Colorectal cancer (CRC) care costs the Australian healthcare system more than any other cancer. We estimated costs and days in hospital for CRC cases, stratified by site  and disease stage, to inform detailed analyses of CRC-related healthcare.Incident CRC patients were identified using the Australian 45 and Up Study cohort linked with cancer registry records. We analysed linked hospital admission records, emergency department records, and reimbursement records for government-subsidised medical services and prescription medicines. Cases’ health system costs  and hospital days were compared with those for cancer-free controls  to estimate excess resources by phase of care, analysed by sociodemographic, health, and disease characteristics.1200 colon and 546 rectal cancer cases were diagnosed 2006–2013, and followed up to June 2016. Eighty-nine percent of cases had surgery, chemotherapy or radiotherapy, and excess costs were predominantly for hospitalisations. Initial phase (12 months post-diagnosis) mean excess health system costs were $50,434 for colon and $60,877 for rectal cancer cases, with means of 16 and 18.5 excess hospital days, respectively. The annual continuing mean excess costs were $6,779 (colon) and $8,336 , with a mean of 2 excess hospital days each. Resources utilised (costs and days) in these phases increased with more advanced disease, comorbidities, and younger age. Mean excess costs in the year before death were $74,952 (colon) and $67,733 , with means of 34 and 30 excess hospital days, respectively–resources utilised were similar across all characteristics, apart from lower costs for cases aged ≥75 at diagnosis.Health system costs and hospital utilisation for CRC care are greater for people with more advanced disease. These findings provide a benchmark, and will help inform future cost-effectiveness analyses of potential approaches to CRC screening and treatment. Colorectal cancer (CRC) has the third highest incidence of all cancers worldwide, with over 1.9 million new cases in 2020 , 4. CRC Australia has a government-funded universal healthcare system for all permanent residents, along with a private healthcare system largely paid for by patients and health insurance providers. There is also a government-funded National Bowel Cancer Screening Program (NBCSP), which aims to reduce the morbidity and mortality from CRC , 7. The A more nuanced characterisation of CRC costs and the way in which they are incurred in Australia is crucial for further economic evaluations of CRC prevention and control strategies. This will inform future analyses of CRC-related healthcare needs, including cost-effectiveness analyses of new approaches to CRC screening and treatment. Past studies have considered costs incurred by specific treatments –16, and In the current study we aimed to estimate health system costs and days spent in hospital by disease stage at diagnosis for individuals with colon or rectal cancer in New South Wales (NSW), Australia’s most populous state. We used detailed, person-level information to generate population-based estimates, analysing administrative health records linked with self-reported data from a large prospective cohort study of NSW residents. Costs and hospital days were estimated by phase of care and year/month around diagnosis and death, compared with those for matched cancer-free controls and disaggregated into the types of health services utilised. We also aimed to disaggregate the estimates using several characteristics potentially related to health services use and/or CRC incidence, such as age, sex, health insurance status and accessibility of services.The source population was all 267,153 participants in the Sax Institute’s NSW 45 and Up Study. A detailed description of the study cohort has been provided previously ; in brieThe linked health records included reimbursements for government-subsidised prescription medicines in the Pharmaceutical Benefits Scheme (PBS), and outpatient and medical services , and some in-hospital procedures subsidised through the Medicare Benefits Schedule (MBS). The Sax Institute linked these records for study participants using a unique identifier. The Centre for Health Record Linkage  probabilWe excluded participants with see :probable linkage errors and those aged <45 years at baseline.a NSWCR record of another invasive cancer prior to the incident CRC.self-reported history of cancer at baseline (except keratinocyte cancers).healthcare subsidised by the Australian Government Department of Veterans’ Affairs (DVA), as their prescription medicines are recorded in a different billing system not available for this study. DVA clients were identified via self-report or any mention of DVA coverage in hospital or ED records.unknown day/month of diagnosis in NSWCR, or CRC first recorded on their death certificate.We identified cases as participants with a NSW Cancer Registry record of incident CRC after recruitment, up until December 2013. Cases were identified using topography codes C18 (colon) and C19-C20  from the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10).Cases were matched to controls who had no NSWCR or self-reported record of cancer at any time and who were alive at the diagnosis date of their matched case. Up to four controls were matched to each case by age (±5 years), sex , Local Government Area of residence (153 areas in NSW) and smoking status . This is an extension of the matching used in previous studies that estimated excess costs by age, sex and some geographical areas –21. InfoDirect health system costs  from the MBS and PBS databases were identified from the costs listed in individual claims records. Inpatient hospital costs were derived from APDC records by linking the Australian Refined Diagnostic Related Group (AR-DRG) code for each hospitalisation to the average public hospital admission cost recorded in the 2010 National Hospital Cost Data Collection . From JuCosts and hospital days were estimated for three phases of care: initial, continuing, and terminal. The phases for each case-control group were assigned based on the case’s diagnosis date, death date and the end of follow-up as follows:For cases who died before July 2016, the final year up to death was designated the terminal phase. If the case died ≤1 year after diagnosis, the terminal phase started at the diagnosis date and no costs/hospital days were attributed to other phases.For cases who survived >1 year but ≤2 years, the initial phase was the period from diagnosis until the start of the 12-month terminal phase.For cases who survived at least two years, the first year after diagnosis was designated the initial phase, and the continuing phase was the period between the end of the initial phase and the start of the terminal phase or 30 June 2016. For cases who died during July 2016—June 2017, the continuing phase ended one year prior to their death. Costs and hospital days for the continuing phase were annualised, and we did not include case-control groups with a continuing phase of <3 months (2% of groups).We also estimated the annual costs and hospital days for each 12-month period around the case’s diagnosis date, from 2 years pre-diagnosis to 5 years post-diagnosis, along with monthly costs and hospital days from 3 months pre-diagnosis to 6 months post-diagnosis. For cases who died, we estimated the monthly excess end-of-life costs and hospital days for the last 6 months relative to their death date. The results for each of these time periods are presented to allow for use in future modelling studies, and to highlight the key time periods for resource use.The excess costs and hospital days due to CRC were estimated for each case by taking their total costs and hospital days and subtracting the average costs and days for their matched controls. If the case was still alive and any of their matched controls died, the case and their remaining matched control(s) were included in any subsequent calculations, or they were censored if there were no remaining matched controls Fig A in . If the 2), overweight (25 to <30), obese (≥30)), comorbidities based on a modified Charlson Comorbidity Index Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: No**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Thank you for the opportunity to review this excellent paper. The study uses a case-control design to investigate mean excess costs and days in hospital associated with a colon or rectal cancer diagnosis. The writing is very clear, methodology sound, and there are some interesting findings. The work is presented mainly as a preliminary paper to assist later modelling studies of cancer screening/prevention or treatment initiatives.There are several analyses / tables presented, and I wondered about the rationale for looking at costs and hospital days in each of these ways – the 3-phase approach, the yearly 2-years pre-diagnosis to 5-years post-diagnosis data, as well as 3-month pre-diagnosis to 6-months post-diagnosis?For an international audience, it would be good to clarify briefly what kind of services (relevant to CRC) are provided by data from MBS, e.g. should note this includes GP attendance.The rationale for choosing socio-demographic/clinical factors associated with cost/days was not very well articulated. Results were then a little difficult to determine what was expected vs unexpected findings.It was helpful to see data not just on mean costs/hospital days, but also median/IQR and SD. Shows some pretty substantial variation, including into negative costs/days at some timeframes, vs controls (Table C) and suggests some cases – possibly a relatively small number – influencing costs. If there is room, it could be helpful to comment on this as it may be relevant for service providers to consider when organising CRC care.Interesting findings regarding insurance status – it is suggested this could be due to different behaviours of people who have insurance, though services use might be related to how care is provided by public/private care providers.Reviewer #2: Thank you for the opportunity to review this well considered manuscript. This will undoubtedly contribute to evaluations of screening