Comparing the Analysis and Results of a Modified Social Accounting Matrix Framework with Conventional Methods of Reporting Indirect Non-Medical Costs

Background Assessing the societal perspective in economic evaluations of new interventions requires estimates of indirect non-medical costs caused by the disease. Different methods exist for measuring the labor input function as a surrogate for these costs. They rarely specify the effect of health on labor and who gains and who loses money. Social accounting matrix (SAM) is an established framework that evaluates public policies with multiple perspectives that could help. Objectives We evaluated the use of a modified SAM to assess money flows between different economic agents resulting in economic transactions following policy changes of medical interventions. Methods We compared conventional methods of measuring indirect non-medical costs related to rotavirus vaccination in the Netherlands with a modified SAM framework. To compare the outcome of each method, we calculated returns on investment (ROI) as the net amount of money per euro invested in the vaccine. One-way and probabilistic sensitivity analyses were carried out for each method, focusing on critical variables with the largest impact on indirect cost estimates. Results The ROI was higher for the modified SAM (1.33) than for the conventional methods assessing income calculations (range - 0.178 to 1.22). Probabilistic sensitivity analyses showed wide distributions in the ROI estimates, with variation in the variable impact on the indirect cost results per method selected. Conclusions In contrast to conventional methods, the SAM approach provides detailed and comprehensive assessments of the impact of new interventions on the indirect non-medical costs and the financial interactions between agents, disclosing useful information for different stakeholders.</p

Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets.

Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results:  The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data

Modelling the synergistic effect of bacteriophage and antibiotics on bacteria: Killers and drivers of resistance evolution.

Bacteriophage (phage) are bacterial predators that can also spread antimicrobial resistance (AMR) genes between bacteria by generalised transduction. Phage are often present alongside antibiotics in the environment, yet evidence of their joint killing effect on bacteria is conflicted, and the dynamics of transduction in such systems are unknown. Here, we combine in vitro data and mathematical modelling to identify conditions where phage and antibiotics act in synergy to remove bacteria or drive AMR evolution. We adapt a published model of phage-bacteria dynamics, including transduction, to add the pharmacodynamics of erythromycin and tetracycline, parameterised from new in vitro data. We simulate a system where two strains of Staphylococcus aureus are present at stationary phase, each carrying either an erythromycin or tetracycline resistance gene, and where multidrug-resistant bacteria can be generated by transduction only. We determine rates of bacterial clearance and multidrug-resistant bacteria appearance, when either or both antibiotics and phage are present at varying timings and concentrations. Although phage and antibiotics act in synergy to kill bacteria, by reducing bacterial growth antibiotics reduce phage production. A low concentration of phage introduced shortly after antibiotics fails to replicate and exert a strong killing pressure on bacteria, instead generating multidrug-resistant bacteria by transduction which are then selected for by the antibiotics. Multidrug-resistant bacteria numbers were highest when antibiotics and phage were introduced simultaneously. The interaction between phage and antibiotics leads to a trade-off between a slower clearing rate of bacteria (if antibiotics are added before phage), and a higher risk of multidrug-resistance evolution (if phage are added before antibiotics), exacerbated by low concentrations of phage or antibiotics. Our results form hypotheses to guide future experimental and clinical work on the impact of phage on AMR evolution, notably for studies of phage therapy which should investigate varying timings and concentrations of phage and antibiotics

What settings have been linked to SARS-CoV-2 transmission clusters?

Background: Concern about the health impact of novel coronavirus SARS-CoV-2 has resulted in widespread enforced reductions in people's movement ("lockdowns"). However, there are increasing concerns about the severe economic and wider societal consequences of these measures. Some countries have begun to lift some of the rules on physical distancing in a stepwise manner, with differences in what these "exit strategies" entail and their timeframes. The aim of this work was to inform such exit strategies by exploring the types of indoor and outdoor settings where transmission of SARS-CoV-2 has been reported to occur and result in clusters of cases. Identifying potential settings that result in transmission clusters allows these to be kept under close surveillance and/or to remain closed as part of strategies that aim to avoid a resurgence in transmission following the lifting of lockdown measures. Methods: We performed a systematic review of available literature and media reports to find settings reported in peer reviewed articles and media with these characteristics. These sources are curated and made available in an editable online database. Results: We found many examples of SARS-CoV-2 clusters linked to a wide range of mostly indoor settings. Few reports came from schools, many from households, and an increasing number were reported in hospitals and elderly care settings across Europe. Conclusions: We identified possible places that are linked to clusters of COVID-19 cases and could be closely monitored and/or remain closed in the first instance following the progressive removal of lockdown restrictions. However, in part due to the limits in surveillance capacities in many settings, the gathering of information such as cluster sizes and attack rates is limited in several ways: inherent recall bias, biased media reporting and missing data

