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Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision-making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, where transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs., Comment: Tutorial with 30 pages, 6 tables and 6 figures. For R code, see https://github.com/DARTH-git/cohort-modeling-tutorial-intro

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions.

Exome sequencing and genome sequencing have the potential to improve clinical utility for patients undergoing genetic investigations. However, evidence of clinical utility is limited to pediatric populations; we aimed to fill this gap by conducting a systematic review of the literature on the clinical utility of exome/genome sequencing across disease indications in pediatric and adult populations. MEDLINE, EMBASE and Cochrane Library were searched between 2016 and 2020. Quantitative studies evaluating diagnostic yield were included; other measures of clinical utility such as changes to clinical management were documented if reported. Two reviewers screened, extracted data, and appraised risk of bias. Fifty studies met our inclusion criteria. All studies reported diagnostic yield, which ranged from 3 to 70%, with higher range of yields reported for neurological indications and acute illness ranging from 22 to 68% and 37–70%, respectively. Diagnoses triggered a range of clinical management changes including surveillance, reproductive-risk counseling, and identifying at-risk relatives in 4–100% of patients, with higher frequencies reported for acute illness ranging from 67 to 95%. The frequency of variants of uncertain significance ranged from 5 to 85% across studies with a potential trend of decreasing frequency over time and higher rates identified in patients of non-European ancestry. This review provides evidence for a higher range of diagnostic yield of exome/genome sequencing compared to standard genetic tests, particularly in neurological and acute indications. However, we identified significant heterogeneity in study procedures and outcomes, precluding a meaningful meta-analysis and certainty in the evidence available for decision-making. Future research that incorporates a comprehensive and consistent approach in capturing clinical utility of exome/genome sequencing across broader ancestral groups is necessary to improve diagnostic accuracy and yield and allow for analysis of trends over time. Prospero registration CRD42019094101.

To assess the association between time of birth and mortality among preterm infants. Population-based study of infants born 22–36 weeks gestation (GA) in Canada from 2010 to 2015 (n = 173 789). Multivariable logistic regression models assessed associations between timing of birth and mortality. Among infants 22–27 weeks GA, evening birth was associated with higher mortality than daytime birth (adjusted odds ratio [AOR] 1.14, 95% CI 1.01–1.29). Among infants 28–32 weeks GA and 33–36 weeks GA, night birth was associated with lower mortality than daytime birth (AOR 0.75, 95% CI 0.59–0.95; AOR 0.78, 95% CI 0.62–0.99, respectively). Sensitivity analysis excluding infants with major congenital anomaly revealed that associations between hour of birth and mortality among infants born 28–32 and 33–36 weeks GA decreased or were not statistically significant. Higher mortality among extremely preterm infants during off-peak hours may suggest variations in available resources based on time of day.

Background Emergence delirium is a common complication in paediatric anaesthesia associated with significant morbidity. Total intravenous anaesthesia (TIVA) and intra-operative dexmedetomidine as an adjuvant to sevoflurane anaesthesia can both reduce the incidence of emergence delirium compared with sevoflurane alone, but no studies have directly compared their relative efficacy. Objective The study objective was to compare the effects of TIVA and dexmedetomidine on the incidence of paediatric emergence delirium. Study design The current study is a systematic review and network meta-analysis (NMA) of randomised controlled trials. Data sources We conducted a systematic search of 12 databases including Medline (Ovid) and Web of Science (Clarivate Analytics) from their respective inception to December 2020. Eligibility Inclusion criteria were randomised controlled trials of paediatric patients undergoing general anaesthesia using sevoflurane, sevoflurane with dexmedetomidine or TIVA. Data were extracted by two reviewers according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and analysed using NMA methodology. Risk ratios and 95% credible intervals (CrI) were calculated for all outcomes [emergence delirium, postoperative nausea and vomiting (PONV), and time to emergence and extubation]. The protocol was registered with PROSPERO (CRD42018091237). Results The systematic review returned 66 eligible studies comprising 5257 patients with crude median emergence delirium incidences of 12.8, 9.1 and 40% in the dexmedetomidine with sevoflurane, TIVA and sevoflurane alone groups, respectively. NMA indicated that compared with TIVA, sevoflurane with adjuvant dexmedetomidine decreased the incidence of emergence delirium without statistical difference (risk ratio 0.88, 95% CrI 0.61 to 1.20, low quality of evidence), but resulted in a higher incidence of PONV (risk ratio: 2.3, 95% CrI 1.1 to 5.6, low quality of evidence). Conclusion Clinical judgement, considering the patient's risk factors for the development of clinically significant outcomes such as emergence delirium and PONV, should be used when choosing between TIVA and sevoflurane with adjuvant dexmedetomidine. These findings are limited by the low quality of evidence (conditional recommendation).

