ObjectivesWe investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.DesignInterim analysis of a prospective cohort study.Setting, participants and interventionsParticipants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.ResultsA total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.ConclusionWearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial.Trial registration numberISRCTN51255782; Pre-results.

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Objectives Systemic autoimmune diseases (SAIDs) have chronic trajectories and share characteristics of self-directed inflammation, as well as aspects of clinical expression. Nonetheless, burden-of-disease studies rarely investigate them as a distinct category. This study aims to assess the mortality rate of SAIDs as a group and to evaluate co-occurring causes of death. Methods We used death certificate data in the Netherlands, 2013–2017 (N = 711 247), and constructed a SAIDs list at the fourth-position ICD-10 level. The mortality rate of SAIDs as underlying cause of death (CoD), non-underlying CoD, and any-mention CoD was calculated. We estimated age-sex-standardized observed/expected (O/E) ratios to assess comorbidities in deaths with SAID relative to the general deceased population. Results We observed 3335 deaths with SAID on their death certificate (0.47% of all deaths). The mortality rate of SAID was 14.6 per million population as underlying CoD, 28.0 as non-underlying CoD, and 39.7 as any-mention CoD. The mortality rate was higher for females and increased exponentially with age. SAID-related deaths were positively associated with all comorbidities except for solid neoplasms and mental conditions. Particularly strong was the association with diseases of the musculoskeletal system (O/E = 3.38; 95% CI: 2.98, 3.82), other diseases of the genitourinary system (O/E = 2.73; 95% CI: 2.18, 3.38), influenza (O/E = 2.71; 95% CI: 1.74, 4.03), blood diseases (O/E = 2.02; 95% CI: 1.70, 2.39), skin and subcutaneous tissue diseases (O/E = 1.95; 95% CI: 1.54, 2.45), and infectious diseases (O/E = 1.85; 95% CI: 1.70, 2.01). Conclusion Systemic autoimmune diseases constitute a rare group of causes of death, but contribute to mortality through multiple comorbidities. Classification systems could be adapted to better encompass these diseases as a category.

Background Previous estimates of the lifetime risk of dementia are restricted to older age groups and may suffer from selection bias. In this study, we estimated the lifetime risk of dementia starting at birth using nationwide integral linked health register data. Methods We studied all deaths in The Netherlands in 2017 (n = 147 866). Dementia was assessed using the cause-of-death registration, individually linked with registers covering long-term care, specialized mental care, dispensed medicines, hospital discharges and claims, and primary care. The proportion of deaths with dementia was calculated for the total population and according to age at death and sex. Results According to all data sources combined, 24.0% of the population dies in the presence of dementia. This proportion is higher for females (29.4%) than for males (18.3%). Using multiple causes of death only, the proportion with dementia is 17.9%. Sequential addition of long-term care and hospital discharge data increased the estimate by 4.0 and 1.5%-points, respectively. Further addition of dispensed medicines, hospital claims and specialized mental care data added another 0.6%-points. Among persons who die at age ≤65–70 years, the proportion with dementia is ≤6.2%. After age 70, the proportion rises sharply, with a peak of 43.9% for females and 33.1% for males at age 90–95 years. Conclusions Around one-fourth of the Dutch population is diagnosed with dementia at some point in life and dies in the presence of dementia. It is a major challenge to arrange optimal care for this group.

End-of-life prevalenceLifetime prevalence Linkage Registries Cause of death A B S T R A C TAims: Although diabetes mellitus at the end of life is associated with complex care, its endof-life prevalence is uncertain. Our aim is to estimate diabetes prevalence in the end-of-life population, to evaluate which medical register has the largest added value to cause-ofdeath data in detecting diabetes cases, and to assess the extent to which reporting of diabetes as a cause of death is associated with disease severity. Methods: Our study population consisted of deaths in the Netherlands (2015-2016) included in Nivel Primary Care Database (Nivel-PCD; N = 18,162). The proportion of deaths with diabetes (Type 1 or 2) within the last two years of life was calculated using individually linked cause-of-death, general practice, medication, and hospital discharge data. Severity status of diabetes was defined with dispensed medicines.Results: According to all data sources combined, 28.7% of the study population had diabetes at the end of life. The estimated end-of-life prevalence of diabetes was 7.7% using multiple cause-of-death data only. Addition of general practice data increased this estimate the most (19.7%-points). Of the cases added by primary care data, 76.3% had a severe or intermediate status.Conclusions: More than one fourth of the Dutch end-of-life population has diabetes. Causeof-death data are insufficient to monitor this prevalence, even of severe cases of diabetes, but could be enriched particularly with general practice data.

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tenofovir alafenamide (TAF) during pregnancy were more likely to experience excessive weight gain, diabetes, and hypertensive disorders.METHODS: This is a retrospective cohort study of pregnant persons with HIV who delivered a live infant during the study period (January 1, 2009, to December 31, 2020. ART was classified as including TAF or no TAF. Median weight gain was compared using Wilcoxon rank sum tests (WRST). A Friedman test was used to compare median weights measured at antenatal visits. We compared the proportion of persons with gestational diabetes (GDM) and hypertensive disorders (HDP) by ART groups using chi-square and Fischer's exact tests. RESULTS:We observed 189 persons who were prescribed ART during pregnancy: 30 with TAF and 159 without TAF. Median weight gain was similar between groups: TAF, 7.8 kg (95% CI, 3.4-13.6 kg); no TAF, 6.8 kg (95% CI, 2-10.8 kg), WRST P5.41. Repeated measures of median weight at antenatal visits were similar between groups (Friedman test P5.72). GDM was an infrequent outcome (TAF, 0; no TAF, 5 [3%]; P51.0). HDP were common but similar between groups (TAF, 11/28 [39%]; no TAF, 58/156 [24%]; P5.89].CONCLUSION: Use of TAF during pregnancy did not result in significant weight gain or increased risk of GDM or HDP. Pregnant persons should not be counseled to avoid TAF during pregnancy to avoid these outcomes.