Late mortality among 5-year survivors of childhood cancer: A systematic review and meta-analysis.

BACKGROUND: Childhood cancer survivors are at increased risk of late mortality (death ≥5 years after diagnosis) from cancer recurrence and treatment-related late effects. The authors conducted a systematic review and meta-analysis to provide comprehensive estimates of late mortality risk among survivors internationally and to investigate differences in risk across world regions. METHODS: Health sciences databases were searched for cohort studies comprised of 5-year childhood cancer survivors in which the risk of mortality was evaluated across multiple cancer types. Eligible studies assessed all-cause mortality risk in survivors relative to the general population using the standardized mortality ratio (SMR). The absolute excess risk (AER) was assessed as a secondary measure to examine excess deaths. Cause-specific mortality risk was also assessed, if reported. SMRs from nonoverlapping cohorts were combined in subgroup meta-analysis, and the effect of world region was tested in univariate meta-regression. RESULTS: Nineteen studies were included, and cohort sizes ranged from 314 to 77,423 survivors. Throughout survivorship, SMRs for all-cause mortality generally declined, whereas AERs increased after 15-20 years from diagnosis in several cohorts. All-cause SMRs were significantly lower overall in North American studies than in European studies (relative SMR, 0.63; 95% confidence interval, 0.49-0.80). SMRs for subsequent malignant neoplasms and for cardiovascular, respiratory, and external causes did not vary significantly between world regions. CONCLUSIONS: The current findings suggest that late mortality risk may differ significantly between world regions, but these conclusions are based on a limited number of studies with considerable heterogeneity. Reasons for regional differences remain unclear but may be better elucidated through future analyses of individual-level data.

Variations in Site-Specific Costs for Infants Born Extremely Preterm in Canadian Neonatal Intensive Care Units.

OBJECTIVE: To quantify site-specific costs and their association with survival without major morbidity (SWMM) in Canada for neonates <28 weeks of gestation admitted to large tertiary neonatal intensive care units. METHODS: We conducted a retrospective analysis of infants born at <28 weeks of gestation and admitted to Canadian Neonatal Network sites from 2010 through 2021. Sites that cared for at least 50 eligible infants by gestational age in weeks over the study period were included. Using a validated costing algorithm that assessed physician, nursing, respiratory therapy, diagnostic imaging, transfusions, procedural, medication, and certain indirect costs, we calculated site and resource-specific costs in 2017 Canadian dollars (CAD) and evaluated their relationship with SWMM. RESULTS: Seven sites with 8180 (range 841-1605) eligible neonates with a mean (SD) gestation of 25.4 [1.3] weeks were included. Survival to discharge or transfer was 85.3% with a mean (SD) length of stay of 75 (46) days. The mean (SD) total and daily costs per neonate varied between $94 992 ($60 283) and $174 438 ($130 501) CAD and $1833 ($916) to $2307 ($1281) CAD, respectively. Between sites, there was no relationship between costs and SWMM. CONCLUSIONS: There was marked variation in costs and SWMM between sites in Canada with universal health care. The lack of concordance between both outcomes and costs among sites may provide possibilities for outcomes improvement and cost containment.

SurvdigitizeR: an algorithm for automated survival curve digitization.

BACKGROUND: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves. METHODS: The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm's performance was also evaluated by Bland-Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves. RESULTS: The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement. CONCLUSIONS: The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ .

Cost-effectiveness of the McGill interactive pediatric oncogenetic guidelines in identifying Li-Fraumeni syndrome in female patients with osteosarcoma.

