Internet searching on the head louse in the UK since the COVID-19 pandemic.

BACKGROUND: Wide-ranging restrictions on social and educational activities were imposed in 2020 in response to the COVID-19 pandemic. These likely influenced the population dynamics of the head louse. Close physical contact between human hosts is required for transmission of this ectoparasite. METHOD: Pre- and post-pandemic internet interest in the head louse in the UK was compared using Google Trends data from March 2017 to March 2022 and modeling using the Meta Prophet package. The influence of school holidays and school attendance was also investigated. RESULTS: There was a sharp decline in internet searching of the term "head louse" from March 2020 onwards coinciding with COVID-19 restrictions, and a large difference between pre- and post-pandemic search volumes. No influence of school holidays or attendance on internet searching on the head louse was found. CONCLUSION: The results suggest pandemic restrictions have had a large effect on head louse incidence.

Delirium in hospitalized COVID-19 patients: Predictors and implications for patient outcome.

INTRODUCTION: Delirium is recognized as a severe complication of coronavirus-disease-2019 (COVID-19). COVID-19-associated delirium has been linked to worse patient outcomes and is considered to be of multifactorial origin. Here we sought to evaluate the incidence and risk factors of delirium in hospitalized COVID-19 patients, along with its impact on clinical outcome. METHODS: Consecutive adult COVID-19 patients admitted to a tertiary academic referral hospital between March 1st and December 31st, 2020 were included. Potential risk factors for delirium were evaluated, including: age, gender, disease severity (as per the highest WHO grading reached during admission), laboratory parameters for infection and renal function (as per their most extreme values), and presence of comorbidities. To assess the relative strength of risk factors for predicting the occurrence of delirium, we performed a random-forest survival analysis. RESULTS: 347 patients with positive COVID-19 PCR test and median age 68.2 [IQR 55.5, 80.5] years were included. Of those, 79 patients (22.8%) developed delirium, 81 (23.3%) were transferred to ICU, 58 (16.7%) died. 163 (73.8%) patients were discharged home, 13 (5.9%) to another hospital, 32 (14.5%) to nursing homes, 13 (5.9%) to rehabilitation with an overall median admission-to-discharge time of 53 [IQR 14, 195] days. The strongest predictors for the occurrence of delirium were blood urea nitrogen (minimal depth value (MD): 3.33), age (MD: 3.75), disease severity (as captured by WHO grading; MD: 3.93), leukocyte count (MD: 4.22), the presence of a neurodegenerative history (MD: 4.43), ferritin (MD: 4.46) and creatinine (MD: 4.59) levels. CONCLUSION: The risk of delirium in COVID-19 can be stratified based on COVID-19 disease severity and-similar to delirium associated with other respiratory infections-the factors advanced age, neurodegenerative disease history, and presence of elevated infection and renal-retention parameters. Screening for these risk factors may facilitate early identification of patients at high-risk for COVID-19-associated delirium.

Social mobility and gonorrhoea in Germany during 2020.

OBJECTIVES: The incidence of STIs is likely to be related to levels of social activity and mobility. Novel datasets detailing levels of social activity were made widely available during the COVID-19 pandemic. These allow the relationship between activity and STI incidence to be examined. METHODS: The correlation between social activities and the reported number of gonorrhoea cases between March and December 2020 in Germany was studied. Regression through Autoregressive Integrated Moving Average (ARIMA) time series modelling identified those activities associated with case numbers. RESULTS: ARIMA regression identified a significant association with 'transit' activity within the Apple data and 'parks' within Google. CONCLUSIONS: This study illustrates the potential newly available measures of social activity provided for STI research. Reductions in STI incidence are likely to have occurred due to COVID-19 social restrictions. Although other studies report reductions in infectious diseases during this period, few examine the potential social factors mediating this. The results illustrate the continual need for sexual health services throughout the pandemic.

A call to caution when hydroxychloroquine is given to elderly patients with COVID-19.

