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2.
European journal of public health ; 32(Suppl 3), 2022.
Article in English | EuropePMC | ID: covidwho-2102860

ABSTRACT

Background The COVID-19 pandemic highlighted the stark health inequities affecting minority ethnic populations in Europe. However, research on ethnic inequities and healthcare utilisation in children has seldom entered the policy discourse. A scoping review was conducted in the UK, summarising and appraising the quantitative evidence on ethnic differences (unequal) and inequities (unequal and unfair or disproportionate to healthcare needs) in paediatric healthcare utilisation. Methods Embase, Medline and grey literature sources were searched for studies published 2001-2021. Studies that found differences and inequities were mapped by ethnic group and healthcare utilisation outcome. They were appraised using the National Institute for Health and Care Excellence appraisal checklists. The distribution of studies was described across various methodological parameters. Results Of the 61 included studies, most found evidence of ethnic variations in healthcare utilisation (n = 54, 89%). Less than half attempted to distinguish between ethnic differences and inequities (n = 27, 44%). Studies were concentrated on primary and preventive care and hospitalisation, with minimal evidence on emergency and outpatient care. The quality of studies was often limited by a lack of theory underpinning analytical decisions, resulting in conflation of difference and inequity, and heterogeneity in ethnic classification. The majority of studies examined children's ethnicity but overlooked parent/caregiver ethnicity, and also didn't investigate patterns across age, year or location. Conclusions To improve the validity, generalisability and comparability of research on ethnicity and paediatric healthcare utilisation, findings from this scoping review were used to develop recommendations for future research. These lessons could be applied more broadly across the European context to improve evidence generation and evidence-based policy-making to reduce inequities in healthcare. Key messages • Quantitative studies of ethnicity and paediatric healthcare utilisation in the UK lack the use of sound theoretical frameworks, and often do not distinguish between ethnic differences and inequities. • The quality of future studies can be improved with greater attention to how ethnicity is classified and analysed, alongside specific considerations for examining healthcare utilisation in children.

