bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges in building image-based diagnostic support deep-learning algorithm for acute burns
Authors: Boissin C , Fransén J , Huss F , Wallis L , Allorto N , Laflamme L , Lundin J 
Published: November 2019

Abstract

Background
Acute burns are complex to diagnose and erroneous assessments impact on the victim’s mortality and morbidity. Specialists operate from a small number of burns centres and the capacity to provide timely assistance to front line clinicians is therefore limited. Diagnostic assistance through artificial intelligence could be an option to provide a timely and equitable diagnosis. This project sheds light on the feasibility of the development of an artificial intelligence algorithm for assisted diagnosis of acute burns and clarifies challenges faced along the way.

Methods
A bank of images has been built from a number of burn centres in South Africa and is continuously being updated (currently about 1200 images). Attempts have been made to train deep learning algorithms to diagnose the burn depth, an element that is challenging both at bedside and using image-based teleconsultation. We came across methodological challenges that need further consideration.

Results
Some challenges are clinical, i.e. inherent to the complexity of burn diagnosis, and imply the need of having an accurate diagnosis for the burn images on which the algorithm is trained. Other challenges pertain to the actual development of an algorithm. Prior to identifying burn depth a complex task relates to the feasibility of finding the burn itself in images of varying body parts and backgrounds. Further, training an algorithm for diagnosing burn depth also requires, large numbers of varying cases, the accurate labelling of the wound area for training, and decisions to be made as regards the best outcome to train upon. Current preliminary results indicate satisfactory identification of the burn area and promising results with regards burn depth diagnosis.

Conclusions
Development of artificial intelligence algorithms require strong collaborations and discussions between technical and clinical experts but are showing promising results.

Key messages
Development of clinical image-based automated diagnosis involve a number of critical challenges on both the technical and clinical sides that need to be addressed prior to optimization. Once optimally developed, deep learning algorithms are a potential solution to assist with the reduction of the burden of burns on the health services by providing timely, and cost-effective advice.
Evaluating a digital sepsis alert in a multi-site hospital: a natural experiment
Authors: Honeyford K , Cooke G S , Kinderlerer A , Williamson E , Gilchrist M , Holmes A , Glampson B , Mulla A , Costelloe C 
Published: November 2019

Abstract

Background
This study investigated the impact of a digital sepsis alert on patient outcomes in a busy London multi-site hospital. Sepsis is a serious illness and common cause of death, but rapid diagnosis and treatment improve patient outcomes. Digital health records allow algorithms to be embedded which ‘alert’ clinicians to patients who are at risk of developing sepsis. Despite the current promotion of ‘digital health’, evidence of the impact of algorithm driven alerts on patient outcomes is limited.

Methods
A retrospective natural experiment utilising the phased introduction of a digital sepsis alert into a large, multi-site hospital in England. Silent alerts (not visible to clinicians) acted as controls. Outcome measures were in-hospital all-cause mortality within 30 days of the alert, extended hospital stay (≥7 days) and timely antibiotics (≤60 minutes of the alert). Inversely weighted multivariable logistic regression was used to determine associations between alert and patient outcomes.

Results
In a sample of 21,183 inpatients, the mortality rate was 5.9%. The active, visible alert was associated with lower odds of death (Odds Ratio (OR):0.76; 95%CI:(0.70, 0.84)). In 9988 emergency department attendances ending in admission, 40.6% had an extended hospital stay and 41.5% received timely antibiotics. The active alert was associated with lower odds of extended hospital stay (OR:0.93; 95%CI:(0.88, 0.99)) and increased odds of receiving timely antibiotics (OR:1.71; 95%CI:(1.57, 1.87)).

Conclusions
This study demonstrates that a move to digital health, through an automated sepsis alert, embedded in digital health records, was associated with improved health outcomes. Further work is needed to identify the causal pathway, which is likely to include more rapid treatment with antibiotics, and possible unintended consequences. These findings support the ongoing roll out of digital alerting and provide a model for robustly evaluating their impact.

