bluuebunny/crossref_metadata_2025_split huggingface dataset:
Frontiers in Operations: Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic
Authors: Luo Joyce , Stellato Bartolomeo 
Published: April 2024

Problem definition: The opioid epidemic is a crisis that has plagued the United States for decades. One central issue of the epidemic is inequitable access to treatment for opioid use disorder (OUD), which puts certain populations at a higher risk of opioid overdose. Methodology/results: We integrate a predictive dynamical model and a prescriptive optimization problem to compute high-quality opioid treatment facility and treatment budget allocations for each U.S. state. Our predictive model is a differential equation-based epidemiological model that captures the dynamics of the opioid epidemic. We use a process inspired by neural ordinary differential equations to fit this model to opioid epidemic data for each state and obtain estimates for unknown parameters in the model. We then incorporate this epidemiological model into a corresponding mixed-integer optimization problem (MIP) that aims to minimize the number of opioid overdose deaths and the number of people with OUD. We develop strong relaxations based on McCormick envelopes to efficiently compute approximate solutions to our MIPs that have a mean optimality gap of 3.99%. Our method provides socioeconomically equitable solutions, as it incentivizes investments in areas with higher social vulnerability (from the U.S. Centers for Disease Control’s Social Vulnerability Index) and opioid prescribing rates. On average, when allowing for overbudget solutions, our approach decreases the number of people with OUD by [Formula: see text], increases the number of people in treatment by [Formula: see text], and decreases the number of opioid-related deaths by [Formula: see text] after 2 years compared with the baseline epidemiological model’s predictions. Managerial implications: Our solutions show that policymakers should target adding treatment facilities to counties that have significantly fewer facilities than their population share and are more socially vulnerable. Furthermore, we demonstrate that our optimization approach, guided by epidemiological and socioeconomic factors, should help inform these strategic decisions, as it yields population health benefits in comparison with benchmarks based solely on population and social vulnerability. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0042 .
Adaptive Lagrangian Policies for a Multiwarehouse, Multistore Inventory System with Lost Sales
Authors: Chao Xiuli , Jasin Stefanus , Miao Sentao 
Published: April 2024

Adaptive Lagrangian Policies for Inventory Control Consider the problem of managing inventory in a multiwarehouse, multistore setting where each store can be periodically replenished from potentially a set of nearby warehouses. This is a practically relevant problem, and its optimal policy is not easy to compute because of the curse of dimensionality. Existing works in the literature have proposed a policy based on Lagrangian relaxation of the original stochastic problem. This vanilla policy applies the control parameters derived from the Lagrangian model and has been shown to perform well in some cases. In “Adaptive Lagrangian Policies for a Multiwarehouse, Multistore Inventory System with Lost Sales,” we go one step farther and develop adaptive Lagrangian policies that update (at certain update times) the control parameters of the vanilla policy based on the historical demand realizations. We analytically show that our proposed adjustment significantly improves the performance of the vanilla policy.
Overlapping infections of Mycobacterium canariasense and Nocardia farcinica in an immunocompetent patient: A case report
Authors: Huang Hai-Yan , Bu Kun-Peng , Liu Jin-Wei , Wei Jing 
Published: April 2024

BACKGROUND
Infections by non-tuberculous mycobacteria (NTM) have become more common in recent years. Mycobacterium canariasense (M. canariasense ) was first reported as an opportunistic pathogen in 2004, but there have been very few case reports since then. Nocardia is a genus of aerobic and Gram-positive bacilli, and these species are also opportunistic pathogens and in the Mycobacteriales order. Conventional methods for diagnosis of NTM are inefficient. Metagenomic next-generation sequencing (mNGS) can rapidly detect many pathogenic microorganisms, even rare species. Most NTM and Nocardia infections occur in immunocompromised patients with atypical clinical symptoms. There are no previous reports of infection by M. canariasense and Nocardia farcinica (N. farcinica ), especially in immunocompetent patients. This case report describes an immunocompetent 52-year-old woman who had overlapping infections of M. canariasense , N. farcinica , and Candida parapsilosis (C. parapsilosis ) based on mNGS.
CASE SUMMARY
A 52-year-old woman presented with a productive cough and chest pain for 2 wk, and recurrent episodes of moderate-grade fever for 1 wk. She received antibiotics for 1 wk at a local hospital, and experienced defervescence, but the productive cough and chest pain persisted. We collected samples of a lung lesion and alveolar lavage fluid for mNGS. The lung tissue was positive for M. canariasense , N. farcinica , and C. parapsilosis , and the alveolar lavage fluid was positive for M. canariasense . The diagnosis was pneumonia, and application of appropriate antibiotic therapy cured the patient.
CONCLUSION
Etiological diagnosis is critical for patients with infectious diseases. mNGS can identify rare and novel pathogens, and does not require a priori knowledge.
Machine learning for predicting Chagas disease infection in rural areas of Brazil
Authors: De Rose Ghilardi Fabio , Silva Gabriel , Vieira Thallyta Maria , Mota Ariela , Bierrenbach Ana Luiza , Damasceno Renata Fiuza , Oliveira Lea Campos de , Dias Porto Chiavegatto Filho Alexandre , Sabino Ester 
Published: April 2024

Introduction
Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis.