Antimicrobial resistance and COVID-19: Intersections and implications

Before the coronavirus 2019 (COVID-19) pandemic began, antimicrobial resistance (AMR) was among the top priorities for global public health. Already a complex challenge, AMR now needs to be addressed in a changing healthcare landscape. Here, we analyse how changes due to COVID-19 in terms of antimicrobial usage, infection prevention, and health systems affect the emergence, transmission, and burden of AMR. Increased hand hygiene, decreased international travel, and decreased elective hospital procedures may reduce AMR pathogen selection and spread in the short term. However, the opposite effects may be seen if antibiotics are more widely used as standard healthcare pathways break down. Over 6 months into the COVID-19 pandemic, the dynamics of AMR remain uncertain. We call for the AMR community to keep a global perspective while designing finely tuned surveillance and research to continue to improve our preparedness and response to these intersecting public health challenges

Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England.

The National Health Service (NHS) Pathways triage system collates data on enquiries to 111 and 999 services in England. Since the 18th of March 2020, these data have been made publically available for potential COVID-19 symptoms self-reported by members of the public. Trends in such reports over time are likely to reflect behaviour of the ongoing epidemic within the wider community, potentially capturing valuable information across a broader severity profile of cases than hospital admission data. We present a fully reproducible analysis of temporal trends in NHS Pathways reports until 14th May 2020, nationally and regionally, and demonstrate that rates of growth/decline and effective reproduction number estimated from these data may be useful in monitoring transmission. This is a particularly pressing issue as lockdown restrictions begin to be lifted and evidence of disease resurgence must be constantly reassessed. We further assess the correlation between NHS Pathways reports and a publicly available NHS dataset of COVID-19-associated deaths in England, finding that enquiries to 111/999 were strongly associated with daily deaths reported 16 days later. Our results highlight the potential of NHS Pathways as the basis of an early warning system. However, this dataset relies on self-reported symptoms, which are at risk of being severely biased. Further detailed work is therefore necessary to investigate potential behavioural issues which might otherwise explain our conclusions

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from &lt;5% of those younger than 20 years to &gt;66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from &lt;1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds. Funding: UK Department for International Development, Wellcome Trust, Health Data Research UK, Medical Research Council, and National Institute for Health Research

Nuclear Receptor HNF4α Binding Sequences are Widespread in Alu Repeats

<p>Abstract</p> <p>Background</p> <p>Alu repeats, which account for ~10% of the human genome, were originally considered to be junk DNA. Recent studies, however, suggest that they may contain transcription factor binding sites and hence possibly play a role in regulating gene expression.</p> <p>Results</p> <p>Here, we show that binding sites for a highly conserved member of the nuclear receptor superfamily of ligand-dependent transcription factors, hepatocyte nuclear factor 4alpha (HNF4α, NR2A1), are highly prevalent in Alu repeats. We employ high throughput protein binding microarrays (PBMs) to show that HNF4α binds > 66 unique sequences in Alu repeats that are present in ~1.2 million locations in the human genome. We use chromatin immunoprecipitation (ChIP) to demonstrate that HNF4α binds Alu elements in the promoters of target genes (<it>ABCC3, APOA4, APOM, ATPIF1, CANX, FEMT1A, GSTM4, IL32, IP6K2, PRLR, PRODH2, SOCS2, TTR</it>) and luciferase assays to show that at least some of those Alu elements can modulate HNF4α-mediated transactivation <it>in vivo </it>(<it>APOM, PRODH2, TTR, APOA4</it>). HNF4α-Alu elements are enriched in promoters of genes involved in RNA processing and a sizeable fraction are in regions of accessible chromatin. Comparative genomics analysis suggests that there may have been a gain in HNF4α binding sites in Alu elements during evolution and that non Alu repeats, such as Tiggers, also contain HNF4α sites.</p> <p>Conclusions</p> <p>Our findings suggest that HNF4α, in addition to regulating gene expression via high affinity binding sites, may also modulate transcription via low affinity sites in Alu repeats.</p