Burden of illness associated with Respiratory Syncytial Virus (RSV)-related hospitalizations among adults in Ontario, Canada: A retrospective population-based study.

BACKGROUND: Globally, RSV is a common viral pathogen that causes 64 million acute respiratory infections annually. Our objective was to determine the incidence of hospitalization, healthcare resource use and associated costs of adults hospitalized with RSV in Ontario, Canada. METHODS: To describe the epidemiology of adults hospitalized with RSV, we used a validated algorithm applied to a population-based healthcare utilization administrative dataset in Ontario, Canada. We created a retrospective cohort of incident hospitalized adults with RSV between September 2010 and August 2017 and followed each person for up to two years. To determine the burden of illness associated with hospitalization and post-discharge healthcare encounters each RSV-admitted patient was matched to two unexposed controls based on demographics and risk factors. Patient demographics were described and mean attributable 6-month and 2-year healthcare costs (2019 Canadian dollars) were estimated. RESULTS: There were 7,091 adults with RSV-associated hospitalizations between 2010 and 2019 with a mean age of 74.6 years; 60.4 % were female. RSV-coded hospitalization rates increased from 1.4 to 14.6 per 100,000 adults between 2010-2011 and 2018-2019. The mean difference in healthcare costs between RSV-admitted patients and matched controls was $28,260 (95 % CI: $27,728 - $28,793) in the first 6 months and $43,721 over 2 years (95 % CI: $40,383 - $47,059) post-hospitalization. CONCLUSIONS: RSV hospitalizations among adults increased in Ontario between 2010/11 to 2018/19 RSV seasons. RSV hospitalizations in adults were associated with increased attributable short-term and long-term healthcare costs compared to matched controls. Interventions that could prevent RSV in adults may reduce healthcare burden.

Healthcare costs and outcomes associated with laboratory-confirmed Lyme disease in Ontario, Canada: A population-based cohort study.

BACKGROUND: The objective of this study was to estimate the economic burden attributable to laboratory-confirmed Lyme disease (LD) in Ontario, Canada and assess health outcomes associated with LD. METHOD: We conducted a cohort study using laboratory-confirmed LD cases accrued between 2006 and 2018. The exposed cohort was matched 1:3 to the unexposed cohort using a combination of hard and propensity score matching. We used phase-of-care costing methods to calculate attributable costs for four phases of illness: pre-diagnosis, acute care, post-acute care, and continuing care in 2018 Canadian dollars. We used ICD-10-CA and OHIP billing codes to identify emergency department visits, physician billings and hospitalizations related to LD sequelae to assess health outcomes. RESULTS: A total of 2,808 cases were identified with a mean age of 46.5 (20.7) years and 44% female. Within 30-days, 404 (14.3%) cases required an ED visit and 63 (2.4%) cases required hospitalization. The mean (95% CI) total costs for LD cases in pre-diagnosis, acute, and post-acute care phases were $209 ($181, 238), $1,084 ($956, $1,212), and $1,714 ($1,499, $1,927), respectively. The highest mean attributable 10-day cost was $275 ($231, $319) during acute care. At 1-year post-infection, LD increased the relative risk of nerve palsies by 62 (20, 197), and polyneuropathy by 24 (3.0, 190). LD resulted in 16 Lyme meningitis events vs. 0 events in the unexposed. CONCLUSION: Individuals with laboratory-confirmed LD have increased healthcare resource use pre-diagnosis and up to six months post-diagnosis, and were more likely to seek healthcare services related to LD sequelae.

Benchmarking ultra-high molecular weight DNA preservation methods for long-read and long-range sequencing.