Methods
In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease. We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. The sample comprised 2,006 patients, divided into 75% for training and 25% for testing algorithm performance. We evaluated the model performance using precision, recall, and AUC-ROC metrics.

Results
The Adaboost algorithm yielded an AUC-ROC of 0.772, a precision of 0.199, and a recall of 0.612. We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. This finding suggests that employing such a threshold could potentially save 22.5% of the cost associated with mass testing of Chagas disease.

Conclusion
Our findings highlight the potential of applying machine learning to improve the sensitivity and effectiveness of Chagas disease diagnosis and prevention. Furthermore, we emphasize the importance of integrating socio-demographic and environmental factors into neglected disease prediction models to enhance their performance.
High uptake of COVID-19 vaccines among healthcare workers in urban Uganda
Authors: Kyakuwa Nasimu , Kimbugwe Geofrey , Nakanjako Flavia , Kalute Hamza , Mpooya Simon , Atuhairwe Christine , Perez Laurent , Kikaire Bernard 
Published: April 2024

Objectives
The aim of the study was to describe the facilitators, barriers to and level of uptake of COVID-19 vaccines among healthcare workers in primary healthcare facilities in an urban setting in Uganda.

Materials and methods
We conducted a cross-sectional study among HCWs in private and public health facilities in Entebbe municipality between July 2021 and August 2021. Data was collected using a structured questionnaire that was shared, via an online link, to consented participants. Uptake of the vaccines among healthcare workers was analysed as proportions, and logistic regression was used to analyse barriers and facilitators to uptake of COVID-19 vaccines.

Results
The study enrolled 360 participants, with 61.7% (n = 222) females. A total of 236 (65.6%) healthcare workers had received at least one dose of COVID-19 vaccine, with higher uptake among females 64% (n = 151). Age above 40 years (OR 2.16), working in a government healthcare facility (OR 3.12), participating in COVID-19 vaccine related activities (OR 4.62), and having tested for SARS-COV-2 (OR 3.05) increased the odds of having been vaccinated. Working in small roadside clinics reduced the odds of being vaccinated by almost 70%, while HCWs in government health services were 3.1 times more likely to have been vaccinated. History of having cared for a COVID-19 patient and having a positive SARS-COV-2 test result did not influence the uptake of the vaccines in the study population.

Conclusion
Vaccine uptake among HCWs was close to the World Health Organisation (WHO) recommended uptake of 70% by mid-2022.
Chest pain in pediatric patients in the emergency department- Presentation, risk factors and outcomes-A systematic review and meta-analysis
Authors: Alsabri Mohammed , Elshanbary Alaa Ahmed , Nourelden Anas Zakarya , Fathallah Ahmed Hashem , Zaazouee Mohamed Sayed , Pincay Jorge , Nakadar Zaid , Wasem Muhammad , Aeder Lita 
Published: April 2024

Objective
This study aimed to assess and determine the presentation, risk factors, and outcomes of pediatric patients who were admitted for cardiac-related chest pain.

Background
Although chest pain is common in children, most cases are due to non-cardiac etiology. The risk of misdiagnosis and the pressure of potentially adverse outcomes can lead to unnecessary diagnostic testing and overall poorer patient experiences. Additionally, this can lead to a depletion of resources that could be better allocated towards patients who are truly suffering from cardiac-related pathology.

Methods
This review was conducted per PRISMA guidelines. This systematic review used several databases including MEDLINE, Embase, Scopus, and Web of Science to obtain its articles for review.

Results
A total of 6,520 articles were identified, and 11 articles were included in the study. 2.5% of our study population was found to have cardiac-related chest pain (prevalence = 0.025, 95% CI [0.013, 0.038]). The most commonly reported location of pain was retrosternal chest pain. 97.5% of the study population had a non-cardiac cause of chest pain, with musculoskeletal pain being identified as the most common cause (prevalence = 0.357, 95% CI [0.202, 0.512]), followed by idiopathic (prevalence = 0.352, 95% CI [0.258, 0.446]) and then gastrointestinal causes (prevalence = 0.053, 95% CI [0.039, 0.067]).

