Cost data requirements for the proposed approach included standardized and disaggregated unit costs (for a limited number of outputs) and information on the facilities network available to the program. The defined functional form will determine the magnitude and shape of costs when outputs and coverage are increasing. This in turn will impact resource allocation decisions. Infectious diseases modelers and economists should use transparent and empirically based cost models for analyses that inform resource allocation decisions. This framework describes a general approach for developing these models.The defined functional form will determine the magnitude and shape of costs when outputs and coverage are increasing. CC-122 This in turn will impact resource allocation decisions. Infectious diseases modelers and economists should use transparent and empirically based cost models for analyses that inform resource allocation decisions. This framework describes a general approach for developing these models. To estimate the effect of filgrastim-sndz market entry on patient out-of-pocket costs and claim payments for filgrastim products. This study used a single interrupted time series design with longitudinal, nationally representative, individual-level claims data from IBM MarketScan. Analyses included all outpatient and prescription claims for branded filgrastim (filgrastim and tbo-filgrastim) and biosimilar filgrastim (filgrastim-sndz) from January 1, 2014, to December 31, 2017. Outcomes of interest included changes in monthly claim payments and monthly patient out-of-pocket costs for filgrastim products. In the baseline period (January 2014 to February 2016), insurers paid an average of $472.21 (95% confidence interval [CI] 465.38-479.03) for 480 mcg of branded filgrastim, whereas patients paid an average of $49.26 (CI 34.25-64.27). Filgrastim-sndz market entry was associated with a statistically significant and immediate 1-month decrease in insurer payment of $30.77 (95% CI -40.59 to -20.94) and a signito beneficiaries enrolled in high cost sharing plans, suggesting the importance of further work assessing the relationship between biosimilar availability and patient out-of-pocket costs. Patient-provider discussion about treatment costs has been recognized as a key component of shared clinical decision making in cancer care. This study examined the association of patient-provider cost discussion with out-of-pocket spending among cancer survivors. Using data from the 2016-2017 Medical Expenditure Panel Survey-Experiences with Cancer Survivorship Supplement, cancer survivors in the United States who reported having a detailed discussion about treatment costs were identified. Multivariable generalized linear model with gamma distribution and log-link was fitted to analyze average total out-of-pocket spending between those who had the discussion and those who did not. We also examined whether having the cost discussion is associated with the likelihood of reporting receipt of all cancer care they believed was necessary using a multivariable logistic regression model. All analyses controlled for patient socioeconomic and health-related characteristics. Among 1525 individuals, representing 14ization of necessary cancer treatments. Three hundred million people living with rare diseases worldwide are disproportionately deprived of in-time diagnosis and treatment compared with other patients. This review provides an overview of global policies that optimize development, licensing, pricing, and reimbursement of orphan drugs. Pharmaceutical legislation and policies related to access and regulation of orphan drugs were examined from 194 World Health Organization member countries and 6 areas. Orphan drug policies (ODPs) were identified through internet search, emails to national pharmacovigilance centers, and systematic academic literature search. Texts from selected publications were extracted for content analysis. One hundred seventy-two drug regulation documents and 77 academic publications from 162 countries/areas were included. Ninety-two of 200 countries/areas (46.0%) had documentation on ODPs. Thirty-four subthemes from content analysis were categorized into 6 policy themes, namely, orphan drug designation, marketing authorizatiothermore, identified policy gaps in price regulation, incentives that encourage market availability, and incentives that encourage research and development should be addressed to improve access to available and affordable orphan drugs. Traditional risk scores improved the definition of the initial therapeutic strategy in acute coronary syndrome (ACS), but they were not designed for predicting long-term individual risks and costs. In parallel, attempts to directly predict costs from clinical variables in ACS had limited success. Thus, novel approaches to predict cardiovascular risk and health expenditure are urgently needed. Our objectives were to predict the risk of major/minor adverse cardiovascular events (MACE) and estimate assistance-related costs. We used a 2-step approach that (1) predicted outcomes with a common pathophysiological substrate (MACE) by using machine learning (ML) or logistic regression (LR) and compared with existing risk scores; (2) derived costs associated with noncardiovascular deaths, dialysis, ambulatory-care-sensitive-hospitalizations (ACSH), strokes, and MACE. With consecutive ACS individuals (n= 1089) from 2 cohorts, we trained in 80% of the population and tested in 20% using a 4-fold cross-validation frames and avoidable costs after ACS.ML methods predicted long-term risks and avoidable costs after ACS. The study had two main aims. First, we assessed the cost-effectiveness of transplanting deceased donor kidneys of differing quality levels based on the Kidney Donor Profile Index (KDPI). Second, we assessed the cost-effectiveness of remaining on the waiting list until a high-quality kidney becomes available compared to transplanting a lower-quality kidney. A decision analytic model to estimate cost-effectiveness was developed using a Markov process. Separate models were developed for 4 separate KDPI bands, with higher values indicating lower quality. Models were simulated in 1-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient from the healthcare payer's perspective. Weibull regression was used to calculate the time-dependent transition probabilities in the base analysis. The impact uncertainty arising in model parameters was included by probabilistic sensitivity analysis using the Monte Carlo simulation method. Willingness to pay was considered as Australian $28 000.