Project description:Objective: Understand the COVID-19-related health literacy of socioeconomically vulnerable migrant groups. Methods: We conducted a survey available in 8 languages among 2,354 members of the target population in Switzerland in 2020. We measured health literacy in four dimensions (finding, understanding, evaluating and applying health information) and assessed adherence to official recommendations during the COVID-19 pandemic. Results: Most migrants felt well informed about the pandemic. Using an extended index of health literacy, we found a moderate correlation (r = -0.28 [-0.24, -0.32]) between COVID-19-related health literacy and socioeconomic vulnerability. The most socioeconomically vulnerable migrants tended to have more difficulty finding and understanding health information about COVID-19 and adhered more to unscientific theses that were not part of the official communication. Conclusion: Special communication efforts by public health authorities have reached most migrants, but socioeconomic vulnerability can be a barrier to taking precautions.
Project description:BackgroundPeople from lower and middle socioeconomic classes and vulnerable populations are among the worst affected by the COVID-19 pandemic, thus exacerbating disparities and the digital divide.ObjectiveTo draw a portrait of e-services as a digital approach to support digital health literacy in vulnerable populations amid the COVID-19 infodemic, and identify the barriers and facilitators for their implementation.MethodsA scoping review was performed to gather published literature with a broad range of study designs and grey literature without exclusions based on country of publication. A search was created in Medline (Ovid) in March 2021 and translated to Medline, PsycINFO, Scopus and CINAHL with Full Text (EBSCOhost). The combined literature search generated 819 manuscripts. To be included, manuscripts had to be written in English, and present information on digital intervention(s) (e.g. social media) used to enable or increase digital health literacy among vulnerable populations during the COVID-19 pandemic (e.g. older adults, Indigenous people living on reserve).ResultsFive articles were included in the study. Various digital health literacy-enabling e-services have been implemented in different vulnerable populations. Identified e-services aimed to increase disease knowledge, digital health literacy and social media usage, help in coping with changes in routines and practices, decrease fear and anxiety, increase digital knowledge and skills, decrease health literacy barriers and increase technology acceptance in specific groups. Many facilitators of digital health literacy-enabling e-services implementation were identified in expectant mothers and their families, older adults and people with low-income. Barriers such as low literacy limited to no knowledge about the viruses, medium of contamination, treatment options played an important role in distracting and believing in misinformation and disinformation. Poor health literacy was the only barrier found, which may hinder the understanding of individual health needs, illness processes and treatments for people with HIV/AIDS.ConclusionsThe literature on the topic is scarce, sparse and immature. We did not find any literature on digital health literacy in Indigenous people, though we targeted this vulnerable population. Although only a few papers were included, two types of health conditions were covered by the literature on digital health literacy-enabling e-services, namely chronic conditions and conditions that are new to the patients. Digital health literacy can help improve prevention and adherence to a healthy lifestyle, improve capacity building and enable users to take the best advantage of the options available, thus strengthening the patient's involvement in health decisions and empowerment, and finally improving health outcomes. Therefore, there is an urgent need to pursue research on digital health literacy and develop digital platforms to help solve current and future COVID-19-related health needs.
Project description:Background The role of health-related disparities including sociodemographic, environmental, and critical care capacity in the COVID-19 pandemic are poorly understood. In the present study, we characterized vulnerable populations located in areas at higher risk of COVID-19 related mortality and low critical healthcare capacity in the U.S. Methods Using Bayesian multilevel analysis and small area disease risk mapping, we assessed the spatial variation of COVID-19 related mortality risk for the U.S. in relation with healthcare disparities including race, ethnicity, poverty, air quality, and critical healthcare capacity. Results Overall, highly populated, regional air hub areas, and minorities had an increased risk of COVID-19 related mortality. We found that with an increase of only 1 ug/m3 in long term PM2.5 exposure, the COVID-19 mortality rate increased by 13%. Counties with major air hubs had 18% increase in COVID-19 related death compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. These counties were primary located at the North- and South-Eastern regions of the country. Conclusion The existing disparity in health and environmental risk factors that exacerbate the COVID-19 related mortality, along with the regional healthcare capacity, determine the vulnerability of populations to COVID-19 related mortality. The results from this study can be used to guide the development of strategies for the identification and targeting preventive strategies in vulnerable populations with a higher proportion of minority groups living in areas with poor air quality and low healthcare capacity.