INTRODUCTION: Use of hydroxychloroquine in patients with coronavirus disease 2019 (COVID-19) was widespread and uncontrolled until recently. Patients vulnerable to severe COVID-19 are at risk of hydroxychloroquine interactions with co-morbidities and co-medications contributing to detrimental, including fatal, adverse treatment effects. METHODS: A retrospective survey was undertaken of health conditions and co-medications of patients with COVID-19 who were pre-screened for enrolment in a randomized, double-blind, placebo-controlled hydroxychloroquine multi-centre trial. RESULTS: The survey involved 305 patients [median age 71 (interquartile range 59-81) years]. The majority of patients (n = 279, 92%) considered for inclusion in the clinical trial were not eligible, mainly due to safety concerns caused by health conditions or co-medications. The most common were QT-prolonging drugs (n = 188, 62%) and haematologic/haemato-oncologic diseases (n = 39, 13%) which prohibited the administration of hydroxychloroquine. In addition, 165 (54%) patients had health conditions and 167 (55%) patients were on co-medications that did not prohibit the use of hydroxychloroquine but had a risk of adverse interactions with hydroxychloroquine. The most common were diabetes (n = 86, 28%), renal insufficiency (n = 69, 23%) and heart failure (n = 58, 19%). CONCLUSION: The majority of hospitalized patients with COVID-19 had health conditions or took co-medications precluding safe treatment with hydroxychloroquine. Therefore, hydroxychloroquine should be administered with extreme caution in elderly patients with COVID-19, and only in clinical trials.

Mobility and COVID-19 mortality across Scandinavia: A modeling study.

BACKGROUND: In response to COVID-19, the Swedish government imposed few travel and mobility restrictions. This contrasted with its Scandinavian neighbours which implemented stringent restrictions. The influence these different approaches had on mobility, and thus on COVID-19 mortality was investigated. METHODS: Datasets indicating restriction severity and community mobility were examined; Google's 'Community Movement Reports' (CMR) show activity at key location categories; the Oxford COVID-19 Government Response Tracker collates legislative restrictions into a 'Stringency Index' (SI). RESULTS: CMR mobility categories were negatively correlated with COVID-19 mortality. The strongest correlations were obtained by negatively time lagging mortality data, suggesting restrictions had a delayed influence. During the 'first wave' a model using SI (AIC 632.87) proved favorable to one using contemporaneous CMR data and SI (AIC 1193.84), or lagged CMR data and SI (AIC 642.35). Validation using 'second wave' data confirmed this; the model using SI solely again being optimal (RMSE: 0.2486 vs. 0.522 and 104.62). Cross-country differences were apparent in all models; Swedish data, independent of SI and CMR, proved significant throughout. There was a significant association for Sweden and the death number across models. CONCLUSION: SI may provide a broader, more accurate, representation of changes in movement in response to COVID-19 restrictions.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data.

BACKGROUND: Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's "Mobility Trends" (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. OBJECTIVE: The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? METHODS: AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. RESULTS: Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with "driving," 2511. "transit," 2513. "walking," 2508). CONCLUSION: These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

Google Trends Data and COVID-19 in Europe: Correlations and model enhancement are European wide.

The current COVID-19 pandemic offers a unique opportunity to examine the utility of Internet search data in disease modelling across multiple countries. Most such studies typically examine trends within only a single country, with few going beyond describing the relationship between search data patterns and disease occurrence. Google Trends data (GTD) indicating the volume of Internet searching on 'coronavirus' were obtained for a range of European countries along with corresponding incident case numbers. Significant positive correlations between GTD with incident case numbers occurred across European countries, with the strongest correlations being obtained using contemporaneous data for most countries. GTD was then integrated into a distributed lag model; this improved model quality for both the increasing and decreasing epidemic phases. These results show the utility of Internet search data in disease modelling, with possible implications for cross country analysis.