Key messages
The introduction of an automated sepsis alert associated with the use of improvement methodology was associated with improved process measures and patient outcomes. Introduction of digital health interventions can, and should, be robustly evaluated with appropriate statistical approaches.
Workshop: Sharing best practices and building effective training at European points of entry
Authors:   Organised by: Netherlands National Institute for Public Health and the Environment Chair persons: Aura Timen - EUPHA (IDC), Christos Hadjichristodoulou - Greece
Published: November 2019

Abstract

Infectious diseases can spread all over the world due to the transportation of people and goods through ships, aircrafts and vehicles. During the transportation on board of conveyances (ships, aircrafts, trains, buses), diseases can spread from person-to-person, by infected food or water or through vectors. The same can happen when travelers pass through points of entry (PoE) such as: ports, airports and ground-crossings.
To properly prevent and respond to public health events at PoE, designated ports, airports and ground-crossings core capacities for PoE are dictated in the IHR (2005), which should be in place at all designated PoE. According to the World Health Organization Global Health Observatory data, the IHR core capacities implementation at designated PoE at the European WHO region is 64%. In order to support countries with this implementation, exchange among countries of effective, legalized practices in accordance with IHR (2005), the so-called best practices, can be of important help.
Besides the goal to improve mere capacity, professionals in charge of communicable diseases at PoE should be prepared to respond to public health events and prevent cross-border spread. Education, training and exercises are common ways to achieve this, but demand extensive expertise, time and financial means. As part of a EU Joint Action, a collective training program for designated PoE is being developed, to share efforts and resources, and to help countries better respond to public health events.
However, regarding this training, many questions are still unanswered. What is effective training for public health events at designated PoE, and what components and methodology should it contain. What are the specific training needs of these people? What competencies should they have? What can we learn from previous trainings and what are best practices for designated PoE? The aim of this workshop is to provide a step-by-step overview of all elements that are essential to improve capacity and develop and organize effective training program for event management at designated points of entry in Europe. This study was funded by the European Union’s Health Programme (2014-2020).

Key messages
Extensive and long-term evaluation of training and exercising regarding infectious disease control at points of entry is needed, in order to design effective trainings and facilitate core capacities. Collection and dissemination of best practices in infectious disease control at points of entry, facilitate the challenging task of IHR core capacity implementation.
Workshop: Predictors and developmental timing of mental health problems among sexual minority individuals
Authors:   Organised by: EUPHA (SGMH) Chair persons: Corina Lelutiu-Weinberger - USA, Richard Bränström - EUPHA (SGMH)
Published: November 2019

Abstract

As consistently shown across studies from various parts of the world, sexual minority individuals (e.g., those identifying as lesbian, gay, and bisexual [LGB]) are significantly at a higher risk for depression, anxiety, and suicidal thoughts and behavior when compared to heterosexuals.
The increased risk for poor mental health among sexual minority populations is believed to be a consequence of LGB individuals’ increased exposure to specific social stressors related to navigating a stigmatized minority identity. Studies trying to explain health inequalities based on sexual orientation have mainly focused on so-called minority stress processes, such as discrimination, internalized homophobia, expectations of rejection, and stress of concealing one’s sexual orientation.
This workshop will give examples of studies from various European countries on mental health predictors and trajectories by using various approaches such as population-based sampling, longitudinal data collection, and comprehensive theoretical frameworks. Dr. Gemma Lewis (University College London, UK) will present results showing that sexual orientation-based disparities in mental health are present already in early adolescence and increase throughout the school years. Arjan van der Star (Karolinska Institutet, Sweden) will present population-based data suggesting that openness about sexual orientation is not directly linked to lower risk of depression among sexual minorities, but is instead dependent on access to social support. Conor Mahon (Dublin City University, Ireland) will present results showing minority stressors as predictors of social anxiety among sexual minority men. Associate professor John Pachankis (Yale University, USA) will present results showing that, in addition to increased exposure to social stressors, barriers to societal integration can partially explain the elevated risk of suicidality among sexual minorities.

Key messages
Sexual minorities are a higher risk of mental health problems, such as depression, social anxiety, and suicidality, as compared to heterosexuals and these disparities can be identified early in life. Sexual orientation-based mental health disparities seem to be based both on disproportionate stigma-related stress and a higher prevalence of general risk factors for poor mental health.
Traumatic brain injury as a risk factor for dementia
Authors: Mezzalira E , Stopa B , Khawaja A , Izzy S , Gormley W 
Published: November 2019

Abstract

Introduction
The Centers for Disease Control and Prevention (CDC) reports that there were 2.87 million cases of traumatic brain injury (TBI) in the United States in 2014, 69 million worldwide. Some studies suggest a connection between TBI and increased risk of dementia, but it remains unclear whether the risk increases with age and TBI severity. Given our aging population, it is essential to better characterize the link between TBI and dementia.

