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Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

Background Effective patient discharge information (PDI) in emergency departments (EDs) is vital and often more crucial than the diagnosis itself. Patients who are well informed at discharge tend to be more satisfied and experience better health outcomes. The combination of written and verbal instructions tends to improve patient recall. However, creating written discharge materials is both time-consuming and costly. With the emergence of generative artificial intelligence (AI) and large language models (LMMs), there is potential for the efficient production of patient discharge documents. This study aimed to investigate several predefined key performance indicators (KPIs) of AI-generated patient discharge information. Methods This study focused on three significant patients’ complaints in the ED: nonspecific abdominal pain, nonspecific low back pain, and fever in children. To generate the brochures, we used an English query for ChatGPT using the GPT-4 LLM and DeepL software to translate the brochures to Dutch. Five KPIs were defined to assess these PDI brochures: quality, accessibility, clarity, correctness and usability. The brochures were evaluated for each KPI by 8 experienced emergency physicians using a rating scale from 1 (very poor) to 10 (excellent). To quantify the readability of the brochures, frequently used indices were employed: the Flesch Reading Ease, Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook, and Coleman-Liau Index on the translated text. Results The brochures generated by ChatGPT/GPT-4 were well received, scoring an average of 7 to 8 out of 10 across all evaluated aspects. However, the results also indicated a need for some revisions to perfect these documents. Readability analysis indicated that brochures require high school- to college-level comprehension, but this is likely an overestimation due to context-specific reasons as well as features inherent to the Dutch language. Conclusion Our findings indicate that AI tools such as LLM could represent a new opportunity to quickly produce patient discharge information brochures. However, human review and editing are essential to ensure accurate and reliable information. A follow-up study with more topics and validation in the intended population is necessary to assess their performance.

Advances in therapies have significantly improved the outcomes of patients with cancer. However, multidimensional symptoms negatively impact patients' quality of life. Traditional symptom analysis methods fail to capture the dynamic and interactive nature of these symptoms, limiting progress in supportive care. Network analysis (NA) is a promising method to evaluate complex medical situations. We performed a systematic review to explore NA's contribution to understanding the complexity of symptom experiences in patients with cancer. The research question was as follows: \"In patients with cancer (population), what is the contribution of NA (intervention) to understanding the complexity of multidimensional symptom experiences (outcome)?\" The keywords \"network analysis\" AND \"symptoms\" AND \"cancer survivors\" OR \"cancer patients\" were searched in MEDLINE, Embase, Google Scholar, and Scopus between 2010 and 2024. Citations were extracted using Covidence software. Two reviewers independently screened the articles and resolved inclusion disagreements through consensus. Data were synthetized, and results have been narratively described. Bias analysis was performed using the Methodological Index for Non-Randomized Studies tool. Among 764 articles initially identified, 22 were included. Studies evaluated mixed solid tumors (n=10), digestive tract cancers (n=4), breast cancer (n=3), head and neck cancer (n=2), gliomas (n=2), and mixed solid and hematological cancers (n=1). Twelve studies used general symptom assessment tools, whereas 10 focused on neuropsychological symptoms. Moreover, 1 study evaluated symptoms at diagnosis, 1 evaluated them during curative radiotherapy, 4 evaluated them during the perioperative period, 5 evaluated them during chemotherapy, 4 evaluated them during ongoing cancer therapies, and 7 evaluated them after acute treatments. Among these, 3 evaluated the longitudinal changes in symptom networks across chemotherapy cycles, and 1 evaluated changes during radiotherapy. Three studies investigated the associations between symptoms and biological parameters. Several NA approaches were used: network visualization (n=1), Bayesian network (n=1), pairwise Markov random field and IsingFit method (n=1), unregularized Gaussian graphical model (n=2), regularized partial correlation network (n=6), network visualization and community NA (n=1), network visualization and Walktrap algorithm (n=1), undirected network model with the Fruchterman-Reingold and edge-betweenness approaches (n=4), biased correlation and concise pattern diagram (n=1), extended Bayesian information criterion graphical LASSO method (n=3), cross-lagged panel network (n=1), and unspecified NA (n=3). Psychological symptoms, particularly anxiety, depression, and distress, were frequently identified as central and stably interconnected. Fatigue consistently emerged as a core symptom, closely linked to sleep disturbances, cognitive impairment, and emotional distress. Associations between symptoms and inflammatory biomarkers (eg, interleukin-6, C-reactive protein, and tumor necrosis factor-α) suggest a biological basis for symptom interconnectivity. NA consistently identified core symptoms, particularly psychological symptoms and fatigue, and associations with inflammatory biomarkers. NA may deepen the understanding of symptom interconnectivity and guide more effective interventions. However, further longitudinal homogeneous studies using standardized methodologies are needed.