Children with Down syndrome (DS) and acute lymphoblastic leukemia (ALL) are at increased risk of treatment-related morbidity and mortality compared to non-DS-ALL, requiring increased supportive care. We examined the healthcare utilization and costs in DS-ALL patients to inform future evaluations of novel therapies.A provincial registry identified all children (1-17 years) diagnosed with B-lineage ALL in Ontario, Canada between 2002 and 2012. Detailed demographic, disease, treatment, and outcome data were abstracted. Linkage to population-based health services databases identified all outpatient and emergency department (ED) visits, hospitalizations, and physician billings. Healthcare utilization costs were available for patients diagnosed during 2006-2012 using validated algorithms (2018 Canadian dollars). Healthcare utilization rates and costs were compared between DS and non-DS patients using regression models, adjusting for all covariates.Of 711 patients, 28 (3.9%) had DS. Adjusting for all covariates, children with DS-ALL experienced substantially higher rates of ED visits (rate ratio [RR] 1.5, 95% confidence interval [95% CI]: 1.2-2.0; p = .001) and inpatient days (RR 2.5, 95% CI: 1.4-4.5; p = .002) compared to non-DS children. Outpatient visit rates were similar (RR 1.1, 95% CI: 0.9-1.3; p = .41). Among patients with available cost data (N = 533, DS = 19), median 5-year healthcare utilization cost was $247,700 among DS patients (interquartile range [IQR]: 200,900-354,500) and $196,200 among non-DS patients (IQR: 148,900-280,300; p = .02). In adjusted analyses, DS-associated costs were 50% higher (RR 1.5, 95% CI: 1.2-1.9; p .002).Healthcare utilization and treatment costs of DS-ALL patients are substantially higher than those of non-DS-ALL. Our data provide a baseline for future DS-specific cost-effectiveness studies.

COVID-19 has had significant mental health impacts internationally and anxiety rates are estimated to have tripled during the pandemic, but the specific causes remain underexplored. This study's purpose was to investigate the associations of sociodemographic factors, COVID-19-related policies, and COVID-19 case/mortality rates with levels of anxiety among Canadians during the pandemic.This study used linear regression models populated with three integrated sources of data: a repeated cross-sectional survey (n = 7008), Oxford COVID-19 Government Response Tracker data, and COVID-19 case/mortality rates. Sociodemographic factors included were age, gender, race, province, income, education, rurality, household composition, and factors related to employment.Local COVID-19 case and mortality rates and stay-at-home orders were positively associated with anxiety symptom severity. Anxiety was most severe among those who: were female, Indigenous, or Middle Eastern; had postsecondary education; lived with others; and became unemployed or had working hours altered during the pandemic. Anxiety was less severe among: older adults; male, Caucasians, and black individuals; those with high incomes, and; those for whom employment did not change during the pandemic.Anxiety was primarily driven by socioeconomic factors among Canadians during the COVID-19 pandemic. Policies that alleviate socioeconomic uncertainty for groups that are most vulnerable may reduce the long-term harm of the pandemic and associated lockdown policies.