BACKGROUND: Studies in vertebrate genomics require sampling from a broad range of tissue types, taxa, and localities. Recent advancements in long-read and long-range genome sequencing have made it possible to produce high-quality chromosome-level genome assemblies for almost any organism. However, adequate tissue preservation for the requisite ultra-high molecular weight DNA (uHMW DNA) remains a major challenge. Here we present a comparative study of preservation methods for field and laboratory tissue sampling, across vertebrate classes and different tissue types. RESULTS: We find that storage temperature was the strongest predictor of uHMW fragment lengths. While immediate flash-freezing remains the sample preservation gold standard, samples preserved in 95% EtOH or 20-25% DMSO-EDTA showed little fragment length degradation when stored at 4°C for 6 hours. Samples in 95% EtOH or 20-25% DMSO-EDTA kept at 4°C for 1 week after dissection still yielded adequate amounts of uHMW DNA for most applications. Tissue type was a significant predictor of total DNA yield but not fragment length. Preservation solution had a smaller but significant influence on both fragment length and DNA yield. CONCLUSION: We provide sample preservation guidelines that ensure sufficient DNA integrity and amount required for use with long-read and long-range sequencing technologies across vertebrates. Our best practices generated the uHMW DNA needed for the high-quality reference genomes for phase 1 of the Vertebrate Genomes Project, whose ultimate mission is to generate chromosome-level reference genome assemblies of all ∼70,000 extant vertebrate species.

Cost-utility analysis of antimicrobial stewardship programme at a tertiary teaching hospital in Ethiopia.

OBJECTIVE: Antimicrobial stewardship (AMS) significantly reduces inappropriate antibiotic use and improves patient outcomes. In low-resource settings, AMS implementation may require concurrent strengthening of clinical microbiology capacity therefore additional investments. We assessed the cost-effectiveness of implementing AMS at Tikur Anbessa Specialised Hospital (TASH), a tertiary care hospital in Ethiopia. DESIGN: We developed a Markov cohort model to assess the cost-utility of pharmacist-led AMS with concurrent strengthening of laboratory capacity compared with usual care from a 'restricted societal' perspective. We used a lifetime time horizon and discounted health outcomes and cost at 3% annually. Data were extracted from a prospective study of bloodstream infections among patients hospitalised at TASH, supplemented by published literature. We assessed parameter uncertainty using deterministic and probabilistic sensitivity analyses. SETTING: Tertiary care hospital in Ethiopia, with 800 beds and serves over half a million patients per year. POPULATION: Cohort of adults and children inpatient population aged 19.8 years at baseline. INTERVENTION: Laboratory-supported pharmacist-led AMS compared with usual care. Usual care is defined as empirical initiation of antibiotic therapy in the absence of strong laboratory and AMS. OUTCOME MEASURES: Expected life-years, quality-adjusted life-years (QALYs), costs (US$2018) and incremental cost-effectiveness ratio. RESULTS: Laboratory-supported AMS strategy dominated usual care, that is, AMS was associated with an expected incremental gain of 38.8 QALYs at lower expected cost (incremental cost savings:US$82 370) per 1000 patients compared with usual care. Findings were sensitive to medication cost, infection-associated mortality and AMS-associated mortality reduction. Probabilistic sensitivity analysis demonstrated that AMS programme was likely to be cost-effective at 100% of the simulation compared with usual care at 1%-51% of gross domestic product/capita. CONCLUSION: Our study indicates that laboratory-supported pharmacist-led AMS can result in improved health outcomes and substantial healthcare cost savings, demonstrating its economic advantage in a tertiary care hospital despite greater upfront investments in a low-resource setting.

Estimating the population health burden of Lyme disease in Ontario, Canada: a microsimulation modelling approach.

BACKGROUND: If untreated, Lyme disease can lead to long-term sequelae and post-treatment Lyme disease syndrome (PTLDS), resulting in reduced health-related quality of life. The objective of this study was to develop a microsimulation model to estimate the population-level health burden of Lyme disease in Ontario, Canada. METHODS: We developed a Lyme disease history model using microsimulation, simulating 100 000 people (mean age 37.6 yr, 51% female) from 2017 in Ontario over a lifetime risk of infection and time horizon. We extracted the sensitivity and specificity of the 2-tier testing recommended by the Canadian Public Health Laboratory Network, probabilities and health state utility values from the published literature and health administrative data. Our reported outcomes from our stochastic analysis include diagnosed cases of Lyme disease (stratified by stage), undiagnosed infections, sequelae, individuals with PTLDS and quality-adjusted life-years (QALYs) lost. RESULTS: Our model estimated 333 (95% confidence interval [CI] 329-337) infections over the lifetime of 100 000 simulated people (mean age 37.6 yr, 51% female), with 92% (95% CI 91%-93%) of infections diagnosed. Of those 308 people with Lyme Disease diagnoses, 67 (95% CI 65-69) developed sequelae (e.g., arthritic, cardiac, neurologic sequelae), and 34 (95% CI 33-35) developed PTLDS. Lyme disease resulted in a loss of 84.5 QALYs (95% CI 82.9-86.2) over the lifetime of the simulated cohort. Sensitivity and scenario analysis showed that increasing incidence rates of Lyme disease, potential underreporting, duration of PTLDS and quality of life (health state utility) associated with PTLDS had the greatest impact on health burden. INTERPRETATION: Lyme disease contributes considerable health burden in terms of QALYs lost. Our analysis provides evidence to understand the disease burden and lays the foundation to assess the cost-effectiveness of pharmaceutical and nonpharmaceutical interventions.

