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1.
Cells ; 10(12)2021 11 30.
Article in English | MEDLINE | ID: covidwho-1613627

ABSTRACT

The COVID-19 pandemic drastically highlighted the vulnerability of the elderly population towards viral and other infectious threats, illustrating that aging is accompanied by dysregulated immune responses currently summarized in terms like inflammaging and immunoparalysis. To gain a better understanding on the underlying mechanisms of the age-associated risk of adverse outcome in individuals experiencing a SARS-CoV-2 infection, we analyzed the impact of age on circulating monocyte phenotypes, activation markers and inflammatory cytokines including interleukin 6 (IL-6), IL-8 and tumor necrosis factor (TNF) in the context of COVID-19 disease progression and outcome in 110 patients. Our data indicate no age-associated differences in peripheral monocyte counts or subset composition. However, age and outcome are associated with differences in monocyte activation status. Moreover, a distinct cytokine pattern of IL-6, IL-8 and TNF in elderly survivors versus non-survivors, which consolidates over the time of hospitalization, suggests that older patients with adverse outcomes experience an inappropriate immune response, reminiscent of an inflammaging driven immunoparalysis. Our study underscores the value, necessity and importance of longitudinal monitoring in elderly COVID-19 patients, as dynamic changes after symptom onset can be observed, which allow for a differentiated insight into confounding factors that impact the complex pathogenesis following an infection with SARS-CoV-2.


Subject(s)
Aging/pathology , COVID-19/blood , COVID-19/pathology , Cytokines/blood , Monocytes/pathology , Acute Disease , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Biomarkers/metabolism , Humans , Longitudinal Studies , Middle Aged , Neutrophils/metabolism , Prospective Studies , SARS-CoV-2 , Young Adult
3.
JAMA Netw Open ; 4(12): e2140364, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1591621

ABSTRACT

Importance: Little is known about the factors associated with COVID-19 vaccine adverse effects in a real-world population. Objective: To evaluate factors potentially associated with participant-reported adverse effects after COVID-19 vaccination. Design, Setting, and Participants: The COVID-19 Citizen Science Study, an online cohort study, includes adults aged 18 years and older with a smartphone or internet access. Participants complete daily, weekly, and monthly surveys on health and COVID-19-related events. This analysis includes participants who provided consent between March 26, 2020, and May 19, 2021, and received at least 1 COVID-19 vaccine dose. Exposures: Participant-reported COVID-19 vaccination. Main Outcomes and Measures: Participant-reported adverse effects and adverse effect severity. Candidate factors in multivariable logistic regression models included age, sex, race, ethnicity, subjective social status, prior COVID-19 infection, medical conditions, substance use, vaccine dose, and vaccine brand. Results: The 19 586 participants had a median (IQR) age of 54 (38-66) years, and 13 420 (68.8%) were women. Allergic reaction or anaphylaxis was reported in 26 of 8680 participants (0.3%) after 1 dose of the BNT162b2 (Pfizer/BioNTech) or mRNA-1273 (Moderna) vaccine, 27 of 11 141 (0.2%) after 2 doses of the BNT162b2 or mRNA-1273 vaccine or 1 dose of the JNJ-78436735 (Johnson & Johnson) vaccine. The strongest factors associated with adverse effects were vaccine dose (2 doses of BNT162b2 or mRNA-1273 or 1 dose of JNJ-78436735 vs 1 dose of BNT162b2 or mRNA-1273; odds ratio [OR], 3.10; 95% CI, 2.89-3.34; P < .001), vaccine brand (mRNA-1273 vs BNT162b2, OR, 2.00; 95% CI, 1.86-2.15; P < .001; JNJ-78436735 vs BNT162b2: OR, 0.64; 95% CI, 0.52-0.79; P < .001), age (per 10 years: OR, 0.74; 95% CI, 0.72-0.76; P < .001), female sex (OR, 1.65; 95% CI, 1.53-1.78; P < .001), and having had COVID-19 before vaccination (OR, 2.17; 95% CI, 1.77-2.66; P < .001). Conclusions and Relevance: In this real-world cohort, serious COVID-19 vaccine adverse effects were rare and comparisons across brands could be made, revealing that full vaccination dose, vaccine brand, younger age, female sex, and having had COVID-19 before vaccination were associated with greater odds of adverse effects. Large digital cohort studies may provide a mechanism for independent postmarket surveillance of drugs and devices.


Subject(s)
/adverse effects , /adverse effects , COVID-19/prevention & control , /administration & dosage , Adult , Age Factors , Aged , Anaphylaxis/chemically induced , Drug Hypersensitivity/etiology , Female , Humans , Immunization Schedule , Logistic Models , Male , Middle Aged , SARS-CoV-2 , Sex Factors
4.
PLoS One ; 16(12): e0261529, 2021.
Article in English | MEDLINE | ID: covidwho-1599654

ABSTRACT

BACKGROUND: Risk factors for the development of severe COVID-19 disease and death have been widely reported across several studies. Knowledge about the determinants of severe disease and mortality in the Indian context can guide early clinical management. METHODS: We conducted a hospital-based case control study across nine sites in India to identify the determinants of severe and critical COVID-19 disease. FINDINGS: We identified age above 60 years, duration before admission >5 days, chronic kidney disease, leucocytosis, prothrombin time > 14 sec, serum ferritin >250 ng/mL, d-dimer >0.5 ng/mL, pro-calcitonin >0.15 µg/L, fibrin degradation products >5 µg/mL, C-reactive protein >5 mg/L, lactate dehydrogenase >150 U/L, interleukin-6 >25 pg/mL, NLR ≥3, and deranged liver function, renal function and serum electrolytes as significant factors associated with severe COVID-19 disease. INTERPRETATION: We have identified a set of parameters that can help in characterising severe COVID-19 cases in India. These parameters are part of routinely available investigations within Indian hospital settings, both public and private. Study findings have the potential to inform clinical management protocols and identify patients at high risk of severe outcomes at an early stage.


