Shielding individuals at high risk of COVID-19: a micro-simulation study

Background One of the proposed interventions for mitigating COVID-19 epidemics, particularly in low-income and crisis-affected settings, is to physically isolate individuals known to be at high risk of severe disease and death due to age or co-morbidities. This intervention, known as 'shielding', could be implemented in various ways. If shielded people are grouped together in residences and isolation is imperfect, any introduction of infections within the shielding group could cause substantial mortality and thus negate the intervention's benefits. We explored the effectiveness of shielding under various modalities of implementation and considered mitigation measures to reduce its possible harms. Methods We used an individual-based mathematical model to simulate the evolution of a COVID-19 epidemic in a population of which a fraction above a given age cut-off are relocated to shielding residences, in which they have variable levels of contacts with their original household, the outside world and fellow shielding residents. We set our simulation with the context of an internally displaced persons' camp in Somaliland, for which we had recently collected data on household demographics and social mixing patterns. We compared an unmitigated epidemic with a shielding intervention accompanied by various measures to reduce the risk of virus introduction and spread within the shielding residences. We did sensitivity analyses to explore parameters such as residence size, reduction in contacts, basic reproduction number, and prior immunity in the population. Results Shielded residences are likely to be breached with infection during the outbreak. Nonetheless, shielding can be effective in preventing COVID-19 infections in the shielded population. The effectiveness of shielding is mostly affected by the size of the shielded residence, and by the degree by which contacts between shielded and unshielded individuals are reduced. Reductions in contacts between shielded individuals could further increase the effectiveness of shielding, but is only effective in larger shielded residences. Large shielded residences increase the risk of infection, unless very large reductions in contacts can be achieved. In epidemics with a lower reproduction number, the effectiveness of shielding could be negative effectiveness. Discussion Shielding could be an effective method to protect the most at-risk individuals. It should be considered where other measures cannot easily be implemented, but with attention to the epidemiological situation. Shielding should only be implemented through small to medium-sized shielding residences, with appropriate mitigation measures such as reduced contact intensity between shielded individuals and self-isolation of cases to prevent subsequent spread.

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Social contacts and other risk factors for respiratory infections among internally displaced people in Somaliland

10.1101/2021.08.20.21262338 ◽

2021 ◽

Author(s):

Kevin van Zandvoort ◽

Mohamed Omer Bobe ◽

Abdirahman Ibrahim Hassan ◽

Mohamed Ismail Abdi ◽

Mohamed Saed Ahmed ◽

...

Keyword(s):

Risk Factors ◽

Respiratory Infections ◽

Severe Disease ◽

Social Contact ◽

Physical Contact ◽

Cross Sectional ◽

Internally Displaced ◽

Internally Displaced People ◽

Social Mixing ◽

Displaced People

Background Populations affected by humanitarian crises experience high burdens of acute respiratory infections (ARI), potentially driven by risk factors for severe disease such as poor nutrition and underlying conditions, and risk factors that may increase transmission such as overcrowding and the possibility of high social mixing. However, little is known about social mixing patterns in these populations. Methods We conducted a cross-sectional social contact survey among internally displaced people (IDP) living in Digaale, a permanent IDP camp in Somaliland. We included questions on household demographics, shelter quality, crowding, travel frequency, health status, and recent diagnosis of pneumonia, and assessed anthropometric status in children. We calculated age-standardised social contact matrices to assess population mixing, and conducted regression analysis on risk factors for recent self-reported pneumonia. Results We found crowded households with high proportions of recent self-reported pneumonia (46% in children). 20% of children younger than five are stunted, and crude death rates are high in all age groups. ARI risk factors are common, but we did not find any significant associations with self-reported pneumonia. Participants reported around 10 direct contacts per day. Social contact patterns are assortative by age, and physical contact rates are very high (78%). Conclusions ARI risk factors are very common in this population, while the large degree of contacts that involve physical touch could further increase transmission. Such IDP settings potentially present a perfect storm of risk factors for ARIs and their transmission, and innovative approaches to address such risks are urgently needed.