Demographic characteristics, acute care resource use and mortality by age and sex in patients with COVID-19 in Ontario, Canada: a descriptive analysis.

BACKGROUND: Understanding resource use for coronavirus disease 2019 (COVID-19) is critical. We conducted a descriptive analysis using public health data to describe age- and sex-specific acute care use, length of stay (LOS) and mortality associated with COVID-19. METHODS: We conducted a descriptive analysis using Ontario's Case and Contact Management Plus database of individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Ontario from Mar. 1 to Sept. 30, 2020, to determine age- and sex-specific hospital admissions, intensive care unit (ICU) admissions, use of invasive mechanical ventilation, LOS and mortality. We stratified analyses by month of infection to study temporal trends and conducted subgroup analyses by long-term care residency. RESULTS: During the observation period, 56 476 individuals testing positive for SARS-CoV-2 were reported; 41 049 (72.7%) of these were younger than 60 years, and 29 196 (51.7%) were female. Proportion of cases shifted from older populations (> 60 yr) to younger populations (10-39 yr) over time. Overall, 5383 (9.5%) of individuals were admitted to hospital; of these, 1183 (22.0%) were admitted to the ICU, and 712 (60.2%) of these received invasive mechanical ventilation. Mean LOS for individuals in the ward, ICU without invasive mechanical ventilation and ICU with invasive mechanical ventilation was 12.8 (standard deviation [SD] 15.4), 8.5 (SD 7.8) and 20.5 (SD 18.1) days, respectively. Among patients receiving care in the ward, ICU without invasive mechanical ventilation and ICU with invasive mechanical ventilation, 911/3834 (23.8%), 124/418 (29.7%) and 287/635 (45.2%) died, respectively. All outcomes varied by age and decreased over time, overall and within age groups. INTERPRETATION: This descriptive study shows use of acute care and mortality varying by age and decreasing between March and September 2020 in Ontario. Improvements in clinical practice and changing risk distributions among those infected may contribute to fewer severe outcomes.

Simulation-Based Estimation of SARS-CoV-2 Infections Associated With School Closures and Community-Based Nonpharmaceutical Interventions in Ontario, Canada.

Importance: Resurgent COVID-19 cases have resulted in the reinstitution of nonpharmaceutical interventions, including school closures, which can have adverse effects on families. Understanding the associations of school closures with the number of incident and cumulative COVID-19 cases is critical for decision-making. Objective: To estimate the association of schools being open or closed with the number of COVID-19 cases compared with community-based nonpharmaceutical interventions. Design, Setting, and Participants: This decision analytical modelling study developed an agent-based transmission model using a synthetic population of 1 000 000 individuals based on the characteristics of the population of Ontario, Canada. Members of the synthetic population were clustered into households, neighborhoods, or rural districts, cities or rural regions, day care facilities, classrooms (ie, primary, elementary, or high school), colleges or universities, and workplaces. Data were analyzed between May 5, 2020, and October 20, 2020. Exposures: School reopening on September 15, 2020, vs schools remaining closed under different scenarios for nonpharmaceutical interventions. Main Outcomes and Measures: Incident and cumulative COVID-19 cases between September 1, 2020, and October 31, 2020. Results: Among 1 000 000 simulated individuals, the percentage of infections among students and teachers acquired within schools was less than 5% across modeled scenarios. Incident COVID-19 case numbers on October 31, 2020, were 4414 (95% credible interval [CrI], 3491-5382) cases in the scenario with schools remaining closed and 4740 (95% CrI, 3863-5691) cases in the scenario for schools reopening, with no other community-based nonpharmaceutical intervention. In scenarios with community-based nonpharmaceutical interventions implemented, the incident case numbers on October 31 were 714 (95% CrI, 568-908) cases for schools remaining closed and 780 (95% CrI, 580-993) cases for schools reopening. When scenarios applied the case numbers observed in early October in Ontario, the cumulative case numbers were 777 (95% CrI, 621-993) cases for schools remaining closed and 803 (95% CrI, 617-990) cases for schools reopening. In scenarios with implementation of community-based interventions vs no community-based interventions, there was a mean difference of 39 355 cumulative COVID-19 cases by October 31, 2020, while keeping schools closed vs reopening them yielded a mean difference of 2040 cases. Conclusions and Relevance: This decision analytical modeling study of a synthetic population of individuals in Ontario, Canada, found that most COVID-19 cases in schools were due to acquisition in the community rather than transmission within schools and that the changes in COVID-19 case numbers associated with school reopenings were relatively small compared with the changes associated with community-based nonpharmaceutical interventions.

Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis.

PURPOSE: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. METHODS: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi-state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states. RESULTS: Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7-fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers. CONCLUSION: This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

Chromosome-scale, haplotype-resolved assembly of human genomes.

Haplotype-resolved or phased genome assembly provides a complete picture of genomes and their complex genetic variations. However, current algorithms for phased assembly either do not generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method named diploid assembly (DipAsm) that uses long, accurate reads and long-range conformation data for single individuals to generate a chromosome-scale phased assembly within 1 day. Applied to four public human genomes, PGP1, HG002, NA12878 and HG00733, DipAsm produced haplotype-resolved assemblies with minimum contig length needed to cover 50% of the known genome (NG50) up to 25 Mb and phased ~99.5% of heterozygous sites at 98-99% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies for the discovery of structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as the human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) regions. DipAsm will facilitate high-quality precision medicine and studies of individual haplotype variation and population diversity.

Point-of-care diagnostic tests for influenza in the emergency department: A cost-effectiveness analysis in a high-risk population from a Canadian perspective.

BACKGROUND: Our objective was to assess the cost-effectiveness of novel rapid diagnostic tests: rapid influenza diagnostic tests (RIDT), digital immunoassays (DIA), rapid nucleic acid amplification tests (NAAT), and other treatment algorithms for influenza in high-risk patients presenting to hospital with influenza-like illness (ILI). METHODS: We developed a decision-analytic model to assess the cost-effectiveness of diagnostic test strategies (RIDT, DIA, NAAT, clinical judgement, batch polymerase chain reaction) preceding treatment; no diagnostic testing and treating everyone; and not treating anyone. We modeled high-risk 65-year old patients from a health payer perspective and accrued outcomes over a patient's lifetime. We reported health outcomes, quality-adjusted life years (QALYs), healthcare costs, and net health benefit (NHB) to measure cost-effectiveness per cohort of 100,000 patients. RESULTS: Treating everyone with no prior testing was the most cost-effective strategy, at a cost-effectiveness threshold of $50,000/QALY, in over 85% of simulations. This strategy yielded the highest NHB of 15.0344 QALYs, but inappropriately treats all patients without influenza. Of the novel rapid diagnostics, NAAT resulted in the highest NHB (15.0277 QALYs), and the least number of deaths (1,571 per 100,000). Sensitivity analyses determined that results were most impacted by the pretest probability of ILI being influenza, diagnostic test sensitivity, and treatment effectiveness. CONCLUSIONS: Based on our model, treating high-risk patients presenting to hospital with influenza-like illness, without performing a novel rapid diagnostic test, resulted in the highest NHB and was most cost-effective. However, consideration of whether treatment is appropriate in the absence of diagnostic confirmation should be taken into account for decision-making by clinicians and policymakers.

Estimation de l'épuisement des ressources hospitalières attribuable à la COVID-19 en Ontario, au Canada.