BACKGROUND: Li-Fraumeni syndrome (LFS) is a penetrant cancer predisposition syndrome (CPS) associated with the development of many tumor types in young people including osteosarcoma and breast cancer (BC). The McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) decision-support tool provides a standardized approach to identify patients at risk of CPSs. METHODS: We conducted a cost-utility analysis, from the healthcare payer perspective, to compare MIPOGG-guided, physician-guided, and universal genetic testing strategies to detect LFS in female patients diagnosed at an age of less than 18 years with osteosarcoma. We developed a decision tree and discrete-event simulation model to simulate the clinical and cost outcomes of the three genetic referral strategies on a cohort of female children diagnosed with osteosarcoma, especially focused on BC as subsequent cancer. Outcomes included BC incidence, quality-adjusted life-years (QALYs), healthcare costs, and incremental cost-utility ratios (ICURs). We conducted probabilistic and scenario analyses to assess the uncertainty surrounding model parameters. RESULTS: Compared to the physician-guided testing, the MIPOGG-guided strategy was marginally more expensive by $105 (-$516; $743), but slightly more effective by 0.003 (-0.04; 0.045) QALYs. Compared to MIPOGG, the universal testing strategy was $1333 ($732; $1953) more costly and associated with 0.011 (-0.043; 0.064) additional QALYs. The ICUR for the MIPOGG strategy was $33,947/QALY when compared to the physician strategy; the ICUR for universal testing strategy was $118,631/QALY when compared to the MIPOGG strategy. DISCUSSION: This study provides evidence for clinical and policy decision-making on the cost-effectiveness of genetic referral strategies to identify LFS in the setting of osteosarcoma. MIPOGG-guided strategy was most likely to be cost-effective at a willingness-to-pay threshold value of $50,000/QALY.

Validation of a Costing Algorithm and Cost Drivers for Neonates Admitted to the Neonatal Intensive Care Unit.

OBJECTIVE: Neonatal intensive care units (NICUs) account for over 35% of pediatric in-hospital costs. A better understanding of NICU expenditures may help identify areas of improvements. This study aimed to validate the Canadian Neonatal Network (CNN) costing algorithm for seven case-mix groups with actual costs incurred in a tertiary NICU and explore drivers of cost. STUDY DESIGN: A retrospective cohort study of infants admitted within 24 hours of birth to a Level-3 NICU from 2016 to 2019. Patient data and predicted costs were obtained from the CNN database and were compared to actual obtained from the hospital accounting system (Coût par Parcours de Soins et de Services). Cost estimates (adjusted to 2017 Canadian Dollars) were compared using Spearman correlation coefficient (rho). RESULTS: Among 1,795 infants included, 169 (9%) had major congenital anomalies, 164 (9%) with <29 weeks' gestational age (GA), 189 (11%) with 29 to 32 weeks' GA, and 452 (25%) with 33 to 36 weeks' GA. The rest were term infants: 86 (5%) with hypoxic-ischemic encephalopathy treated with therapeutic hypothermia, 194 (11%) requiring respiratory support, and 541 (30%) admitted for other reasons. Median total NICU costs varied from $6,267 (term infants admitted for other reasons) to $211,103 (infants born with <29 weeks' GA). Median daily costs ranged from $1,613 to $2,238. Predicted costs correlated with actual costs across all case-mix groups (rho range 0.78-0.98, p < 0.01) with physician and nursing representing the largest proportion of total costs (65-82%). CONCLUSION: The CNN algorithm accurately predicts NICU total costs for seven case-mix groups. Personnel costs account for three-fourths of in-hospital total costs of all infants in the NICU. KEY POINTS: · Very preterm infants born below 33 weeks of gestation account for most of NICU resource use.. · Human resources providing direct patient care represented the largest portion of costs.. · The algorithm strongly predicted total costs for all case-mix groups..

Cost and cost-effectiveness of immunotherapy in childhood ALL: A systematic review.