The coronavirus infectious disease 19 (COVID-19) pandemic has resulted in significant morbidities, severe acute respiratory failures and subsequently emergency departments' (EDs) overcrowding in a context of insufficient laboratory testing capacities. The development of decision support tools for real-time clinical diagnosis of COVID-19 is of prime importance to assist patients' triage and allocate resources for patients at risk. From March 2 to June 15, 2020, clinical patterns of COVID-19 suspected patients at admission to the EDs of Liège University Hospital, consisting in the recording of eleven symptoms (i.e. dyspnoea, chest pain, rhinorrhoea, sore throat, dry cough, wet cough, diarrhoea, headache, myalgia, fever and anosmia) plus age and gender, were investigated during the first COVID-19 pandemic wave. Indeed, 573 SARS-CoV-2 cases confirmed by qRT-PCR before mid-June 2020, and 1579 suspected cases that were subsequently determined to be qRT-PCR negative for the detection of SARS-CoV-2 were enrolled in this study. Using multivariate binary logistic regression, two most relevant symptoms of COVID-19 were identified in addition of the age of the patient, i.e. fever (odds ratio [OR] = 3.66; 95% CI: 2.97-4.50), dry cough (OR = 1.71; 95% CI: 1.39-2.12), and patients older than 56.5 y (OR = 2.07; 95% CI: 1.67-2.58). Two additional symptoms (chest pain and sore throat) appeared significantly less associated to the confirmed COVID-19 cases with the same OR = 0.73 (95% CI: 0.56-0.94). An overall pondered (by OR) score (OPS) was calculated using all significant predictors. A receiver operating characteristic (ROC) curve was generated and the area under the ROC curve was 0.71 (95% CI: 0.68-0.73) rendering the use of the OPS to discriminate COVID-19 confirmed and unconfirmed patients. The main predictors were confirmed using both sensitivity analysis and classification tree analysis. Interestingly, a significant negative correlation was observed between the OPS and the cycle threshold (Ct values) of the qRT-PCR. The proposed approach allows for the use of an interactive and adaptive clinical decision support tool. Using the clinical algorithm developed, a web-based user-interface was created to help nurses and clinicians from EDs with the triage of patients during the second COVID-19 wave.

Background Management of unscheduled urgent care is a complex concern for many healthcare providers. Facing the challenge of appropriately dispatching unscheduled care, primary and emergency physicians have collaboratively implemented innovative strategies such as telephone triage. Currently, new original solutions tend to emerge with the development of new technologies. We created an interactive patient self-triage platform, ODISSEE, and aimed to explore its accuracy and potential factors affecting its performance using clinical case scenarios. Methods The ODISSEE platform was developed based on previously validated triage protocols for out-of-hours primary care. ODISSEE is composed of 18 icons leading to algorithmic questions that finally provide an advised orientation (emergency or primary care services). To investigate ODISSEE performance, we used 100 clinical case scenarios, each associated with a preestablished orientation determined by a group of experts. Fifteen volunteers were asked to self-triage with 50 randomly selected scenarios using ODISSEE on a digital tablet. Their triage results were compared with the experts’ references. Results The 15 participants performed a total of 750 self-triages, which matched the experts references regarding the level of care in 85.6% of the cases. The orientation was incorrect in 14.4%, with an undertriage rate of 1.9% and an overtriage rate of 12.5%. The tool’s specificity and sensitivity to advise participants on the appropriate level of care were 69% (95% CI: 64—74) and 97% (95% CI: 95—98) respectively. When combined with advice on the level of urgency, the tool only found the correct orientation in 68.4% with 9.2% of undertriages and 22.4% of overtriages. Some participant characteristics and the types of medical conditions demonstrated a significant association with the tool performance. Conclusion Self-triage apps, such as the ODISSEE platform, could represent an innovative method to allow patients to self-triage to the most appropriate level of care. This study based on clinical vignettes highlights some positive arguments regarding ODISSEE safety, but further research is needed to assess the generalizability of such tools to the population without equity issues.

Background Since the beginning of the pandemic, hospitals have been constantly overcrowded, with several observed waves of infected cases and hospitalisations. To avoid as much as possible this situation, efficient tools to facilitate the diagnosis of COVID-19 are needed. Objective To evaluate and compare prediction models to diagnose COVID-19 identified in a systematic review published recently using performance indicators such as discrimination and calibration measures. Methods A total of 1618 adult patients present at two Emergency Department triage centers and for whom qRT-PCR tests had been performed were included in this study. Six previously published models were reconstructed and assessed using diagnostic tests as sensitivity (Se) and negative predictive value (NPV), discrimination (Area Under the Roc Curve (AUROC)) and calibration measures. Agreement was also measured between them using Kappa’s coefficient and IntraClass Correlation Coefficient (ICC). A sensitivity analysis has been conducted by waves of patients. Results Among the 6 selected models, those based only on symptoms and/or risk exposure were found to be less efficient than those based on biological parameters and/or radiological examination with smallest AUROC values (< 0.80). However, all models showed good calibration and values above > 0.75 for Se and NPV but poor agreement (Kappa and ICC < 0.5) between them. The results of the first wave were similar to those of the second wave. Conclusion Although quite acceptable and similar results were found between all models, the importance of radiological examination was also emphasized, making it difficult to find an appropriate triage system to classify patients at risk for COVID-19.