Methods
We conducted a retrospective cohort study of two major academic medical centers for years 2000-2015. We identified all patients with TBI, aged 45 and older. Variables included age, TBI severity, pre-existing dementia, dementia diagnosed after TBI, years to dementia, and follow-up time. TBI severity was determined by head/neck AIS score, using ICD-PIC software. Mild TBI was defined as AIS 0-2, and Moderate/Severe as AIS 3-6. Analysis was done in R.v.3.0.1 software.

Results
Overall, there were 14,199 patients with TBI, of which 9,938 (70%) were mild and 4,261 (30%) were moderate/severe. Mean age was 70.5 (±14.0). There were 1,422 cases (10%) of pre-existing dementia, and 850 (6%) cases of dementia diagnosed after TBI. The mean follow-up time was 1,129 (±1,474) days.
The 75-84 age group had the highest incidence of TBI (28%). When compared by age group and TBI severity, the proportion of moderate/severe TBI increased with increasing age. The proportion of pre-existing dementia increased with age, as expected. Notably, there is increased incidence of dementia after TBI in patients aged 65 and older (7-10%, p &lt; 0.001). There was no observed effect of TBI severity on the risk of dementia after TBI.

Conclusions
Our results indicate that TBI is a risk factor for the development of dementia, especially in patients aged 65 and older. Given the global public health burden of these two diseases it is critical to develop effective TBI primary prevention strategies.

Key messages
TBI is a risk factor for the development of dementia. Need for public health measures to mitigate the risk of TBI in the patient population 65 and older.
Occupational health effects during periods with heat
Authors: van den Hazel P , Zuurbier M , Flouris A , Nybo L , Kingma B 
Published: November 2019

Abstract
Hydration status, awareness and the attitude to health and performance effects of preventing dehydration was evaluated in five industries across Europe. The prevalence of dehydration was assessed via analyses of urine samples from 139 workers and questionnaires collected from employers as well as employees collected from ten different work places. In total 80 % of all workers were either suboptimal hydrated at the onset of work or became dehydrated during their work shift with levels equal to or higher than those associated with impaired function in cognitively dominated tasks and complex motor function. The high prevalence of dehydration is in conflict with ∼75% of all workers that emphasize drinking as the most important mitigation strategy during periods with elevated heat strain. Also, work safety and prevention of negative health effect was stressed as very important both by employers and employees (average score 9 out of 10). Although, hydration is emphasized by work-safety advisories, it seems clear that more effective 24/7 hydration strategies are warranted. We propose that future protection of workers against detrimental effects of heat should consider personalized alerts that can integrate the importance of timing to facilitate the development of appropriate hydration habits that accounts for the large inter-individual variation in sweating and hence differences in water and electrolyte needs.
People with a disability status in the French claim database: which characteristics and diseases?
Authors: Colinot N , Thomas M , Verboux D , Rachas A , Gastaldi-Ménager C h 
Published: November 2019

Abstract

Background
Due to a lack of data, populations with disabilities have been poorly studied. Here, we described the characteristics and diseases of people receiving a financial support for a disability and compared them to the general population.

Methods
We extracted data of all health insurance general scheme beneficiaries (87% of the French population) 20-64 years of age in 2016 from the national health database, including information on adult disability allowance (ADA) and disability pension (DP). Diseases were identified using algorithms mainly based on ICD-10 diagnoses from hospital stays, long-term diseases information and specific medications. The proportion of patients with a given disease was compared between people with ADA or DP or without a disability status using relative risks adjusted for age and sex (aRR), estimated by Poisson regression.

Results
Among 32.5 million people, 770,566 (2.5%) had an ADA (mean age 47, 49% women) and 733,599 (2.4%) a DP (mean age 53, 55% women). Disability status was associated with a higher proportion for all studied diseases, with high aRR found for mental illness (ADA: 44.8% (aRR: 17); DP: 26.5% (aRR: 8.9) versus 2.5% in those without disability status), in particular mental retardation (aRR: 108 and 4.7 respectively), neurologic or degenerative diseases (ADA: 13.5% (aRR: 16); DP: 9.0% (aRR: 9.8) versus 0.8% in those without disability status), in particular paraplegia (aRR: 36 and 25), but also cancers, diabetes and chronic and acute cardiovascular diseases.

Conclusions
About 5% of French health insurance beneficiaries 20-64 years old received a financial support for a disability in 2016. All studied diseases were more frequent in this population than in those without a disability status. These results are useful to inform public authorities on health status of these populations and to implement public health policies targeting these populations with special healthcare needs.