Conclusions
The overwhelming majority of pediatric chest pain cases stem from benign origins. This comprehensive analysis found musculoskeletal pain as the predominant culprit behind chest discomfort in children. Scrutinizing our study cohort revealed that retrosternal chest pain stands as the unequivocal epicenter of this affliction. Thorough evaluation of pediatric patients manifesting with chest pain is paramount for the delivery of unparalleled care, especially in the context of potential cardiac risks in the emergency department.
Mental health and burnout during medical school: Longitudinal evolution and covariates
Authors: Carrard Valerie , Berney Sylvie , Bourquin Céline , Ranjbar Setareh , Castelao Enrique , Schlegel Katja , Gaume Jacques , Bart Pierre-Alexandre , Schmid Mast Marianne , Preisig Martin , Berney Alexandre 
Published: April 2024

Background
Medical students’ rate of depression, suicidal ideation, anxiety, and burnout have been shown to be higher than those of the same-age general population. However, longitudinal studies spanning the whole course of medical school are scarce and present contradictory findings. This study aims to analyze the longitudinal evolution of mental health and burnout from the first to the last year of medical school using a wide range of indicators. Moreover, biopsychosocial covariates that can influence this evolution are explored.

Method
In an open cohort study design, 3066 annual questionnaires were filled in by 1595 different students from the first to the sixth year of the Lausanne Medical School (Switzerland). Depression symptoms, suicidal ideation, anxiety symptoms, stress, and burnout were measured along with biopsychosocial covariates. The longitudinal evolution of mental health and burnout and the impact of covariates were modelled with linear mixed models.

Results
Comparison to a same-aged general population sample shows that medical students reported significantly more depression symptoms and anxiety symptoms. Medical students’ mental health improved during the course of the studies in terms of depression symptoms, suicidal ideation, and stress, although suicidal ideation increased again in the last year and anxiety symptoms remained stable. Conversely, the results regarding burnout globally showed a significant worsening from beginning to end of medical school. The covariates most strongly related to better mental health and less burnout were less emotion-focused coping, more social support, and more satisfaction with health.

Conclusion
Both improvement of mental health and worsening of burnout were observed during the course of medical school. This underlines that the beginning and the end of medical school bring specific challenges with the first years’ stressors negatively impacting mental health and the last year’s difficulties negatively impacting burnout.
Experiences of help-seeking from professional services for a child or young person’s mental health concerns during the pandemic: A qualitative study
Authors: Mathews Frances , Benham-Clarke Simon , Ford Tamsin J. , Hill Suzanne , Sadler Katharine , Newlove-Delgado Tamsin 
Published: April 2024

Introduction
The immediate response to the Covid-19 pandemic saw school closures and a shift in provision to online health services for children and young people experiencing mental health concerns. This study provides mental health and referral services with an insight into difficulties experienced as well as recommendations on potential improvements.

Methods
Semi-structured interviews with 11 parents and six young people. Reflexive thematic analysis was used to analyse the data.

Results
Parents and young people reported mixed experiences on accessing mental health support. Priorities and pressures on health services impacted the likelihood of choosing to seek and being able to obtain help. Parents and young people had varying expectations and experiences in help-seeking during the pandemic which were also impacted by others’ experiences and views. For many, the relationship with the professional they were in contact with impacted their mental health treatment. Provision was sometimes accessed via private services due to long waiting lists or problems that did not “meet threshold”.

Conclusion
Understanding the experiences of seeking mental healthcare during the pandemic can inform improvements to access to services at a time when people are most vulnerable. Accessible provision other than private services needs to be made for those on waiting lists. For those who do not meet service threshold, intermediary support needs to be secured to prevent unnecessary exacerbation of symptoms and prolonged problems. If schools are to remain the hub for children and young people’s mental health services, they should be considered essential services at all times.
Interpreter usage and associations with latent tuberculosis infection treatment acceptance and completion in the USA among non-U.S.–born persons, 2012–2017
Authors: Gonzalez-Reyes Rebeca , Katz Dolly , Lambert Lauren , Sorri Yoseph , Narita Masahiro , Horne David J. ,   for the Tuberculosis Epidemiologic Studies Consortium
Published: April 2024

Background
Latent tuberculosis infection (LTBI) screening and treatment interventions that are tailored to optimize acceptance among the non-U.S.–born population are essential for U.S. tuberculosis elimination. We investigated the impact of medical interpreter use on LTBI treatment acceptance and completion among non-U.S.–born persons in a multisite study.

Methods
The Tuberculosis Epidemiologic Studies Consortium was a prospective cohort study that enrolled participants at high risk for LTBI at ten U.S. sites with 18 affiliated clinics from 2012 to 2017. Non-U.S.–born participants with at least one positive tuberculosis infection test result were included in analyses. Characteristics associated with LTBI treatment offer, acceptance, and completion were evaluated using multivariable logistic regression with random intercepts to account for clustering by enrollment site. Our primary outcomes were whether use of an interpreter was associated with LTBI treatment acceptance and completion. We also evaluated whether interpreter usage was associated treatment offer and whether interpreter type was associated with treatment offer, acceptance, or completion.

