Changes in prescribing of oral capecitabine versus intravenous (IV) 5-fluorouracil (5-FU) in gastrointestinal (GI) cancers during the COVID-19 pandemic.

e18596 Background: Several oncology guidelines recommend using oral drugs vs. IV to minimize COVID-19 risk for patients with cancer. We examined the association between prescribing patterns of oral capecitabine vs. IV 5FU for GI cancers and social distancing, measured by the change in population mobility patterns in response to shelter-in place policies, during the pandemic. Methods: Using claims data for commercially insured members, we included patients 18 years of age or older with colorectal, gastroesophageal, or pancreatic cancer, who had continuous health plan coverage for at least 2 months before and 1 month after initiating chemotherapy with capecitabine or 5-FU from January 2017 to August 2020. We analyzed unadjusted trends in proportion of chemotherapy that was oral during pandemic (March 1st to August 31st, 2020) compared to previous years. Then, we conducted difference-in-differences analysis using COVID-19 Community Mobility Reports, by Google, and utilizing different levels of changes in mobility trends across states over time. In our main model, we used a 20% decrease in retail and recreation visits as our threshold and compared the prescribing rates in states below and above the threshold as well as before and after the pandemic began. We also used different thresholds and categories of places to check the sensitivity of our findings. Models are adjusted for age, gender, month of year, urban status, comorbidities, and state of residence at chemotherapy start date. Results: A total of 17,414 nationally distributed patients (69% colorectal, 13% gastroesophageal, 18% pancreatic) were included (mean age, 58.8 years; 41% female). During the pandemic, 1,875 patients (65% colorectal, 15% gastroesophageal, 20% pancreatic) were identified. The proportion of oral regimens did not change significantly for colorectal and gastroesophageal patients and decreased by 7.4 percentage points (pp) (p < 0.01) for pancreatic patients. In regression modelling with mobility data, oral prescribing rates for colorectal patients increased by 3.1 pp (p < 0.01), largely driven by increases for female patients (9.2 pp, p = 0.02). We observed a decrease in oral prescribing rates among pancreatic patients (-1.20 pp, p = 0.04) and did not observe a significant change for gastroesophageal patients. Our results are not sensitive to different social distancing specifications. Conclusions: We observed differential impact of the pandemic on oral prescribing rates by GI cancer type and gender. Oral prescribing increased among colorectal cancer patients driven mostly by higher oral prescribing in females. For pancreatic and gastroesophageal patients, oral prescriptions either remained unchanged or decreased. This observation may reflect a variable impact of the pandemic on women as compared to men and might involve heightened caregiving responsibilities for women.

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Reduction in mobility and COVID-19 transmission

Nature Communications ◽

10.1038/s41467-021-21358-2 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 7

Author(s):

Pierre Nouvellet ◽

Sangeeta Bhatia ◽

Anne Cori ◽

Kylie E. C. Ainslie ◽

Marc Baguelin ◽

...

Keyword(s):

Predictive Ability ◽

Control Measures ◽

Social Distancing ◽

Beneficial Effects ◽

Mobility Data ◽

Strict Control ◽

Before And After ◽

Proxy Measure ◽

Lower Transmission ◽

The Relationship

AbstractIn response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27–77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49–91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12–48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.

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Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

Frontiers in Big Data ◽

10.3389/fdata.2021.718351 ◽

2021 ◽

Vol 4 ◽

Author(s):

A. Potgieter ◽

I. N. Fabris-Rotelli ◽

Z. Kimmie ◽

N. Dudeni-Tlhone ◽

J. P. Holloway ◽

...

Keyword(s):

Human Mobility ◽

Data Sources ◽

Population Mobility ◽

Mobility Patterns ◽

Mobility Data ◽

The World ◽

Spatial Weight ◽

Data Source ◽

Reduced Mobility ◽

Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

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Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data

International Journal of Health Geographics ◽

10.1186/s12942-021-00299-5 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Harry E. R. Shepherd ◽

Florence S. Atherden ◽

Ho Man Theophilus Chan ◽

Alexandra Loveridge ◽

Andrew J. Tatem

Keyword(s):

United Kingdom ◽

Spatial Scales ◽

International Travel ◽

Population Mobility ◽

International Mobility ◽

Mobility Patterns ◽

Social Distancing ◽

The United Kingdom ◽

The Uk ◽

The Impact

Abstract Background Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility. Methods Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021. Results We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination. Conclusions While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.