Background Chemical insecticides are crucial to malaria control and elimination programmes. The frontline vector control interventions depend mainly on pyrethroids; all long-lasting insecticidal nets (LLINs) and more than 80% of indoor residual spraying (IRS) campaigns use chemicals from this class. This extensive use of pyrethroids imposes a strong selection pressure for resistance in mosquito populations, and so continuous resistance monitoring and evaluation are important. As pyrethroids have also been used for many years in the Manhiça District, an area in southern Mozambique with perennial malaria transmission, an assessment of their efficacy against the local malaria vectors was conducted. Methods Female offspring of wild-caught Anopheles funestus s.s. females were exposed to deltamethrin, lambda-cyhalothrin and permethrin using the World Health Organization (WHO) insecticide-resistance monitoring protocols. The 3-min WHO cone bioassay was used to evaluate the effectiveness of the bed nets distributed or available for purchase in the area (Olyset, permethrin LLIN; PermaNet 2.0, deltamethrin LLIN) against An. funestus . Mosquitoes were also exposed to PermaNet 2.0 for up to 8 h in time-exposure assays. Results Resistance to pyrethroids in An. funestus s.s. was extremely high, much higher than reported in 2002 and 2009. No exposure killed more than 25.8% of the mosquitoes tested (average mortality, deltamethrin: 6.4%; lambda-cyhalothrin: 5.1%; permethrin: 19.1%). There was no significant difference in the mortality generated by 3-min exposure to any net (Olyset: 9.3% mortality, PermaNet 2.0: 6.0%, untreated: 2.0%; p = 0.2). Six hours of exposure were required to kill 50% of the An. funestus s.s. on PermaNet 2.0. Conclusions Anopheles funestus s.s. in Manhiça is extremely resistant to pyrethroids, and this area is clearly a pyrethroid-resistance hotspot. This could severely undermine vector control in this district if no appropriate countermeasures are undertaken. The National Malaria Control Programme (NMCP) of Mozambique is currently improving its resistance monitoring programme, to design and scale up new management strategies. These actions are urgently needed, as the goal of the NMCP and its partners is to reach elimination in southern Mozambique by 2020.

Anthrax, caused by Bacillus anthracis, is endemic in the Kruger National Park (KNP). The epidemiology of B. anthracis is dependent on various factors including vectors. The aims of this study were to examine non-biting blowflies for the presence of B. anthracis externally and internally after feeding on an anthrax-infected carcass and to determine the role of flies in disseminating B. anthracis onto the surrounding vegetation. During an anthrax outbreak in 2014 in the endemic Pafuri region, blowflies associated with two 2–3-day-old anthrax-positive carcasses (kudu and impala) as well as surrounding vegetation were collected and investigated for the presence of B. anthracis spores. The non-biting blowflies (n = 57) caught included Chrysomya albiceps, Ch. marginalis and Lucilia spp. Bacillus anthracis spores were isolated from 65.5% and 25.0% of blowflies collected from the kudu and impala carcasses, respectively. Chrysomya albiceps and Ch. marginalis have the potential to disseminate B. anthracis to vegetation from infected carcasses and may play a role in the epidemiology of anthrax in the KNP. No B. anthracis spores were initially isolated from leaves of the surrounding vegetation using selective media. However, 170 and 500 spores were subsequently isolated from Abutilon angulatum and Acacia sp. leaves, respectively, when using sheep blood agar. CONSERVATION IMPLICATIONS : The results obtained in this study have no direct conservation implications and only assist in the understanding of the spread of the disease.

Objectives. To assess the associations between the executive order that Texas governor Greg Abbott issued on March 22, 2020, postponing procedures deemed not immediately medically necessary, and patients’ access to abortion care in Texas. Methods. We used 17 515 individual-level patient records from 13 Texas abortion facilities for matched periods in 2019 and 2020 to examine differences in return rates for abortion after completion of a state-mandated ultrasound and median wait times between ultrasound and abortion visits for those who returned. Results. Patients were less likely to return for an abortion if they had an ultrasound while the executive order was under effect (82.8%) than in the same period in 2019 (90.4%; adjusted odds ratio = 2.06; 95% confidence interval = 1.12, 3.81). Compared with patients at or before 10.0 weeks’ gestation at ultrasound, patients at more than 10 weeks’ gestation had higher odds of not returning for an abortion or, if they returned, experienced greater wait times between ultrasound and abortion visits. Conclusions. Texas’ executive order prohibiting abortion during the COVID-19 pandemic disrupted patients’ access to care and disproportionately affected patients who were past 10 weeks’ gestation. ( Am J Public Health. 2024;114(10):1013–1023. https://doi.org/10.2105/AJPH.2024.307747 )