and CRC management in Australia in the coming years.The matched controls are described as'matched by age, sex, geography, smoking' whilst the latter two variables offer some measure of correction for risk factors please clarify any precedents for this approach and acknowledge any potential shortcomings i.e. the limitation of obesity, alcohol consumption, or other SES related factors as known risk factors for CRC - This is pertinent since the subsequent stratification uses obesity as a classifier.Line 165 onward'This left 1% of admissions without an AR-DRG – almost all >2 years after diagnosis – and these were assigned a cost of $1500 for the first day (compared with single-day costs of ~$2200 for a colonoscopy and ~$1000 for rehabilitation) and $300 per additional day. A sensitivity test using double these assigned costs made <1% difference to the results.'This comes across a little unclear? What do you mean? Is it that an alternative A or B costing was compared ... or that speculatively that a colonoscopy would be the rationale for any admission during this time? if so why ? Surely after a defined time frame it would not be an accepted standard that just because someone had a CRC that a scope would be indicated during an admission... surely a CT might be the first test of preference clinically?Having read on it appeared this was a comparison ... therefore please frame the intro to the preceding sentence with the word 'either' and consider providing a rationale as to why a colonoscopy is the default test of preference at such a time point - in the absence of specific GI symptoms it would be more clinically likely that a CT would be ordered first as an inpatient if Hx of CRC were noted and the presenting condition thought in any way connected - ie they would want to exclude mets, rather than per se to re-examine the bowel itself +/- the chances that the individual had a rectal tumour and stoma placement etc...Statistical analysis line 197It might be useful if you had a small diagram to show thisegCase -----------------C1 --------XC2 ----------------XC3 ----------------or something similarCase -----XC1 -----X------------C2 -----X------------C3 -----X------------Line 207'Hospitalisations could span multiple time periods, so costs were apportioned across time periods using the proportion of days in each time period out of the total length of stay'This is not super clear what that means... ? as in if a patient had a 3 week inpt stay which crossed monthly cut points? or straddled a given year end? It would be helpful to clarify (based on the assumption that this work may form the basis for future updates of the work.)Line 211How does the defined staging system compare to TNM Stage 1-4 ? and will that be useful for future work where the treatment indications are not by this staging system? Was TNM not available?Table 1I would assume the layout will differ in print... but having the title layout not cross / doesn't hang over multiple pages makes it hard to read (at 6pm on a Friday...)RE Private insurance coverage - How would this compare to the general Australian population? is that an expected level of private coverage.4th paragraph in the Discussion on age (last sentence)'A substantially smaller proportion of participants aged ≥75 years received chemotherapy or rectal surgery than was observed for those aged <75, and older participants may have had less diagnostic work-up'How is this statement supported by the data... are they more represented in the stage unknown? just confused here, one can't be both staged and then have less diagnostic work up?They would have less surgical assessments, less ongoing CT monitoring, less ongoing blood tests ... follow up of care still in the time frame of your categorization of initial care ... but they would still have similar diagnostics to determine stage?6th paragraph'Compared with current smokers, ex-smokers who quit >15 years earlier had fewer hospital days during the initial and continuing phases. ' Please provide an effect size, or direct the reader to the section in the supplementary information.In limitations you state'We did not include all possible resource-related information, with missing information on some non-admitted hospital costs (e.g. community-based care), treatment that may have been supplied by access programs (38), and numbers of hospital days did not include outpatient (non-admitted) procedures'Have you clarified if colonoscopy is actually an outpatient procedure (as would be standard in the UK) - if so the attribution of a scope above and the $2200 shortfall should be acknowledged in the use of these costs for future researchers modelling as you suggest - clarification here would therefore be essential2nd last paragraph'important to note that our study focused on direct healthcare costs incurred after a cancer diagnosis. '.....Why then in the discussion did you differentiate / suggest that diagnostic work up in older patients was likely… you couldn’t therefore determine thatPerhaps I wasn’t fully alert above – but a clear statement in the methods should indicate that no diagnostic costs are included – or at least that all costs are incurred after diagnosis.Conclusions'The results from this analysis will inform more detailed evaluations of the impact of CRC control interventions through costs and hospital requirements, and extend existing evaluations (9-13) to reinforce the benefit of interventions through changes in hospital utilization.'Why are there references in the conclusion? Surely that’s for the discussion… but also you’re preempting the results of future work that hasn’t been done here…Focus on your own conclusions…'This will help identify the best use of future healthcare resources and determine cost-effective strategies to reduce the CRC burden. 'This is not a direct conclusion of this work, amend the previous sentence to consider some of the framing you wish… but otherwise move into the discussion.Supplementary Info reviewed - tracked comments enclosed - minor clarifications.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, AttachmentPONE-D-21-14030_reviewer.docxSubmitted filename: Click here for additional data file.AttachmentS1_File.docxSubmitted filename: Click here for additional data file. 15 Oct 2021(See attached file)AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 3 Nov 2021Health system costs and days in hospital for colorectal cancer patients in New South Wales, AustraliaPONE-D-21-14030R1Dear Dr. Goldsbury,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Anna Ugalde, PhDAcademic EditorPLOS ONEThank you for responding to the reviewers comments, I think this manuscript will be an important contribution to the journal and our understanding of hospital costs for colorectal cancer patients.Reviewers' comments: 17 Nov 2021PONE-D-21-14030R1 Health system costs and days in hospital for colorectal cancer patients in New South Wales, Australia Dear Dr. Goldsbury:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Anna Ugalde Academic EditorPLOS ONE


Many viruses, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human immunodeficiency virus (HIV), have a structure consisting of spikes protruding from an underlying spherical surface. Research in biological and colloidal sciences has revealed secrets of why spikes exist on virus surfaces. Specifically, the spikes favor virus attachment on surfaces via receptor-specific interactions (RSIs), mediate the membrane fusion, and determine or change viral tropism. The spikes also facilitate viruses to approach surfaces before attachment and subsequently escape back to the environment if RSIs do not occur . Therefore, virus spikes create the paradox of having a large capacity for binding with cells (high infectivity) and meanwhile great mobility in the environment. Such structure-function relationships have important implications for the fabrication of virus-like particles and analogous colloids  for applications such as the development of antiviral vaccines and drug delivery. Many viruses that pose a significant risk to public health have a structure consisting of spikes protruding from an underlying spherical surface, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza virus, and human immunodeficiency virus (HIV) , 2. ThesSpiked viruses invade their host cells by interaction of their spikes with molecules on surfaces of the cells. For example, spike proteins of coronaviruses can be divided into two important functional subunits, S1 subunits and the S2 domain  , 5. The The spikes not only play a critical role in attachment to host cell surfaces via receptor-specific interactions, as shown above, but they also facilitate the approach of viruses to cell surfaces before the attachment, as revealed by colloid science . SpecifikTs (where k is the Boltzmann constant and T is the absolute temperature) can essentially inhibit the attachment of particles to the surface by Brownian diffusion because the rate of attachment decreases exponentially with the magnitude of the energy barrier (kT) in order for it to be overcome by Brownian diffusion. Energy barriers between viruses and surfaces are commonly larger than 10 kTs under prevalent biological and environmental conditions  are commonly negatively charged under prevalent biological and environmental conditions , 11. In  barrier . The enenditions . Therefonditions .Surface roughness has been identified as a critical factor that facilitates particle attachment in a primary minimum , 14, 15.The primary minimum can even be completely eliminated when repulsion from the bulk particle surface overcomes the attraction between nanoscale asperities and the surface  4). In . In 4). in situ soil remediation, and drug delivery. For example, the receptor-binding domains in spike proteins of viruses  may serve as targets for developing vaccines to inhibit corresponding infectious diseases . If the RSIs do not occur, the viruses are readily released back into the environment because the spikes reduce the primary-minimum depth or the adhesion that acts on the viruses at surfaces. These findings have important implications for the development of novel technologies for virus disease control .