Primary Subject area Neonatal-Perinatal Medicine Background Neonatal Intensive Care Units (NICUs) account for over 35% of pediatric in-hospital clinical costs, thus implying that a better understanding of care expenditures within these units is the first step for improving efficiency of care. The Canadian Neonatal Network (CNN) algorithm is the first to provide case-specific costs based on resource usage among preterm infants born < 37 weeks but has not yet been validated for other populations in the NICU. Objectives To validate the CNN costing algorithm in six case-mix categories with real-time costs obtained from hospital-specific financial software (CPSS) in a tertiary-level NICU and assess the variations in proportion of cost centers across case-mixes. Design/Methods A retrospective cohort study of all patients admitted within 24h of birth to a Level 3 medico-surgical NICU 2016-2019. Patient demographics, clinical information and CNN predicted costs were obtained from the CNN database. Real-time costs were obtained from the hospital financial software (CPSS). Total and daily costs were compared between sources using Pearson correlation coefficient (r) and paired Student’s t-test. Costs were adjusted to account for inter-institutional and -provincial price variations using the Cost of Standard Hospitalization Stay from the Canadian Institute for Health Information. Proportions of each cost center across the different case-mix categories were compared using Chi-square analyses. Results Among the 1795 live infants admitted into the NICU, 167 (9.3%) were < 29 weeks gestational age (GA), 193 (11%) were 29-32 weeks GA, 457 (25.5%) were 33-36 weeks GA, 144 (8%) had major congenital anomalies, 179 (10%) were term infants diagnosed with Hypoxic-Ischemic Encephalopathy (HIE) and 672 (37%) were term infants with no HIE or major congenital anomalies. Median NICU costs varied according to each case-mix from $10,025 for term infants without HIE or congenital anomaly to $180,145 for infants born < 29 weeks (Figure 1). Despite high variation in total NICU costs, there were small variations in median daily costs (range: $1,312-$1,941). Overall, the CNN algorithm strongly correlated with CPSS total costs across all 6 case-mix categories (r range 0.90-1.00, p-value < 0 .01) (Figure 2). We report a consistent strong predictive performance of the algorithm in 5/8 pre-specified cost centers among preterm infants (r range 0.77-0.99, p-value < 0 .01). Unit producing personnel (nurses and physicians) consistently comprised the largest proportion of total costs (64-78%) for all case-mix categories. Conclusion The CNN algorithm accurately predicts NICU total costs for six case-mix categories. Costs per day were comparable across different case-mix categories, and unit producing personnel represented the highest proportion of costs suggesting that reductions in length of stay would be the most efficient method to reduce NICU costs.

Aim: To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants. Method: We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models. Results: Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68–0.77) and 0.70 (95% CI 0.65–0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74–100) and 91% (95% CI 83–93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80–0.93). Interpretation: Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

Background/Aims: Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. Methods: This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. Results: With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. Conclusions: By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.

Objective To evaluate the changes in the associations of antenatal corticosteroids (ACS) with neonatal mortality and severe neurological injury over time (2003-17). Design National, population-representative, retrospective cohort study. Setting Level III neonatal intensive care units participating in the Canadian Neonatal Network. Population All infants born at 230/7 -336/7 weeks of gestation (n = 43 456). Methods We estimated the associations between exposure to ACS and neonatal outcomes by year of birth. Year of birth was considered both continuously and categorically as three consecutive epochs. Main outcome measure Neonatal mortality and severe neurological injury. Results The absolute rates of neonatal mortality and severe neurological injury decreased during the study period in both the ACS and No ACS groups. For infants born at 230/7 -306/7 weeks of gestation, ACS was associated with similar reductions in neonatal mortality across the three epochs (9.0% versus 18.1%, adjusted relative risk [aRR] 0.54, 95% CI 0.47-0.61 in 2003-09; 7.6% versus 19.6%, aRR 0.51, 95% CI 0.44-0.59 in 2010-13; and 7.3% versus 14.5%, aRR 0.56, 95% CI 0.46-0.68 in 2014-17) and in severe neurological injury (13.2% versus 25.8%, aRR 0.57, 95% CI 0.50-0.64 in 2003-09; 7.4% versus 17.4%, aRR 0.53, 95% CI 0.43-0.66 in 2010-14; and 7.2% versus 14.8%, aRR 0.59, 95% CI 0.48-0.74 in 2014-17). Conclusion Despite the ongoing improvements in neonatal care of preterm infants, as reflected by the gradual decrease in the absolute rates of neonatal mortality and severe neurological injury, the association of ACS treatment with neonatal mortality and severe neurological injury among extremely preterm infants born at 23-30 weeks of gestation has remained stable throughout the study period of 15 years. Tweetable abstract Despite the gradual decrease in the rates of neonatal mortality and severe neurological injury, antenatal corticosteroids remain an important intervention in the current era of neonatal care.