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Prognostic and therapeutic role of vitamin D in COVID-19: systematic review and meta-analysis

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/clinem/dgab892 ◽

2021 ◽

Author(s):

Harsha Anuruddhika Dissanayake ◽

Nipun Lakshitha de Silva ◽

Manilka Sumanatilleke ◽

Sawanawadu Dilantha Neomal de Silva ◽

Kavinga Kalhari Kobawaka Gamage ◽

...

Keyword(s):

Vitamin D ◽

High Risk ◽

Vitamin D Deficiency ◽

Observational Studies ◽

Meta Analysis ◽

Severe Disease ◽

Risk Of Bias ◽

Sensitivity Analyses ◽

Cochrane Library

Abstract Purpose Vitamin D deficiency/insufficiency may increase the susceptibility to COVID-19. We aimed to determine the association between vitamin D deficiency/insufficiency and susceptibility to COVID-19, its severity, mortality and role of vitamin D in its treatment. Methods We searched CINHAL, Cochrane library, EMBASE, PubMED, Scopus, and Web of Science up to 30.05.2021 for observational studies on association between vitamin D deficiency/insufficiency and susceptibility to COVID-19, severe disease and death among adults, and, randomized controlled trials (RCTs) comparing vitamin D treatment against standard care or placebo, in improving severity or mortality among adults with COVID-19. Risk of bias was assessed using Newcastle-Ottawa scale for observational studies and AUB-KQ1 Cochrane tool for RCTs. Study-level data were analyzed using RevMan 5.3 and R (v4∙1∙0). Heterogeneity was determined by I 2 and sources were explored through pre-specified sensitivity analyses, subgroup analyses and meta-regressions. Results Of 1877 search results, 76 studies satisfying eligibility criteria were included. Seventy-two observational studies were included in the meta-analysis (n=1976099). Vitamin D deficiency/insufficiency increased the odds of developing COVID-19 (OR 1∙46, 95% CI 1∙28–1∙65, p<0∙0001, I 2=92%), severe disease (OR 1∙90, 95% CI 1∙52–2∙38, p<0.0001, I 2=81%) and death (OR 2∙07, 95% CI 1∙28–3∙35, p=0.003, I 2=73%). 25-hydroxy vitamin D (25(OH)D) concentration were lower in individuals with COVID-19 compared to controls (mean difference [MD] -3∙85 ng/mL, 95% CI -5∙44,-2∙26, p=<0.0001), in patients with severe COVID-19 compared to controls with non-severe COVID19 (MD -4∙84 ng/mL, 95% CI -7∙32,-2∙35, p=0∙0001) and in non-survivors compared to survivors (MD -4∙80 ng/mL, 95%-CI -7∙89,-1∙71, p=0∙002). The association between vitamin D deficiency/insufficiency and death was insignificant when studies with high risk of bias or studies reporting unadjusted effect estimates were excluded. Risk of bias and heterogeneity were high across all analyses. Discrepancies in timing of vitamin D testing, definitions of severe COVID-19 and vitamin D deficiency/insufficiency partly explained the heterogeneity. Four RCTs were widely heterogeneous precluding meta-analysis. Conclusion Multiple observational studies involving nearly two million adults suggest vitamin D deficiency/insufficiency increases susceptibility to COVID-19 and severe COVID-19, although with a high risk of bias and heterogeneity. Association with mortality was less robust. Heterogeneity in RCTs precluded their meta-analysis.

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Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK

10.1101/2020.04.28.20082222 ◽

2020 ◽

Cited By ~ 6

Author(s):

Huayu Zhang ◽

Ting Shi ◽

Xiaodong Wu ◽

Xin Zhang ◽

Kun Wang ◽

...