Survival rates for pediatric acute lymphoblastic leukemia (pALL) have improved dramatically; relapsed/refractory (r/r) acute lymphoblastic leukemia (ALL) remains challenging. Immunotherapies are rapidly evolving treatments for r/r ALL with limited cost-effectiveness data. This study identifies existing economic evaluations of immunotherapy in pALL and summarizes cost-effectiveness. Medline, Embase, and other databases were searched from inception to October 2022. Cost-effectiveness analyses evaluating immunotherapy in pALL were included. Costs reported in 2021 USD. Of 2960 studies, 11 met inclusion criteria. Tisagenlecleucel was compared to standard of care, clofarabine monotherapy, clofarabine combination therapy, or blinatumomab. No studies have evaluated blinatumomab or inotuzumab ozogamicin. Six studies found tisagenlecleucel to be cost-effective, five of which were supported by Novartis. Four found that it had the potential to be cost-effective, and one found that it was not cost-effective. The cost-effectiveness of tisagenlecleucel was highly dependent on list price and cure rates. This study can inform the use of tisagenlecleucel in pALL.

Barriers and Facilitators of Using R for Decision Analytic Modeling in Health Technology Assessment: Focus Group Results.

BACKGROUND AND OBJECTIVE: Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators. METHODS: We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes. RESULTS: Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures. CONCLUSIONS: There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.

Preterm Birth, Family Income, and Intergenerational Income Mobility.

Importance: Preterm birth (PTB) has been associated with lower income in adulthood, but associations with intergenerational income mobility and the role of family socioeconomic status (SES) as modifying factor are unclear. Objectives: To assess whether the association between PTB and income differs according to family SES at birth and to assess the association between PTB and intergenerational income mobility. Design, Setting, and Participants: This study comprised a matched cohort of live births in Canada between January 1, 1990, and December 31, 1996, with follow-up until December 31, 2018. Statistical analysis was performed between May 2023 and March 2024. Exposure: Preterm birth, defined as birth between 24 and 37 weeks' gestational age (with gestational age subcategories of 34-36, 32-33, 28-31, and 24-27 weeks) vs early and full term births (gestational age, 37-41 weeks). Main Outcomes and Measures: Associations between PTB and annual adulthood income in 2018 Canadian dollars were assessed overall (current exhange rate: $1 = CAD $1.37) and stratified by family income quintiles, using generalized estimating equation regression models. Associations between PTB and percentile rank change (ie, difference between the rank of individuals and their parents in the income distribution within their respective generations) and upward or downward mobility (based on income quintile) were assessed using linear and multinomial logistic regressions, respectively. Results: Of 1.6 million included births (51.1% boys and 48.9% girls), 6.9% infants were born preterm (5.4% born at 34-36 weeks, 0.7% born at 32-33 weeks, 0.5% born at 28-31 weeks, and 0.2% born at 24-27 weeks). After matching on baseline characteristics (eg, sex, province of birth, and parental demographics) and adjusting for age and period effects, PTB was associated with lower annual income (mean difference, CAD -$687 [95% CI, -$788 to -$586]; 3% lower per year), and the differences were greater among those belonging to families in the lowest family SES quintile (mean difference, CAD -$807 [95% CI, -$998 to -$617]; 5% lower per year). Preterm birth was also associated with lower upward mobility and higher downward mobility, particularly for those born earlier than 31 weeks' gestational age (24-27 weeks: mean difference in percentile rank change, -8.7 percentile points [95% CI, -10.5 to -6.8 percentile points]). Conclusions and Relevance: In this population-based matched cohort study, PTB was associated with lower adulthood income, lower upward social mobility, and higher downward mobility, with greater differences among those belonging to economically disadvantaged families. Interventions to optimize socioeconomic outcomes of preterm-born individuals would need to define target population considering SES.

The impact of referring hospital resources on interfacility overtriage: A population-based analysis.