Health care costs of migraine constitute a major issue in health economics. Several publications analyzed health care costs for adult migraine patients, based on questionnaires or secondary  data. Although migraine often starts already in primary school age, data on migraine related costs in children is scarce. In this paper we aimed to assess the migraine-related health care costs in 6 to 11 year old children in Germany.n = 2597) were compared to a control group of 6 to 11 year old children without a headache diagnosis between 2013 and 2017 . The association of migraine and costs was modeled by generalized linear regression (Gamma regression) with adjustment for sex, age and comorbidities.Using claims data of a large German health insurer (BARMER), overall annual health care costs of 6 to 11 year old children with a diagnosis of migraine in 2017 . Excess costs directly related to migraine amounted to € 115. The remaining excess costs were related to comorbidities, which were more frequent in the migraine group. Mental and behavioural disorders constituted the most expensive comorbidity, accounting for € 105 of the € 400 annual excess costs in the migraine group.6 to 11 year old children with a migraine diagnosis cause significant direct and comorbidity related excess costs in the German health care system.The online version contains supplementary material available at 10.1186/s10194-021-01349-w. One reason for this is that the more expensive drugs for acute treatment (triptans) and all preventive drugs are only very rarely needed in young children and not recommended by the German guideline . Also, tHeadache may also produce costs related to special diagnostic tests, especially radiology and laboratory assessments. In the present study, these amounted to € 19 or 5% of the total excess costs in the migraine group. In the Eurolight project, diagnostic tests for migraine in adults amounted to € 19 or 22% of the total direct migraine related costs . The facMental and behavioural disorders), which was 13% points more common in the migraine group. High prevalence and significant excess costs of depression and anxiety have been reported previously, both in children and adolescents ,,3], indiAs we calculated migraine related costs in the year 2017 based on migraine diagnoses in 2017, costs might be underestimated because part of the children may have received their first migraine diagnosis only towards the end of 2017.It is also important to consider that the present study only included costs of children that received a ‘confirmed’ migraine diagnosis. Part of the children who suffer from migraine may not receive a migraine diagnosis because migraine characteristics are less specific in children compared with adults . FurtherFinally, although we included age and sex in all analyses and performed extensive analysis of the impact of comorbidities, there might be other important covariates that could not be considered in the present study. In contrast to the US, the German population is smaller and more homogeneous, and race and geographical origin are usually not included in this type of analysis. It should also be noted that all participants in the present study were insured with the same health insurance company, and therefore had the same access to healthcare.Direct health care costs for children were principally caused by visits to primary care physicians and specialists. Only a small part of the costs was due to prescription drugs . A large proportion of the costs turned out not to be directly related to migraine, but rather to the treatment of comorbidities associated with migraine, which therefore should be a focus of future research and treatment strategies. The fact that the largest proportion of migraine-related costs in children stemmed from visits to paediatricians and general practitioners shows that these are the most promising targets for implementation of migraine prevention strategies in children.Additional file 1: Table S1. Frequencies and relative risk of comorbidities by chapters of ICD-10-GM in the control and the migraine group.Additional file 2: Table S2. Costs of painkillers and other possibly headache-related drugs.Additional file 3: Table S3. Comparison of primary care physician and specialist raw costs in 2017 for control group and migraine group.Additional file 4: Table S4. Excess costs of specific diseases and disease related difference in migraine and control group. * G Diagnosis without ICD G43 and G44,** R diagnosis without ICD R51.


Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically. Rapid label-free detection of SARS-CoV-2 using phase imaging ) with computational specificity. Different virus types captured in SLIM image are detected and classified by 2D U-Net. The global impact of the disease on the healthcare systems and its socioeconomic ramifications are severe and, likely, long-lasting2. The prompt response and public health measures have proven effective in limiting the spread of the virus, decreasing the number of active cases, and ultimately the mortality rate3. Fast, accurate, and scalable testing has been recognized unanimously as crucial for mitigating the impact of COVID-19 and future pandemics4.COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which reached pandemic proportions in 20204. Briefly, nucleic acid tests (NATs) rely on the viral RNA being amplified via polymerase chain reaction (PCR) and are the most broadly used in the clinic today. NATs have been implemented on automated instruments and provide a result in several hours. Their accuracy may vary, with false-negative rates reported in the order of 30%5. Serological tests assess the patient’s response to the viral infection through proteins such as immunoglobulin G. The efficacy of these tests relies on prior knowledge about the patient’s immune status as well as potential previous exposures to other virus types. The accuracy of serological tests is very high when performed ~20 days after the infection or first symptoms, but may lead to high false-negative rates for early patients and false positives for patients previously exposed to other viruses4. Common antigen tests can be performed using nasopharyngeal swabs and yield results in less than one hour. These tests operate on detecting proteins associated with the SARS-CoV-2 virus (nucleocapsid or spike proteins) using lateral flow or enzyme-linked immunosorbent assay (ELISA) tests.Diagnostic test accuracy is characterized by the sensitivity, defined as the probability of a positive result in a diseased patient, and specificity, given by the probability of a negative result in a healthy patient. Furthermore, the negative predictive value represents the chance of an individual with a negative test to be disease-free and, conversely, the positive predictive value is the chance that a person with a positive test is infected. In addition to these accuracy metrics, throughput and cost are important for deploying testing at scale. Recently, Weissleder et al. have reviewed the current status of the COVID-19 diagnostic tests8, fluorescence imaging of labeled virus particles and detection through machine learning9, microfluidic immunoassays coupled with fluorescence detections10, etc. While these approaches represent advances in SARS-CoV-2 detection methodologies, they still require either labeling or addition of foreign particles/solutions for the detection of SARS-CoV-2.Recently, accelerated efforts have been devoted to developing alternative testing procedures. These alternative detection schemes involve the use of plasmonic biosensors11. This study is an important precursor of our method and shares important similarities as well as differences. While both are quantitative-phase imaging techniques, methods of bringing specificity are different. Reference 11 uses chemical specificity based on antibody–antigen-based binding of virus particles on the glass slide. Virus particles are then counted based on their size estimate through computational means. In our study, for the generation of ground-truth data, we used fluorescence-stained virus particles and then used deep neural network for detection and classification of different virus types present in the sample. Other label-free imaging modalities, such as interferometric scattering microscopy (iSCAT)13 have shown tremendous potential for the detection of diffraction-limited samples, like nanoparticles14 and viruses18.Holographic quantitative-phase imaging methods have been used for virus sensing and counting of HSV particlesHere, we present a new approach for SARS-CoV-2 detection, which relies on direct, label-free imaging of viral particles. We employed spatial light-interference microscopy (SLIM), a highly sensitive interferometric method, to image viruses deposited on a glass slide. Although individual viruses are below the diffraction limit of the microscope, the optical path-length information retrieved by SLIM unravels the nanoscale distribution of the refractive index associated with the individual and aggregated viral particles. We paired these data with deep-learning algorithms, specifically optimized for viral-particle detection and classification. Using fluorescence markers for specific virus tagging, we retrieved “ground truth” data by imaging the same field of view with both SLIM and epifluorescence. To emulate a more realistic application environment, we synthesized datasets where different virus types were “digitally mixed” onto the same SLIM image for deep-learning development and evaluation. Thus, in addition to SARS-CoV-2, we imaged H1N1, HAdV, and ZIKV. While a situation where a patient is exposed simultaneously to these four viruses is highly unlikely, we wanted to test it as a challenging task for our method and evaluate the specificity of our deep-learning model. Following the training process, we tested the convolutional neural network (CNN) on unseen samples, classifying one virus type vs. the rest. Our results indicated a 96% area under the receiver-operating characteristic curve for SARS-CoV-2, 99% for H1N1, 92% for HAdV, and 91% for ZIKV.This preclinical study demonstrates that sensitive imaging of unlabeled particles, paired with artificial intelligence (AI), can provide the foundation for a rapid, high-throughput, scalable test. The fact that the assay can be performed on the specimen placed on a glass slide allows for simple and fast sample collection, via, e.g., breath condensates. The image acquisition and inference take 100 ms in total, which means that the entire test, including specimen collection, can be performed within a minute. Throughput can be scaled up by borrowing engineering concepts from whole-slide scanners in digital pathology, where hundreds of slides can be automatically fed into the imaging instrument. As the specimen requires minimum preparation and the instrument can be made portable, in principle, the technology can be deployed as a point-of-care solution.The paper is structured as follows. First, we present the workflow for multimodal