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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that less frequent repositioning of long‐term care residents at moderate to high risk of developing pressure ulcers (PrUs) is noninferior to current repositioning standards in preventing PrUs, but the long‐term health and economic consequences of less frequent repositioning have not been adequately estimated. Our objective was to estimate the cost‐effectiveness of different repositioning strategies (2‐, 3‐, 4‐hour intervals). We conducted a cost‐utility analysis using a lifetime horizon based on data from a randomized clinical trial and the literature. We updated a published PrU decision model with resource usage, unit costs, and epidemiological estimates from the literature and from a small observational study. The Ontario Ministry of Health and Long‐Term Care perspective was taken. We estimated lifetime costs to be CAN$5,425 (95% credible interval (CrI)=$922–12,166) less per resident with 3‐hour repositioning than with 2‐hour repositioning and CAN$3,296 (95% CrI=$483–9,738) less than with 4‐hour repositioning. The gain in expected quality‐adjusted life years from a 3‐ to a 2‐hour repositioning strategy was 0.008, (95% CrI=0.005–0.016) and from a 3‐ to a 4‐hour repositioning strategy was 0.009 (95% CrI=0.007–0.018). Repositioning at 3‐hour intervals was the dominant strategy with respect to the incremental cost‐effectiveness ratio against the 2‐ and 4‐hour strategies. Sensitivity analysis showed a 99% probability that 3‐hour repositioning was a dominant strategy. We concluded that repositioning at 3‐hour intervals for residents at moderate or high risk of PrUs and who were cared for on high‐density foam mattresses appeared to be the most cost‐effective strategy. [ABSTRACT FROM AUTHOR]

The volume and technical complexity of both academic and commercial research using decision analytic modelling has increased rapidly over the last two decades. The range of software programs used for their implementation has also increased, but it remains true that a small number of programs account for the vast majority of cost-effectiveness modelling work. We report a comparison of four software programs: TreeAge Pro, Microsoft Excel, R and MATLAB. Our focus is on software commonly used for building Markov models and decision trees to conduct cohort simulations, given their predominance in the published literature around cost-effectiveness modelling. Our comparison uses three qualitative criteria as proposed by Eddy et al.: "transparency and validation", "learning curve" and "capability". In addition, we introduce the quantitative criterion of processing speed. We also consider the cost of each program to academic users and commercial users. We rank the programs based on each of these criteria. We find that, whilst Microsoft Excel and TreeAge Pro are good programs for educational purposes and for producing the types of analyses typically required by health technology assessment agencies, the efficiency and transparency advantages of programming languages such as MATLAB and R become increasingly valuable when more complex analyses are required. [ABSTRACT FROM AUTHOR]

Background: There are numerous health-related quality of life (HRQol) measurements used in coronary heart disease (CHD) in the literature. However, only values assessed with preference-based instruments can be directly applied in a cost-utility analysis (CUA). Objective: To summarize and synthesize instrument-specific preference-based values in CHD and the underlying disease-subgroups, stable angina and post-acute coronary syndrome (post-ACS), for developed countries, while accounting for study-level characteristics, and within- and between-study correlation. Methods: A systematic review was conducted to identify studies reporting preference-based values in CHD. A multivariate meta-analysis was applied to synthesize the HRQoL values. Meta-regression analyses examined the effect of study level covariates age, publication year, prevalence of diabetes and gender. Results: A total of 40 studies providing preference-based values were detected. Synthesized estimates of HRQoL in post-ACS ranged from 0.64 (Quality of Well-Being) to 0.92 (EuroQol European”tariff”), while in stable angina they ranged from 0.64 (Short form 6D) to 0.89 (Standard Gamble). Similar findings were observed in estimates applying to general CHD. No significant improvement in model fit was found after adjusting for study-level covariates. Large between-study heterogeneity was observed in all the models investigated. Conclusions: The main finding of our study is the presence of large heterogeneity both within and between instrument-specific HRQoL values. Current economic models in CHD ignore this between-study heterogeneity. Multivariate meta-analysis can quantify this heterogeneity and offers the means for uncertainty around HRQoL values to be translated to uncertainty in CUAs. [ABSTRACT FROM AUTHOR]

