The impact of the SARS-CoV-2 pandemic on global influenza surveillance: Insights from 18 National Influenza Centers based on a survey conducted between November 2021 and March 2022.

Background: National Influenza Centers (NICs) have played a crucial role in the surveillance of SARS-CoV-2. The FluCov project, covering 22 countries, was initiated to monitor the impact of the SARS-CoV-2 pandemic on influenza activity. Methods: This project consisted of an epidemiological bulletin and NIC survey. The survey, designed to assess the impact of the pandemic on the influenza surveillance system, was shared with 36 NICs located across 22 countries. NICs were invited to reply between November 2021 and March 2022. Results: We received 18 responses from NICs in 14 countries. Most NICs (76%) indicated that the number of samples tested for influenza decreased. Yet, many NICs (60%) were able to increase their laboratory testing capacity and the "robustness" (e.g., number of sentinel sites) (59%) of their surveillance systems. In addition, sample sources (e.g., hospital or outpatient setting) shifted. All NICs reported a higher burden of work following the onset of the pandemic, with some NICs hiring additional staff or partial outsourcing to other institutes or departments. Many NICs anticipate the future integration of SARS-CoV-2 surveillance into the existing respiratory surveillance system. Discussion: The survey shows the profound impact of SARS-CoV-2 on national influenza surveillance in the first 27 months of the pandemic. Surveillance activities were temporarily disrupted, whilst priority was given to SARS-CoV-2. However, most NICs have shown rapid adaptive capacity underlining the importance of strong national influenza surveillance systems. These developments have the potential to benefit global respiratory surveillance in the years to come; however, questions about sustainability remain.

Detection of primary Sjögren's syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning.

BACKGROUND: Primary Sjögren's Syndrome (pSS) is a rare autoimmune disease that is difficult to diagnose due to a variety of clinical presentations, resulting in misdiagnosis and late referral to specialists. To improve early-stage disease recognition, this study aimed to develop an algorithm to identify possible pSS patients in primary care. We built a machine learning algorithm which was based on combined healthcare data as a first step towards a clinical decision support system. METHOD: Routine healthcare data, consisting of primary care electronic health records (EHRs) data and hospital claims data (HCD), were linked on patient level and consisted of 1411 pSS and 929,179 non-pSS patients. Logistic regression (LR) and random forest (RF) models were used to classify patients using age, gender, diseases and symptoms, prescriptions and GP visits. RESULTS: The LR and RF models had an AUC of 0.82 and 0.84, respectively. Many actual pSS patients were found (sensitivity LR = 72.3%, RF = 70.1%), specificity was 74.0% (LR) and 77.9% (RF) and the negative predictive value was 99.9% for both models. However, most patients classified as pSS patients did not have a diagnosis of pSS in secondary care (positive predictive value LR = 0.4%, RF = 0.5%). CONCLUSION: This is the first study to use machine learning to classify patients with pSS in primary care using GP EHR data. Our algorithm has the potential to support the early recognition of pSS in primary care and should be validated and optimized in clinical practice. To further enhance the algorithm in detecting pSS in primary care, we suggest it is improved by working with experienced clinicians.

Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning: Algorithm Development and Validation.

BACKGROUND: Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but they focused on screening for a single predefined disorder instead of multiple disorders simultaneously. OBJECTIVE: The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment. METHODS: On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search. RESULTS: The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier. CONCLUSIONS: A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process.

Fitness for purpose of routinely recorded health data to identify patients with complex diseases: The case of Sjögren's syndrome.