imaging and ground-truth data acquisition. Next, we describe the SLIM imaging system and its sensitivity to the nanoscale ultrastructure of viral particles. We show 3D tomograms of the four virus types, to illustrate the subtle texture difference that the instrument captures, which the AI tools exploit for classification. We describe the convolutional neural network, which is a version of U-Net optimized for this problem. Finally, we present the accuracy of classifying the four virus types. We end with a discussion of the next steps necessary to implement this technology as a reliable clinical testing solution.Figure 19, which have found broad applications in biomedicine31 due to their ability to image unlabeled, highly transparent structures. SLIM is implemented as an add-on module to an existing phase-contrast microscope and, in essence, controls rigorously the phase shift between the incident and scattered field emerging from the specimen33. We used a Nikon Eclipse Ti-inverted microscope outfitted with a SLIM module , which allows for fully automated data acquisition. The microscope objective pupil is relayed onto the surface of a phase-only spatial-light modulator (SLM), such that the phase shift between the incident and scattered light is controlled precisely . Using this operation, one can see that clumps of particles can be separated via deconvolution Fig. .36. Figure Resolution of our imaging system is approximately 335 nm . Following Rayleigh’s resolution criterion, two objects with separation less than the width of point spread function (PSF), cannot be fully resolved. The individual virus particles used in this study have an average diameter of less than 150 nm, which makes them subdiffraction objects for optical imaging. In order to push the resolution beyond the diffraction limit, we performed a deconvolution with the microscope’s PSF (Supplementary Information Section S3). To estimate the PSF, we identified the smallest spot in the images via a Matlab script. Using this PSF, the images were deblurred by employing the iterative Richardson–Lucy algorithm with total variation regularization (see Supplementary Information Section S3 for more details)p-values 1.35e-12, 8.84e-6 and 1.23e-5, respectively, demonstrates the statistical significance of the dry mass-density differences between SARS-CoV-2 and H1N1, HAdV, and ZIKV, respectively, obtained by applying Kruskal–Wallis test (in MATLAB) to single-virus data. These results indicate that dry mass density, which is incorporated into the SLIM data, is a marker that helps the machine-learning algorithm to detect SARS-CoV-2.One advantage of SLIM over fluorescence is the inherent ability to measure not only shape descriptors like diameter, orientation, circularity, etc., but also quantify the phase information associated with the sample, which can then be used to extract biophysical information, such as cell dry mass density. From the SLIM images, we extracted the total dry mass and surface dry mass density for each measured particle (see Supplementary Information Section S3 for details). We observed shifts in the dry mass density for different virus classes as shown in Fig. 38 and H1N139, hexagonal cross-section for HAdV40, and smoother surface of ZIKV42. These reconstructions suggest that signatures of structural information still exist in the diffraction-limited SLIM images, due to the nanoscale path-length sensitivity of SLIM. These subtle features help the machine-learning algorithm to successfully classify these particles.To get a better understanding of the viral particles, we performed a tomographic reconstruction of diffraction-limited SLIM, using the Amira (Thermo Scientific) software (see Supplementary Information Section S4 for details). The results are shown in Fig. To assess the structural differences on a large scale, we performed volumetric reconstructions of groups of particles. Movies The ultrastructure is less evident in the 2D images as its visibility is highly dependent on the orientation of the particle and focus during imaging. The effect of z-level slicing on the visibility of the ultrastructure is shown in Fig. We formulated the virus-detection task as a semantic segmentation problem: given an input SLIM image containing several virus particles, our model predicts a probability distribution for each pixel, denoting the chance of this pixel belonging to one of the five classes: background, SARS-CoV-2, H1N1, HAdV, and ZIKV. An argmax operation turns the model output into a class label for each pixel. As all our raw SLIM images were of pure-culture virus particles, we synthesized a new dataset via “digital mixing” for machine-learning development and evaluation (see Supplementary Information Section S7 for details).43. Our model was trained using the digitally mixed SLIM images as input and the corresponding segmentation maps as ground truth . We emulated a real-life scenario where the concentration and position of particles per sample can vary. So, each image in our digitally mixed dataset had between 2 and 8 particles of each virus type, resulting in between 8 and 32 virus particles in total. In this dataset, all 4 types of virus particles were randomly placed onto over 1600, 240 × 240 blank (background removed by segmentation) images (see Supplementary Information Sections S2 and S7 for more details of the procedure). We randomly selected around 1000 images for training and kept the remaining 564 SLIM images as the test dataset to evaluate our model. Similar as the first dataset, we enforced instance-level consistency on our model prediction via the same postprocessing step , Influenza A virus (H1N1) (ATCC® VR-1894™), Human adenovirus 2 (HAdV) (ATCC® VR-846™), and Zika ). HAdV and H1N1 were deactivated by UV. For fluorescence imaging, each virus solution was stained with Rhodamine B isothiocyanate, separately for each experiment. The Rhodamine B isothiocyanate (RBITC) can target any protein through the binding between isothiocyanate and amine group on the protein. Since the virus particles have a protein shell, it is effective to use RBITC to label them. Dialysis was carried out to remove unbound fluorophores from the stained solution. Stained virus sample was dropped on a glass slide, fixed with 90% ethyl alcohol, and air-dried (more information in Supplementary Information Section S1).The viruses used in this study are: Heat-inactivated SARS-CoV-2 . Images were acquired with Nikon Plan-Apo 100x/1.45, phase-contrast oil objective. Exposure was kept at 30 ms and 200 ms for SLIM and fluorescence, respectively. For 3D reconstructions, we acquired a z-scan passing through focus, with a step size of 5 nm for the SLIM channel only. After the image acquisition, offline processing involved image registration of SLIM and fluorescence through MATLAB (see Supplementary Information Section S2). For the first dataset, we extracted 48 × 48 crops from SLIM and fluorescence images. We then segmented SLIM images to prepare the masks, which served as labels for the corresponding virus type during automated classification. For the second dataset, we first segmented the SLIM and fluorescence images and then performed automatic cropping based on bounding-box information (more information in Supplementary Information Section S2).36. We first converted the phase map obtained from SLIM to complex field. This complex field was then used as an input to the algorithm. We derived an initial estimate for PSF from the images themselves, by choosing the smallest spot in the images. Utilizing the properties obtained from segmentation , we carried out quantitative analysis on single-virus particles using MATLAB (see Supplementary Information Section S3).We performed deconvolution using Richardson–Lucy iterative algorithm with total variation (TV) regularizationWe produced tomographic reconstructions using Amira software (Thermo Scientific). We cropped out single particles from the whole image and upsampled them by a factor of 10 with bilinear interpolation to remove pixelations. We then used Volren and Isosurface rendering to reconstruct volume and surface tomograms (see Supplementary Information Section S4) for each virus type.Calibration of spatial optical path-length sensitivity of our SLIM system was done by imaging sample-free area on a glass slide, as outlined in Supplementary Information Section S5. Our spatial optical path-length sensitivity was determined to be 0.7 nm.49 against a categorical cross-entropy loss function. During evaluation, we found that in some cases, our model inferred more than 1 label for different parts of the same particle. To enforce instance-level consistency onto our model prediction, we performed a postprocessing step via connected-component analysis to ensure that all pixels in each individual particle are predicted as one class. After this postprocessing step (see Supplementary Information Section S7), our model’s performance was summarized into a confusion matrix on over 10,000 virus particles from the test dataset for the second dataset.For both the first  and second (automatic selection without background) datasets, we prepared digitally mixed images to train and test our network. We placed single-cropped viruses from each class, randomly in a 240 × 240 image, in fixed concentration for the first dataset (five particles per class) and varying concentrations (2–8 particles per class per image) for the second dataset. It is to be emphasized here that the digital mixing provides no distinction between the viruses, it only serves as a ground truth for model training. During training, the model weights were updated using the Adam optimizerSupplementary InformationMovie S1Movie S2Movie S3Movie S4Movie S5