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Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study (Preprint)

10.2196/preprints.24591 ◽

2020 ◽

Author(s):

Romain Garnier ◽

Jan R Benetka ◽

John Kraemer ◽

Shweta Bansal

Keyword(s):

United States ◽

Black People ◽

The United States ◽

Control Measures ◽

Mobility Patterns ◽

Poverty Level ◽

Social Distancing ◽

Mobility Data ◽

Black Populations ◽

The Impact

BACKGROUND Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

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Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030121 ◽

2021 ◽

Vol 10 (3) ◽

pp. 121

Author(s):

Gisliany Lillian Alves de Oliveira ◽

Luciana Lima ◽

Ivanovitch Silva ◽

Marcel da Câmara Ribeiro-Dantas ◽

Kayo Henrique Monteiro ◽

...

Keyword(s):

South America ◽

Population Mobility ◽

Mobility Patterns ◽

Mobility Index ◽

Social Distancing ◽

Government Response ◽

Community Mobility ◽

Data Driven Approach ◽

Local Moran ◽

Global And Local

Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees.

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Early social distancing policies in Europe, changes in mobility & COVID-19 case trajectories: Insights from Spring 2020

PLoS ONE ◽

10.1371/journal.pone.0253071 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253071

Author(s):

Liana R. Woskie ◽

Jonathan Hennessy ◽

Valeria Espinosa ◽

Thomas C. Tsai ◽

Swapnil Vispute ◽

...

Keyword(s):

Comprehensive Evaluation ◽

Population Mobility ◽

School Closures ◽

Social Distancing ◽

Government Response ◽

Mobility Data ◽

Aggregate Population ◽

The Impact ◽

The Relationship ◽

At Home

Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies’ relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.

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Socio-economic disparities in social distancing during the COVID-19 pandemic in the United States

10.1101/2020.11.07.20201335 ◽

2020 ◽

Author(s):

Romain Garnier ◽

Jan R. Benetka ◽

John Kraemer ◽

Shweta Bansal

Keyword(s):

United States ◽

Black People ◽

The United States ◽

Control Measures ◽

Mobility Patterns ◽

Poverty Level ◽

Social Distancing ◽

Mobility Data ◽

Black Populations ◽

The Impact

AbstractImportanceEliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic.ObjectiveTo assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations.Design, Setting, and ParticipantsWe used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level between February and May 2020. Using linear mixed models, we assessed the associations between social distancing and socio-economic variables, including the proportion of people below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density.Main outcomes and ResultsWe find that the speed, depth, and duration of social distancing in the United States is heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; and in contrast, that social distancing is intense in counties with higher population densities and larger Black populations.Conclusions and relevanceSocio-economic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of COVID-19 in communities across the United States. This is likely to amplify existing health disparities, and needs to be addressed to ensure the success of ongoing pandemic mitigation efforts.

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No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic (Preprint)

10.2196/preprints.19862 ◽

2020 ◽

Author(s):

Dursun Delen ◽

Enes Eryarsoy ◽

Behrooz Davazdahemami

Keyword(s):

Disease Transmission ◽

Transmission Rate ◽

Disease Spread ◽

Data Sets ◽

Mobility Patterns ◽

Social Distancing ◽

Mobility Data ◽

Simulation Based ◽

Transactional Data ◽

Cross National

BACKGROUND In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. OBJECTIVE The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. METHODS Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (<i>β</i>) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and <i>β</i> values. RESULTS Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. CONCLUSIONS Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.

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The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic

10.1101/2020.07.31.20143016 ◽

2020 ◽

Cited By ~ 7

Author(s):

Xiao Huang ◽

Zhenlong Li ◽

Yuqin Jiang ◽

Xinyue Ye ◽

Chengbin Deng ◽

...

Keyword(s):

Time Series ◽

Human Mobility ◽

County Level ◽

Lower Income ◽

Mobility Patterns ◽

Weighted Distance ◽

Social Distancing ◽

Mobility Data ◽

Positive Correlations ◽

The U.S

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S. during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google community mobility reports, 3) mobility data from Descartes Labs, and 4) Twitter mobility calculated via weighted distance. We further design a Responsive Index (RI) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the RI between either two data sources, revealing their general similarity, albeit with varying Pearson r coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The positive correlation between RI and income at the county level is significant in all mobility datasets, suggesting that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in RI between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. To our best knowledge, this is the first study that cross-compares multi-source mobility datasets. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.

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Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

10.20944/preprints202106.0211.v1 ◽

2021 ◽

Author(s):

Arminn Potgieter ◽

Inger Fabris-Rotelli ◽

Zaid Kimmie ◽

Nontembeko Dudeni-Tlhone ◽

Jenny Holloway ◽

...

Keyword(s):

South Africa ◽

Human Mobility ◽

Data Sources ◽

Population Mobility ◽

Mobility Patterns ◽

Mobility Data ◽

The World ◽

Spatial Weight ◽

Reduced Mobility ◽

Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

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