Depression is a common mental health illness that affects hundreds of millions of people worldwide. In Saudi Arabia, depression is considered a major public health concern. Recently, many people have used AI chatbots to seek health information. This paper aims to explore the challenges that people with depression face when using AI chatbots in Saudi Arabia. A qualitative method was employed to analyze 19 interviews: data were thematically analyzed using Braun and Clarke's approach. Four main themes were identified: increased loneliness associated with AI use, user frustration triggered by AI's tendency to over please users, limited understanding of the Saudi context, and a lack of recognition of subjective experiences. The findings from this study can raise awareness among people with depression about potential challenges when encountering AI chatbots.

PurposeThe numbers of people with both diagnosed and suspected autism have risen exponentially worldwide over the last few decades. Autistic individuals face significant health and social disparities, including higher rates of poor health, early mortality and limited access to essential services. Understanding autistic people's information needs and information journeys is therefore crucial.Design/methodology/approachThe research consisted of two consecutive qualitative studies. First, a sample of posts from an online autism group, made by group users who described themselves as autistic, was generated and analysed using reflexive thematic analysis. Fifteen semi-structured interviews were then undertaken with adults who used online groups for autistic people and stated that they had received a professional diagnosis of autism or were awaiting an autism assessment. Interview transcripts were analysed using thematic analysis.FindingsThe results identified some distinctive information behaviours connected by the participants to autism. A descriptive model was developed to represent the information journeys of people who use online groups for autistic people and are autistic or believe that they might be. The model shows how people who have been diagnosed with autism or who are awaiting professional assessment, seek and encounter online information about autism and the role of online groups for autistic people within users' information journeys.Originality/valueThis research is the first attempt within the literature to describe and model the information journeys of people who are autistic or likely to be autistic and use online groups to find and share information. This is also the first study focussing specifically on the information behaviours of autistic adults.

Introduction. Depression is a major contributor to disability and will be a leading cause of global disease burden by 2030. There is a high depression rate in Saudi Arabia. Online Mental health communities (OMHCs) are important for people with mental health conditions who are concerned about the stigma associated with these conditions. Paralinguistic digital affordances (PDAs) are a useful social affordance enabled by social media. This study aimed to understand the role of using PDAs in Saudi OMHCs. Method. Qualitative methods were employed to analyse a sample of 1331 posts from two Saudi OMHCs. Analysis. Data were thematically analysed using Braun and Clarke’s method. Results. PDAs fostered receiving emotional and informational support and were used to communicate frustrating emotions. A variety of emojis was used to provide encouragement to other users in response to their posts and conveyed different forms of support. PDAs were also used to show empathy and to acknowledge other people’s feelings and to reassure them. However, on occasion, PDAs were used in a negative way. Conclusion. PDAs provide additional emotional and informational support to encourage people with depression in OMHCs. Future research could interview OMHC users to develop a deeper understanding of how PDAs provide support.

Estimating the quality of published research is important for evaluations of departments, researchers, and job candidates. Citation-based indicators sometimes support these tasks, but do not work for new articles and have low or moderate accuracy. Previous research has shown that ChatGPT can estimate the quality of research articles, with its scores correlating positively with an expert scores proxy in all fields, and often more strongly than citation-based indicators, except for clinical medicine. ChatGPT scores may therefore replace citation-based indicators for some applications. This article investigates the clinical medicine anomaly with the largest dataset yet and a more detailed analysis. The results showed that ChatGPT 4o-mini scores for articles submitted to the UK's Research Excellence Framework (REF) 2021 Unit of Assessment (UoA) 1 Clinical Medicine correlated positively (r = 0.134, n = 9872) with departmental mean REF scores, against a theoretical maximum correlation of r = 0.226. ChatGPT 4o and 3.5 turbo also gave positive correlations. At the departmental level, mean ChatGPT scores correlated more strongly with departmental mean REF scores (r = 0.395, n = 31). For the 100 journals with the most articles in UoA 1, their mean ChatGPT score correlated strongly with their departmental mean REF score (r = 0.495) but negatively with their citation rate (r=-0.148). Journal and departmental anomalies in these results point to ChatGPT being ineffective at assessing the quality of research in prestigious medical journals or research directly affecting human health, or both. Nevertheless, the results give evidence of ChatGPT's ability to assess research quality overall for Clinical Medicine, where it might replace citation-based indicators for new research.