BACKGROUND: This study is part of the EU-funded project HarmonicSS, aimed at improving the treatment and diagnosis of primary Sjögren's syndrome (pSS). pSS is an underdiagnosed, long-term autoimmune disease that affects particularly salivary and lachrymal glands. OBJECTIVES: We assessed the usability of routinely recorded primary care and hospital claims data for the identification and validation of patients with complex diseases such as pSS. METHODS: pSS patients were identified in primary care by translating the formal inclusion and exclusion criteria for pSS into a patient selection algorithm using data from Nivel Primary Care Database (PCD), covering 10% of the Dutch population between 2006 and 2017. As part of a validation exercise, the pSS patients found by the algorithm were compared to Diagnosis Related Groups (DRG) recorded in the national hospital insurance claims database (DIS) between 2013 and 2017. RESULTS: International Classification of Primary Care (ICPC) coded general practitioner (GP) contacts combined with the mention of "Sjögren" in the disease episode titles, were found to best translate the formal classification criteria to a selection algorithm for pSS. A total of 1462 possible pSS patients were identified in primary care (mean prevalence 0.7‰, against 0.61‰ reported globally). The DIS contained 208 545 patients with a Sjögren related DRG or ICD10 code (prevalence 2017: 2.73‰). A total of 2 577 577 patients from Nivel PCD were linked to the DIS database. A total of 716 of the linked pSS patients (55.3%) were confirmed based on the DIS. CONCLUSION: Our study finds that GP electronic health records (EHRs) lack the granular information needed to apply the formal diagnostic criteria for pSS. The developed algorithm resulted in a patient selection that approximates the expected prevalence and characteristics, although only slightly over half of the patients were confirmed using the DIS. Without more detailed diagnostic information, the fitness for purpose of routine EHR data for patient identification and validation could not be determined.

Recognizing hotspots in Brief Eclectic Psychotherapy for PTSD by text and audio mining.

Background: Identifying and addressing hotspots is a key element of imaginal exposure in Brief Eclectic Psychotherapy for PTSD (BEPP). Research shows that treatment effectiveness is associated with focusing on these hotspots and that hotspot frequency and characteristics may serve as indicators for treatment success. Objective: This study aims to develop a model to automatically recognize hotspots based on text and speech features, which might be an efficient way to track patient progress and predict treatment efficacy. Method: A multimodal supervised classification model was developed based on analog tape recordings and transcripts of imaginal exposure sessions of 10 successful and 10 non-successful treatment completers. Data mining and machine learning techniques were used to extract and select text (e.g. words and word combinations) and speech (e.g. speech rate, pauses between words) features that distinguish between 'hotspot' (N = 37) and 'non-hotspot' (N = 45) phases during exposure sessions. Results: The developed model resulted in a high training performance (mean F 1-score of 0.76) but a low testing performance (mean F 1-score = 0.52). This shows that the selected text and speech features could clearly distinguish between hotspots and non-hotspots in the current data set, but will probably not recognize hotspots from new input data very well. Conclusions: In order to improve the recognition of new hotspots, the described methodology should be applied to a larger, higher quality (digitally recorded) data set. As such this study should be seen mainly as a proof of concept, demonstrating the possible application and contribution of automatic text and audio analysis to therapy process research in PTSD and mental health research in general.

Antecedentes:La identificación y el abordaje de los puntos críticos (hotspots en inglés) es un elemento clave para exposición imaginaria en la Psicoterapia Ecléctica Breve para TEPT (BEPP por sus siglas en inglés). La investigación muestra que la efectividad del tratamiento se asocia con la focalización en estos puntos críticosy que la frecuencia y características de los puntos críticos podría servir de indicador para el éxito terapéutico.Objetivo: Este estudio tiene como objetivo desarrollar un modelo para reconocer automáticamente los puntos críticos basados en características de texto y discurso, lo que podría ser una forma eficiente de seguir los progresos del paciente y predecir la eficacia del tratamiento.Metodo: Se desarrolló un modelo de clasificación supervisada multimodal basado en grabaciones y transcripciones de cintas analógicas de sesiones de exposición imaginaria de diez de tratamiento exitosos y diez no exitosos. Se usaron técnicas de minería de datos y técnicas de aprendizaje automático para extraer y seleccionar las características de texto (ej., palabras y combinaciones de palabras) y discurso (ej., velocidad del discurso, pausas entre las palabras) que distinguen entre las fases de 'puntos críticos' (N= 37) y ' puntos no críticos' (N= 45) durante las sesiones de exposición.Resultados: El modelo desarrollado resultó en un alto rendimiento de entrenamiento (puntaje F1 promedio de 0.76) pero un bajo rendimiento de prueba (puntaje F1 promedio = 0.52). Esto muestra que las características de los textos y discursos seleccionados podrían distinguir claramente entre puntos críticos y puntos no críticos en el conjunto de datos actual, pero probablemente no reconocerá muy bien los puntos críticos de nuevos datos de entrada.Conclusiones: Para mejorar el reconocimiento de nuevos puntos críticos, la metodología descrita debería ser aplicada a un conjunto de datos más grande y de mejor alta calidad (grabado digital). Como tal, este estudio debe verse principalmente como una prueba de concepto, demostrando la posible aplicación y contribución del análisis automático de texto y audio para la investigación del proceso terapéutico en TEPT e investigación en salud mental en general.