CONTEXTE: La propagation à l'échelle planétaire de la maladie à coronavirus 2019 (COVID-2019) se poursuit dans plusieurs pays, mettant à rude épreuve les systèmes de santé. Cette étude avait pour but de prédire les répercussions de la pandémie sur les issues des patients et l'utilisation des ressources hospitalières en Ontario (Canada). MÉTHODES: Nous avons conçu un modèle de simulation axé sur les personnes illustrant le flux de patients atteints de la COVID-19 dans les hôpitaux ontariens. Nous avons simulé diverses combinaisons de trajectoires épidémiques et de capacités de soins hospitaliers. Les paramètres à l'étude étaient le nombre de patients devant être admis au service d'hospitalisation ou à l'unité des soins intensifs (USI) ­avec ou sans respirateur mécanique ­, le nombre de jours jusqu'à l'épuisement des ressources, le nombre de patients en attente de ressources et le nombre de décès. RÉSULTATS: Nous avons constaté que la mise en place rapide de mesures de santé publique efficaces éviterait l'épuisement des ressources hospitalières. Les simulations dans lesquelles les mesures d'éloignement sanitaire étaient inefficaces ou adoptées tardivement ont montré que l'épuisement des ressources prendrait de 14 à 26 jours et qu'il y aurait, dans le pire des cas, 13 321 décès de personnes en attente de ressources. Cet épuisement pourrait être évité ou retardé par la mise en place de mesures rigoureuses visant à améliorer la capacité des hôpitaux en matière de soins intensifs, de respirateurs mécaniques et de soins hospitaliers. INTERPRÉTATION: Sans l'adoption de mesures d'éloignement sanitaire rigoureuses, le système de santé ontarien n'aurait pas eu les ressources nécessaires pour prendre en charge le nombre attendu de patients atteints de la COVID-19, même en cas d'augmentation rapide de sa capacité hospitalière. Les pénuries auraient fait augmenter le taux de mortalité. En ralentissant la transmission de la maladie par la mise en place de mesures de santé publique et l'augmentation de la capacité des hôpitaux, l'Ontario a probablement évité que ces derniers subissent une pression catastrophique.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia / COVID-19: Predicción de las necesidades de recursos sanitarios en el Valle del Cauca, Colombia

Abstract Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making

Resumen Introducción: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. Métodos: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. Resultados: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

Estimation of COVID-19-induced depletion of hospital resources in Ontario, Canada.

BACKGROUND: The global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing substantial strain to health care systems. The purpose of our study was to predict the effect of the COVID-19 pandemic on patient outcomes and use of hospital resources in Ontario, Canada. METHODS: We developed an individual-level simulation to model the flow of patients with COVID-19 through the hospital system in Ontario. We simulated different combined scenarios of epidemic trajectory and hospital health care capacity. Our outcomes included the number of patients who needed admission to the ward or to the intensive care unit (ICU) with or without the need for mechanical ventilation, number of days to resource depletion, number of patients awaiting resources and number of deaths. RESULTS: We found that with effective early public health measures, hospital system resources would not be depleted. For scenarios with late or ineffective implementation of physical distancing, hospital resources would be depleted within 14-26 days, and in the worst case scenario, 13 321 patients would die while waiting for needed resources. Resource depletion would be avoided or delayed with aggressive measures to increase ICU, ventilator and acute care hospital capacities. INTERPRETATION: We found that without aggressive physical distancing measures, the Ontario hospital system would have been inadequately equipped to manage the expected number of patients with COVID-19 despite a rapid increase in capacity. This lack of hospital resources would have led to an increase in mortality. By slowing the spread of the disease using public health measures and by increasing hospital capacity, Ontario may have avoided catastrophic stresses to its hospitals.

The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic.

In Ontario on March 16, 2020, a directive was issued to all acute care hospitals to halt nonessential procedures in anticipation of a potential surge in COVID-19 patients. This included scheduled outpatient cardiac surgical and interventional procedures that required the use of intensive care units, ventilators, and skilled critical care personnel, given that these procedures would draw from the same pool of resources required for critically ill COVID-19 patients. We adapted the COVID-19 Resource Estimator (CORE) decision analytic model by adding a cardiac component to determine the impact of various policy decisions on the incremental waitlist growth and estimated waitlist mortality for 3 key groups of cardiovascular disease patients: coronary artery disease, valvular heart disease, and arrhythmias. We provided predictions based on COVID-19 epidemiology available in real-time, in 3 phases. First, in the initial crisis phase, in a worst case scenario, we showed that the potential number of waitlist related cardiac deaths would be orders of magnitude less than those who would die of COVID-19 if critical cardiac care resources were diverted to the care of COVID-19 patients. Second, with better local epidemiology data, we predicted that across 5 regions of Ontario, there may be insufficient resources to resume all elective outpatient cardiac procedures. Finally in the recovery phase, we showed that the estimated incremental growth in waitlist for all cardiac procedures is likely substantial. These outputs informed timely data-driven decisions during the COVID-19 pandemic regarding the provision of cardiovascular care.