3.
Cramer, Estee Y.; Huang, Yuxin, Wang, Yijin, Ray, Evan L.; Cornell, Matthew, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, House, Katie, Jayawardena, Dasuni, Kanji, Abdul Hannan, Khandelwal, Ayush, Le, Khoa, Mody, Vidhi, Mody, Vrushti, Niemi, Jarad, Stark, Ariane, Shah, Apurv, Wattanchit, Nutcha, Zorn, Martha W.; Reich, Nicholas G.; Gneiting, Tilmann, Mühlemann, Anja, Gu, Youyang, Chen, Yixian, Chintanippu, Krishna, Jivane, Viresh, Khurana, Ankita, Kumar, Ajay, Lakhani, Anshul, Mehrotra, Prakhar, Pasumarty, Sujitha, Shrivastav, Monika, You, Jialu, Bannur, Nayana, Deva, Ayush, Jain, Sansiddh, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Adiga, Aniruddha, Hurt, Benjamin, Lewis, Bryan, Marathe, Madhav, Peddireddy, Akhil Sai, Porebski, Przemyslaw, Venkatramanan, Srinivasan, Wang, Lijing, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G.; Tec, Mauricio, Woody, Spencer, Srivastava, Ajitesh, Xu, Tianjian, Cegan, Jeffrey C.; Dettwiller, Ian D.; England, William P.; Farthing, Matthew W.; George, Glover E.; Hunter, Robert H.; Lafferty, Brandon, Linkov, Igor, Mayo, Michael L.; Parno, Matthew D.; Rowland, Michael A.; Trump, Benjamin D.; Chen, Samuel, Faraone, Stephen V.; Hess, Jonathan, Morley, Christopher P.; Salekin, Asif, Wang, Dongliang, Zhang-James, Yanli, Baer, Thomas M.; Corsetti, Sabrina M.; Eisenberg, Marisa C.; Falb, Karl, Huang, Yitao, Martin, Emily T.; McCauley, Ella, Myers, Robert L.; Schwarz, Tom, Gibson, Graham Casey, Sheldon, Daniel, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yu, Rose, Jin, Xiaoyong, Wang, Yu-Xiang, Yan, Xifeng, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Chen, Jinghui, Gu, Quanquan, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Chattopadhyay, Ishanu, Huang, Yi, Lu, Guoqing, Pfeiffer, Ruth, Sumner, Timothy, Wang, Dongdong, Wang, Liqiang, Zhang, Shunpu, Zou, Zihang, Biegel, Hannah, Lega, Joceline, Hussain, Fazle, Khan, Zeina, Van Bussel, Frank, McConnell, Steve, Guertin, Stephanie L.; Hulme-Lowe, Christopher, Nagraj, V. P.; Turner, Stephen D.; Bejar, Benjamín, Choirat, Christine, Flahault, Antoine, Krymova, Ekaterina, Lee, Gavin, Manetti, Elisa, Namigai, Kristen, Obozinski, Guillaume, Sun, Tao, Thanou, Dorina, Ban, Xuegang, Shi, Yunfeng, Walraven, Robert, Hong, Qi-Jun, van de Walle, Axel, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Turtle, James, Cao, Duy, Galasso, Joseph, Cho, Jae H.; Jo, Areum, DesRoches, David, Forli, Pedro, Hamory, Bruce, Koyluoglu, Ugur, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Ivy, Julie S.; Mayorga, Maria E.; Mele, Jessica, Rosenstrom, Erik T.; Swann, Julie L.; Kraus, Andrea, Kraus, David, Bian, Jiang, Cao, Wei, Gao, Zhifeng, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Chinazzi, Matteo, Vespignani, Alessandro, Xiong, Xinyue, Davis, Jessica T.; Mu, Kunpeng, Piontti, Ana Pastore y, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Zheng, Andrew, Lami, Omar Skali, Bennouna, Amine, Ndong, David Nze, Perakis, Georgia, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Weisberg, Shane, Jadbabaie, Ali, Sarker, Arnab, Shah, Devavrat, Celi, Leo A.; Penna, Nicolas D.; Sundar, Saketh, Berlin, Abraham, Gandhi, Parth D.; McAndrew, Thomas, Piriya, Matthew, Chen, Ye, Hlavacek, William, Lin, Yen Ting, Mallela, Abhishek, Miller, Ely, Neumann, Jacob, Posner, Richard, Wolfinger, Russ, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Osthus, Dave, Wolffram, Daniel, Karlen, Dean, Panaggio, Mark J.; Kinsey, Matt, Mullany, Luke C.; Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Brenner, Michael, Coram, Marc, Edwards, Jessie K.; Joshi, Keya, Klein, Ellen, Hulse, Juan Dent, Grantz, Kyra H.; Hill, Alison L.; Kaminsky, Kathryn, Kaminsky, Joshua, Keegan, Lindsay T.; Lauer, Stephen A.; Lee, Elizabeth C.; Lemaitre, Joseph C.; Lessler, Justin, Meredith, Hannah R.; Perez-Saez, Javier, Shah, Sam, Smith, Claire P.; Truelove, Shaun A.; Wills, Josh, Gardner, Lauren, Marshall, Maximilian, Nixon, Kristen, Burant, John C.; Budzinski, Jozef, Chiang, Wen-Hao, Mohler, George, Gao, Junyi, Glass, Lucas, Qian, Cheng, Romberg, Justin, Sharma, Rakshith, Spaeder, Jeffrey, Sun, Jimeng, Xiao, Cao, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Yueying, Wang, Guannan, Wang, Lily, Yu, Shan, Jain, Chaman, Bhatia, Sangeeta, Nouvellet, Pierre, Barber, Ryan, Gaikedu, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris, Pigott, David, Reiner, Robert C.; Baccam, Prasith, Gurung, Heidi L.; Stage, Steven A.; Suchoski, Bradley T.; Fong, Chung-Yan, Yeung, Dit-Yan, Adhikari, Bijaya, Cui, Jiaming, Prakash, B. Aditya, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Asplund, John, Baxter, Arden, Keskinocak, Pinar, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan O.; Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long T.; Li, Chun-Liang, Pfister, Tomas, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Wilson, Daniel, Belov, Artur A.; Chow, Carson C.; Gerkin, Richard C.; Yogurtcu, Osman N.; Ibrahim, Mark, Lacroix, Timothee, Le, Matthew, Liao, Jason, Nickel, Maximilian, Sagun, Levent, Abbott, Sam, Bosse, Nikos I.; Funk, Sebastian, Hellewell, Joel, Meakin, Sophie R.; Sherratt, Katharine, Kalantari, Rahi, Zhou, Mingyuan, Karimzadeh, Morteza, Lucas, Benjamin, Ngo, Thoai, Zoraghein, Hamidreza, Vahedi, Behzad, Wang, Zhongying, Sen, Pei, Shaman, Jeffrey, Yamana, Teresa K.; Bertsimas, Dimitris, Li, Michael L.; Soni, Saksham, Bouardi, Hamza Tazi, Adee, Madeline, Ayer, Turgay, Chhatwal, Jagpreet, Dalgic, Ozden O.; Ladd, Mary A.; Linas, Benjamin P.; Mueller, Peter, Xiao, Jade, Bosch, Jurgen, Wilson, Austin, Zimmerman, Peter, Wang, Qinxia, Wang, Yuanjia, Xie, Shanghong, Zeng, Donglin, Bien, Jacob, Brooks, Logan, Green, Alden, Hu, Addison J.; Jahja, Maria, McDonald, Daniel, Narasimhan, Balasubramanian, Politsch, Collin, Rajanala, Samyak, Rumack, Aaron, Simon, Noah, Tibshirani, Ryan J.; Tibshirani, Rob, Ventura, Valerie, Wasserman, Larry, Drake, John M.; O’Dea, Eamon B.; Abu-Mostafa, Yaser, Bathwal, Rahil, Chang, Nicholas A.; Chitta, Pavan, Erickson, Anne, Goel, Sumit, Gowda, Jethin, Jin, Qixuan, Jo, HyeongChan, Kim, Juhyun, Kulkarni, Pranav, Lushtak, Samuel M.; Mann, Ethan, Popken, Max, Soohoo, Connor, Tirumala, Kushal, Tseng, Albert, Varadarajan, Vignesh, Vytheeswaran, Jagath, Wang, Christopher, Yeluri, Akshay, Yurk, Dominic, Zhang, Michael, Zlokapa, Alexander, Pagano, Robert, Jain, Chandini, Tomar, Vishal, Ho, Lam, Huynh, Huong, Tran, Quoc, Lopez, Velma K.; Walker, Jo W.; Slayton, Rachel B.; Johansson, Michael A.; Biggerstaff, Matthew, Reich, Nicholas G..
Scientific Data ; 9(1):462-462, 2022.
Article in English | PMC | ID: covidwho-1967614