Results
Among 8,761 non-U.S.–born participants, those who used an interpreter during the initial interview had a significantly greater odds of accepting LTBI treatment than those who did not use an interpreter. There was no association between use of an interpreter and a clinician’s decision to offer treatment or treatment completion once accepted. Characteristics associated with lower odds of treatment being offered included experiencing homelessness and identifying as Pacific Islander persons. Lower treatment acceptance was observed in Black and Latino persons and lower treatment completion by participants experiencing homelessness. Successful treatment completion was associated with use of shorter rifamycin-based regimens. Interpreter type was not associated with LTBI treatment offer, acceptance, or completion.

Conclusions
We found greater LTBI treatment acceptance was associated with interpreter use among non-U.S.–born individuals.
Professional quality of life in animal research personnel is linked to retention & job satisfaction: A mixed-methods cross-sectional survey on compassion fatigue in the USA
Authors: Young Lauren , Ferrara Fabienne , Kelly Lisa , Martin Tara , Thompson-Iritani Sally , LaFollette Megan R. 
Published: April 2024

Background
Working with research animals can be both rewarding and challenging. The rewarding part of the work is associated with understanding the necessity for animal research to improve the health of humans and animals and the knowledge that one can provide care and compassion for the animals. Challenges with animal research include witnessing stress/pain in animals necessitated by scientific requirements, end of study euthanasia, and societal stigmatization about animal research. These challenges could be compounded with more general workplace stresses, in turn, impacting job retention and satisfaction. However, these factors have yet to be formally evaluated. Therefore, the purpose of this survey was to comprehensively evaluate professional quality of life’s correlation with key workplace metrics.

Methods
Six institutions were recruited to participate in a longitudinal intervention trial on compassion fatigue resiliency. This manuscript reports key baseline metrics from this survey. A cross-sectional mixed methods survey was developed to evaluate professional quality of life, job satisfaction, retention, and factors influencing compassion fatigue resiliency. Quantitative data were analyzed via general linear models and qualitative data were analyzed by theme.

Results
Baseline data was collected from 198 participants. Personnel who reported higher compassion satisfaction also reported higher retention and job satisfaction. Conversely, personnel who reported higher burnout also reported lower job satisfaction. In response to open-ended questions, participants said their compassion fatigue was impacted by institutional culture (70% of participants), animal research (58%), general mental health (41%), and specific compassion fatigue support (24%).

Conclusions
In conclusion, these results show that professional quality of life is related to important operational metrics of job satisfaction and retention. Furthermore, compassion fatigue is impacted by factors beyond working with research animals, including institutional culture and general mental health support. Overall, this project provides rationale and insight for institutional support of compassion fatigue resiliency.
Land as a binding constraint to cluster-based development in Ethiopia: To cluster or not to cluster?
Authors: Dureti Guyo Godana , Tabe-Ojong Martin Paul JR. 
Published: April 2024

Introduction
As one of the agglomeration models targeting cluster-based rural development, cluster farming has been promoted in Ethiopia and it is already reported to have significant welfare implications, but participation rates are not as high as expected. This study examines the role of land as a constraint to the development of cluster-based development in Ethiopia both using extensive and intensive measures of cluster farming. The study further disaggregates farm households based on their farm size to better understand potential heterogeneities in the relationship between farm size and cluster farming. The paper also documents other household socio-economic and network characteristics that may matter in cluster farming.

Methods
We use a large-scale farm household data from 3,969 households coupled with some expert insights on cluster farming in Ethiopia. Households in the study areas grow major staples such as maize, wheat, teff, malt barley, and sesame in four main regions of Ethiopia. We employ a double hurdle model to examine both the decision to participate and the extent to which households participate in cluster farming. By extent of participation, we refer to the amount of land and share of land farm households contribute to cluster farming. For robustness purposes, we also estimate the Tobit and Linear Probability Models.

Results
We show a positive association between farm size and cluster farming both at the extensive and intensive margins. This relationship turns negative for large amounts of land. This shows that cluster farming increases with farm size up to a threshold beyond which it declines. We also find suggestive evidence that participation rates are lower for small-scale farms, but also declines for large-scale farms. In addition, we show that access to information and network characteristics also matter in enabling cluster farming.

Conclusion
The findings of this study are relevant in the framework of plans to upscale the cluster-based development initiative in Ethiopia. Attention to landholding issues is key and may be an important area where policy action can be geared to boost cluster farming. Moreover, our results inform potential targeting plans that aim to increase the participation of small-scale farmers who are usually the intended targets of such programs.