Prognostic accuracy of eight triage scores in suspected COVID-19 in an Emergency Department low-income setting: An observational cohort study.

Introduction: Previous studies deriving and validating triage scores for patients with suspected COVID-19 in Emergency Department settings have been conducted in high- or middle-income settings. We assessed eight triage scores' accuracy for death or organ support in patients with suspected COVID-19 in Sudan. Methods: We conducted an observational cohort study using Covid-19 registry data from eight emergency unit isolation centres in Khartoum State, Sudan. We assessed performance of eight triage scores including: PRIEST, LMIC-PRIEST, NEWS2, TEWS, the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS in suspected COVID-19. A composite primary outcome included death, ventilation or ICU admission. Results: In total 874 (33.84 %, 95 % CI:32.04 % to 35.69 %) of 2,583 patients died, required intubation/non-invasive ventilation or HDU/ICU admission . All risk-stratification scores assessed had worse estimated discrimination in this setting, compared to studies conducted in higher-income settings: C-statistic range for primary outcome: 0.56-0.64. At previously recommended thresholds NEWS2, PRIEST and LMIC-PRIEST had high estimated sensitivities (≥0.95) for the primary outcome. However, the high baseline risk meant that low-risk patients identified at these thresholds still had a between 8 % and 17 % risk of death, ventilation or ICU admission. Conclusion: None of the triage scores assessed demonstrated sufficient accuracy to be used clinically. This is likely due to differences in the health care system and population (23 % of patients died) compared to higher-income settings in which the scores were developed. Risk-stratification scores developed in this setting are needed to provide the necessary accuracy to aid triage of patients with suspected COVID-19.

Training and testing of a gradient boosted machine learning model to predict adverse outcome in patients presenting to emergency departments with suspected covid-19 infection in a middle-income setting.

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.

Can all healthy adults use the current evidential breath alcohol analysers? An investigation using a large spirometry database.

People failing to give a specimen of breath at a police station are assumed to be deliberately obstructive and are charged with Failure to Provide under the Road Traffic Act 1988. However, spirometry records of 281,210 healthy individuals from UK BioBank showed that a significant minority cannot use existing evidential breath analysis machines. Women were three times more likely to be unable to use them than men (1.64% vs 0.54%) with the risk rising with age six-fold from those in their 40s (0.43%) to 2.7% in their 70s, with women more affected (0.65% to 3.8%). Short stature was a further risk factor: 2.6% of men and 3.8% of women below the 2nd percentile of height could not use the current machines, with almost one in ten elderly, short women unable to do so, while smokers aged 50+ were twice as likely as non-smokers of the same age to be unable to provide breath specimens.

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting.

BACKGROUND: Uneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic. METHODS: Routinely collected data for public hospitals in the Western Cape, South Africa from the 27th August 2020 to 11th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort. RESULTS: We analysed 305,564 derivation, 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment. CONCLUSION: The LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings.

External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study.

BACKGROUND: Tools proposed to triage ED acuity in suspected COVID-19 were derived and validated in higher income settings during early waves of the pandemic. We estimated the accuracy of seven risk-stratification tools recommended to predict severe illness in the Western Cape, South Africa. METHODS: An observational cohort study using routinely collected data from EDs across the Western Cape, from 27 August 2020 to 11 March 2022, was conducted to assess the performance of the PRIEST (Pandemic Respiratory Infection Emergency System Triage) tool, NEWS2 (National Early Warning Score, version 2), TEWS (Triage Early Warning Score), the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS (Pandemic Medical Early Warning Score) in suspected COVID-19. The primary outcome was intubation or non-invasive ventilation, death or intensive care unit admission at 30 days. RESULTS: Of the 446 084 patients, 15 397 (3.45%, 95% CI 34% to 35.1%) experienced the primary outcome. Clinical decision-making for inpatient admission achieved a sensitivity of 0.77 (95% CI 0.76 to 0.78), specificity of 0.88 (95% CI 0.87 to 0.88) and the negative predictive value (NPV) of 0.99 (95% CI 0.99 to 0.99). NEWS2, PMEWS and PRIEST scores achieved good estimated discrimination (C-statistic 0.79 to 0.82) and identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.41 to 0.64. Use of the tools at recommended thresholds would have more than doubled admissions, with only a 0.01% reduction in false negative triage. CONCLUSION: No risk score outperformed existing clinical decision-making in determining the need for inpatient admission based on prediction of the primary outcome in this setting. Use of the PRIEST score at a threshold of one point higher than the previously recommended best approximated existing clinical accuracy.