ABSTRACT

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

4.
Journal of Mental Health Policy and Economics ; 25(SUPPL 1):S36-S37, 2022.
Article in English | EMBASE | ID: covidwho-1913292

ABSTRACT

Background: Mental health problems are increasingly prevalent among children and adolescents. Children from low income families are likely to have worse mental health than their wealthier peers. Understanding the association between economic deprivation and poor child mental health, how it varies across ages from early childhood to teen years, and the mechanisms underlying the association is of paramount importance to tackle this increasing public health problem which has been further exacerbated by the COVID-19 pandemic. Aim: This study aims to investigate the relationship between family income and child mental health problems from childhood to adolescence in the UK, its potential variation with age, and the potential mechanisms that may explain the relationship. Methods: Using data from the UK Millennium Cohort Study, child mental health was measured by the Total Difficulties Score (TDS), Internalising and Externalising subscales, all derived from the Strengths and Difficulties Questionnaire (SDQ) at ages 3, 5, 7, 11 and 14 years. Family income was operationalised as permanent income, with lagged transitory income used as robustness check. A secondary exposure was frequency of poverty. Cross-sectional analysis using multivariable logistic regression was conducted at each survey age, based on the Grossman health production function. Results: Results were available for 8,096 children, the prevalence of mental health problems (TDS) ranged from 4.6% to 11.1% across all ages. Unadjusted results indicated significant protective effects of higher family income on the likelihood of the child having poorer mental health in all age groups. The relationship weakened after adjustment for confounding and potential mediating factors, and marginal effects of income on TDS were -0.024(SE=0.009), -0.014(SE=0.004), -0.009(SE=0.006), -0.048(SE=0.010) and -0.041(SE=0.011) at age 3, 5, 7, 11, and 14 years, respectively (p<0.001 in all age groups except age 7 where p=0.163). Adjust- ment for poor maternal mental health and low mother-to-infant attachment reduced the strength of the association between income and child mental health. Fully adjusted model suggested an increased independent effect of poor maternal mental health on children's mental health as children grew older. Discussion: While family income is strongly associated with a child's mental health, much of this effect is explained by other risk factors such as maternal depression, and therefore the direct effects are relatively small. This may suggest that policies targeting income redistribution may reduce child mental health inequalities, and also be beneficial to the wider family, reducing the prevalence of other associated risk factors. This is even more important as the ongoing COVID-19 pandemic pushes more families into poverty.

