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The military of today depends on the availability and readiness of high-technology weapon systems. As the military often has to focus on core tasks (the usage of systems), logistics and support tasks are outsourced to industry, which means that industrial suppliers gain importance for making weapon systems available and mission ready. However, companies are profit-maximizing and invest their best resources in the most promising business areas, which might be clients other than the (domestic) military customer. This raises the question of how the military can ensure that the defense industry provides the best performance: preferential treatment for the military. The purpose of this study is to investigate preferential treatment in the specific context of defense.

This paper reports on the examination of factors influencing preferential treatment for the military. The analysis uses structural equation modeling and data from a sample of German defense suppliers.

The results show that the perceived customer attractiveness has a strong effect on preferential customer treatment. Attractiveness is influenced by trust, commitment and a comparison with other customers.

There are several implications for defense theory and practice, including the need for further consideration of relational in contrast to transactional practices in military–industrial supply collaboration, as these seem highly relevant for getting the best resources for producing and maintaining weapon systems. In addition, increasing customer attractiveness, in particular if the military lacks a domestic defense industry base, is proposed.

The findings are based on a focus sample of only defense suppliers. This paper transfers the industrial discussion about the buyer–supplier relationships and preferential customer treatment to the defense logistics research context.

This study aims to examine the relationship between medication for opioid use disorder (MOUD) among pregnant individuals, referral source, mental health, political affiliation and substance use policies in all 50 states in the USA.

This study describes MOUD receipt among pregnant people with an opioid use disorder (OUD) in 2018. The authors explored sociodemographic differences in MOUD receipt, referrals and co-occurring mental health disorders. The authors included a comparison of MOUD receipt among states that have varying substance use policies and examined the impact of these policies and the political affiliation on MOUD. The authors used multilevel binary logistic regression to examine effects of individual and state-level characteristics on MOUD.

Among 8,790 pregnant admissions with OUD, the majority who received MOUD occurred in the Northeast region (71.52%), and 14.99% were referred by the criminal justice system (n = 1,318). Of those who were self-referred, 66.39% received MOUD, while only 30.8% of referrals from the criminal justice system received MOUD. Those referred from the criminal justice system or who had a co-occurring mental health disorder were least likely to receive MOUD. The multilevel model showed that while policies were not a significant predictor, a state’s political affiliation was a significant predictor of MOUD.

The study has some methodological limitations; a state-level analysis, even when considering the individual factors, may not provide sufficient description of community-level or other social factors that may influence MOUD receipt. This study adds to the growing literature on the ineffectiveness of prenatal substance use policies designed specifically to increase the use of MOUD. If such policies are consistently assessed as not contributing to substantial increase in MOUD among pregnant women over time, it is imperative to investigate potential mechanisms in these policies that may not facilitate MOUD access the way they are intended to.

Findings from this study aid in understanding the impact that a political affiliation may have on treatment access; states that leaned more Democratic were more likely to have higher rates of MOUD, and this finding can lead to research that focuses on how and why this contributes to greater treatment utilization. This study provides estimates of underutilization at a state level and the mechanisms that act as barriers, which is a stronger assessment of how state-specific policies and practices are performing in addressing prenatal substance use and a necessary step in implementing changes that can improve the links between pregnant women and MOUD.

To the best of the authors’ knowledge, this is the first study to explore individual-level factors that include mental health and referral sources to treatment that lead to MOUD use in the context of state-level policy and political environments. Most studies estimate national-level rates of treatment use only, which can be useful, but what is necessary is to understand what mechanisms are at work that vary by state. This study also found that while substance use policies were designed to increase MOUD for pregnant women, this was not as prominent a predictor as other factors, like mental health, being referred from the criminal justice system, and living in a state with more Democratic-leaning affiliations.

The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

The opioid epidemic in the USA, the new psychoactive substances emerging on the market and the recent increase in cocaine treatment demands in Western Europe, all emphasise the importance of monitoring the use and harms of drugs over time. To be informed about new consumption patterns, this study aims to study the trends among people entering treatment for substance use in Belgium.

Belgian data from the Treatment Demand Indicator collected between 2015 and 2019 were used. A reference group of treatment units was selected to allow for comparisons between the different years. Trend analysis was performed by using a joinpoint regression among different regions and groups of clients.

The drugs of choice that were most frequently mentioned among the 23,000 analysed treatment episodes were alcohol and cannabis. Both remained relatively stable over time. Heroin seemed to be decreasing significantly at the national level, but increased in Brussels. Benzodiazepines decreased significantly in Flanders and Brussels, but not in Wallonia. On the other hand, reports of crack cocaine increased significantly in the three regions with a more pronounced trend in Wallonia and Brussels. Substances such as fentanyl, methamphetamine, ketamine or volatile inhalants have been mentioned significantly more by people entering treatment in 2019, although their contribution to the total number is still limited.