Background Adhesive small bowel obstruction (aSBO) is a potentially recurrent disease. Although non‐operative management is often successful, it is associated with greater risk of recurrence than operative intervention, and may have greater downstream morbidity and costs. This study aimed to compare the current standard of care, trial of non‐operative management (TNOM), and early operative management (EOM) for aSBO. Methods Patients admitted to hospital between 2005 and 2014 in Ontario, Canada, with their first episode of aSBO were identified and propensity‐matched on their likelihood to receive EOM for a cost–utility analysis using population‐based administrative data. Patients were followed for 5 years to determine survival, recurrences, adverse events and inpatient costs to the healthcare system. Utility scores were attributed to aSBO‐related events. Cost–utility was presented as the incremental cost‐effectiveness ratio (ICER), expressed as Canadian dollars per quality‐adjusted life‐year (QALY). Results Some 25 150 patients were admitted for aSBO and 3174 (12·6 per cent) were managed by EOM. Patients managed by TNOM were more likely to experience recurrence of aSBO (20·9 per cent versus 13·2 per cent for EOM; P, This population‐based cost–utility analysis compared early operative management (EOM) for adhesive small bowel obstruction (SBO) to the current standard of care of a trial of non‐operative management. Although EOM was more costly, it was associated with a significantly lower risk of recurrence and reduced exposure to the morbidity and costs associated with multiple admissions for adhesive SBO. With longer follow‐up, EOM becomes increasingly cost‐effective, and crosses published willingness‐to‐pay thresholds within 5 years of the first admission. Early operative management in small bowel occlusion

The objective of the study was to assess clinical mental and physical health outcomes of siblings of children with chronic health condition(s) compared with siblings of healthy children or normative data.We searched Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, and CINAHL through August 9, 2021. We included English-language studies that reported clinically diagnosable mental or physical health outcomes among siblings of children (18 years old) with a chronic health condition, included a comparison group, and used an experimental or observational study design. Two reviewers extracted data and independently assessed risk of bias using the Newcastle Ottawa Scale.Of 9899 screened studies, 34 were included; 28 studies reported on mental health, 3 reported on physical health, and 3 reported on mortality. Siblings of children with chronic conditions had greater depression rating scale scores than their comparison groups (standardized mean difference = 0.53; 95% CI = 0.38-0.68; P .001 [6 studies]), whereas anxiety scores were not substantially increased (standardized mean difference = 0.21; 95% CI = -0.02 to 0.43; P = .07 [7 studies]). The effects for confirmed psychiatric diagnoses (7 studies), mortality (3 studies), or physical health outcomes (3 studies) could not be meta-analyzed given the limited number of studies and between-study heterogeneity.Siblings of children with chronic health conditions may be at an increased risk of depression. Our findings suggest the need for targeted interventions to support the psychological well-being of siblings of children with chronic health conditions.

ObjectiveTo evaluate the diagnostic accuracy of teleretinal screening compared with face-to-face examination for detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD).Methods and analysisThis study adhered to the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA). A comprehensive search of OVID MEDLINE, EMBASE and Cochrane CENTRAL was performed from January 2010 to July 2021. QUADAS-2 tool was used to assess methodological quality and applicability of the studies. A bivariate random effects model was used to perform the meta-analysis. Referrable DR was defined as any disease severity equal to or worse than moderate non-proliferative DR or diabetic macular oedema (DMO).Results28 articles were included. Teleretinal screening achieved a sensitivity of 0.91 (95% CI: 0.82 to 0.96) and specificity of 0.88 (0.74 to 0.95) for any DR (13 studies, n=7207, Grading of Recommendations, Assessment, Development and Evaluation (GRADE) low). Accuracy for referrable DR (10 studies, n=6373, GRADE moderate) was lower with a sensitivity of 0.88 (0.81 to 0.93) and specificity of 0.86 (0.79 to 0.90). After exclusion of ungradable images, the specificity for referrable DR increased to 0.95 (0.90 to 0.98), while the sensitivity remained nearly unchanged at 0.85 (0.76 to 0.91). Teleretinal screening achieved a sensitivity of 0.71 (0.49 to 0.86) and specificity of 0.88 (0.85 to 0.90) for detection of AMD (three studies, n=697, GRADE low).ConclusionTeleretinal screening is highly accurate for detecting any DR and DR warranting referral. Data for AMD screening is promising but warrants further investigation.PROSPERO registration numberCRD42020191994.

We sought to determine the cost-effectiveness of noninvasive fetal RhD blood group genotyping in nonalloimmunized and alloimmunized pregnancies in Canada.We developed two probabilistic state-transition (Markov) microsimulation models to compare fetal genotyping followed by targeted management versus usual care (i.e., universal Rh immunoglobulin [RhIG] prophylaxis in nonalloimmunized RhD-negative pregnancies, or universal intensive monitoring in alloimmunized pregnancies). The reference case considered a healthcare payer perspective and a 10-year time horizon. Sensitivity analysis examined assumptions related to test cost, paternal screening, subsequent pregnancies, other alloantibodies (e.g., K, Rh c/C/E), societal perspective, and lifetime horizon.Fetal genotyping in nonalloimmunized pregnancies (at per-sample test cost of C$247/US$311) was associated with a slightly higher probability of maternal alloimmunization (22 vs. 21 per 10,000) and a reduced number of RhIG injections (1.427 vs. 1.795) than usual care. It was more expensive (C$154/US$194, 95% Credible Interval [CrI]: C$139/US$175-C$169/US$213) and had little impact on QALYs (0.0007, 95%CrI: -0.01-0.01). These results were sensitive to the test cost (threshold achieved at C$88/US$111), and inclusion of paternal screening. Fetal genotyping in alloimmunized pregnancies (at test cost of C$328/US$413) was less expensive (-C$6280/US$7903, 95% CrI: -C$6325/US$7959 to -C$6229/US$7838) and more effective (0.19 QALYs, 95% CrI 0.17-0.20) than usual care. These cost savings remained robust in sensitivity analyses.Noninvasive fetal RhD genotyping saves resources and represents good value for the management of alloimmunized pregnancies. If the cost of genotyping is substantially decreased, the targeted intervention can become a viable option for nonalloimmunized pregnancies.