There has been a large increase in the incidence of invasive fungal infections (IFIs) over the past decades, largely because of the increasing size of the population at risk. One of the major risk groups for IFIs are patients with haematological malignancies treated with cytotoxic chemotherapy or undergoing haematopoietic stem cell transplantation. These IFIs are associated with high morbidity and mortality rates. Consequently, as the diagnosis of IFIs is difficult, antifungal prophylaxis is desirable in high-risk patients. Furthermore, as the economic impact of IFIs is also significant, it is important to assess the cost benefit and cost effectiveness of each prophylactic agent in order to aid decisions concerning which prophylactic agent provides the best value for limited healthcare resources. This article systematically reviews the available pharmacoeconomic evidence regarding antifungal prophylaxis in immunocompromised patients treated for haematological malignancies. Furthermore, specific points of interest concerning economic analyses of antifungal prophylaxis are briefly discussed. Considering the available evidence, antifungal prophylaxis in immunocompromised patients treated for haematological malignancies seems to be an intervention with favourable cost-benefit, cost-effectiveness and costsaving potential. Furthermore, recently introduced antifungal agents seem to be attractive alternatives to fluconazole from a pharmacoeconomic point of view. However, due to wide heterogeneity in patient characteristics, underlying diseases, hospital settings and study methods in the included economic studies, as well as the lack of 'head-to-head' trials, it is difficult to find clear evidence of the economic advantages of a single prophylactic agent. Furthermore, we show that the results of cost-effectiveness analyses are highly dependent on several crucial factors that influence the baseline IFI incidence rates and, therefore, differ per patient population or region. [ABSTRACT FROM AUTHOR]

Background: Albuminuria is a marker for renal and cardiovascular (CV) risk, allowing early diagnosis of subjects with elevated renal and CV risk. Objective: This study aimed to estimate the costeffectiveness and budget impact of various population-based screen-and-treat scenarios for elevated albuminuria levels (ie, microalbuminuria) in the Netherlands. Methods: A multistate transition Markov model was developed to simulate the natural course of albuminuria-based disease progression to dialysis and occurrence of CV events. Several population-based strategies directed at screening for elevated albuminuria were evaluated. These strategies depended on urinary albumin concentration (UAC), urinary albumin excretion (UAE), and age. Transition probabilities were derived from the observational community-based Prevention of Renal and Vascular End Stage Disease (PREVEND) cohort study. Health care costs (in year-2008 euros) and life-years gained were calculated over an 8-year period. In the base-case analysis, we analyzed screening for and treatment of microalbuminuria. Screening for microal-buminuria involved prescreening for UAC ≥20 mg/L, followed by a confirmation test for UAE ≥30 mg/d. Other options based on combinations of albuminuria for UAC prescreening (no prescreening, and ≥10, ≥20, ≥100, and ≥200 mg/L) and UAE confirmation test (≥15, ≥30, and ≥300 mg/d) for treatment were investigated in scenario analyses. Furthermore, these various strategies based on UAC and UAE values were analyzed in different subgroups based on age (all ages, aged ≥50 years, and aged ≥60 years). Results: The PREVEND study included 8592 Dutch residents aged 28 to 75 years at the time of initial screening. Among a hypothetical cohort of 1000 subjects identified and treated in the base-case analysis, it was estimated (based on PREVEND follow-up data) that, in the screening/treatment and no-screening scenarios, 76 versus 124 CV events occurred, 16 versus 27 CV deaths, and 3 versus 5 dialysis cases, respectively. The per-person difference in net costs for screening was calculated at €926 (€2003 vs €1077), and prevention of CV deaths was estimated to gain 0.0421 discounted life-year per person. Correspondingly, the cost-effectiveness was estimated at €22,000 per life-year gained. In the base-case analysis, probabilistic sensitivity analysis indicated that the likelihood of cost-effectiveness of a screen-and-treat strategy was 54%, 90%, and 95% for a maximum acceptable cost-effectiveness threshold of €20,000, €50,000, and €80,000 per life-year gained, respectively. Higher albuminuria thresholds for screening and start of treatment further improved the cost-effectiveness but reduced the overall health gains achieved. Limiting screening to those subjects aged ≥50 and ≥60 years resulted in more favorable cost-effectiveness compared with population-based screening without age restriction. Conclusions: Our analyses suggest the potentially favorable cost-effectiveness of population-based screening for albuminuria in the general Dutch population. The results offer health care decision-makers new tools for considering actual implementation of such screening. [Copyright &y& Elsevier]