Ensuring and Sustaining a Pandemic Workforce

Fraher, Erin; Pittman, Patricia; Frogner, Bianca K.; Spetz, Joanne; Moore, Jean; Beck, Angela J.; Armstrong, David M.; Buerhaus, Peter I.

doi:10.1056/nejmp2006376

Cited by 95 publications

(105 citation statements)

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“…Major Medicare and Medicaid telehealth policy updates in the 10 states with the highest reported coronavirus positive cases are summarized ( Table 1). Physical distancing and shelter-in-place orders are likely to result in considerable psychological distress, which prompts healthcare providers and organizations to ensure and sustain a pandemic workforce that addresses crisis-related health problems [12].…”

Section: Discussionmentioning

confidence: 99%

Social Media Data Analytics on Telehealth During the COVID-19 Pandemic

Massaad¹,

Cherfan²

2020

Cureus

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Introduction: Physical distancing during the coronavirus Covid-19 pandemic has brought telehealth to the forefront to keep up with patient care amidst an international crisis that is exhausting healthcare resources. Understanding and managing health-related concerns resulting from physical distancing measures are of utmost importance. Objectives: To describe and analyze the volume, content, and geospatial distribution of tweets associated with telehealth during the Covid-19 pandemic. Methods: We inquired Twitter public data to access tweets related to telehealth from March 30, 2020 to April 6, 2020. We analyzed tweets using natural language processing (NLP) and unsupervised learning methods. Clustering analysis was performed to classify tweets. Geographic tweet distribution was correlated with Covid-19 confirmed cases in the United States. All analyses were carried on the Google Cloud computing service "Google Colab" using Python libraries (Python Software Foundation). Results: A total of 41,329 tweets containing the term "telehealth" were retrieved. The most common terms appearing alongside 'telehealth' were "covid", "health", "care", "services", "patients", and "pandemic". Mental health was the most common health-related topic that appeared in our search reflecting a high need for mental healthcare during the pandemic. Similarly, Medicare was the most common appearing health plan mirroring the accelerated access to telehealth and change in coverage policies. The geographic distribution of tweets related to telehealth and having a specific location within the United States (n=19,367) was significantly associated with the number of confirmed Covid-19 cases reported in each state (p<0.001). Conclusion: Social media activity is an accurate reflection of disease burden during the Covid-19 pandemic. Widespread adoption of telehealth-favoring policies is necessary and mostly needed to address mental health problems that may arise in areas of high infection and death rates.

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Section: Discussionmentioning

confidence: 99%

Social Media Data Analytics on Telehealth During the COVID-19 Pandemic

Massaad¹,

Cherfan²

2020

Cureus

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“…The COVID-19 pandemic is straining public health systems worldwide and major nonpharmaceutical interventions have been implemented to slow its spread [1][2][3][4] . During the initial phase of the outbreak the spread was primarily determined by human mobility 5,6 .…”

Section: Discussionmentioning

confidence: 99%

Crowding and the epidemic intensity of COVID-19 transmission

Rader

Scarpino

Nande

et al. 2020

Preprint

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34The COVID-19 pandemic is straining public health systems worldwide and major non-35 pharmaceutical interventions have been implemented to slow its spread [1][2][3][4] . During the initial phase 36 of the outbreak the spread was primarily determined by human mobility 5,6 . Yet empirical evidence 37 on the effect of key geographic factors on local epidemic spread is lacking 7 . We analyse highly-38 resolved spatial variables for cities in China together with case count data in order to investigate 39 the role of climate, urbanization, and variation in interventions across China. Here we show that 40 the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense 41cities are more spread out through time, and denser cities have larger total incidence. Observed 42 differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that 43 explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more 44 prolonged epidemics. Whilst stringent interventions can shorten the time length of these local 45 epidemics, although these may be difficult to implement in many affected settings. 46 47 Main text identify drivers of local transmission in Chinese cities, with a focus on epidemic intensity among 68 provinces in China. 69 70To explore the impact of urbanization, temperature, and humidity, we used daily incidence data of 71 confirmed COVID-19 cases (date of onset) aggregated at the prefectural level (n = 293) in China. 72Prefectures are administrative units that typically have one urban center (Figure 1). We aggregate 73 individual level data that were collected from official government reports 18 . Epidemiological data in each 74 prefecture were truncated to exclude dates before the first and after the last day of reported cases. The 75 shape of epidemic curves varied between prefectures with some showing rapid rises and declines in cases 76 and others showing more prolonged epidemics (Figure 1A). We estimate epidemic intensity for each 77 prefecture from these data by calculating the inverse Shannon entropy of the distribution of incident 78 cases 9 . We define the incidence distribution "# for a given city to be the proportion of COVID-19 cases 79

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“…The flu pandemics of 1918 and 2009 have equipped the world to deal with similar situation fairly successfully (17). But the world has changed with increase in the number of factories, colleges, schools, hospitals, nursing homes and offices emerging all around the world (5).…”

Section: Resultsmentioning

confidence: 99%

COVID-19 in occupational settings: lessons from 100 years ago and addressing the disease today

Joseph

Kallumkal

2020

Indian J Community Health

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COVID-19 is a disease caused by the novel coronavirus (SARS-CoV-2). On January 30th, 2020 the outbreak of Novel Coronavirus 2019 was declared a Public Health Emergency of International Concern by the World Health Organization. COVID-19 was declared a pandemic on the 11th of March 2020. The virus spreads from person to person through close contact, touching contaminated surfaces and then touching the eyes, nose or mouth. Preventive measures are taken at all workplaces to stop the spread of COVID-19. Social distancing, regular hand washing with soap and water for 20 seconds, use of alcohol- based hand sanitizer and cough etiquette must be practiced at all times. Hospitals, schools, colleges, hospitality, apparel and manufacturing industries now practice hand hygiene, social distancing, regular disinfection of contaminated surfaces, staggered work shifts, correct use of face mask if mild symptoms develop and shutting down of establishments as per instructions of the local health authorities. COVID-19 is already affecting tens of millions of informal workers all around the world. India has a share of almost 90 percent of working people which amount to 400 million workers in the informal economy, who are at the risk of falling into poverty during this crisis. The current lock down measures in India have affected these workers, compelling them to return to rural areas. Poverty along with the spread of the COVID-19 would further worsen the situation. Without dwelling on the healthcare setting, this paper discusses various preventive measures to be implemented in three different sectors. These interventions can be adapted to others as well, to stop the transmission of COVID-19.

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Ensuring and Sustaining a Pandemic Workforce

Cited by 95 publications

References 1 publication

Social Media Data Analytics on Telehealth During the COVID-19 Pandemic

Social Media Data Analytics on Telehealth During the COVID-19 Pandemic

Crowding and the epidemic intensity of COVID-19 transmission

COVID-19 in occupational settings: lessons from 100 years ago and addressing the disease today

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