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Data breaches in the US healthcare sector have more than tripled in the last decade across all states. However, to this day, no established framework ranks all states from most to least at risk for healthcare data breaches. This gap has led to a lack of proper risk identification and understanding of cyber environments at state levels.

Based on the security action cycle, the National Institute of Standards and Technology (NIST) cybersecurity framework, the risk-planning model, and the multicriteria decision-making (MCDM) literature, the paper offers an integrated multicriteria framework for prioritization in cybersecurity to address this lack and other prioritization issues in risk management in the field. The study used historical breach data between 2015 and 2021.

The findings showed that California, Texas, New York, Florida, Indiana, Pennsylvania, Massachusetts, Minnesota, Ohio, and Georgia are the states most at risk for healthcare data breaches.

The findings highlight each US state faces a different level of healthcare risk. The findings are informative for patients, crucial for privacy officers in understanding the nuances of their risk environment, and important for policy-makers who must grasp the grave disconnect between existing issues and legislative practices. Furthermore, the study suggests an association between positioning state risk and such factors as population and wealth, both avenues for future research.

Theoretically, the paper offers an integrated framework, whose basis in established security models in both academia and industry practice enables utilizing it in various prioritization scenarios in the field of cybersecurity. It further emphasizes the importance of risk identification and brings attention to different healthcare cybersecurity environments among the different US states.

Due to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items after occurring a disaster is significant, especially when some of the relief items are perishable. Therefore, the purpose of this paper is to create a resilient and integrated decision-making structure to distribute relief items at demand points, considering two dimensions of sustainability, under disruption.

This study developed a mixed-integer nonlinear mathematical model to handle the pre- and post-disaster planning when a disaster occurs. The represented model has two objective functions: minimizing weighted unmet demand and total costs. Therefore, to convert this multi-objective problem into a single objective one, the e-constraint method was applied.

The main results showed that considering some resilience strategies has a significant effect in reducing the weighted amount of unmet demand and saves the total costs. More precisely, considering resilience strategies results in a 60% reduction in total unmet demand and 11% reduction in total pre-positioning costs. On the other hand, reducing the maximum response time with applying resilience strategies is another achievement of the present study. For these reasons, the use of these strategies can reduce people’s pain and suffer from natural disasters. In general, the application and effectiveness of sustainability dimensions and resilience strategies in the introduced humanitarian relief chain network were analyzed.

To verify the applicability of this study, this model is applied on a probable real-life case study in Tehran. Finally, some managerial insights are discussed to help humanitarian organizations, managers and stakeholders to make better decisions to reduce negative effects of natural disasters.

This paper introduced a two-stage stochastic mathematical model for designing a resilient humanitarian relief chain network under disruption, at pre- and post-disaster stages. Also, economic and social dimensions of sustainability are considered in this study. Moreover, assembling perishable and im-perishable relief items as relief kits, dynamically is a main contribution of this research.