Effect of a telephonic alert system (Healthy Outlook) for patients with chronic obstructive pulmonary disease: cohort study with matched controls

Background: Healthy Outlook was a telephonic alert system for patients with Chronic Obstructive Pulmonary Disease (COPD) in the United Kingdom. It used routine meteorological and communicable disease reports to identify times of increased risk to health. We tested its effect on hospital use and mortality. Methods: Enrolees with a history of hospital admissions were linked to hospital administrative data. They were compared with control patients from local general practices, matched for demographic characteristics, health conditions, previous hospital use and predictive risk scores. We compared unplanned hospital admissions, admissions for COPD, outpatient attendances, planned admissions and mortality, over 12 months following enrolment. Results: Intervention and matched control groups appeared similar at baseline (n=1,413 in each group). Over the 12 months following enrolment, Healthy Outlook enrolees experienced more COPD admissions than matched controls (adjusted rate ratio 1.26, 95% CI, 1.05 to 1.52) and more outpatient attendances (adjusted rate ratio 1.08, 95% CI 1.03 to 1.12). Enrolees also had lower mortality rates over 12 months (adjusted odds ratio 0.61, 95% CI, 0.45 to 0.84). Conclusion: Healthy Outlook did not reduce admission rates, though mortality rates were lower. Findings for hospital utilisation were unlikely to have been affected by confounding

A comparison of alternative strategies for choosing control populations in observational studies.

Various approaches have been used to select control groups in observational studies: (1) from within the intervention area; (2) from a convenience sample, or randomly chosen areas; (3) from areas matched on area-level characteristics; and (4) nationally. The consequences of the decision are rarely assessed but, as we show, it can have complex impacts on confounding at both the area and individual levels. We began by reanalyzing data collected for an evaluation of a rapid response service on rates of unplanned hospital admission. Balance on observed individual-level variables was better with external than local controls, after matching. Further, when important prognostic variables were omitted from the matching algorithm, imbalances on those variables were also minimized using external controls. Treatment effects varied markedly depending on the choice of control area, but in the case study the variation was minimal after adjusting for the characteristics of areas. We used simulations to assess relative bias and means-squared error, as this could not be done in the case study. A particular feature of the simulations was unexplained variation in the outcome between areas. We found that the likely impact of unexplained variation for hospital admissions dwarfed the benefits of better balance on individual-level variables, leading us to prefer local controls in this instance. In other scenarios, in which there was less unexplained variation in the outcome between areas, bias and mean-squared error were optimized using external controls. We identify some general considerations relevant to the choice of control population in observational studies

A deep learning approach for staging embryonic tissue isolates with small data

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches

A deep learning approach for staging embryonic tissue isolates with small data

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches

Healthcare utilization among migrants to the UK: cross-sectional analysis of two national surveys

Funder: The Health FoundationObjective: To contribute objective evidence on health care utilization among migrants to the UK to inform policy and service planning. Methods: We analysed data from Understanding Society, a household survey with fieldwork from 2015 to 2017, and the European Health Interview Survey with data collected between 2013 and 2014. We explored health service utilization among migrants to the UK across primary care, inpatient admissions and maternity care, outpatient care, mental health, dental care and physiotherapy. We adjusted for age, sex, long-term health conditions and time since moving to the UK. Results: Health care utilization among migrants to the UK was lower than utilization among the UK-born population for all health care dimensions except inpatient admissions for childbirth; odds ratio (95%CI) range 0.58 (0.50–0.68) for dental care to 0.88 (0.78–0.98) for primary care). After adjusting for differences in age and self-reported health, these differences were no longer observed, except for dental care (odds ratio 0.57, 95%CI 0.49–0.66, P < 0.001). Across primary care, outpatient and inpatient care, utilization was lower among those who had recently migrated, increasing to the levels of the nonmigrant population after 10 years or more since migrating to the UK. Conclusions: This study finds that newly arrived migrants tend to utilize less health care than the UK population and that this pattern was at least partly explained by better health, and younger age. Our findings contribute nationally representative evidence to inform public debate and decision-making on migration and health

Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial

Objective To assess the effect of home based telehealth interventions on the use of secondary healthcare and mortality

Effect of telecare on use of health and social care services: findings from the Whole Systems Demonstrator cluster randomised trial

Objective: to assess the impact of telecare on the use of social and health care. Part of the evaluation of the Whole Systems Demonstrator trial. Participants and setting: a total of 2,600 people with social care needs were recruited from 217 general practices in three areas in England. Design: a cluster randomised trial comparing telecare with usual care, general practice being the unit of randomisation. Participants were followed up for 12 months and analyses were conducted as intention-to-treat. Data sources: trial data were linked at the person level to administrative data sets on care funded at least in part by local authorities or the National Health Service. Main outcome measures: the proportion of people admitted to hospital within 12 months. Secondary endpoints included mortality, rates of secondary care use (seven different metrics), contacts with general practitioners and practice nurses, proportion of people admitted to permanent residential or nursing care, weeks in domiciliary social care and notional costs. Results: 46.8% of intervention participants were admitted to hospital, compared with 49.2% of controls. Unadjusted differences were not statistically significant (odds ratio: 0.90, 95% CI: 0.75–1.07, P = 0.211). They reached statistical significance after adjusting for baseline covariates, but this was not replicated when adjusting for the predictive risk score. Secondary metrics including impacts on social care use were not statistically significant. Conclusions: telecare as implemented in the Whole Systems Demonstrator trial did not lead to significant reductions in service use, at least in terms of results assessed over 12 months

Are self-reported telemonitored blood pressure readings affected by end-digit preference: a prospective cohort study in Scotland

Objective Simple forms of blood pressure (BP) telemonitoring require patients to text readings to central servers creating an opportunity for both entry error and manipulation. We wished to determine if there was an apparent preference for particular end digits and entries which were just below target BPs which might suggest evidence of data manipulation. Design Prospective cohort studySetting 37 socio-economically diverse primary care practices from South East NHS Lothian, Scotland.Participants Patients were recruited with hypertension to a telemonitoring service in which patients submitted home BP readings by manually transcribing the measurements into text messages for transmission (‘patient-texted system’). These readings were compared to those from primary care patients with uncontrolled hypertension using a system in which readings were automatically transmitted, eliminating the possibility of manipulation of values (‘automatic-transmission system’).Methods A Generalised Estimating Equations method was used to compare BP readings between the patient-texted and automatic-transmission systems, while taking into account clustering of readings within patients.Results A total of 44,150 BP readings were analysed on 1,068 patients using the patient-texted system compared to 20,705 readings on 199 patients using the automatic-transmission system. Compared to the automatic-transmission data, the patient-texted data showed a significantly higher proportion of occurrences of both systolic and diastolic BP having a zero end digit (OR 2.1, 95% CI 1.7 to 2.6) although incidence was less than 2% of readings. Similarly, there was a preference for systolic 134 and diastolic 84 (the threshold for alerts was 135/85) (134 systolic BP OR 1.52, 95% CI 1.28 to 1.82; 84 diastolic BP OR 1.54, 95% CI 1.28 to 1.86). Conclusion End-digit preference for zero numbers and specific value preference for readings just below the alert threshold exists among patients self-reporting their BP using telemonitoring. However, the proportion of readings affected is small and unlikely to be clinically important