Andreas Gilsdorf

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The free app Ada allows users to enter symptoms they are experiencing, and applies a probabilistic reasoning model to provide a list of possible causes for those symptoms. Objective: The objective of our study was to explore the potential contribution of Ada data to syndromic surveillance, by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. Methods: We extracted data for all assessments performed by Ada users in Germany over three seasons (2017/18, 2018/19 and 2019/20), and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age-group), standardised for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson’s correlation coefficient. Results: In total 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age-groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson’s correlations: 2017-18: r = 0.86 (95% CI 0.76-0.92), P<.0001; 2018-19: r = 0.90 (95% CI 0.84-0.94), P <.0001; 2019-20: r = 0.64 (95% CI 0.44-0.78), P <.0001). However, there were differences in the exact timing and nature of the epidemic curves between years. Conclusions: With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems, or to the syndromic surveillance of symptoms not covered by existing systems.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).

BACKGROUND Participatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms. OBJECTIVE The objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. METHODS We extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient. RESULTS In total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation—2017-18: r=0.86, 95% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years. CONCLUSIONS With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). The area explored in this publication is interesting and somewhat necessary given the novelty of COVID-19 screeners, but the paper does not compare like with like and is not an appropriate evaluation of the use case of the screeners, including the Ada COVID-19 screener. When comparing the screeners appropriately, which is reported in indices but not reported rigorously in the paper, it is shown that Symptoma does not indeed perform superiorly.

Background: Unprecedented lockdown measures have been introduced in countries worldwide to mitigate the spread and consequences of COVID-19. Although attention has been focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to the limitations of existing syndromic surveillance data and tools. Objective: The aim of this study is to explore the added value of mobile phone app–based symptom assessment tools as real-time health insight providers to inform public health policy makers. Methods: A comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an assessment within the Ada app in Germany and the United Kingdom was conducted between two periods, namely before and after the implementation of “Phase One” COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analyzed using a Pearson chi-square test and reported as log2 fold changes. Results: Overall, 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Overall, 34,200-37,400 symptomatic users in the United Kingdom reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany: 68,600/103,200, 66.52%; United Kingdom: 51,200/71,600, 72.74%). The majority were aged 10-29 years (Germany: 68,500/100,000, 68.45%; United Kingdom: 50,900/68,800, 73.91%), and about one-quarter were aged 30-59 years (Germany: 26,200/100,000, 26.15%; United Kingdom: 14,900/68,800, 21.65%). Overall, 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures period as compared to the Baseline period, and 34 of these were reported in both countries. The following mental health symptoms (log2 fold change, P value) were reported less often during the Measures period: inability to manage constant stress and demands at work (–1.07, P<.001), memory difficulty (–0.56, P<.001), depressed mood (–0.42, P<.001), and impaired concentration (–0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. In total, 14 of the 34 symptoms had statistically significant associations with weather variables. Conclusions: Symptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system.

BACKGROUND Unprecedented lockdown measures have been introduced in countries worldwide to mitigate the spread and consequences of COVID-19. Although attention has been focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to the limitations of existing syndromic surveillance data and tools. OBJECTIVE The aim of this study is to explore the added value of mobile phone app–based symptom assessment tools as real-time health insight providers to inform public health policy makers. METHODS A comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an assessment within the Ada app in Germany and the United Kingdom was conducted between two periods, namely before and after the implementation of “Phase One” COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analyzed using a Pearson chi-square test and reported as log2 fold changes. RESULTS Overall, 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Overall, 34,200-37,400 symptomatic users in the United Kingdom reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany: 68,600/103,200, 66.52%; United Kingdom: 51,200/71,600, 72.74%). The majority were aged 10-29 years (Germany: 68,500/100,000, 68.45%; United Kingdom: 50,900/68,800, 73.91%), and about one-quarter were aged 30-59 years (Germany: 26,200/100,000, 26.15%; United Kingdom: 14,900/68,800, 21.65%). Overall, 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures period as compared to the Baseline period, and 34 of these were reported in both countries. The following mental health symptoms (log2 fold change, P value) were reported less often during the Measures period: inability to manage constant stress and demands at work (–1.07, P<.001), memory difficulty (–0.56, P<.001), depressed mood (–0.42, P<.001), and impaired concentration (–0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. In total, 14 of the 34 symptoms had statistically significant associations with weather variables. CONCLUSIONS Symptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system.

The German Infection Protection Act requires notifying certain cases of infectious diseases to local health departments (LHD) in Germany. LHDs transmit notifications meeting case definitions to the national health authority, where the proportion of discarded notifications is not known. The proportion of discarded cases at the level of LHDs can be expressed as the positive predictive value (PPV) of the notification system. The PPV can be used to assess the efficiency of the system. We quantified the proportion of discarded notifications to calculate the PPV of the German notification system at the level of LHDs using electronic notification data from Berlin LHDs from 2012. We also analysed reasons for discarding notifications by reviewing notification forms. Data was available from eight LHDs (67%) receiving 10,113 notifications in 2012. Overall PPV was 89% (minimum-maximum = 77–97% across LHDs) and ranging from 30% (Hepatitis B) to 99% (Rotavirus). Of 166 individual investigation forms 84% were on hepatitis B or C cases, most of them discarded because of previously diagnosed chronic disease. LHDs investigate many notifications that do not lead to public health action and useful surveillance data leading to inefficient use of resources. Adaptation of case definitions or the legal framework concerning notifications may increase the efficiency of the notification system and lead to better use of data from notified cases.

Mandatory notification of invasive methicillin-resistant Staphylococcus aureus (MRSA) infections was introduced for laboratories in Germany in 2009. The aims were to support local health authorities (LHAs) in their mandate to prevent and control infections in hospitals and to improve population-based nationwide surveillance of healthcare associated infections. We evaluated the MRSA surveillance system to assess whether its aims were met and to identify areas for improvement. Using the updated guidelines for evaluating public health surveillance systems by the Centers for Disease Control and Prevention we assessed the attributes simplicity, timeliness, data quality, acceptability, and usefulness. In 2016/2017 we interviewed staff in LHAs, state health authorities (SHAs), and laboratories and analyzed surveillance data of cases notified between 2009 and 2016. We interviewed 10% of LHAs (n = 38), 63% of SHAs (n = 10), 5 selected laboratories and analyzed information on 27,706 notified MRSA cases. LHAs reported that on receiving notifications from laboratories they contacted hospitals for clinical information, which was time-consuming and complicated as physicians were hard to reach or refused to answer questions, citing doctor-patient confidentiality. LHAs suggested reducing the amount of information collected as some clinical information was unnecessary for implementing control measures. LHAs stated that they received notifications on time, however surveillance data analysis showed some delay. Data completeness exceeded 90% for most variables, however it was only 68% and 80% for dates of disease onset and hospital admission respectively making it impossible to discriminate between hospital and community acquired infections in half of the cases. The surveillance system was well accepted by half of the interviewees. A third however stated that the benefits of the surveillance system were outweighed by the work associated with it. The majority rated the system to be useful for recognizing trends in the MRSA incidence and the ability to check up on infection control measures in hospitals. The surveillance system proved to be useful by fulfilling its aims. It was timely, acceptable and provided complete data for most variables. However, the system was complicated; ensuring that only relevant variables are reported could simplify the system without losing any of its usefulness.