Towards text mining therapeutic change: A systematic review of text-based methods for Therapeutic Change Process Research.

Therapeutic Change Process Research (TCPR) connects within-therapeutic change processes to outcomes. The labour intensity of qualitative methods limit their use to small scale studies. Automated text-analyses (e.g. text mining) provide means for analysing large scale text patterns. We aimed to provide an overview of the frequently used qualitative text-based TCPR methods and assess the extent to which these methods are reliable and valid, and have potential for automation. We systematically reviewed PsycINFO, Scopus, and Web of Science to identify articles concerning change processes and text or language. We evaluated the reliability and validity based on replicability, the availability of code books, training data and inter-rater reliability, and evaluated the potential for automation based on the example- and rule-based approach. From 318 articles we identified four often used methods: Innovative Moments Coding Scheme, the Narrative Process Coding Scheme, Assimilation of Problematic Experiences Scale, and Conversation Analysis. The reliability and validity of the first three is sufficient to hold promise for automation. While some text features (content, grammar) lend themselves for automation through a rule-based approach, it should be possible to automate higher order constructs (e.g. schemas) when sufficient annotated data for an example-based approach are available.

Obstructive Sleep Apnea Syndrome in Company Workers: Development of a Two-Step Screening Strategy with a New Questionnaire.

STUDY OBJECTIVES: To develop and evaluate a screening questionnaire and a two-step screening strategy for obstructive sleep apnea syndrome (OSAS) in healthy workers. METHODS: This is a cross-sectional study of 1,861 employees comprising healthy blue- and white-collar workers in two representative plants in the Netherlands from a worldwide consumer electronic company who were approached to participate. Employees were invited to complete various sleep questionnaires, and undergo separate single nasal flow recording and home polysomnography on two separate nights. RESULTS: Of the 1,861 employees, 249 provided informed consent and all nasal flow and polysomnography data were available from 176 (70.7%). OSAS was diagnosed in 65 (36.9%). A combination of age, absence of insomnia, witnessed breathing stops, and three-way scoring of the Berlin and STOPBANG questionnaires best predicted OSAS. Factor analysis identified a six-factor structure of the resulting new questionnaire: snoring, snoring severity, tiredness, witnessed apneas, sleep quality, and daytime well-being. Subsequently, some questions were removed, and the remaining questions were used to construct a new questionnaire. A scoring algorithm, computing individual probabilities of OSAS as high, intermediate, or low risk, was developed. Subsequently, the intermediate risk group was split into low and high probability for OSAS, based on nasal flow recording. This two-step approach showed a sensitivity of 63.1%, and a specificity of 90.1%. Specificity is important for low prevalence populations. CONCLUSION: A two-step screening strategy with a new questionnaire and subsequent nasal flow recording is a promising way to screen for OSAS in a healthy worker population. CLINICAL TRIAL REGISTRATION: Development and validation of a screening instrument for obstructive sleep apnea syndrome in healthy workers. Netherlands Trial Register (www.trailregister.nl), number: NTR2675.