs over time.FindingsWe found 2,286 publications about Zika virus in 2016 and 21,990 about SARS-CoV-2 up to 24 May 2020, of which we analysed a random sample of 5294. For both infections, there were more epidemiological than laboratory science studies. Amongst epidemiological studies for both infections, case reports, case series and cross-sectional studies emerged first, cohort and case-control studies were published later. Trials were the last to emerge. Mathematical modelling studies were more common in SARS-CoV-2 research. The number of preprints was much higher for SARS-CoV-2 than for Zika virus.InterpretationSimilarities in the overall pattern of publications might be generalizable, whereas differences are compatible with differences in the characteristics of a disease. Understanding how evidence accumulates during disease outbreaks helps us understand which types of public health questions we can answer and when.FundingMJC and HI are funded by the Swiss National Science Foundation (SNF grant number 176233). NL acknowledges funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (grant agreement number 101003688). DBG is funded by the Swiss government excellence scholarship (2019.0774) and the Swiss School of Public Health Global P3HS.
Objectives Moderate to severe plaque psoriasis is a chronic inflammatory disease. In Germany, guidelines recommend fumaric acid esters (FAEs) as first-line systemic treatment. Despite treatment with FAEs, disease burden remains high in Germany. Secukinumab, a fully human monoclonal antibody, has demonstrated greater efficacy and safety than FAEs in the PRIME trial. The aim of the current study, hence, is to quantify the potential societal economic impact of secukinumab in systemic treatment-naïve patients with moderate to severe plaque psoriasis in Germany. Methods We employed a semi-Markov model to capture health gains at an individual level and a dynamic population model to extrapolate the findings in the population of interest. We quantified the health outcomes in two scenarios: (i) patients receiving secukinumab and (ii) patients receiving FAEs. Using estimates on change in work productivity and societal economic parameters, we translated the health outcomes into paid and unpaid economic gains. We used gross value added (GVA) to value these gains and calculated the macroeconomic indirect and induced value-chain effects. Results Our calculations show that patients treated with secukinumab spend on average 94% of their time in Psoriasis Area and Severity Index (PASI) ≥ 75 state compared with 80% for patients in the FAEs scenario. When assuming that FAEs are the sole comparator to secukinumab, this difference could lead to 4.3 million active hours gained until 2030. These gained hours translate to a total societal economic impact of €308 million till 2030 for the whole patient population in GVA terms. Conclusion This study demonstrated that using secukinumab instead of FAEs in moderate to severe plaque psoriasis could lead to substantial macroeconomic GVA gains.
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