Socioeconomic Background and Self-Reported Sleep Quality in Older Adults during the COVID-19 Pandemic: An Analysis of the English Longitudinal Study of Ageing (ELSA).

The COVID-19 pandemic negatively impacted sleep quality. However, research regarding older adults' sleep quality during the pandemic has been limited. This study examined the association between socioeconomic background (SEB) and older adults' sleep quality during the COVID-19 pandemic. Data on 7040 adults aged ≥50 were acquired from a COVID-19 sub-study of the English Longitudinal Study of Ageing (ELSA). SEB was operationalized using educational attainment, previous financial situation, and concern about the future financial situation. Sociodemographic, mental health, physical health, and health behavior variables were included as covariates. Chi-squared tests and binary logistic regression were used to examine associations between SEB and sleep quality. Lower educational attainment and greater financial hardship and concerns were associated with poor sleep quality. The relationship between educational attainment and sleep quality was explained by the financial variables, while the relationship between previous financial difficulties and sleep quality was explained by physical health and health behavior variables. Greater financial concerns about the future, poor mental health, and poor physical health were independent risk factors for poor sleep quality in older adults during the pandemic. Healthcare professionals and service providers should consider these issues when supporting older patients with sleep problems and in promoting health and wellness.

An unsupervised learning approach to diagnosing Alzheimer's disease using brain magnetic resonance imaging scans.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. Due to the lack of effectiveness of manual diagnosis by doctors, machine learning is now being applied to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. OBJECTIVE: This study applied a statistical method and unsupervised learning methods to discriminate between scans from cognitively normal (CN) and people with AD using a limited number of labelled structural MRI scans. METHODS: We used two-sample t-tests to detect the AD-relevant regions, and then employed an unsupervised learning neural network to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between CN and AD data based on the extracted features. The approach was tested on baseline brain structural MRI scans from 429 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI), of which 231 were CN and 198 had AD. RESULTS: The abnormal regions around the lower parts of limbic system were indicated as AD-relevant regions based on the two-sample t-test (p < 0.001), and the proposed method yielded an accuracy of 0.84 for discriminating between CN and AD. CONCLUSION: The study combined statistical and unsupervised learning methods to identify scans of people with AD. This method can detect AD-relevant regions and could be used to accurately diagnose AD; it does not require large amounts of labelled MRI scans. Our research could help in the automatic diagnosis of AD and provide a basis for diagnosing stable mild cognitive impairment (stable MCI) and progressive mild cognitive impairment (progressive MCI).

Social Support in a Diabetes Online Community: Mixed Methods Content Analysis.

BACKGROUND: Patients with diabetes may experience different needs according to their diabetes stage. These needs may be met via online health communities in which individuals seek health-related information and exchange different types of social support. Understanding the social support categories that may be more important for different diabetes stages may help diabetes online communities (DOCs) provide more tailored support to web-based users. OBJECTIVE: This study aimed to explore and quantify the categorical patterns of social support observed in a DOC, taking into consideration users' different diabetes stages, including prediabetes, type 2 diabetes (T2D), T2D with insulin treatment, and T2D remission. METHODS: Data were collected from one of the largest DOCs in Europe: Diabetes.co.uk. Drawing on a mixed methods content analysis, a qualitative content analysis was conducted to explore what social support categories could be identified in users' posts. A total of 1841 posts were coded by 5 human annotators according to a modified version of the Social Support Behavior Code, including 7 different social support categories: achievement, congratulations, network support, seeking emotional support, seeking informational support, providing emotional support, and providing informational support. Subsequently, quantitative content analysis was conducted using chi-square post hoc analysis to compare the most prominent social support categories across different stages of diabetes. RESULTS: Seeking informational support (605/1841, 32.86%) and providing informational support (597/1841, 32.42%) were the most frequent categories exchanged among users. The overall distribution of social support categories was significantly different across the diabetes stages (χ218=287.2; P<.001). Users with prediabetes sought more informational support than those in other stages (P<.001), whereas there were no significant differences in categories posted by users with T2D (P>.001). Users with T2D under insulin treatment provided more informational and emotional support (P<.001), and users with T2D in remission exchanged more achievement (P<.001) and network support (P<.001) than those in other stages. CONCLUSIONS: This is the first study to highlight what, how, and when different types of social support may be beneficial at different stages of diabetes. Multiple stakeholders may benefit from these findings that may provide novel insights into how these categories can be strategically used and leveraged to support diabetes management.