Cryptococcus neoformans and Cryptococcus gattii species complexes are the main etiological agents of cryptococcosis, a life-threatening fungal infection affecting mostly immunocompromised people, but also immunocompetent hosts or those with unrecognized risk factors. These encapsulated basidiomycete yeasts, which are widely distributed in the environment, are responsible for thousands of cases of pneumonia and meningoencephalitis globally. Despite important improvements in antifungal and antiretroviral therapy, cryptococcal meningitis, the main presentation of cryptococcosis, is still associated with high morbidity and mortality around the world and remains a significant clinical and economic burden in adults from many countries where there is a high HIV seroprevalence. Every year, about 250,000 people suffer from cryptococcal meningitis, with an estimation of more than 180,000 attributable deaths [C. neoformans and C. gattii, as well as on the epidemiology, clinical aspects, diagnosis, and treatment of cryptococcosis, both through reviews and original research articles.The members of the C. neoformans and C. gattii species complexes, which have long been recognized, and can be distinguished by the polysaccharide that makes up the capsule of these yeasts. However, by combining data from different genotyping studies and methodologies carried out in several laboratories from around the world, it was possible to initially identify four major molecular types in C. neoformans (VNI to VNIV) and four in C. gattii (VGI to VGIV). These molecular types have been extensively recovered not only from human clinical samples, but also from veterinary samples and environmental sources, some of them being responsible for outbreaks. More recently, by extending the molecular studies of the agents of cryptococcosis, three other molecular types, represented by fewer isolates and being more restricted geographically, were recognized. In C. neoformans, the molecular type VNB was reported in Botswana and later in Brazil [C. gattii the molecular type VGV was reported in environmental isolates in the Zambezian region [Currently, there are four serotypes  amongst the members of the n Brazil ,3, whilen region , and VGVn region ,6. Even n region , the issC. neoformans and C. gattii recovered worldwide from endemic areas, as well as from sporadic cases, have been sequenced. This has contributed to a better understanding of the origin, speciation, evolution, and diversification of these yeasts, which has allowed for the recognition of associations between genetic variants and virulence [C. neoformans and C. gattii species complexes are such successful pathogens.With the increasing affordability of whole genome sequencing (WGS), several genomes of strains of irulence . Using Wirulence ,9. This C. neoformans and C. gattii species complexes have been studied for several years to establish the relevance and role of these fungal factors and phenotypes in human disease. The capsule, melanin, and phospholipase activity are the most studied virulence factors as they are protective components against the attacks of the host immune system [C. neoformans and C. gattii [Pathogenesis and virulence factors in the e system . During . gattii ,11. NeveTherapy for cryptococcal infection is still a major challenge, and from the scarce arsenal of antifungal agents that exist, only three classes, namely polyenes, flucytosine, and azoles, are currently used to treat this mycosis . The comC. neoformans and C. gattii species complex as well as investigating their global spread. Even though these yeasts have served as a fungal model for more than 3 decades, new clinical and laboratory studies are needed to gain better insights into Cryptococcus and cryptococcosis. This special issue is planned to coincide with the 11th International Conference on Cryptococcus and Cryptococcosis, which is scheduled to be held in Kampala, Uganda, in January 2023. The community working on new studies related to these yeast pathogens and this mycosis are therefore welcome to contact the guest editors and submit their manuscripts to be considered for publication in this special issue “Recent Advances in Cryptococcus and Cryptococcosis”.Given the ongoing high global incidence and mortality from cryptococcal meningitis, not only in the rising HIV population, but also in solid organ transplant recipients, chemotherapy patients, other immunosuppressed hosts, and even in those immunocompetent, the cryptococcal species pose a significant risk to modern medicine . The eme


Hazardous chemicals like toxic organic dyes are very harmful to the environment and their removal is quite challenging. Therefore there is a necessity to develop techniques, which are environment friendly, cost-effective and easily available in nature for water purification and remediation. The present research work is focused on the development` and characterization of the ecofriendly semi-interpenetrating polymer network (semi-IPN) nanocomposite hydrogels composed of polyvinyl alcohol (PVA) and alginate (Alg) hydrogel beads incorporating natural bentonite (Bent) clay as a beneficial adsorbent for the removal of toxic methylene blue (MB) from aqueous solution. PVA−Alg/Bent nanocomposite hydrogel beads with different Bent content  were synthesized via external ionic gelation method. The designed porous and steady structure beads were characterized by the use of Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). The performance of the beads as MB adsorbents was investigated by treating aqueous solutions in batch mode. The experimental results indicated that the incorporation of Bent (30 wt%) in the nanocomposite formulation sustained the porous structure, preserved water uptake, and increased MB removal efficiency by 230% compared to empty beads. Designed beads possessed higher affinity to MB at high pH 8, 30 °C, and fitted well to pseudo-second-order kinetic model with a high correlation coefficient. Moreover, the designed beads had good stability and reusability as they exhibited excellent removal efficiency (90%) after six consecutive adsorption-desorption cycles. The adsorption process was found be combination of both monolayer adsorption on homogeneous surface and multilayer adsorption on heterogeneous surface. The maximum adsorption capacity of the designed beads system as calculated by Langmuir isotherm was found to be 51.34 mg/g, which is in good agreement with the reported clay-related adsorbents. The designed semi-IPN PVA−Alg/Bent nanocomposite hydrogel beads demonstrated good adsorbent properties and could be potentially used for MB removal from polluted water. Methylene blue (MB) is a well-known cationic phenothiazine dye, with versatile industrial applications in the textile, leather, plastics, paper and cosmetics industry and also used as a biological stain and photosensitizer in photodynamic therapy ,4,5,6,7.Various conventional physicochemical techniques have been proposed and introduced for the purification of toxic dyes from water from including photocatalysis, chemical oxidation, ozonation, membrane filtration, coagulation and flocculation, as well as adsorption ,15,16,17In recent years, semi-interpenetrating polymer network (semi-IPN) biodegradable hydrogels consisting of linear polymer chains of one component entangled with other crosslinked polymer network have attracted wide attention as adsorbents for removal of pollutants from industrial water . This is−) groups of Alg are able to attract cationic MB from aqueous solutions  and n are the Freundlich constants, which represent the adsorption capacity and adsorption intensity, respectively. For favorable and multilayer cooperative adsorption, 1/n value should be between 0 and 1 [The adsorption mechanisms were studied by Freundlich and Langmuir isotherm models. The Freundlich isotherm adsorption formula is expressed in Equation (4) ,51.(4)lmq (mg/g) is the maximum adsorption capacity, LK (L/mg) is the Langmuir constant related to the affinity of the binding sites, and LR is the constant separation factor. RL value between 0 and 1 states a favorable adsorption process [The Langmuir isotherm adsorption Equations (5) and (6) ,51 were  process .eq and tq (mg/g) are the amounts of MB adsorbed at equilibrium and at time t, respectively. K1 (1/min) and K2 (g/mg.min) are the rate constants of pseudo-first order and pseudo-second-order kinetics, respectively.Both pseudo-first and pseudo-second order kinetic models were adopted to estimate the adsorption kinetics of MB dye onto PVA−Alg/Bent nanocomposite hydrogel beads. The corresponding Equations (7) and (8) ,51 displt-test. All data were represented as mean ± standard deviation (M ± SD) and each experiment being performed in at least five replicates. A p value < 0.05 was considered significant.The significant differences between the data among the experimental groups were analyzed using a one-way ANOVA test followed by a Tukey’s post-hoc test and a Student 2+ ions as crosslinkers [The semi-IPN PVA‒Alg/Bent nanocomposite hydrogel beads were successfully prepared by external ionic gelation method with Caslinkers ,47. The slinkers . Figure In this work, the PVA: Alg weight ratio and the content of Bent nanoparticles were fixed at (1:1) and 30 wt%, respectively. These values were selected because they represent an optimum performance of efficient adsorptive removal of MB dye and mechanical stability of the hydrogel beads . To asse−1. The absorption peak at 1054 cm−1 is attributed to the stretching vibrations of Si–O–Si group, whereas the peak at 950 cm−1 is related to Al–OH–Al bending vibrations [−1 are corresponding to O–H and Si–O stretching vibrations of silanol group of bent. The spectrum of the PVA–Alg hydrogel, shows a broad overlapped absorption band in the region of 3300−3400 cm−1 which is related the stretching vibration of O–H of the PVA and Alg [−1 and 1428 cm−1 are due to asymmetric and symmetrical stretching vibrations of the –COO of Alg [−1 was the typical stretching vibration for C–H in PVA. All the characteristic peaks of PVA, Alg, and Bent were observed in the FTIR spectrum of the nanocomposite hydrogel. In addition, the small shift in the position of the band near 1300 cm−1 would be related to the presence of hydrogen bonds between O–H groups of PVA and Alg or with Si-O groups of Bent [brations . Besides and Alg ,47. AbsoO of Alg ,55. The  of Bent ,53,55. TThe presence of Bent nanoparticles homogeneously distributed in the hydrogel matrix was further confirmed by EDX elemental analysis as shown in − to –COOH groups of the hydrogel matrix. Consequently, the beads existed in collapsed state via the hydrophobic interaction, lead to more rigid gel-networks structure, which restricted traversing of water [−, which establishing repulsions between the negatively charged chains, facilitating the expansion of hydrogel and thus enhancing the swelling rate [The swelling behavior of the semi-IPN PVA–Alg/Bent (30 wt%) nanocomposite hydrogel beads in aqueous solution at different pH values is depicted in of water ,51,57. ITo study the suitability of the designed semi-IPN PVA–Alg/Bent nanocomposite hydrogel beads as adsorbent for MB dye, adsorption experiments were first conducted utilizing a batch experimental setup. The influences of various parameters on adsorption behaviors of the beads, such as pH, contact time, initial dye concentration, beads dosage and temperature, were explored.The pH of dye solution is the most vital parameter controlling the efficiency of dye adsorption process . The effContact time is a significant factor that indicates whether the adsorbent removes the target contaminant to reach the equilibrium . The varIn batch adsorption, the initial concentration of the MB molecules is the main driving force offsetting the mass diffusion resistance force . Figure Adsorbent dosage is another key factor in the dye adsorption system for maximizing the cost efficiency and benefits of adsorbents . Figure The influence of temperature on the removal rate is an important factor to investigate dye adsorption in industrial wastewater at different temperatures ,58. The For acceptance of a system at industrial scale, the system should be economically feasible and repetitive. The reusability performance of adsorbent determines the feasibility of large-scale practical application ,49. The 2 values of 0.9932 and 0.9968, respectively, which indicated the suitability of both models to describe the adsorption of MB on hydrogel beads [L values lay between 0.005 to 0.03 (0 < LR< 1) and 1/n was about 0.57 (0 < 1/n < 1) as well as mq of 51.34 mg/g that confirmed the feasibility and favorability of the MB adsorption on the surface of the beads [qm value for MB removal of the PVA–Alg/Bent nanocomposite hydrogel beads with other adsorbents is listed in The isotherm models of Langmuir and Freundlich were employed to get more insights into the removal process of MB dye by semi-IPN PVA–Alg/Bent nanocomposite hydrogel beads. The Langmuir isotherm model is assumed to indicate affinity for homogenous monolayer adsorption, while the Freundlich isotherm model is used to recognize the adsorption of multiple adsorption layer surfaces ,51. Figuel beads . Accordihe beads ,63. A coln(eq − tq) and t/tq versus contact time are shown in 2 value (0.9974) of the pseudo-second-order kinetic model was higher than that of the pseudo-first-order kinetic model (0.9615), hence, the pseudo-second-order kinetic model is more suitable for describing the adsorption behavior of MB onto the beads, and the main adsorption process of MB was dominated by a chemisorption mechanism. This result is in accord with previous reports on dye adsorption on polysaccharide-based adsorbents [The kinetics of adsorption are an important index in defining the efficiency of sorption . In thissorbents .