Project description:AimWhile accumulating evidence suggests that people modified their smoking during the ongoing COVID-19 pandemic, it remains unclear whether those most at risk for tobacco-related health disparities did so. The current study examined changes in smoking among several vulnerable smoker populations during the COVID-19 pandemic.MethodsA web-based survey was distributed in 2020 to 709 adults with socioeconomic disadvantage, affective disorders, or opioid use disorder who participated in a previous study investigating the effects of very low nicotine content (VLNC) cigarettes on smoking. Current smoking status and rate, and adoption of protective health behaviors in response to the pandemic (eg social distancing, mask wearing) were examined.ResultsAmong 332 survey respondents (46.8% response rate), 84.6% were current smokers. Repeated measures ANOVA showed that current cigarettes/day (CPD) was higher during COVID than pre-COVID (12.9 ± 1.0 versus 11.6 ± 1.0; p < .001). Most respondents had adopted protective health behaviors to prevent infection (>79% for all behaviors). More than half indicated that they were still leaving their homes specifically to buy cigarettes (64.6%) and were buying more packs per visit to the store (54.5%) than pre-COVID. Individuals unemployed at the time of the survey experienced greater increases in CPD (from 11.4 ± 1.4 to 13.3 ± 1.4, p = .024) as did those with higher levels of anxiety (from 11.5 ± 1.1 to 13.6 ± 1.1, p < .001).ConclusionsSmoking increased during the COVID-19 pandemic in this sample of adults from vulnerable populations, even while most adopted protective health measures to prevent infection. Unemployment and anxiety might identify those at greatest risk for increases in tobacco use.ImplicationsIndividuals from populations especially vulnerable to smoking might be at risk for greater harm from cigarette smoking during times of pandemic-related stress. Public health interventions are warranted to ameliorate increases in smoking among these populations. Special attention should be paid to those experiencing unemployment and high anxiety.
Project description:COVID-19 incidence and case fatality rates (CFR) differ among ethnicities, stimulating efforts to pinpoint genetic factors that could explain these phenomena. In this regard, the multiallelic apolipoprotein E (APOE) gene has recently been interrogated in the UK biobank cohort, demonstrating associations of the APOE ε4/ε4 genotype with COVID-19 severity and mortality. The frequency of the ε4 allele and thus the distribution of APOE ε4/ε4 genotype may differ among populations. We have assessed APOE genotypes in 1638 Greek individuals, based on haplotypes derived from SNP rs7412 and rs429358 and found reduced frequency of ε4/ε4 compared to the British cohort. Herein we discuss this finding in relation to CFR and hypothesize on the potential mechanisms linking APOE ε4/ε4 to severe COVID-19. We postulate that the metabolic deregulation ensued by APOE4, manifested by elevated cholesterol and oxidized lipoprotein levels, may be central to heightened pneumocyte susceptibility to infection and to exaggerated lung inflammation associated with the ε4/ε4 genotype. We also discuss putative dietary and pharmacological approaches for the prevention and management of COVID-19 in APOE ε4/ε4 individuals.
Project description:There is not in Argentina publications regarding the presentation of patients with COVID-19 requiring hospitalized and emergency care in vulnerable populations (lower incomes and less education tend at greater risk for poor health status and healthcare access), and it has few reports in developing countries. The objective is to determine whether in the care of vulnerable patients, to succeed against COVID-19, multiple public health tools and interventions will be needed to minimize morbidity and mortality. The study is a prospective cohort investigation of patients with lab-confirmed COVID-19, who required to any of the Health Centers response from April 8, 2020, to August 18, 2020. In Buenos Aires Metropolitan Area (AMBA), April 8, 2020 the virus was identified in patients hospitalized in the "Southeast Network" (SN), AMBA. SN covering an area of 661 square kilometers, with 1.8 million inhabitants residing in urban, and rural areas. A total of 14 health centers with different levels of care complexity provide care to patients in the region. The information of each patient with COVID-19 evaluated by SN, was incorporated in an Epidemiological Dashboard. The investigation was designed and reported with consideration of observational studies in epidemiology. We describe the hospitals presentation and care of persons who required SN response and were ultimately diagnosed with COVID-19. From April 8, 2020, to August 18, 2020, were included 1495 patients with lab-confirmed COVID-19 in SN. A total of 58% patients were men, and the mean age (SD) was 48.9 (15.59) years. Eighty one percent patients with pre-existing diseases, most frequent hypertension and diabetes, but hypertension, chronic lung disease, and cardiovascular disease presented higher risk. A total of 13% were hospitalized in Intensive Therapy Unit. The mortality of the cohort was 9.77%. Mortality was higher for patients aged 65 or more (OR 5.09), and for those had some pre-existing disease (OR 2.61). Our observations are consistent with reports demonstrating older persons, and those with comorbidities have the highest risk of mortality related to COVID-19. However, unlike other reports from developed or some developing countries, the mortality in our study is lower. This finding may be related to age of our cohort is younger than other published. Also, the health system was able to respond to the demand.