Use of Online Health Forums by People Living With Breast Cancer During the COVID-19 Pandemic: Thematic Analysis.

BACKGROUND: At the time of the UK COVID-19 lockdowns, online health forums (OHFs) were one of the relatively few remaining accessible sources of peer support for people living with breast cancer. Cancer services were heavily affected by the pandemic in many ways, including the closure of many of the customary support services. Previous studies indicate that loneliness, anxiety, distress, and depression caused by COVID-19 were common among people living with breast cancer, and this suggests that the role of OHFs in providing users with support, information, and empathy could have been of increased importance at that time. OBJECTIVE: This study aimed to examine how people living with breast cancer shared information, experiences, and emotions in an OHF during the COVID-19 pandemic. METHODS: This qualitative study thematically analyzed posts from the discussion forums of an OHF provided by the UK charity, Breast Cancer Now. We selected 1053 posts from the time of 2 UK lockdowns: March 16, 2020, to June 15, 2020 (lockdown 1), and January 6, 2021, to March 8, 2021 (lockdown 3), for analysis, from 2 of the forum's boards (for recently diagnosed people and for those undergoing chemotherapy). We analyzed the data using the original 6 steps for thematic analysis by Braun and Clarke but by following a codebook approach. Descriptive statistics for posts were also derived. RESULTS: We found that COVID-19 amplified the forum's value to its users. As patients with cancer, participants were in a situation that was "bad enough already," and the COVID-19 pandemic heightened this difficult situation. The forum's value, which was already high for the information and peer support it provided, increased because COVID-19 caused some special information needs that forum users were uniquely well placed to fulfill as people experiencing the combined effects of having breast cancer during the pandemic. The forum also met the emotional needs generated by the COVID-19 pandemic and was valued as a place where loneliness during the pandemic may be relieved and users' spirits lifted in a variety of ways specific to this period. We found some differences in use between the 2 periods and the 2 boards-most noticeable was the great fear and anxiety expressed at the beginning of lockdown 1. Both the beginning and end of lockdown periods were particularly difficult for participants, with the ends seen as potentially increasing isolation. CONCLUSIONS: The forums were an important source of support and information to their users, with their value increasing during the lockdowns for a variety of reasons. Our findings will be helpful to organizations offering OHFs and to health care workers advising people living with breast cancer about sources of support.

Association between Internet Usage and Quality of Life of Elderly People in England: Evidence from the English Longitudinal Study of Ageing (ELSA).

The WHO has stated that the number of senior citizens above age 65 across the world will double by the year 2050: in the UK, the whole population is projected to grow by about 2.5% over a decade, from mid-2018. Although people are living longer, they are not healthier in old age, and there is an increasing number of illnesses and disabilities in the ageing population, which have an impact on their overall well-being and quality of life (QoL). Alongside these trends, Internet technologies have improved and provide a wide range of information, including on medical and health issues. This study aimed to examine the association between the utilisation of the internet among older people in England and their QoL. This study utilised the English Longitudinal Study of Aging (ELSA), a longitudinal study of a representative sample of people aged 50 and over in England. The data from Wave 9 were analysed using bivariate analysis and logistic regression. The results show a strong association between QoL and utilisation of the Internet in older people, even when adjusting for demographic variables and health. Higher use of the internet was associated with older people being less likely to have higher QoL. The excessive use of the internet for communication and gathering information also contributed to lower QoL. From the findings, poorer QoL was also found in people in older age groups, in those who are married, and those who never suffer from chronic diseases. Our findings suggest that the quality of life in older people might not only be associated with the frequency of usage but also the purpose for which the internet is used; however, this relationship is complex and further research should explore this in greater depth. Further research should also investigate how older people's use of the Internet changed during the COVID-19 pandemic and the effects of this on the QoL in older age.

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting.

Background: Uneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic. Methods: Routinely collected data for public hospitals in the Western Cape, South Africa from the 27 th August 2020 to 11 th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort. Results: We analysed 305,564, derivation 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment. Conclusion: The LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings. What is already known on this subject: Uneven vaccination in low- and middle-income countries (LMICs) coupled with less resilient health care provision mean that emergency health care systems in LMICs may still be at risk of being overwhelmed during periods of increased COVID-19 infection.Risk-stratification scores may help rapidly triage need for hospitalisation. However, those proposed for use in the ED for patients with suspected COVID-19 have been developed and validated in high-income settings. What this study adds: The LMIC-PRIEST score has been robustly developed using a large routine dataset from the Western Cape, South Africa and is directly applicable to existing triage practices in LMICs.External validation across both income settings and COVID-19 variants showed good discrimination and high sensitivity (at lower thresholds) to a composite outcome indicating need for inpatient admission from the ED. How this study might affect research practice or policy: Use of the LMIC-PRIEST score at thresholds of three or less would allow identification of very low-risk patients (negative predictive value ≥0.99) across all settings assessedDuring periods of increased demand, this could allow the rapid identification and discharge of patients from the ED using information collected at initial assessment.