Key messages
High frequency of diseases, including mental illness and neurodegenerative diseases, but also a large panel of other diseases was observed in people with a financial support for a disability. Public health policies targeting people with a disability should account for the large spectrum of diseases in these patients.
Housing facilities for asylum seekers in Germany: Ethical concerns regarding social exclusion, othering and negative effects on health
Authors: Agbih S 
Published: November 2019

Abstract

Background
Asylum seekers face many barriers in accessing health care and their well-being is impaired in manifold ways. According to Public Health research the impact on health of social determinants is much higher than the influence of access to health care. Therefore, it is important to take other living conditions like accommodation into serious consideration.

Methods
The focus will be on how housing conditions allow or prevent opportunities for social participation and autonomy. As a philosophical-ethical paper this contribution attempts to identify moral concerns and ethical questions regarding housing situations under the lens of inclusion and exclusion. This will lead to the question whether they constitute forms of othering. In a first step it will be clarified how inclusion and exclusion are used as analytic frame, particularly whether and how far they are not only descriptive terms but also normative ones. Exemplary cases of housing facilities for asylum seekers in Germany will be given and questioned regarding practices of inclusion and exclusion and how this affects participation and autonomy.

Conclusions
Apart from contributing to barriers in accessing health care different types of “exclusive” housing for asylum seekers often impedes on social participation and disempowers autonomous choices. Since social exclusion is known to affect (mental) health this raises concerns for health and well-being as well as several other ethical questions.
Questions for further discussion:
What is the responsibility of Public Health regarding the housing conditions of asylum seekers and the negative effects of othering on health?
How do we perceive this in the light of solidarity?
Spiritual participation and its relation to housing conditions for refugees in Germany
Authors: Özisik S 
Published: November 2019

Abstract

Background
Many studies claim a positive relation between religious / spiritual participation and individual wellbeing. Thus, religious communities play a supportive and socializing role, and supply group assistance, security and solidarity also for refugees. Some dimensions of religion, such as the opportunity of practicing congregational rituals, transmitting religious traditions to the next generations, and building a religious/spiritual community may be linked to the housing conditions.
Objectives: The contribution aims to answer the following questions by showing the effects of housing conditions on the well-being of refugees: Is the spirituality of refugees affected after settlement in collective accommodation sites, and if so how?Does the distance or proximity to collective worship places affect the spiritual participation of refugees and their activities in religious organizations?

Methods
The data is derived from an ongoing research project at Bielefeld University, Germany. The research uses a multimethod approach. Semi-structured qualitative interviews (28) are supported by participatory observations and questionnaire application (N = 120).

Results
Both in the Interviews and quantitative survey, many Christian, Yezidi and Muslim respondents explicitly argued about their religious participation / nonparticipation due to their housing conditions either in collective accommodation or private housing.

Conclusions
Christians and Yezidis have fewer opportunities of collective practises of religion due to remote accommodations. The Muslim participants on the contrary have more options of practising with their coreligionists. The availability of religious institutions near to housing places affects this directly.
Artificial Intelligence-based tools to control healthcare associated infections: where do we stand
Authors: Scardoni A , Odone A 
Published: November 2019

Abstract

Background
Control of Healthcare associated infections (HAI) is a key public health concern in Europe. Current HAI surveillance systems are based on manual medical records review, vulnerable to misclassification & expensive. Artificial intelligence (AI), as a digital tool, offers great potential to HAI control. Still, scant evidence is available on both its practice and impact.

Methods
As part of a broader multidisciplinary project, we conducted a systematic review to retrieve, pool and critically apprize all the available evidence on practice, performance and impact of AI-based HAI control programmes. We followed PRISMA and searched the Medline and Embase databases for relevant studies. Included studies were stratified by HAI type and outcomes of interest, including all possible performance measures, clinical, organizational and economic outcomes.

Results
We screened 2873 records, resulting in 27 papers included in the review. Studies were carried out in 9 countries, the majority in the US (56%), 18.5% in EU countries, 25.9% published in 2018. Two thirds of studies focused on selected types of infections. Study designs were very diverse and performance observed for HAI detection were very heterogeneous, precluding pooled calculation of summary diagnostic accuracy estimates in most instances, but generally higher than non AI-based models. Overall performance measures of AI algorithms were: sensitivity range 19%-92%, accuracy 70.2%-96.1%.

Conclusions
Use of AI for HAI surveillance of HAI has increased reliability compared to traditional surveillance or to automated surveillance models. With ongoing improvements in information technology, implementation of AI models will improve the quality and capacity of surveillance will support hospital HAI surveillance.

Main messages
AI offer great potential to healthcare associated infections control. Preliminary evidence show AI-based models have perform better than manual or automated models for HAIs detection.