Estimating healthcare resource needs for COVID-19 patients in Nigeria.

INTRODUCTION: continuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria. METHODS: we adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon. RESULTS: in our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources. CONCLUSION: substantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia.

BACKGROUND: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. METHODS: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. RESULTS: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. CONCLUSION: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making.

INTRODUCCIÓN: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. MÉTODOS: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. RESULTADOS: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

Health outcomes attributable to carbapenemase-producing Enterobacteriaceae infections: A systematic review and meta-analysis.

BACKGROUND: Carbapenemase-producing Enterobacteriaceae (CPE) pose a significant global health threat. OBJECTIVE: To conduct a systematic review of health outcomes and long-term sequelae attributable to CPE infection. METHODS: We followed PRISMA reporting guidelines and published our review protocol on PROSPERO (CRD42018097357). We searched Medline, Embase, CINAHL and the Cochrane Library. We included primary studies with a carbapenem-susceptible control group in high-income countries, published in English. Quality appraisal was completed using Joanna Briggs Institute checklists. We qualitatively summarized frequently reported outcomes and conducted a meta-analysis. RESULTS: Our systematic review identified 8,671 studies; 17 met the eligibility criteria for inclusion. All studies reported health outcomes; none reported health-related quality-of-life. Most studies were from Europe (65%), were conducted in teaching or university-affiliated hospitals (76%), and used case-control designs (53%). Mortality was the most commonly reported consequence of CPE-infections; in-hospital mortality was most often reported (62%). Our meta-analysis (n = 5 studies) estimated an absolute risk difference (ARD) for in-hospital bloodstream infection mortality of 0.25 (95% confidence interval [CI], 0.17-0.32). Duration of antibiotic therapy (range, 4-29.7 vs 1-23.6 days) and length of hospital stay (range, 21-87 vs 15-43 days) were relatively higher for CPE-infected patients than for patients infected with carbapenem-susceptible pathogens. Most studies (82%) met >80% of their respective quality appraisal criteria. CONCLUSIONS: The risk of in-hospital mortality due to CPE bloodstream infection is considerably greater than carbapenem-susceptible bloodstream infection (ARD, 0.25; 95% CI, 0.17-0.32). Health outcome studies associated with CPE infection are focused on short-term (eg, in-hospital) outcomes; long-term sequelae and quality-of-life are not well studied. TRIAL REGISTRATION: PROSPERO (CRD42018097357).

Long-Term Sequelae and Health-Related Quality of Life Associated With Lyme Disease: A Systematic Review.

Lyme disease (LD) is the most commonly reported vector-borne disease, but its clinical consequences remain uncertain. We conducted a systematic review of the long-term sequelae and health-related quality of life (HRQoL) associated with LD in North America and Europe. We performed searches in 6 electronic databases up to December 2018 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, including observational studies reporting long-term sequelae, HRQoL, and prognostic factors. We included 46 studies, published between 1994 and 2019. Based on 21 studies reporting attributable outcomes, higher proportions of sequelae reported from exposed patients were: neck pain, myalgia, arthralgia, paresthesia, sleep disorder, poor appetite, and concentration difficulties. Patients with PTLDS reported impaired HRQoL compared to the general US population. Included studies were highly heterogeneous in terms of study design, settings, patient characteristics, and quality. Patients with LD are more likely to report nonspecific long-term sequelae, especially those experiencing persistent symptoms posttreatment. Opportunities exist for prospective longitudinal studies to better understand LD outcomes.

Estimating healthcare resource needs for COVID-19 patients in Nigeria

Introduction: continuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria.Methods: we adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon.Results: in our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources.Conclusion: substantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further