− and –SiO−, respectively. Thus, the removal of MB by the designed adsorbent is due to the electrostatic interactions between the positive charges on the iminium groups (=N+) present in the MB dye molecules and the abundant negative charges of the nanocomposite adsorbent. In addition, hydrogen bonding occurs between the –OH groups present in the adsorbent surface and nitrogen atom of MB [om of MB ,73.In the present study, a semi-IPN PVA–Alg/Bent nanocomposite hydrogel was successfully developed as efficient adsorbent for the removal of MB dye from aqueous solution. The designed adsorbent beads possesses a porous structure, high specific surface area and net negative surface charge and provide many channels for the rapid adsorption of MB dye. Adsorbent beads had a good stability and reusability after six repeated cycles of adsorption and desorption. Moreover, the adsorbent beads showed outstanding MB removal efficiency with easy handling and extraction after adsorption, as well as eco-friendliness and low cost. Therefore, they could be suitable for the removal of other toxic cationic dyes and harmful pollutants from wastewater at an industrial scale.


To enable people to live in old age in their own homes often requires specialised home care services. Despite the high importance of these services, the finance of home nursing care (HNC) is still under-investigated in many countries. The aim of this paper was to describe the finance of HNC in the Czech Republic.Balancing of revenues and costs was done using structured questionnaires from the closed accounting year 2018 as a monthly average. Nonparametric Kruskal-Wallis and Wilcoxon signed ranks tests were used to test hypotheses.Data from 62 providers were analysed. The data included information from a total of 2297 patients and 995 employees. The average of total costs were € 17,591.7  and average of total revenues were € 17,276.5 . The average cost per a patient was € 516.0 (95% CI 465.9–566.1) and the average revenues were € 500.1 (95% CI 457.0–543.3).The overall financial balance of HNC providers seems to be balanced in the Czech Republic. Nevertheless, insurance, although it should, did not cover all the costs. Micro- providers tended to be cheaper regarding the hours worked by nurses. It is an indisputable fact that the world’s population is ageing. This trend mainly concerns developed countries. Globally, it was estimated that one in six people in the world will be over 65 16%) by 2050, and in Europe and North America, it could be even one in four people older 65 in 2050. It was also estimated that the number of people aged 80 and over would triple, from 143 million in 2019 to 426 million in 2050 6% by 205. AccordiThe survey was created by balancing of revenues and costs. Cost and revenue items were collected using structured questionnaires from the closed accounting year 2018 as a monthly average from the primary accounting documents of the entities concerned for the “profit and loss account” (or the “income statement”). In total, the 481 home nursing care providers were included in the official health statistics of the Ministry of Health in 2018. They were contacted during the conciliation procedure (regular meetings of care providers). Data collection began in May 2019 and ended in September 2019. Seventy-six home care providers agreed to participate in the survey, from which 62 establishments delivered the relevant data by the end of the data collection procedure. Therefore, the response rate to the section on participating providers was 12.9%; that is relatively a small number that should be mentioned as a limitation of the study.The basic analytical chart of accounts was uniform for all the providers. At this point, it should be noted that synthetic accounts  are regulated in the Czech Republic by the law , while aIn the first step, we divided providers according to the number of staff into three categories - micro-provider < 10, medium-provider < 30, and larger-provider > 30. After, we calculated basic descriptive statistics for the entire sample and each category separately. After that, we calculated averages of balance sheets for the entire sample and each category separately as well. Finally, we calculated averages of selected variables per patient and per hour of the nurse’s work. Differences in those variables among the categories were tested by the nonparametric Kruskal-Wallis test. Within-group difference between costs and revenues were tested by nonparametric paired Wilcoxon signed ranks test. For both tests, the level of significance was set on α = 0.05. We calculated 95% confidence interval (CI) for each variable. To briefly explain how it works, if 95% CI crosses 0 in the cash flow, it means the financial loss or earning was not statistically significant at significance level α = 0.05. All the statistics were carried out in statistical software IBM SPSS Statistics 24.Data from 62 providers were analysed; 30 were micro, 24 medium and 8 larger providers. The data included information from a total of 2297 patients and 995 employees. There were 713 nurses out of all the employees. A vast majority of nurses worked on a part-time basis. The average number of all the employees per provider was 16.1 (95% CI 12.5–19.6); for nurses, it was 11.5 (95% CI 9.1–19.9). The average FTE for nurses was 7.7 (95% CI 6.3–9.2). Nurses worked on the average 1824.3 (95% CI 1510.4 - 2198.2) hours a month. All the personnel data logically increased with the size of the provider . The direct costs € 12,871.6  were higher than indirect costs € 4656.2 (95% CI 3775.4 - 5537.1). This trend was apparent among all the providers. However, whereas the direct costs were three times higher than indirect costs in the larger-provider category, it was only twice bigger in the micro-providers. The average of total revenues were € 17,276.5  and the majority was created by the health insurance revenues € 15,895.7 . However, the health insurance revenues would not be enough to cover all the costs, because the average insurance revenues to total costs difference were € − 1696.0 (95% CI -2356.1 - -1036.0), which was statistically significant. The overall economy showed a negative trend with the average of cash flow was € -315.5 (95% CI -730.8 - 100.4). However, this trend was not statistically significant in all the providers together as well as in each group separately. The averages of balance sheets are presented in Table The average cost per a patient was € 516.0 (95% CI 465.9–566.1) and the average revenues were € 500.1 (95% CI 457.0–543.3). There was a significant negative cash flow when the total costs were deducted from the total insurance revenues € -64.3 (95% CI -97.6 - -31.0). Similar statistically significant negative cash flow was also in all the groups. Negative cash flow was also found after deducting the total revenues from total costs. However, these negative cash flows were not statistically significant. The averages of all these variables differed across the groups but not significantly. The averages cash flow per patient is presented in Table If we calculated all the costs and revenues based on a nurse’s working hour, the total cash flow was negative. The total cost per hour was € 9.2 (95% CI 8.8–9.6) and total revenues € 9.1 (95% CI 8.6–9.5). With the size of the provider the negative difference increased, however, the differences were not statistically significant in any cases. Nevertheless, differences between the total costs and payments from health insurance companies were statistically significant in all the cases and the difference increased with the size of the provider (Table This study can be seen as a first attempt to analyse the HNC financing on the microeconomic level. At this point, it should be mentioned that the overall financial balance of HNC providers was not well balanced. Even though a majority of HNC was reimbursed through general health insurance, providers needed to obtain additional resources to achieve financial balances. As it was mentioned above, mainly non-profit organisations provide HNC in the Czech Republic. A long-term unbalanced financial situation can lead these organisations into debt .Comparing costs for HNC among different countries is a quite complicated process that might not make sense because economic power differs across countries, which may skew the comparison. For example, the diversity of rural areas in terms of functions, economic development level and access to social and technical infrastructure  and the Another important question is whether HNC is more economical or effective than institutional care. According to some studies, the HNC had potential on reduction in public expenditure, and according to some others, HNC did not have that effect , 22. HowDespite high importance, only a little piece of information is available regarding its financing from public resources in many countries . BecauseIn conclusion, the overall financial balance of HNC providers seems to be balanced in the Czech Republic. Nevertheless, that happened mostly because of additional financial resources. Insurance, although it should, did not cover all the costs. The size of the provider did not play a crucial role. Nevertheless, for micro- providers, it tended to be cheaper regarding the hours worked by nurses. Price assessment or costs comparison between institutional and homecare was not possible because relevant data from institutional care were not available. In the future, therefore, extensive investigations should be carried out into both HNC and institutional care costs.