To the best of the authors’ knowledge, this study is the first to evaluate trends for all drugs of choice at a national and regional level. These results might not only benefit national policymakers but also other countries with similar alcohol or drug use patterns.

This paper aims to examine the time it would take to provide medical prophylaxis for a large urban population in the wake of an airborne anthrax attack and the effect that various parameters have on the total logistical time.

A mathematical model that evaluates key parameters and suggests alternatives for improvement is formulated. The objective of the model is to minimize the total logistical time required for prophylaxis by balancing three cycles as follows: the loading cycle, the shipping cycle and the service cycle.

Applying the model to two representative cases reveals the effect of various parameters on the process. For example, the number of distribution centers and the number of servers in each center are key parameters, whereas the number of central depots and the local shipping method is less important.

Various psychological factors such as mass panic are not included in the model.

There are few papers analyzing the logistical response to an anthrax attack, and most focus mainly on the strategic level. The study deals with the tactical logistical level. The authors focus on the distribution process of prophylaxis and other medical supplies during the crisis, analyze it and identify the parameters that influence the time between the detection of the attack and the provision of effective medical treatment to the exposed population.

In Mexico, only 19.3 per cent of industrial water is treated (Green-Peace, 2014, pp. 3-4), whereas municipal treatment levels are approximately 50 per cent (CONAGUA, 2014a). This paper aims to focus on how the wastewater treatment plant policy, from a circular economy perspective, is affected by the governance context at the Presa Guadalupe sub-basin. Circular economy can contribute to water innovations that help in improving water quality. However, such benefits are not easily achieved. This case provides an example of the complexity and challenges that the implementation of a circular economy model can face.

Data are collected via semi-structured in-depth interviews with the stakeholders that are members of the Presa Guadalupe Commission. The contextual interaction theory (CIT) is the theoretical basis for this analysis (Boer de and Bressers, 2011; Bressers, 2009).

The findings show that the wastewater treatment plant policy plays an important role in a circular economy model. Some incentives towards a circular economy model are already in place; however, the hurdles of a top-down implementation perspective, low availability of resources, prioritisation of short-term results, lack of enforcement of the “polluter pays” principle and a linear model of water systems need to be overcome. If Mexico wants to move towards a circular economy model and if the government wants to enforce sustainable development principles, wastewater treatment is a challenge that must be addressed.

There are few studies in the circular economy literature that have analysed its implementation under a governance arrangement perspective.

This study aims to examine the effectiveness of an integrated intervention program for alcoholism (IIPA) for improving verbal encoding and memory, visuospatial construction, visual memory and quality of life (QoL) in persons with alcohol dependence.

The sample comprised treatment-seeking alcohol-dependent persons (n = 50), allotted into two groups: (1) the treatment as usual (TAU) group (n = 25) and (2) the treatment group (n = 25)]. The groups were matched on age (±1 year) and education (±1 year). The TAU group received standard pharmacological treatment, psychotherapeutic sessions on relapse prevention and yoga for 18 days, while the treatment group received IIPA sessions in addition to the usual treatment. Auditory verbal learning test, complex figure test and QoL scale were administered at pre- and post-treatment along with screening measures.

The two groups were comparable on demographic variables, clinical characteristics and outcome measures at baseline. Pre- to post-treatment changes (gain scores) comparison between the treatment and TAU groups revealed a significant difference in verbal encoding, verbal and visual memory, verbal recognition, visuospatial construction and QoL.

This study suggests that IIPA is effective for improving learning and memory in both modality (verbal and visual) and QoL in persons with alcoholism. The IIPA may help in better treatment recovery.

The IIPA may help in treatment for alcoholism and may enhance treatment efficacy.

IIPA is effective for improving learning and memory in both modalities and QoL in persons with alcohol dependence. The IIPA may help in better treatment recovery.

The purpose of this study was to assess tuberculosis (TB) treatment outcomes among new smear-positive pulmonary tuberculosis (PTB) patients and identify the risk factors of unsuccessful treatment outcomes in Kepong district, Kuala Lumpur, Malaysia.

A retrospective cohort study was conducted using registry-based data from the Tuberculosis Information System (TBIS) between 2014 and 2018. Simple random sampling was used to select 734 males and 380 females from the TBIS registry. Smear-positive PTB patient's sociodemographic, clinical and behavioral characteristics were extracted and analyzed. Logistic regression was used to find the possible independent risk factors for unsuccessful treatment outcomes.

The treatment success rate was 77.20% (n = 860) which was still below the target set by the WHO (>90%). In total, 254 patients showed an unsuccessful treatment outcome: 106 died, 99 defaulted, 47 not evaluated and 2 showed treatment failure. Unsuccessful treatment outcome was significantly associated with older age, male gender, non-citizen, unemployment and being HIV positive.

The study focuses on all these contributing factors of unsuccessful treatment outcome for a better risk assessment and stratification of TB patients and identify effective surveillance and management strategies to strengthen the control programs of tuberculosis in Kepong district.