Keyword(s):

High Risk ◽

Risk Prediction ◽

Low Income ◽

Lymphocyte Count ◽

Laboratory Tests ◽

Prediction Models ◽

Severe Disease ◽

External Validation ◽

Internal Validation ◽

Poor Outcome

AbstractBackgroundAccurate risk prediction of clinical outcome would usefully inform clinical decisions and intervention targeting in COVID-19. The aim of this study was to derive and validate risk prediction models for poor outcome and death in adult inpatients with COVID-19.MethodsModel derivation using data from Wuhan, China used logistic regression with death and poor outcome (death or severe disease) as outcomes. Predictors were demographic, comorbidity, symptom and laboratory test variables. The best performing models were externally validated in data from London, UK.Findings4.3% of the derivation cohort (n=775) died and 9.7% had a poor outcome, compared to 34.1% and 42.9% of the validation cohort (n=226). In derivation, prediction models based on age, sex, neutrophil count, lymphocyte count, platelet count, C-reactive protein and creatinine had excellent discrimination (death c-index=0.91, poor outcome c-index=0.88), with good-to-excellent calibration. Using two cut-offs to define low, high and very-high risk groups, derivation patients were stratified in groups with observed death rates of 0.34%, 15.0% and 28.3% and poor outcome rates 0.63%, 8.9% and 58.5%. External validation discrimination was good (c-index death=0.74, poor outcome=0.72) as was calibration. However, observed rates of death were 16.5%, 42.9% and 58.4% and poor outcome 26.3%, 28.4% and 64.8% in predicted low, high and very-high risk groups.InterpretationOur prediction model using demography and routinely-available laboratory tests performed very well in internal validation in the lower-risk derivation population, but less well in the much higher-risk external validation population. Further external validation is needed. Collaboration to create larger derivation datasets, and to rapidly externally validate all proposed prediction models in a range of populations is needed, before routine implementation of any risk prediction tool in clinical care.FundingMRC, Wellcome Trust, HDR-UK, LifeArc, participating hospitals, NNSFC, National Key R&D Program, Pudong Health and Family Planning CommissionResearch in contextEvidence before this studySeveral prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay in COVID-19 have been published.1 Commonly reported predictors of severe prognosis in patients with COVID-19 include age, sex, computed tomography scan features, C-reactive protein (CRP), lactic dehydrogenase, and lymphocyte count. Symptoms (notably dyspnoea) and comorbidities (e.g. chronic lung disease, cardiovascular disease and hypertension) are also reported to have associations with poor prognosis.2 However, most studies have not described the study population or intended use of prediction models, and external validation is rare and to date done using datasets originating from different Wuhan hospitals.3 Given different patterns of testing and organisation of healthcare pathways, external validation in datasets from other countries is required.Added value of this studyThis study used data from Wuhan, China to derive and internally validate multivariable models to predict poor outcome and death in COVID-19 patients after hospital admission, with external validation using data from King’s College Hospital, London, UK. Mortality and poor outcome occurred in 4.3% and 9.7% of patients in Wuhan, compared to 34.1% and 42.9% of patients in London. Models based on age, sex and simple routinely available laboratory tests (lymphocyte count, neutrophil count, platelet count, CRP and creatinine) had good discrimination and calibration in internal validation, but performed only moderately well in external validation. Models based on age, sex, symptoms and comorbidity were adequate in internal validation for poor outcome (ICU admission or death) but had poor performance for death alone.Implications of all the available evidenceThis study and others find that relatively simple risk prediction models using demographic, clinical and laboratory data perform well in internal validation but at best moderately in external validation, either because derivation and external validation populations are small (Xie et al3) and/or because they vary greatly in casemix and severity (our study). There are three decision points where risk prediction may be most useful: (1) deciding who to test; (2) deciding which patients in the community are at high-risk of poor outcomes; and (3) identifying patients at high-risk at the point of hospital admission. Larger studies focusing on particular decision points, with rapid external validation in multiple datasets are needed. A key gap is risk prediction tools for use in community triage (decisions to admit, or to keep at home with varying intensities of follow-up including telemonitoring) or in low income settings where laboratory tests may not be routinely available at the point of decision-making. This requires systematic data collection in community and low-income settings to derive and evaluate appropriate models.

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Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2004978117 ◽

2020 ◽

Vol 117 (19) ◽

pp. 10484-10491 ◽

Cited By ~ 190

Author(s):

Marino Gatto ◽

Enrico Bertuzzo ◽

Lorenzo Mari ◽

Stefano Miccoli ◽

Luca Carraro ◽

...