Non-alcoholic steatohepatitis (NASH) – a progressive subset of non-alcoholic fatty liver disease (NAFLD) – is a chronic liver disease that can progress to advanced fibrosis, cirrhosis, and end-stage liver disease (ESLD) if left untreated. Early-stage NASH is usually asymptomatic, meaning a large proportion of the prevalent population are undiagnosed. Receiving a NASH diagnosis increases the probability that a patient will receive interventions for the purpose of managing their condition. The purpose of this study was to estimate the disease burden and economic impact of diagnosed NASH in the United Kingdom (UK) adult population in 2018.The socioeconomic burden of diagnosed NASH from a societal perspective was estimated using cost-of-illness methodology applying a prevalence approach. This involved estimating the number of adults with diagnosed NASH in the UK in a base period (2018) and the economic and wellbeing costs attributable to diagnosed NASH in that period. The analysis was based on a targeted review of the scientific literature, existing databases and consultation with clinical experts, health economists and patient groups.Of the prevalent NASH population in the UK in 2018, an estimated 79.8% were not diagnosed. In particular, of the prevalent population in disease stages F0 to F2, only 2.0% (F0), 2.0% (F1) and 16.5% (F2), respectively, were diagnosed. Total economic costs of diagnosed NASH in the UK ranged from £2.3 billion  to £4.2 billion . In 2018, people with NASH in the UK were estimated to experience 94,094 to 174,564 disability-adjusted life years (DALYs) overall. Total wellbeing costs associated with NASH in 2018 were estimated to range between £5.6 to £10.5 billion.The prevention and appropriate management of adult NASH patients could result in reduced economic costs and improvements in wellbeing.The online version contains supplementary material available at 10.1007/s10198-020-01256-y. Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease characterised by excessive fat deposition in the liver that is not primarily attributable to consumption of alcohol and occurs in the absence of competing liver disease aetiologies such as chronic viral hepatitis , 2. NAFLThe number of NASH cases is projected to increase significantly in the near future, with estimates  suggestiDue to the practical and ethical difficulties involved in screening large samples of the population for NASH, limited evidence exists regarding the epidemiology and treatment patterns of NASH, and hence its associated costs over time. NASH has an economic cost through its effect on health, productivity, carers, efficiency of government administration and payments, and other economic costs. Furthermore, NASH has an adverse effect on wellbeing through a reduction in healthy life years lived. Therefore, a comprehensive examination of these economic and wellbeing costs is warranted.The purpose of this study was to estimate the disease burden and economic impact of diagnosed NASH in the UK adult population in 2018. The development of such research may help to foster public health measures to tackle this important healthcare challenge. Increasing the proportion of NASH patients in the early stages of fibrosis (F0 to F2) with a diagnosis would provide opportunities for intervention to prevent progression to late stage disease.The socioeconomic burden of diagnosed NASH in the UK was estimated from a societal perspective using cost-of-illness methodology applying a prevalence approach  via ExceEconomic costs estimated include health system costs, productivity costs, formal and informal care costs, deadweight loss (DWL) from inefficiencies associated with forgone taxation revenue and transfer payments, and other economic costs such as funeral costs brought forward due to premature mortality. Economic costs were estimated via a combination of bottom-up and top-down approaches. Disease burden and wellbeing costs were estimated using World Health Organization (WHO) burden of disease methodology  and convThe data available to estimate the socioeconomic burden of diagnosed NASH was limited and imperfect, requiring a considered methodological approach in the selection of inputs for the modelling. A targeted review of the scientific literature was conducted to obtain inputs for modelling (Supplementary Table 1.1), supplemented with information obtained from existing databases and through consultation with clinical experts, health economists, and patient groups. The review involved a targeted (non-systematic) search of the PubMed and Cochrane Library databases supplemented by hand searching. The review searched for publications released from 2011 onwards and in the English language. Relevant articles found at the title/abstract level, where the full text was not published in English were flagged. The selection of inputs for this study followed a structured, hierarchical approach considering three factors – quality, generalisability and internal consistency. NASH-specific UK studies were used, where available. Otherwise, each available input was considered in terms of its associated quality, generalisability and internal consistency trade-offs. The selection of a particular source; therefore, embodied a degree of judgement. Furthermore, where more than one methodology was available for estimating any of the inputs included in this analysis, consideration was given to its suitability to the overarching intent of this analysis and the available data in selecting the included methodology.The inputs and methodologies selected were validated by the aforementioned expert panel via a series of consultations leveraging principles of expert elicitation , 13. InpNASH prevalence estimates (lower and higher scenarios) according to age, sex, and disease stage, and other epidemiological parameters including incidence, attributable liver transplants and attributable mortality were obtained from peer-reviewed literature and other published data (described below). Nine disease stages were included in the analysis – fibrosis stage zero (F0), fibrosis stage one (F1), fibrosis stage two (F2), fibrosis stage three (F3), fibrosis stage four compensated cirrhosis (F4 CC), decompensated cirrhosis (DCC), hepatocellular carcinoma (HCC), liver transplant, and death (including both liver- and cardiovascular-related death) .An upper scenario estimate of the total prevalence rate of NASH in the adult population of the UK was derived from peer-reviewed literature . The aduLiver transplants attributable to NASH were derived by applying the number of adult elective liver transplants in the UK due to the ‘metabolic’ indication, to the age and sex distribution of NASH prevalence , 19. PeoThe diagnosis rate of NASH by disease stage was determined based on consultation with clinical experts, highlighting that NASH diagnoses are primarily made incidentally during routine clinical investigations in the UK after patients are referred by their general practitioner (GP) to a liver specialist. It was suggested that a large proportion of the prevalent NASH population are undiagnosed prior to developing advanced fibrosis or cirrhosis. This is important as disease-specific intervention can only be offered after NASH diagnosis has been established. In the absence of a NASH diagnosis, no disease modifying interventions will be offered.The probability of diagnosis at each disease stage was initially derived from a UK economic evaluation of an alternative diagnostic pathway, which reported the probability of detection from a UK prospective cross-sectional feasibility study –13. In tThe estimates were presented and validated with a panel of clinical experts via a series of consultations leveraging principles of expert elicitation and amended to reflect local clinical practice. This involved presenting the panel with the estimated number of prevalent and diagnosed patients using the aforementioned sources. The panel drew from their knowledge of the number of diagnosed patients within their collective care and the number and size of specialist centres in the UK to provide feedback on the estimates. Estimates of the number of diagnosed patients with end stage liver disease (ESLD) were further validated against published estimates where available. Revised estimates were shared with the panel following their feedback for validation. This Delphi-type technique was repeated until consensus was achieved. These derived estimates are applied throughout the results section (base probability of diagnosis scenario). Sensitivity analysis on the probability of diagnosis was conducted via lower and higher scenarios. The results of the base scenario applied to the lower and higher prevalence scenarios are reported. The supplementary materials report the results of these lower and higher probability of diagnosis scenario applied to the lower and higher prevalence scenarios (Supplementary Tables 1.2–1.7).Health system costs estimated include primary care, secondary care, diagnostic