ImportanceUniversal paid sick-leave (PSL) policies have been implemented in jurisdictions to mitigate the spread of SARS-CoV-2. However empirical data regarding health and economic consequences of PSL policies is scarce.ObjectiveTo estimate effects of a universal PSL policy in Ontario, Canada’s most populous province.DesignAn agent-based model (ABM) to simulate SARS-CoV-2 transmission informed by data from Statistics Canada, health administrative sources, and from the literature.SettingOntario from January 1st to May 1st, 2021.ParticipantsA synthetic population (1 million) with occupation and household characteristics representative of Ontario residents (14.5 million).ExposureA base case of existing employer-based PSL alone versus the addition of a 3-or 10-day universal PSL policy to facilitate testing and self-isolation among workers infected with SARS-CoV-2 themselves or because of infected household members.Main Outcome(s) and Measure(s)Number of SARS-CoV-2 infections and COVID-19 hospitalizations, worker productivity, lost wages, and presenteeism (going to a workplace while infected).ResultsIf a 3- and 10-day universal PSL were implemented over the 4-month study period, then compared with the base-case, the PSL policies were estimated to reduce cumulative SARS-CoV-2 cases by 85,531 (95% credible interval, CrI -2,484; 195,318) and 215,302 (81,500; 413,742), COVID-19 hospital admissions by 1,307 (-201; 3,205) and 3,352 (1,223; 6,528), numbers of workers forgoing wages by 558 (-327;1,608) and 7,406 (6,764; 8,072), and numbers of workers engaged in presenteeism by 24,499 (216; 54,170) and 279,863 (262,696; 295,449). Hours of productivity loss were estimated to be 10,854,379 (10,212,304; 11,465,635) in the base case, 17,446,525 (15,934,321; 18,854,683) in the 3-day scenario, and 26,127,165 (20,047,239; 29,875,161) in the 10-day scenario. Lost wages were $5,256,316 ($4,077,280; $6,804,983) and $12,610,962 ($11,463,128; $13,724,664) lower in the 3 day and 10 day scenarios respectively, relative to the base case.Conclusions and RelevanceExpanded access to PSL is estimated to reduce total numbers of COVID-19 cases, reduce presenteeism of workers with SARS-CoV-2 at workplaces, and mitigate wage loss experienced by workers.Competing interestsThe authors have no competing interests relevant to this article to disclose.FundingSupported by COVID-19 Rapid Research Funding (C-291-2431272-SANDER). This research was further supported, in part, by a Canada Research Chair in Economics of Infectious Diseases held by Beate Sander (CRC-950-232429). The study sponsor had no role in the design, collection, analysis, interpretation of the data, manuscript preparation or the decision to submit for publication.Author ContributionsConceptualization: PP, JDR, BS, DNData Curation: PP, JDR, BS, DNFormal Analysis: PP, JDR, DNMethodology: PP, JDR, BS, DNSupervision: PP, DN, BSValidation: PP, JDR, BS, DNFirst Draft: PP, JDR, BS, DNReview and EditPP, JDR, BS, DNKey pointsQuestionWhat could be the health and economic consequence of more generous paid sick leave policies in the context of the COVID-19 pandemic?FindingsMore generous policies are estimated to reduce SARS-CoV-2 infections (and thus COVID-19 hospitalizations), lost wages and presence of individuals with infection at workplaces.MeaningMore generous paid sick leave can be a valuable addition to other COVID-19 public health interventions.

Background Clinical care is moving from a “one size fits all” approach to a setting in which treatment decisions are based on individual treatment response, needs, preferences, and risk. Research into personalized treatment strategies aims to discover currently unknown markers that identify individuals who would benefit from treatments that are nonoptimal at the population level. Before investing in research to identify these markers, it is important to assess whether such research has the potential to generate value. Thus, this article aims to develop a framework to prioritize research into the development of new personalized treatment strategies by creating a set of measures that assess the value of personalizing care based on a set of unknown patient characteristics. Methods Generalizing ideas from the value of heterogeneity framework, we demonstrate 3 measures that assess the value of developing personalized treatment strategies. The first measure identifies the potential value of personalizing medicine within a given disease area. The next 2 measures highlight specific research priorities and subgroup structures that would lead to improved patient outcomes from the personalization of treatment decisions. Results We graphically present the 3 measures to perform sensitivity analyses around the key drivers of value, in particular, the correlation between the individual treatment benefits across the available treatment options. We illustrate these 3 measures using a previously published decision model and discuss how they can direct research in personalized medicine. Conclusion We discuss 3 measures that form the basis of a novel framework to prioritize research into novel personalized treatment strategies. Our novel framework ensures that research targets personalized treatment strategies that have high potential to improve patient outcomes and health system efficiency. Highlights It is important to undertake research prioritization before conducting any research that aims to discover novel methods (e.g., biomarkers) for personalizing treatment. The value of unexplained heterogeneity can highlight disease areas in which personalizing treatment can be valuable and determine key priorities within that area. These priorities can be determined under assumptions of the magnitude of the individual-level treatment effect, which we explore in sensitivity analyses.