Keyword(s):

Reproduction Number ◽

Human Mobility ◽

Transmission Model ◽

Spatially Explicit ◽

Human Interactions ◽

First Case ◽

Social Mixing ◽

Containment Measures ◽

Mixing Patterns ◽

Emergency Measures

The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible–Exposed–Infected–Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number (R0 = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.

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Social mixing patterns relevant to infectious diseases spread by close contact in urban Blantyre, Malawi.

10.1101/2021.12.16.21267959 ◽

2021 ◽

Author(s):

Deus Thindwa ◽

Kondwani C Jambo ◽

John Ojal ◽

Peter MacPherson ◽

Mphatso D Phiri ◽

...

Keyword(s):

Infectious Diseases ◽

Low Income ◽

Negative Binomial ◽

Close Contact ◽

Hiv Positive ◽

Skin Contact ◽

Social Mixing ◽

Contact Behaviour ◽

Contact Frequency ◽

Mixing Patterns

Introduction: Understanding human mixing patterns relevant to infectious diseases spread through close contact is vital for modelling transmission dynamics and optimisation of disease control strategies. Mixing patterns in low-income countries like Malawi are not well understood. Methodology: We conducted a social mixing survey in urban Blantyre, Malawi between April and July 2021 (between the 2nd and 3rd wave of COVID-19 infections). Participants living in densely-populated neighbourhoods were randomly sampled and, if they consented, reported their physical and non-physical contacts within and outside homes lasting at least 5 minutes during the previous day. Age-specific mixing rates were calculated, and a negative binomial mixed effects model was used to estimate determinants of contact behaviour. Results: Of 1,201 individuals enrolled, 702 (58.5%) were female, the median age was 15 years (interquartile range [IQR] 5-32) and 127 (10.6%) were HIV-positive. On average, participants reported 10.3 contacts per day (range: 1-25). Mixing patterns were highly age-assortative, particularly those within the community and with skin-to-skin contact. Adults aged 20-49y reported the most contacts (median:11, IQR: 8-15) of all age groups; 38% (95%CI: 16-63) more than infants (median: 8, IQR: 5-10), who had the least contacts. Household contact frequency increased by 3% (95%CI 2-5) per additional household member. Unemployed participants had 15% (95%CI: 9-21) fewer contacts than other adults. Among long range (>30 meters away from home) contacts, secondary school children had the largest median contact distance from home (257m, IQR 78-761). HIV-positive status in adults >18 years-old was not associated with increased contact patterns (1%, 95%CI -9-12). During this period of relatively low COVID-19 incidence in Malawi, 301 (25.1%) individuals stated that they had limited their contact with others due to COVID-19 precautions; however, their reported contacts were not fewer (8%, 95%CI 1-13). Conclusion: In urban Malawi, contact rates, are high and age-assortative, with little behavioural change due to either HIV-status or COVID-19 circulation. This highlights the limits of contact-restriction-based mitigation strategies in such settings and the need for pandemic preparedness to better understand how contact reductions can be enabled and motivated. Keywords: Social contacts, Transmission, Mixing data, Infectious disease, Malawi, Africa

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Mitigating the COVID-19 pandemic in India: an in-depth exploration of challenges and opportunities for three vulnerable population groups

Equality Diversity and Inclusion An International Journal ◽

10.1108/edi-09-2020-0264 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Joe Thomas ◽

Emma Emily de Wit ◽

R.K. Radhakrishnan ◽

Nupur Kulkarni ◽

Joske G.F. Bunders-Aelen

Keyword(s):