Depression is one of the most prevalent mental health illnesses and a significant public health concern in Saudi Arabia. Due to misconceptions about mental health diseases, such as depression, in Saudi Arabia, there is widespread stigma. Many people with depression, therefore, seek health information via social media such as blogs, microblogs, and online communities. Online mental health communities (OMHCs) have been developed only recently in the country. This study explores the challenges people experience when engaging in OMHCs. A sample of 1,422 posts was generated from two OMHCs and analyzed using inductive thematic analysis. Three main themes were identified: misinformation, triggering vulnerability, and judgment. Findings from this study will be shared with the OMHCs and will help administrators to develop strategies and policies to enhance the experience of people with depression in using OMHCs.

Substantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators. This ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency. The overall prevalence of multimorbidity was 33.4

Objective To assess accuracy of telephone triage in identifying need for emergency care among those with suspected COVID-19 infection and identify factors which affect triage accuracy. Design Observational cohort study. Setting Community telephone triage provided in the UK by Yorkshire Ambulance Service NHS Trust (YAS). Participants 40 261 adults who contacted National Health Service (NHS) 111 telephone triage services provided by YAS between 18 March 2020 and 29 June 2020 with symptoms indicating COVID-19 infection were linked to Office for National Statistics death registrations and healthcare data collected by NHS Digital. Outcome Accuracy of triage disposition was assessed in terms of death or need for organ support up to 30 days from first contact. Results Callers had a 3% (1200/40 261) risk of serious adverse outcomes (death or organ support). Telephone triage recommended self-care or non-urgent assessment for 60% (24 335/40 261), with a 1.3% (310/24 335) risk of adverse outcomes. Telephone triage had 74.2% sensitivity (95% CI: 71.6 to 76.6%) and 61.5% specificity (95% CI: 61% to 62%) for the primary outcome. Multivariable analysis suggested respiratory comorbidities may be overappreciated, and diabetes underappreciated as predictors of deterioration. Repeat contact with triage service appears to be an important under-recognised predictor of deterioration with 2 contacts (OR 1.77, 95% CI: 1.14 to 2.75) and 3 or more contacts (OR 4.02, 95% CI: 1.68 to 9.65) associated with false negative triage. Conclusion Patients advised to self-care or receive non-urgent clinical assessment had a small but non-negligible risk of serious clinical deterioration. Repeat contact with telephone services needs recognition as an important predictor of subsequent adverse outcomes.

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/noninvasive 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 lowrisk 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.

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.

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.

BackgroundUneven 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.MethodsRoutinely 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.ResultsWe 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.ConclusionThe 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.

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 discrim-inating 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).

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 31 st 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.

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.

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 2 nd 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.

BackgroundTools 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. MethodsAn 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. ResultsOf 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. ConclusionNo 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.

Across the world, health systems grapple with the burden of infectious diseases, particularly in low and middle-income countries. In Sri Lanka, the effectiveness of notifiable disease surveillance heavily relies on the data collected from government hospitals within the Western medical system. Unfortunately, the absence of notifications from other pertinent sources hinders comprehensive reporting of notifiable diseases, consequently compromising the quality of epidemiological data. To address this issue, an exploratory study was undertaken to identify alternative notification sources, examine the challenges associated with these sources, and propose an integrated surveillance model (using Soft Systems Methodology) for infectious disease notification. The study employed a qualitative approach, involving interviews with 38 healthcare professionals engaged in notifiable disease surveillance activities in Jaffna, Sri Lanka. The gathered information was transcribed and analysed using thematic analysis techniques. The findings of the study highlighted incompleteness as a major factor contributing to the substandard reporting of surveillance data in Jaffna. To enhance the completeness of reporting in the region, it is crucial to involve various stakeholders in the notification system. This includes indigenous medical practitioners, private sector Western medical practitioners, public health workers, medical laboratories, educational institutions, civil service officers, and the general public. Incorporating these additional sources would result in more comprehensive reporting of notifiable diseases, thereby strengthening the overall surveillance efforts in Jaffna, Sri Lanka.

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.

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.

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.