To describe the trend in costs over 10 years for tertiary-level neonatal care of infants born 22Clinical characteristics, outcomes, and third-party payor costs for the tertiary neonatal care of infants born 22Between 2010 and 2019, the number of infants born24 weeks of gestation increased from 4.4% to 7.7%. The average length of stay increased from 68 days to 75 days. Unadjusted average ± SD total costs per neonate were $120 717 ± $93 062 CAD in 2010 and $132 774 ± $93 161 CAD in 2019. After adjustment for year, center, and gestation, total costs and length of stay increased significantly, by $13 612 CAD (P .01) and 8.1 days (P .01) over 10 years, respectively; whereas costs accounting for LOS remained stable.The total costs and length of stay for infants 22

Purpose We aimed to provide clinicians with introductory guidance for interpreting and assessing confidence in on Network meta-analysis (NMA) results. Methods We reviewed current literature on NMA and summarized key points. Results Network meta-analysis (NMA) is a statistical method for comparing the efficacy of three or more interventions simultaneously in a single analysis by synthesizing both direct and indirect evidence across a network of randomized clinical trials. It has become increasingly popular in healthcare, since direct evidence (head-to-head randomized clinical trials) are not always available. NMA methods are categorized as either Bayesian or frequentist, and while the two mostly provide similar results, the two approaches are theoretically different and require different interpretations of the results. Conclusions We recommend a careful approach to interpreting NMA results and the validity of an NMA depends on its underlying statistical assumptions and the quality of the evidence used in the NMA.

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transitions probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time-dependent). This tutorial illustrates adding two types of time-dependency using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation., 34 pages, 7 figures. arXiv admin note: text overlap with arXiv:2001.07824

Chronic hepatitis C (CHC) is among the most burdensome infectious diseases in the world. Health utilities are a valuable tool for quantifying this burden and conducting cost-utility analysis.Our study summarizes the available data on utilities in CHC patients. This will facilitate analyses of CHC treatment and elimination strategies.We searched MEDLINE, Embase, and the Cochrane Library for studies measuring utilities in CHC patients. Utilities were pooled by health state and utility instrument using meta-analysis. A further analysis used meta-regression to adjust for the effects of clinical status and methodological variation.Fifty-one clinical studies comprising 15 053 patients were included. Based on the meta-regression, patients' utilities were lower for more severe health states (predicted mean EuroQol-5D-3L utility for mild/moderate CHC: 0.751; compensated cirrhosis: 0.671; hepatocellular carcinoma: 0.662; decompensated cirrhosis: 0.602). Patients receiving interferon-based treatment had lower utilities than those on interferon-free treatment (0.647 vs 0.733). Patients who achieved sustained virologic response (0.786) had higher utilities than those with mild to moderate CHC. Utilities were substantially higher for patients in experimental studies compared to observational studies (coefficient: +0.074, P.05). The time tradeoff instrument was associated with the highest utilities, and the Health Utilities Index 3 was associated with the lowest utilities.Chronic hepatitis C is associated with a significant impairment in global health status, as measured by health utility instruments. Impairment is greater in advanced disease. Experimental study designs yield higher utilities-an effect not previously documented. Curative therapy can alleviate the burden of CHC, although further research is needed in certain areas, such as the long-term impacts of treatment on utilities.

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.