BACKGROUND: Nearly half of patients transferred from non-trauma to trauma centres have minor injuries. The transfer of patients with minor injuries to trauma centres is not associated with any known patient benefit and represents an opportunity to reduce healthcare costs and improve patient experience. In this study, we evaluated the relationship between hospital resources and overtriage, with the objective of identifying targets for system-level intervention. METHODS: We conducted a population-based cohort study of adults, age ≥ 16, presenting with minor injuries to non-trauma centres in Ontario, Canada (2009-2020). The primary outcome was overtriage, defined as transfer to a trauma centre. Hierarchical logistic regression was used to evaluate the association between hospital resources and a patient's likelihood of being overtriaged, adjusting for case-mix. RESULTS: amongst 165,302 patients with minor injuries, 15,641 (9.5 %) were transferred to a trauma centre (overtriage). Presence of a CT scanner, surgical support, or intensive care unit had no impact on a patient's likelihood of overtriage. Relative to community hospitals, presentation to a teaching hospital was independently associated with greater odds of overtriage (OR 2.97, 95 % CI: 1.26-7.00). Accounting for case-mix and resources, the median difference in a patient's odds of overtriage varied 3.7-fold across non-trauma centres (MOR 3.76). CONCLUSIONS: There is significant variability in overtriage across non-trauma centres, even after adjusting for case-mix and hospital resources. These finding suggests that some centres have developed processes to minimize overtriage independent of available resources. Broad implementation of these processes may represent an opportunity for system-wide quality improvement.

Application of Multi-Criteria Decision Analysis (MCDA) to Prioritize Real-World Evidence Studies for Health Technology Management: Outcomes and Lessons Learned by the Canadian Real-World Evidence for Value of Cancer Drugs (CanREValue) Collaboration.

Multi-criteria decision analysis (MCDA) is a value assessment tool designed to help support complex decision-making by incorporating multiple factors and perspectives in a transparent, structured approach. We developed an MCDA rating tool, consisting of seven criteria evaluating the importance and feasibility of conducting potential real-world evidence (RWE) studies aimed at addressing uncertainties stemming from initial cancer drug funding recommendations. In collaboration with the Canadian Agency for Drugs and Technologies in Health's Provincial Advisory Group, a validation exercise was conducted to further evaluate the application of the rating tool using RWE proposals varying in complexity. Through this exercise, we aimed to gain insight into consensus building and deliberation processes and to identify efficiencies in the application of the rating tool. An experienced facilitator led a multidisciplinary committee, consisting of 11 Canadian experts, through consensus building, deliberation, and prioritization. A total of nine RWE proposals were evaluated and prioritized as low (n = 4), medium (n = 3), or high (n = 2) priority. Through an iterative process, efficiencies and recommendations to improve the rating tool and associated procedures were identified. The refined MCDA rating tool can help decision-makers prioritize important and feasible RWE studies for research and can enable the use of RWE for the life-cycle evaluation of cancer drugs.

The PSY-SIM Model: Using Real-World Data to Inform Health Care Policy for Individuals With Chronic Psychotic Disorders.

BACKGROUND AND HYPOTHESIS: Few microsimulation models have been developed for chronic psychotic disorders, severe and disabling mental disorders associated with poor medical and psychiatric outcomes, and high costs of care. The objective of this work was to develop a microsimulation model for individuals with chronic psychotic disorders and to use the model to examine the impact of a smoking cessation initiative on patient outcomes. STUDY DESIGN: Using health records and survey data from Ontario, Canada, the PSY-SIM model was developed to simulate health and cost outcomes of individuals with chronic psychotic disorders. The model was then used to examine the impact of the Smoking Treatment for Ontario Patients (STOP) program from Ontario on the development of chronic conditions, life expectancy, quality of life, and lifetime health care costs. STUDY RESULTS: Individuals with chronic psychotic disorders had a lifetime risk of 63% for congestive heart failure and roughly 50% for respiratory disease, cancer and diabetes, and a life expectancy of 76 years. The model suggests the STOP program can reduce morbidity and lead to survival and quality of life gains with modest increases in health care costs. At a long-term quit rate of 4.4%, the incremental cost-effectiveness ratio of the STOP program was $41,936/QALY compared with status quo. CONCLUSIONS: Smoking cessation initiatives among individuals with chronic psychotic disorders can be cost-effective. These findings will be relevant for decision-makers and clinicians looking to improving health outcomes among this patient population.

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.