Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach.

Once unplanned urban rail disruptions occur, it is essential to evaluate the impacts on public transport passengers since impact estimation results enable transit agencies to verify whether alternative transit services have adequate capacity to evacuate the affected rail passengers and to adopt effective emergency measures in response to the disruptions. This paper focuses on estimating the impacts of unplanned rail line segment disruptions on rail passengers as well as original bus passengers, as the latter are overlooked in existing studies. A method of identifying affected rail passengers based on passenger tap-in time is proposed, which is helpful for evaluating the scale and origin-destination distribution of the affected passengers. Passengers' response behaviors are analyzed and modeled in a multi-agent simulation system. The system realizes the simulation of the multimodal evacuation process, in which a rule-based logit model is employed to describe passengers' travel selection behavior and the Monte Carlo method is utilized to address the issue of uncertainty in passengers' travel selection. In particular, the original bus passengers are integrated into the simulation and interact with rail passengers. Finally, some indicators assessing the impacts on rail passengers and bus passengers are presented, and a case study based on the Ningbo urban rail transit network is conducted.

Assessing multidimensional sustainability: Lessons from Brazil's social protection programs.

Examining linkages among multiple sustainable development outcomes is key for understanding sustainability transitions. Yet rigorous evidence on social and environmental outcomes of sustainable development policies remains scarce. We conduct a national-level analysis of Brazil's flagship social protection program, Zero Hunger (ZH), which aims to reduce food insecurity and poverty. Using data from rural municipalities across Brazil and quasi-experimental causal inference techniques, we assess relationships between social protection investment and outcomes related to sustainable development goals (SDGs): "no poverty" (SDG 1), "zero hunger" (SDG 2), and "health and well being" (SDG 3). We also assess potential perverse outcomes arising from agricultural development impacting "climate action" (SDG 13) and "life on land" (SDG 15) via clearance of natural vegetation. Despite increasing daily per capita protein and kilocalorie production, summed ZH investment did not alleviate child malnutrition or infant mortality and negligibly influenced multidimensional poverty. Higher investment increased natural vegetation cover in some biomes but increased losses in the Cerrado and especially the Pampa. Effects varied substantially across subprograms. Conditional cash transfer (Bolsa Familia [BF]) was mainly associated with nonbeneficial impacts but increased protein production and improved educational participation in some states. The National Program to Strengthen Family Farming (PRONAF) was typically associated with increased food production (protein and calories), multidimensional poverty alleviation, and changes in natural vegetation. Our results inform policy development by highlighting successful elements of Brazil's ZH program, variable outcomes across divergent food security dimensions, and synergies and trade-offs between sustainable development goals, including environmental protection.

A novel method for predicting the budget impact of innovative medicines: validation study for oncolytics.

BACKGROUND: High budget impact (BI) estimates of new drugs have led to decision-making challenges potentially resulting in restrictions in patient access. However, current BI predictions are rather inaccurate and short term. We therefore developed a new approach for BI prediction. Here, we describe the validation of our BI prediction approach using oncology drugs as a case study. METHODS: We used Dutch population-level data to estimate BI where BI is defined as list price multiplied by volume. We included drugs in the antineoplastic agents ATC category which the European Medicines Agency (EMA) considered a New Active Substance and received EMA marketing authorization (MA) between 2000 and 2017. A mixed-effects model was used for prediction and included tumor site, orphan, first in class or conditional approval designation as covariates. Data from 2000 to 2012 were the training set. BI was predicted monthly from 0 to 45 months after MA. Cross-validation was performed using a rolling forecasting origin with e^|Ln(observed BI/predicted BI)| as outcome. RESULTS: The training set and validation set included 25 and 44 products, respectively. Mean error, composed of all validation outcomes, was 2.94 (median 1.57). Errors are higher with less available data and at more future predictions. Highest errors occur without any prior data. From 10 months onward, error remains constant. CONCLUSIONS: The validation shows that the method can relatively accurately predict BI. For payers or policymakers, this approach can yield a valuable addition to current BI predictions due to its ease of use, independence of indications and ability to update predictions to the most recent data.

Affordability of oncology drugs: accuracy of budget impact estimations.

Background: In many countries, Budget Impact (BI) informs reimbursement decisions. Evidence has shown that decision-makers have restricted access based on high BI estimates but studies show that BI estimates are often inaccurate. Objective: To assess the accuracy of BI estimations used for informing access decisions on oncology drugs in the Netherlands. Study Design: Oncology products for which European Medicines Agency Marketing Authorisation was granted between 1-1-2000 and 1-10-2017 were selected. Observed BI data were provided by FarmInform. BI estimates were extracted from the reimbursement dossiers of the Dutch Healthcare Institute. Products without an estimated BI in the reimbursement dossier were excluded. Accuracy is defined as the ratio observed BI/estimated BI. Setting: General community, the Netherlands. Results: Ten products were included in the base case analysis. Mean accuracy was 0.64 and observed BI deviated by more than 40% and 100% from the estimated BI for 4 and 5 products, respectively. For all products together, €141 million BI was estimated and €82 million BI was observed, a €59 million difference. Conclusions: The findings indicate that BI estimates for oncology drugs in the Netherlands are inaccurate. The role and use of BI in reimbursement decisions for these potentially life-saving drugs should therefore be considered carefully, as well as BI estimation methodology.

Risk-management tool for environmental prioritization of pharmaceuticals based on emissions from hospitals.

Over the past decade, the health care sector has become increasingly aware of the impact of pharmaceutical emissions to the environment. Yet, it remains unclear which compounds are the most relevant to address and at what point emission control is most effective. This study presents a modelling framework to prioritize pharmaceuticals based on their relative risks for aquatic organisms, using purchase and prescription data from hospitals. The framework consists of an emission prediction module and a risk prioritization module. The emission prediction module accounts for three different routes of intake (oral, intravenous, rectal), for non-patient consumption, and for delayed athome excretion due to relatively long half-lives or prescription durations of selected pharmaceuticals. We showcase the modelling framework with 16 pharmaceuticals administered at two Dutch academic hospitals. Predictions were validated with experimental data from passive sampling in the sewer system. With the exception of metformin, all predictions were within a factor of 10 from measurements. The risk prioritization module ranks each pharmaceutical based on its predicted relative risk for aquatic organisms. The resulting prioritization suggests that emission mitigation strategies should mainly focus on antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs).