Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study).

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches.

Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP).

OBJECTIVES: We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN: Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS: Participants 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. RESULTS: A 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. CONCLUSION: Wearable 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 number ISRCTN51255782; Pre-results.

The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review.

Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52-0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence.

Estimating the lifetime risk of dementia using nationwide individually linked cause-of-death and health register data.

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.

Systemic autoimmune disease as a cause of death: mortality burden and comorbidities.

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.

How large should a cause of death be in order to be included in mortality trend analysis? Deriving a cut-off point from retrospective trend analyses in 21 European countries.

OBJECTIVES: The International Classification of Diseases (ICD-10) distinguishes a large number of causes of death (CODs) that could each be studied individually when monitoring time-trends. We aimed to develop recommendations for using the size of CODs as a criterion for their inclusion in long-term trend analysis. DESIGN: Retrospective trend analysis. SETTING: 21 European countries of the WHO Mortality Database. PARTICIPANTS: Deaths from CODs (3-position ICD-10 codes) with ≥5 average annual deaths in a 15-year period between 2000 and 2016. PRIMARY AND SECONDARY OUTCOME MEASURES: Fitting polynomial regression models, we examined for each COD in each country whether or not changes over time were statistically significant (with α=0.05) and we assessed correlates of this outcome. Applying receiver operating characteristicROC curve diagnostics, we derived COD size thresholds for selecting CODs for trends analysis. RESULTS: Across all countries, 64.0% of CODs had significant long-term trends. The odds of having a significant trend increased by 18% for every 10% increase of COD size. The independent effect of country was negligible. As compared to circulatory system diseases, the probability of a significant trend was lower for neoplasms and digestive system diseases, and higher for infectious diseases, mental diseases and signs-and-symptoms. We derived a general threshold of around 30 (range: 28-33) annual deaths for inclusion of a COD in trend analysis. The relevant threshold for neoplasms was around 65 (range: 61-70) and for infectious diseases was 20 (range: 19-20). CONCLUSIONS: The likelihood that long-term trends are detected with statistical significance is strongly related to COD size and varies between ICD-10 chapters, but has no independent relation to country. We recommend a general size criterion of 30 annual deaths to select CODs for long-term mortality-trends analysis in European countries.

Prevalence of diabetes mellitus at the end of life: An investigation using individually linked cause-of-death and medical register data.

AIMS: Although diabetes mellitus at the end of life is associated with complex care, its end-of-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-of-death 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. Cause-of-death data are insufficient to monitor this prevalence, even of severe cases of diabetes, but could be enriched particularly with general practice data.

Detecting Mortality Trends in the Netherlands Across 625 Causes of Death.

Cause of death (COD) data are essential to public health monitoring and policy. This study aims to determine the proportion of CODs, at ICD-10 three-position level, for which a long-term or short-term trend can be identified, and to examine how much the likelihood of identifying trends varies with COD size. We calculated annual age-standardized counts of deaths from Statistics Netherlands for the period 1996-2015 for 625 CODs. We applied linear regression models to estimate long-term trends, and outlier analysis to detect short-term changes. The association of the likelihood of a long-term trend with COD size was analyzed with multinomial logistic regression. No long-term trend could be demonstrated for 216 CODs (34.5%). For the remaining 409 causes, a trend could be detected, following a linear (211, 33.8%), quadratic (126, 20.2%) or cubic model (72, 11.5%). The probability of detecting a long-term trend increased from about 50% at six mean annual deaths, to 65% at 22 deaths and 75% at 60 deaths. An exceptionally high or low number of deaths in a single year was found for 16 CODs. When monitoring long-term mortality trends, one could consider a much broader range of causes of death, including ones with a relatively low number of annual deaths, than commonly used in condensed lists.