tests, pharmaceuticals and medical research. Health system utilisation data for patients diagnosed with NASH in disease stages F0 to HCC were extracted from two cost utility studies, containing information on NASH patient management and resource utilisation in the UK , 21. TheHealth system utilisation for patients in F0 to HCC was estimated for patients who were ‘newly diagnosed’ and ‘in monitoring’ in 2018. Newly diagnosed refers to patients who, in 2018, are in their first year of care following diagnosis. In monitoring are those patients who, in 2018, are in their second or subsequent years of care, following a previous diagnosis. Health system unit costs were sourced from the National Schedule of Reference Costs , NationaTotal health system costs by disease stage were calculated by applying the average, per-person health system cost for that disease stage to the respective diagnosed population. The costs of treating comorbidities associated with NASH were not captured in this study, as the comorbidities are separate conditions. The distribution of costs across payers – such as individuals and families, government, and other parties  – was estimated by applying proportions of healthcare expenditure proportions sourced from UK Health Accounts . All cosProductivity costs were estimated via a human capital approach and include reduced workforce participation, lost productive time due to absenteeism and presenteeism, forgone income due to premature mortality, and search; hiring and training costs. This approach involves estimating the number of hours of productivity that are lost due to NASH. This estimate is then converted to a monetary value by multiplying the number of hours by average weekly earnings adjusted for age, gender, and general population employment rates . The impProductivity costs incurred through reduced workforce participation were estimated by applying reduced workforce participation attributable to NASH to the UK general population employment rates and average weekly earnings by age and gender. In lieu of a UK-specific estimate, reduced workforce participation attributable to ESLD comprising DCC to liver transplant was obtained from a cross-sectional analysis of 230,406 adult US Medical Expenditure Panel Survey (MEPS) participants with chronic liver disease including NAFLD and NASH . SimilarOther economic costs of NASH include formal and informal care costs, DWL from inefficiencies associated with forgone taxation revenue and transfer payments, and other economic costs such as funeral costs brought forward due to premature mortality.The average cost of formal care received by NASH patients with ESLD was based on a retrospective cost-of-illness study of patients with chronic liver disease conducted over 1 year in Italy, adjusted to 2018 UK pounds sterling . The oppDWL was estimated from inefficiencies associated with forgone taxation revenue and transfer payments . TransfeDisease burden and wellbeing costs were estimated using the WHO burden of disease methodology via Excel. This is a non-financial approach, where pain, suffering and premature mortality are measured in terms of Disability-Adjusted Life Years (DALYs).DALYs are composed of premature mortality (years of life lost due to premature death – YLL) and morbidity  components. DALYs are calculated by assigning disability weights to various health states, where zero represents a year of perfect health and one represents death. Disability weights for DCC and HCC were sourced from the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease Study . A disabThe burden of disease as measured in DALYs was converted into pounds using an estimate of the VSLY, an estimate of the value society places on an anonymous life. A VSLY of £60,000 per person in the UK was sourced from ‘The Green Book’ published by HM Treasury and inflated using the UK Health Consumer Price Index (CPI) .Sensitivity analysis was conducted in Excel on the population prevalence rate and probabilities of diagnosis (described above) and are reported in the supplementary materials. In addition, aggregate economic cost variables which were most likely to influence the results were adjusted from their base value to a high or low case (default ± 10%) at each disease stage to determine the magnitude of their influence on the results estimated.Of the prevalent adult NASH population in the UK in 2018, those patients with a diagnosis ranged between 0.23 million persons  to 0.44 million persons   of these costs were attributable to people with NASH in F3, while £553 to £1,026 million (24%) and £164 to £303 million (7%) were attributed to people with NASH F4 CC to DCC  . The costs of forgone income made up the largest share of productivity costs at 58%. Reduced workforce participation, absenteeism and presenteeism made up 3%, 13% and 46% of total productivity costs, respectively. The government incurred 22% of total costs through lost taxes, and employers and individuals incurred 46% and 32% of costs, respectively.Overall, other economic costs attributable to diagnosed NASH were estimated to range between £81 to £149 million in 2018. This consists of carer costs of £14 to £25 million, DWL of £58 to £107 million and other economic costs  of £10 to £18 million. The total annual cost of formal and informal care for NASH patients was estimated to range between £13 to £24 million and £1 (both scenarios) million in 2018, respectively. Excluding LT, people with NASH in disease stage HCC incurred the greatest per person carer costs, estimated at £1224 (both scenarios) in 2018. DWL made up the majority of the other economic costs category ranging from £58 to £107 million. Since DWL is borne by the whole of society, the majority of ‘other costs’ were consequently also borne by society. The discounted value of funeral costs associated with premature deaths due to NASH was estimated to range between £10 to £18 million.In 2018, people diagnosed with NASH in the UK were estimated to experience 94,094 to 174,564 DALYs overall. Total wellbeing costs associated with NASH in 2018 were estimated to range between £5.6 to £10.5 billion  first needs to understand whether improved diagnosis and management of early-stage NASH is a cost-effective approach to reducing the rate at which these patients progress to more expensive ESLD. It is not practical or ethical to screen an entire population for NASH; however, it is worth exploring whether screening patients at increased risk of progressing to ESLD  is cost-effective. This question was explored in a modelled cohort study of 1,000 NAFLD patients in the UK over 1 year from a healthcare payer perspective. This study found that, compared with standard care, non-invasive liver testing in primary care was cost-effective in supporting early detection of patients at risk of progressing to ESLD while reducing unnecessary patient referrals with early stage liver disease . FurtherIn light of global concern regarding NASH in the context of a worldwide epidemic of T2DM and obesity, it is important that diagnostic and management pathways for NASH be improved , 6. A paCost-of-illness studies are conducted with the intent of describing the economic burden imposed by a disease, on a specific population . The resThe targeted review of the scientific literature undertaken found one previously published estimate of the economic burden of NAFLD or NASH in the UK in 2016 [Many of the epidemiological, economic and wellbeing parameters required to model the socioeconomic burden of NASH in the adult population in the UK in 2018 were supported by limited available evidence, providing estimates of these parameters for NASH in the UK, specifically. Due to these limitations, evidence was sourced from studies investigating conditions with comparable aetiology and countries with comparable demographics and validated via a series of consultations leveraging principles of expert elicitation , 13. ThiThis study found NASH imposed significant economic and wellbeing costs on the UK population in 2018. As it currently stands, people with NASH incur significant economic costs, in addition to severe reductions in their quality of life. The greatest economic burden is estimated to be borne by individuals diagnosed with NASH; however, significant economic costs are also incurred by families, friends, the government, employers and society/others.These results demonstrate the need to develop effective non-interventional diagnoses and treatments for NASH in addition to preventative measures. Educational programs aimed at increasing awareness of NASH, and associated risks, among general practitioners and the general population will be important for reducing the rate of progression from early to late stage liver disease. This is important, because the late, fibrotic stages of NASH are estimated to incur the greatest economic and wellbeing costs to individuals and others.Supplementary file1 (DOCX 201 KB)Below is the link to the electronic supplementary material.