5.
J Surg Educ ; 79(6): 1334-1341, 2022.
Article in English | MEDLINE | ID: covidwho-1895271

ABSTRACT

OBJECTIVE: General surgery residency programs have increased their social media presence to educate and recruit prospective residents. This study aims to understand the impact of general surgery residency program social media on the 2020-2021 applicants' evaluation of prospective programs, particularly during the COVID-19 pandemic. DESIGN: An optional 20-item online survey regarding specialty choice, sub-internship rotation completion, social media resource use, social media impact, and general demographic information. SETTING: Large academic medical center, United States. PARTICIPANTS: A total of 1191 Participants to our general surgery residency program were sent a survey. Six hundred thirteen completed the survey. RESULTS: Surveys were sent to all general surgery residency applicants of a single program (1,191) and 613 (51.4%) responded. Overall, social media resources use included official residency program website (92.4%), Doximity (36.5%), and Twitter (35.6%). The most frequently relied upon resources by applicants were the official residency program website (64.9%) Twitter (10.9%) and Instagram (10.8%). Most respondents agreed that social media was an effective means to inform applicants (70.9%) and that it positively impacted their perception of the program (62.6%). The most commonly cited benefits were helping the program exhibit its culture and comradery among residents, faculty, and staff (79.2%), with posts of social events and camaraderie as being the most helpful in learning about residency programs. Of all applicants, 71.3% noted that social media had a significant impact on perceptions of programs during the application cycles that were limited by COVID-19 safety and travel restrictions. However, most applicants disagree with (35.3%) or are neutral toward (32.1%) the statement that social media will have less of an impact on future cycles not limited by COVID-19. CONCLUSION: During the 2020-2021 application cycle, the majority of applicants utilized social media to inform and educate themselves about the general surgery programs they applied to. Residency-based social media had a positive impact on the majority of applicants, especially in terms of allowing a program to demonstrate its culture and camaraderie. Investing time and resources into residency social media accounts appears to be a meaningful pursuit for general surgery programs and is an important aspect in today's recruitment effort.


Subject(s)
COVID-19 , Internship and Residency , Social Media , Humans , United States , Prospective Studies , COVID-19/epidemiology , Pandemics
6.
Physica A ; 598: 127318, 2022 Jul 15.
Article in English | MEDLINE | ID: covidwho-1783700

ABSTRACT

The novel coronavirus SARS CoV-2 responsible for the COVID-19 pandemic and SARS CoV-1 responsible for the SARS epidemic of 2002-2003 share an ancestor yet evolved to have much different transmissibility and global impact 1. A previously developed thermodynamic model of protein conformations hypothesized that SARS CoV-2 is very close to a new thermodynamic critical point, which makes it highly infectious but also easily displaced by a spike-based vaccine because there is a tradeoff between transmissibility and robustness 2. The model identified a small cluster of four key mutations of SARS CoV-2 that predicts much stronger viral attachment and viral spreading compared to SARS CoV-1. Here we apply the model to the SARS-CoV-2 variants Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1) and Delta (B.1.617.2)3 and predict, using no free parameters, how the new mutations will not diminish the effectiveness of current spike based vaccines and may even further enhance infectiousness by augmenting the binding ability of the virus.

8.
Front Immunol ; 12: 754127, 2021.
Article in English | MEDLINE | ID: covidwho-1518487

ABSTRACT

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed "patient archetypes", whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.