Accuracy of emergency medical service telephone triage of need for an ambulance response in suspected COVID-19: an observational cohort study.

OBJECTIVE: To assess accuracy of emergency medical service (EMS) telephone triage in identifying patients who need an EMS response and identify factors which affect triage accuracy. DESIGN: Observational cohort study. SETTING: Emergency telephone triage provided by Yorkshire Ambulance Service (YAS) National Health Service (NHS) Trust. PARTICIPANTS: 12 653 adults who contacted EMS telephone triage services provided by YAS between 2 April 2020 and 29 June 2020 assessed by COVID-19 telephone triage pathways were included. OUTCOME: Accuracy of call handler decision to dispatch an ambulance was assessed in terms of death or need for organ support at 30 days from first contact with the telephone triage service. RESULTS: Callers contacting EMS dispatch services had an 11.1% (1405/12 653) risk of death or needing organ support. In total, 2000/12 653 (16%) of callers did not receive an emergency response and they had a 70/2000 (3.5%) risk of death or organ support. Ambulances were dispatched to 4230 callers (33.4%) who were not conveyed to hospital and did not deteriorate. Multivariable modelling found variables of older age (1 year increase, OR: 1.05, 95% CI: 1.04 to 1.05) and presence of pre-existing respiratory disease (OR: 1.35, 95% CI: 1.13 to 1.60) to be predictors of false positive triage. CONCLUSION: Telephone triage can reduce ambulance responses but, with low specificity. A small but significant proportion of patients who do not receive an initial emergency response deteriorated. Research to improve accuracy of EMS telephone triage is needed and, due to limitations of routinely collected data, this is likely to require prospective data collection.

Prognostic accuracy of triage tools for adults with suspected COVID-19 in a prehospital setting: an observational cohort study.

BACKGROUND: Tools proposed to triage patient acuity in COVID-19 infection have only been validated in hospital populations. We estimated the accuracy of five risk-stratification tools recommended to predict severe illness and compared accuracy to existing clinical decision making in a prehospital setting. METHODS: An observational cohort study using linked ambulance service data for patients attended by Emergency Medical Service (EMS) crews in the Yorkshire and Humber region of England between 26 March 2020 and 25 June 2020 was conducted to assess performance of the Pandemic Respiratory Infection Emergency System Triage (PRIEST) tool, National Early Warning Score (NEWS2), WHO algorithm, CRB-65 and Pandemic Medical Early Warning Score (PMEWS) in patients with suspected COVID-19 infection. The primary outcome was death or need for organ support. RESULTS: Of the 7549 patients in our cohort, 17.6% (95% CI 16.8% to 18.5%) experienced the primary outcome. The NEWS2 (National Early Warning Score, version 2), PMEWS, PRIEST tool and WHO algorithm identified patients at risk of adverse outcomes with a high sensitivity (>0.95) and specificity ranging from 0.3 (NEWS2) to 0.41 (PRIEST tool). The high sensitivity of NEWS2 and PMEWS was achieved by using lower thresholds than previously recommended. On index assessment, 65% of patients were transported to hospital and EMS decision to transfer patients achieved a sensitivity of 0.84 (95% CI 0.83 to 0.85) and specificity of 0.39 (95% CI 0.39 to 0.40). CONCLUSION: Use of NEWS2, PMEWS, PRIEST tool and WHO algorithm could improve sensitivity of EMS triage of patients with suspected COVID-19 infection. Use of the PRIEST tool would improve sensitivity of triage without increasing the number of patients conveyed to hospital.

Factors Affecting General Practitioners' Readiness to Accept and Use an Electronic Health Record System in the Republic of North Macedonia: A National Survey of General Practitioners.