Project description:COVID-19 has disproportionately impacted underserved populations, including racial/ethnic minorities. Prior studies have demonstrated that mobile health units are effective at expanding preventive services for hard-to-reach populations, but this has not been studied in the context of COVID-19 vaccination. Our objective was to determine if voluntary participants who access mobile COVID-19 vaccination units are more likely to be racial/ethnic minorities and adolescents compared with the general vaccinated population. We conducted a cross-sectional study of individuals who presented to three different mobile COVID-19 vaccination units in the Greater Boston area from May 20, 2021, to August 18, 2021. We acquired data regarding the general vaccinated population in the state and of target communities from the Massachusetts Department of Public Health. We used chi-square testing to compare the demographic characteristics of mobile vaccination unit participants and the general state and community populations that received COVID-19 vaccines during the same time period. We found that during this three-month period, mobile vaccination units held 130 sessions and administered 2622 COVID-19 vaccine doses to 1982 unique participants. The median (IQR) age of participants was 31 (16-46) years, 1016 (51%) were female, 1575 (80%) were non-White, and 1126 (57%) were Hispanic. Participants in the mobile vaccination units were more likely to be younger (p < 0.001), non-White race (p < 0.001), and Hispanic ethnicity (p < 0.001) compared with the general vaccinated population of the state and target communities. This study suggests that mobile vaccination units have the potential to improve access to COVID-19 vaccination for diverse populations.
Project description:Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. A fast and accurate grasp of public attitudes toward vaccination is critical to addressing vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualization of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes in public COVID-19 vaccination attitudes.
Project description:Recent studies have produced inconsistent findings regarding the association between community social vulnerability and COVID-19 incidence and death rates. This inconsistency may be due, in part, to the fact that these studies modeled cases and deaths separately, ignoring their inherent association and thus yielding imprecise estimates. To improve inferences, we develop a Bayesian multivariate negative binomial model for exploring joint spatial and temporal trends in COVID-19 infections and deaths. The model introduces smooth functions that capture long-term temporal trends, while maintaining enough flexibility to detect local outbreaks in areas with vulnerable populations. Using multivariate autoregressive priors, we jointly model COVID-19 cases and deaths over time, taking advantage of convenient conditional representations to improve posterior computation. As such, the proposed model provides a general framework for multivariate spatiotemporal modeling of counts and rates. We adopt a fully Bayesian approach and develop an efficient posterior Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs steps. We use the model to examine incidence and death rates among counties with high and low social vulnerability in the state of Georgia, USA, from 15 March to 15 December 2020.
Project description:The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online. This has motivated the need for accurate semantic search and retrieval of reliable COVID-19 information across millions of documents, in multiple languages. To address this challenge, this paper proposes a novel high precision and high recall neural Multistage BiCross encoder approach. It is a sequential three-stage ranking pipeline which uses the Okapi BM25 retrieval algorithm and transformer-based bi-encoder and cross-encoder to effectively rank the documents with respect to the given query. We present experimental results from our participation in the Multilingual Information Access (MLIA) shared task on COVID-19 multilingual semantic search. The independently evaluated MLIA results validate our approach and demonstrate that it outperforms other state-of-the-art approaches according to nearly all evaluation metrics in cases of both monolingual and bilingual runs.