Subject(s)
Alarmins/immunology , COVID-19 , Models, Biological , SARS-CoV-2 , Adrenal Cortex Hormones/therapeutic use , Adult , Aged , Anti-Inflammatory Agents/therapeutic use , Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , COVID-19/diagnosis , COVID-19/drug therapy , COVID-19/immunology , COVID-19/virology , COVID-19 Vaccines , Humans , Middle Aged , Reproducibility of Results , Viral Load
9.
Clin Oncol (R Coll Radiol) ; 34(1): 19-27, 2022 01.
Article in English | MEDLINE | ID: covidwho-1487658

ABSTRACT

AIMS: In response to the COVID-19 pandemic, guidelines on reduced fractionation for patients treated with curative-intent radiotherapy were published, aimed at reducing the number of hospital attendances and potential exposure of vulnerable patients to minimise the risk of COVID-19 infection. We describe the changes that took place in the management of patients with stage I-III lung cancer from April to October 2020. MATERIALS AND METHODS: Lung Radiotherapy during the COVID-19 Pandemic (COVID-RT Lung) is a prospective multicentre UK cohort study. The inclusion criteria were: patients with stage I-III lung cancer referred for and/or treated with radical radiotherapy between 2nd April and 2nd October 2020. Patients who had had a change in their management and those who continued with standard management were included. Data on demographics, COVID-19 diagnosis, diagnostic work-up, radiotherapy and systemic treatment were collected and reported as counts and percentages. Patient characteristics associated with a change in treatment were analysed using multivariable binary logistic regression. RESULTS: In total, 1553 patients were included (median age 72 years, 49% female); 93 (12%) had a change to their diagnostic investigation and 528 (34%) had a change to their treatment from their centre's standard of care as a result of the COVID-19 pandemic. Age ≥70 years, male gender and stage III disease were associated with a change in treatment on multivariable analysis. Patients who had their treatment changed had a median of 15 fractions of radiotherapy compared with a median of 20 fractions in those who did not have their treatment changed. Low rates of COVID-19 infection were seen during or after radiotherapy, with only 21 patients (1.4%) developing the disease. CONCLUSIONS: The COVID-19 pandemic resulted in changes to patient treatment in line with national recommendations. The main change was an increase in hypofractionation. Further work is ongoing to analyse the impact of these changes on patient outcomes.


Subject(s)
COVID-19 , Lung Neoplasms , Aged , COVID-19 Testing , Cohort Studies , Female , Humans , Lung Neoplasms/epidemiology , Lung Neoplasms/radiotherapy , Male , Pandemics , Prospective Studies , SARS-CoV-2 , United Kingdom/epidemiology
10.
Nat Chem Biol ; 17(10): 1012-1013, 2021 10.
Article in English | MEDLINE | ID: covidwho-1454793
16.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.02.06.21251276

ABSTRACT

Quantifying how accurate epidemiological models of COVID-19 forecast the number of future cases and deaths can help frame how to incorporate mathematical models to inform public health decisions. Here we analyze and score the predictive ability of publicly available COVID-19 epidemiological models on the COVID-19 Forecast Hub. Our score uses the posted forecast cumulative distributions to compute the log-likelihood for held-out COVID-19 positive cases and deaths. Scores are updated continuously as new data become available, and model performance is tracked over time. We use model scores to construct ensemble models based on past performance. Our publicly available quantitative framework may aid in improving modeling frameworks, and assist policy makers in selecting modeling paradigms to balance the delicate trade-offs between the economy and public health.


Subject(s)
59585
17.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.11.03.20225409

ABSTRACT

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.


Subject(s)
59585 , 3103
18.
19.
Ultrasound in Obstetrics & Gynecology ; 56:262-263, 2020.
Article in English | Academic Search Complete | ID: covidwho-875970
20.
International Journal of Sports Science ; 10(3):57-61, 2020.
Article in English | GIM | ID: covidwho-831270

ABSTRACT

The COVID-19 virus outbreak was declared a pandemic by the World Health Organization (WHO) as of March 2020. Hand washing and a 2-meter social distancing became the norm worldwide as front-line mitigation interventions. Subsequently many States in the U.S. and countries worldwide adopted a more aggressive mitigation strategy known as "shelter-in-place". The "shelter-in-place" intervention also known as "stay at home" requires individuals to stay at home except trips for essential needs and to work remotely for businesses considered as non-essential. However, there are concerns regarding the "stay at home" isolation effects on an already sedentary (inactive) world population. The authors provide a pointed summary of scientific literature identifying the potential ramifications of large sectors of the population becoming both isolated and sedentary. Likewise, the authors suggest straightforward strategies for physical activity that could help mitigate the negative ramifications of isolation and sedentary behavior. The authors consider their remarks as promoting the value of physical activity and not as professional medical advice. If you are feeling isolated or alone, seek the council of your health care provider.

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