Local initiative supports case isolation and contact tracing during a SARS-CoV-2 surge in summer 2020: a community case study in Antwerp, Belgium.

In Antwerp, Belgium's second largest city, a COVID-19 surge in July 2020 predominantly affected neighborhoods with high ethnic diversity. Local volunteers reacted and set up an initiative to support contact tracing and self-isolation. We describe the origin, implementation, and transfer of this local initiative, based on semi-structured interviews of five key informants and document review. The initiative started in July 2020, when family physicians signaled a surge of SARS-CoV-2 infections among people of Moroccan descent. Family physicians feared that the mainstream contact tracing organized by the Flemish government through centralized call centers would not be efficient in halting this outbreak. They anticipated language barriers, mistrust, inability to investigate case clusters, and practical problems with self-isolation. It took 11 days to start up the initiative, with logistical support from the province and city of Antwerp. Family physicians referred SARS-CoV-2-infected index cases with complex needs (including language and social situation) to the initiative. Volunteer COVID coaches contacted cases, got a contextualized understanding of their living conditions, assisted with backward and forward contact tracing, offered support during self-isolation, and checked if infected contacts also needed support. Interviewed coaches were positive about the quality of the interaction: they described extensive open conversations with cases. The coaches reported back to referring family physicians and coordinators of the local initiative, who took additional action if necessary. Although interactions with affected communities were perceived as good, respondents considered that the number of referrals by family physicians was too low to have a meaningful impact on the outbreak. In September 2020, the Flemish government assigned the tasks of local contact tracing and case support to the local health system level (primary care zones). While doing so, they adopted elements of this local initiative, such as COVID coaches, tracing system, and extended questionnaires to talk with cases and contacts. This community case study illustrates how urgency can motivate people to action yet support from people with access to resources and coordination capacity is vital for effective organization and transition to long-term sustainability. From their conception, health policies should consider adaptability of new interventions to local contexts.

Predicting COVID-19 Symptoms From Free Text in Medical Records Using Artificial Intelligence: Feasibility Study.

BACKGROUND: Electronic medical records have opened opportunities to analyze clinical practice at large scale. Structured registries and coding procedures such as the International Classification of Primary Care further improved these procedures. However, a large part of the information about the state of patient and the doctors' observations is still entered in free text fields. The main function of those fields is to report the doctor's line of thought, to remind oneself and his or her colleagues on follow-up actions, and to be accountable for clinical decisions. These fields contain rich information that can be complementary to that in coded fields, and until now, they have been hardly used for analysis. OBJECTIVE: This study aims to develop a prediction model to convert the free text information on COVID-19-related symptoms from out of hours care electronic medical records into usable symptom-based data that can be analyzed at large scale. METHODS: The design was a feasibility study in which we examined the content of the raw data, steps and methods for modelling, as well as the precision and accuracy of the models. A data prediction model for 27 preidentified COVID-19-relevant symptoms was developed for a data set derived from the database of primary-care out-of-hours consultations in Flanders. A multiclass, multilabel categorization classifier was developed. We tested two approaches, which were (1) a classical machine learning-based text categorization approach, Binary Relevance, and (2) a deep neural network learning approach with BERTje, including a domain-adapted version. Ethical approval was acquired through the Institutional Review Board of the Institute of Tropical Medicine and the ethics committee of the University Hospital of Antwerpen (ref 20/50/693). RESULTS: The sample set comprised 3957 fields. After cleaning, 2313 could be used for the experiments. Of the 2313 fields, 85% (n=1966) were used to train the model, and 15% (n=347) for testing. The normal BERTje model performed the best on the data. It reached a weighted F1 score of 0.70 and an exact match ratio or accuracy score of 0.38, indicating the instances for which the model has identified all correct codes. The other models achieved respectable results as well, ranging from 0.59 to 0.70 weighted F1. The Binary Relevance method performed the best on the data without a frequency threshold. As for the individual codes, the domain-adapted version of BERTje performs better on several of the less common objective codes, while BERTje reaches higher F1 scores for the least common labels especially, and for most other codes in general. CONCLUSIONS: The artificial intelligence model BERTje can reliably predict COVID-19-related information from medical records using text mining from the free text fields generated in primary care settings. This feasibility study invites researchers to examine further possibilities to use primary care routine data.

Applying systems thinking to identify enablers and challenges to scale-up interventions for hypertension and diabetes in low-income and middle-income countries: protocol for a longitudinal mixed-methods study.

INTRODUCTION: There is an urgent need to reduce the burden of non-communicable diseases (NCDs), particularly in low-and middle-income countries, where the greatest burden lies. Yet, there is little research concerning the specific issues involved in scaling up NCD interventions targeting low-resource settings. We propose to examine this gap in up to 27 collaborative projects, which were funded by the Global Alliance for Chronic Diseases (GACD) 2019 Scale Up Call, reflecting a total funding investment of approximately US$50 million. These projects represent diverse countries, contexts and adopt varied approaches and study designs to scale-up complex, evidence-based interventions to improve hypertension and diabetes outcomes. A systematic inquiry of these projects will provide necessary scientific insights into the enablers and challenges in the scale up of complex NCD interventions. METHODS AND ANALYSIS: We will apply systems thinking (a holistic approach to analyse the inter-relationship between constituent parts of scaleup interventions and the context in which the interventions are implemented) and adopt a longitudinal mixed-methods study design to explore the planning and early implementation phases of scale up projects. Data will be gathered at three time periods, namely, at planning (TP), initiation of implementation (T0) and 1-year postinitiation (T1). We will extract project-related data from secondary documents at TP and conduct multistakeholder qualitative interviews to gather data at T0 and T1. We will undertake descriptive statistical analysis of TP data and analyse T0 and T1 data using inductive thematic coding. The data extraction tool and interview guides were developed based on a literature review of scale-up frameworks. ETHICS AND DISSEMINATION: The current protocol was approved by the Monash University Human Research Ethics Committee (HREC number 23482). Informed consent will be obtained from all participants. The study findings will be disseminated through peer-reviewed publications and more broadly through the GACD network.

The impact of COVID-19 on chronic care according to providers: a qualitative study among primary care practices in Belgium.

BACKGROUND: The COVID-19 pandemic affects the processes of routine care for chronic patients. A better understanding helps to increase resilience of the health system and prepare adequately for next waves of the pandemic. METHODS: A qualitative study was conducted in 16 primary care practices: 6 solo working, 4 monodisciplinary and 7 multidisciplinary. Twenty-one people (doctors, nurses, dieticians) were interviewed, using semi-structured video interviews. A thematic analysis was done using the domains of the Chronic Care Model (CCM). RESULTS: Three themes emerged: changes in health care organization, risk stratification and self-management support. All participating practices reported drastic changes in organization with a collective shift towards COVID-19 care, and reduction of chronic care activities, less consultations, and staff responsible for self-management support put on hold. A transition to digital support did not occur. Few practitioners had a systematic approach to identify and contact high-risk patients for early follow-up. A practice with a pre-established structured team collaboration managed to continue most chronic care elements. Generally, practitioners expected no effects of the temporary disruption for patients, although they expressed concern about patients already poorly regulated. CONCLUSION: Our findings show a disruption of the delivery of chronic care in the Belgium prim care context. In such contexts, the establishment of the CCM can facilitate continuity of care in crisis times. Short term actions should be directed to facilitate identifying high-risk patients and to develop a practice organization plan to organize chronic care and use digital channels for support, especially to vulnerable patients, during next waves of the epidemic.