BACKGROUND: Electronic health records (EHRs) represent an important aspect of digital health care, and to promote their use further, we need to better understand the drivers of their acceptance among health care professionals. EHRs are not simple computer applications; they should be considered as a highly integrated set of systems. Technology acceptance theories can be used to better understand users' intentions to use EHRs. It is recommended to assess factors that determine the future acceptance of a system before it is implemented. OBJECTIVE: This study uses a modified version of the Unified Theory of Acceptance and Use of Technology with the aim of examining the factors associated with intentions to use an EHR application among general practitioners (GPs) in the Republic of North Macedonia, a country that has been underrepresented in extant literature. More specifically, this study aims to assess the role of technology acceptance predictors such as performance expectancy, effort expectancy, social influence, facilitating conditions, job relevance, descriptive norms, and satisfaction with existing eHealth systems already implemented in the country. METHODS: A web-based invitation was sent to 1174 GPs, of whom 458 completed the questionnaire (response rate=40.2%). The research instrument assessed performance expectancy, effort expectancy, facilitating conditions, and social influence in relation to the GPs' intentions to use future EHR systems. Job relevance, descriptive norms, satisfaction with currently used eHealth systems in the country, and computer/internet use were also measured. RESULTS: Hierarchical linear regression analysis showed that effort expectancy, descriptive norms, social influence, facilitating conditions, and job relevance were significantly associated with intentions to use the future EHR system, but performance expectance was not. Multiple mediation modeling analyses further showed that social influence (z=2.64; P<.001), facilitating conditions (z=4.54; P<.001), descriptive norms (z=4.91; P<.001), and effort expectancy (z=5.81; P=.008) mediated the association between job relevance and intentions. Finally, moderated regression analysis showed that the association between social influence and usage intention was significantly moderated (P=.02) by experience (Bexperience×social influence=.005; 95% CI 0.001 to 0.010; ß=.080). In addition, the association between social influence and intentions was significantly moderated (P=.02) by age (Bage×social influence=.005; 95% CI 0.001 to 0.010; ß=.077). CONCLUSIONS: Expectations of less effort in using EHRs and perceptions on supportive infrastructures for enabling EHR use were significantly associated with the greater acceptance of EHRs among GPs. Social norms were also associated with intentions, even more so among older GPs and those with less work experience. The theoretical and practical implications of these findings are also discussed.

Sensor-based platforms for remote management of chronic diseases in developing regions: A qualitative approach examining the perspectives of healthcare professionals.

The continuous monitoring of chronic diseases serves as one of the cornerstones in the efforts to improve the quality of life of patients and maintain the healthcare services provided to them. This study aims to provide an in-depth understanding of the perspectives of healthcare professionals on using sensor-based networks (SBN) used for remote and continuous monitoring of patients with chronic illness in Kosovo, a developing country. A qualitative research method was used to interview 26 healthcare professionals. The study results demonstrate the positive attitudes of participants to using SBN, and considers their concerns on the impact of these platforms on the patient's life, the number of visits in the medical centre, data privacy concerning interactions between patients and their medical personnel and the costs of the platform. Further to that, the study makes an important contribution to knowledge by identifying the challenges and drawbacks of these platforms and provides recommendations for system designers.

The Use of Data Mining Methods for the Prediction of Dementia: Evidence From the English Longitudinal Study of Aging.

Dementia in older age is a major health concern with the increase in the aging population. Preventive measures to prevent or delay dementia symptoms are of utmost importance. In this study, a large and wide variety of factors from multiple domains were investigated using a large nationally representative sample of older people from the English Longitudinal Study of Ageing. Seven machine learning algorithms were implemented to build predictive models for performance comparison. A simple model ensemble approach was used to combine the prediction results of individual base models to further improve predictive power. A series of important factors in each domain area were identified. The findings from this study provide new evidence on factors that are associated with the dementia in later life. This information will help our understanding of potential risk factors for dementia and identify warning signs of the early stages of dementia. Longitudinal research is required to establish which factors may be causative and which factors may be a consequence of dementia.

Online Support Groups as a Source of Empowerment for People with Type 2 Diabetes.

People with Type 2 Diabetes (T2D) control much of their illness by making daily decisions regarding their health behaviours. They require certain skills, information, and support, which might not be obtainable from healthcare providers, and they may seek support through other media, such as online support groups (OSGs). This study seeks to understand the role of OSGs in empowering people with T2D by thematically analysing threads and posts from two UK OSGs.

'Someone like me': User experiences of the discussion forums of non-12-step alcohol online support groups, June 2019.