Low Income ◽

State Level ◽

Population Group ◽

Mitigation Measures ◽

Low Income Countries ◽

Vulnerable Groups ◽

Mixed Method Research ◽

Population Groups ◽

Content Type ◽

Internally Displaced

PurposeThe COVID-19 pandemic is certain to have an unprecedented impact on the global population, but marginalized and vulnerable groups in low-income countries (LICs) are predicted to carry the largest burden. This study focuses on the implications of COVID-19-related measures on three population groups in India, including (1) migrant laborers (of which a majority come from Scheduled Castes (SCs) and Scheduled Tribes (STs), as well as Other Backward Classes (OBCs)), (2) children from low-income families and, (3) refugees and internally displaced persons (IDPs).Design/methodology/approachThis study adopts a sequential mixed-method research design. A desk-based study of a selection of government reports was undertaken on the COVID-19-related mitigation measures. The desk study was followed by in-depth interviews with purposively recruited high-ranking experts in specific sectors of policy implementation and service delivery across the country.FindingsThe outcomes of this study shed light on (1) the most urgent needs that need to be addressed per population group, (2) the variety of state-level responses as well as best practices observed to deal with mitigation issues and (3) opportunities for quick relief as well as more long-term solutions.Practical implicationsThe COVID-19 pandemic has not only reduced people's means of maintaining a livelihood but has simultaneously revealed some of India's long-standing problems with infrastructure and resource distribution in a range of sectors, including nutrition and health, education, etc. There is an urgent need to construct effective pathways to trace and respond to those people who are desolate, and to learn from – and support – good practices at the grassroot level.Originality/valueThe current study contributes to the discussion on how inclusive public health might be reached.

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Hepatitis A: Epidemiology, High-Risk Groups, Prevention and Research on Antiviral Treatment

Viruses ◽

10.3390/v13101900 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1900

Author(s):

Marion Migueres ◽

Sébastien Lhomme ◽

Jacques Izopet

Keyword(s):

High Risk ◽

Low Income ◽

Hepatitis A ◽

Hepatitis A Virus ◽

Antiviral Treatment ◽

Severe Disease ◽

Risk Groups ◽

Developed Countries ◽

Low Income Countries ◽

Sex With Men

The hepatitis A virus (HAV) is a leading cause of acute viral hepatitis worldwide. It is transmitted mainly by direct contact with patients who have been infected or by ingesting contaminated water or food. The virus is endemic in low-income countries where sanitary and sociodemographic conditions are poor. Paradoxically, improving sanitary conditions in these countries, which reduces the incidence of HAV infections, can lead to more severe disease in susceptible adults. The populations of developed countries are highly susceptible to HAV, and large outbreaks can occur when the virus is spread by globalization and by increased travel and movement of foodstuffs. Most of these outbreaks occur among high-risk groups: travellers, men who have sex with men, people who use substances, and people facing homelessness. Hepatitis A infections can be prevented by vaccination; safe and effective vaccines have been available for decades. Several countries have successfully introduced universal mass vaccination for children, but high-risk groups in high-income countries remain insufficiently protected. The development of HAV antivirals may be important to control HAV outbreaks in developed countries where a universal vaccination programme is not recommended.

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Welfare Reform and the Delivery of Welfare-to-Work Programs to AAPIs: What Works?

10.36650/nexus5.2_77-97_chun-chungchowetal ◽

2007 ◽

Vol 5 (2) ◽

pp. 77-97 ◽

Cited By ~ 1

Author(s):

Julian Chow ◽

Grace Yoo ◽

Catherine Vu

Keyword(s):

High Risk ◽

Welfare Reform ◽

Asian American ◽

Low Income ◽

Personal Responsibility ◽

Welfare To Work ◽

Work Program ◽

What Works ◽

One Stop ◽

The Impact

The passage of the Personal Responsibility and Work Opportunity Act (PRWORA) of 1996 has major implications for low-income Asian American and Pacific Islander (AAPI) populations. The purpose of this paper is to provide an overview of the research currently examining the impact of welfare reform on AAPI recipients and the welfare-to-work services available to this population. This article highlights AAPI participation and their timing-out rates in California’s CalWORKs program and their barriers to transitioning to work. Four welfare-to-work program models and recommendations are presented to illustrate strategies that can be used to address the unique needs of AAPI in order to alleviate their high risk for timing-out: one-stop-shops, transitional jobs programs, providing comprehensive and family focused services, and additional research and evaluation of programs specific to assisting the AAPI population on CalWORKs.

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Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

10.2196/preprints.22030 ◽

2020 ◽

Author(s):

Carson Lam ◽

Jacob Calvert ◽

Gina Barnes ◽

Emily Pellegrini ◽

Anna Lynn-Palevsky ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Learning Algorithm ◽

Severe Disease ◽

High Specificity ◽

Population Level ◽

The United States ◽

Health Condition ◽

Available P ◽

Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

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