BACKGROUND: Peer support is widely acknowledged to be an important factor in recovery from problem drinking. Many seek this from support groups, including those online. Whilst Alcoholics Anonymous (AA) and other 12-step groups have provided help to many people, some individuals do not find them useful. This paper aims to contribute to the current limited knowledge on non-12-step groups, i.e., those that do not follow the approach of AA. METHODS: Twenty-five semi-structured interviews were carried out with users of five non-12-step alcohol online support groups (AOSGs) which differed in approach to recovery from problem drinking, size and location. The study was publicised via the groups, and interviewees self-selected. Data were analysed using thematic and template analysis. RESULTS: The most important benefit of the groups, according to most interviewees was finding 'someone like me': something that many did not feel they could do elsewhere, including in AA. Another key perceived difference from 12-step groups was that their groups provided support without requiring them to follow a set programme for recovery. The groups respected individuals' rights to choose their own goal for sobriety (e.g., abstinence, moderate drinking) and to choose how they achieved it. Other key benefits included seeing that recovery is possible and sharing experiential information. Some disadvantages of using the forums are also discussed. CONCLUSION: The findings report the experiences and perceptions of twenty-five users of non-12-step AOSGs. These are groups that have received little research attention so the findings offer a rare insight into users' opinions on these sources of peer support.

Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data.

BACKGROUND: Infectious disease outbreaks have the potential to cause a high number of fatalities and are a very serious public health risk. OBJECTIVES: Our aim was to utilise an indepth method to study a period of time where the H1N1 Pandemic of 2009 was at its peak. METHODS: A data set of n = 214 784 tweets was retrieved and filtered, and the method of thematic analysis was used to analyse the data. RESULTS: Eight key themes emerged from the analysis of data: emotion and feeling, health related information, general commentary and resources, media and health organisations, politics, country of origin, food, and humour and/or sarcasm. DISCUSSION: A major novel finding was that due to the name 'swine flu', Twitter users had the belief that pigs and pork could host and/or transmit the virus. Our paper also considered the methodological implications for the wider field of library and information science as well as specific implications for health information and library workers. CONCLUSIONS: Novel insights were derived on how users communicate about disease outbreaks on social media platforms. Our study also provides an innovative methodological contribution because it was found that by utilising an indepth method it was possible to extract greater insight into user communication.

Sharing and Empathy in Digital Spaces: Qualitative Study of Online Health Forums for Breast Cancer and Motor Neuron Disease (Amyotrophic Lateral Sclerosis).

BACKGROUND: The availability of an increasing number of online health forums has altered the experience of living with a health condition, as more people are now able to connect and support one another. Empathy is an important component of peer-to-peer support, although little is known about how empathy develops and operates within online health forums. OBJECTIVE: The aim of this paper is to explore how empathy develops and operates within two online health forums for differing health conditions: breast cancer and motor neuron disease (MND), also known as amyotrophic lateral sclerosis. METHODS: This qualitative study analyzed data from two sources: interviews with forum users and downloaded forum posts. Data were collected from two online health forums provided by UK charities: Breast Cancer Care and the Motor Neurone Disease Association. We analyzed 84 threads from the breast cancer forum and 52 from the MND forum. Threads were purposively sampled to reflect varied experiences (eg, illness stages, topics of conversation, and user characteristics). Semistructured interviews were conducted with 14 Breast Cancer Care forum users and five users of the MND forum. All datasets were analyzed thematically using Braun and Clarke's six-phase approach and combined to triangulate the analysis. RESULTS: We found that empathy develops and operates through shared experiences and connections. The development of empathy begins outside the forum with experiences of illness onset and diagnosis, creating emotional and informational needs. Users came to the forum and found their experiences and needs were shared and understood by others, setting the empathetic tone and supportive ethos of the forum. The forum was viewed as both a useful and meaningful space in which they could share experiences, information, and emotions, and receive empathetic support within a supportive and warm atmosphere. Empathy operated through connections formed within this humane space based on similarity, relationships, and shared feelings. Users felt a need to connect to users who they felt were like themselves (eg, people sharing the same specific diagnosis). They formed relationships with other users. They connected based on the emotional understanding of ill health. Within these connections, empathic communication flourished. CONCLUSIONS: Empathy develops and operates within shared experiences and connections, enabled by structural possibilities provided by the forums giving users the opportunity and means to interact within public, restricted, and more private spaces, as well as within groups and in one-to-one exchanges. The atmosphere and feeling of both sites and perceived audiences were important facilitators of empathy, with users sharing a perception of virtual communities of caring and supportive people. Our findings are of value to organizations hosting health forums and to health professionals signposting patients to additional sources of support.