Subject(s)
COVID-19/blood , COVID-19/epidemiology , Hospitalization , SARS-CoV-2 , Severity of Illness Index , Adolescent , Adult , Age Factors , C-Reactive Protein/analysis , Case-Control Studies , Female , Fibrin Fibrinogen Degradation Products/analysis , Hospitals , Humans , India/epidemiology , Interleukin-6/blood , L-Lactate Dehydrogenase/blood , Male , Middle Aged , Procalcitonin/blood , Risk Factors , Young Adult
6.
Front Immunol ; 12: 758294, 2021.
Article in English | MEDLINE | ID: covidwho-1581342

ABSTRACT

Objective: This meta-analysis compared the efficacy and safety of five kinds of COVID-19 vaccines in different age groups (young adults and older adults), aiming to analyze the difference of adverse events (AEs) rate and virus geometric mean titer (GMT) values between young and older people, in order to find a specific trend, and explore the causes of this trend through meta-analysis. Method: Meta-analysis was used to analyze the five eligible articles. The modified Jadad scoring scale was used to evaluate the quality of eligible literature with a scoring system of 1 to 7. The primary endpoint of the effectiveness index was GMT. The primary endpoints of the safety index were the incidence of local AEs and systemic AEs. Stata 12.0 software was used for meta-analysis. Revman 5.0 software was used to map the risk of publication bias, and Egger's test was used to analyze publication bias. Results: The GMT values of young adults were higher than older adults (SMD = 1.40, 95% CI (0.79, 2.02), P<0.01). There was a higher incidence of local and systemic AEs in young people than in the elderly (OR = 1.10, 95% CI (1.08, 1.12), P<0.01; OR = 1.18, 95% CI (1.14, 1.22), P<0.01). Conclusion: The immune effect of young people after being vaccinated with COVID-19 vaccines was better than that of the elderly, but the safety was worse than that of old people, the most common AEs were fever, rash, and local muscle pain, which were tolerable for young people. As the AEs of the elderly were lower, they can also be vaccinated safely; the reason for the low level of GMT in the elderly was related to Immunosenescence. The vaccine tolerance of people of different ages needs to be studied continuously.


Subject(s)
Antibodies, Viral/blood , COVID-19 Vaccines/adverse effects , COVID-19 Vaccines/immunology , COVID-19/prevention & control , Immunogenicity, Vaccine , Adult , Age Factors , Aged , Aged, 80 and over , Antibodies, Neutralizing/blood , Female , Humans , Male , Middle Aged , SARS-CoV-2
7.
PLoS One ; 16(3): e0248675, 2021.
Article in English | MEDLINE | ID: covidwho-1574573

ABSTRACT

BACKGROUND: In December 2019, a new disease named coronavirus disease 2019 (COVID-19) was occurred. Patients who are critically ill with COVID-19 are more likely to die, especially elderly patients. We aimed to describe the effect of age on the clinical and immune characteristics of critically ill patients with COVID-19. METHODS: We retrospectively included 32 patients with COVID-19 who were confirmed to have COVID-19 by the local health authority and who were admitted to the first affiliated hospital of Zhengzhou University in Zhengzhou, China between January 3 and March 20, 2020. Clinical information and experimental test data were retrospectively collected for the patients. The 32 patients in this study were all in a critical condition and were classified as severe, according to the guidelines of 2019-nCoV infection from the National Health Commission of the People's Republic of China. Data were compared between those <60 years old and ≥60 years old. RESULTS: Of 32 patients, 13 were under 60 years old, and 19 patients were ≥60 years old. The most common symptom among all patients upon admission was fever (93.8%, 30/32). Compared to younger patients, older patients exhibited increased comorbidities. Among patients who were 60 years and older, platelet count, direct bilirubin (DBIL), indirect bilirubin(IBIL), lactate dehydrogenase (LDH), B-type natriuretic peptide (BNP), C-reactive protein (CRP), procalcitonin (PCT), and interleukin-10 (IL-10) were significantly higher than in younger patients who were less than 60 years old. CD4+ T lymphocytes, CD8+ T lymphocytes, and NKT lymphocytes were decreased, CD4+/CD8+ T lymphocytes were significantly increased in all 32 patients, while there were no evident differences between younger and older patients. The CURB-65 (confusion, urea, respiratory, rate, blood pressure plus age ≥65 years), Acute Physiology and Chronic Health Evaluation (APACHE) II and pH value were significantly higher in older patients than in patients who were under 60 years old. However, the PaO2 and PaO2:FiO2 were lower in older patients than the younger. Compared to patients under 60 years old, patients who were 60 years and older tended to develop ARDS (15 [78.9%] vs 5 [38.5%]), septic shock (7 [36.8%] vs 0 [0.0%]) and were more likely to receive mechanical ventilation (13 [68.4%] vs 3[23.1%]). Dynamic trajectories of seven laboratory parameters were tracked on days 1, 3, 5 and 7, and significant differences in lymphocyte count (P = 0.026), D-dimer (P = 0.010), lactate dehydrogenase (P = 0.000) and C-reactive protein (P = 0.000) were observed between the two age groups. CONCLUSIONS: A high proportion of critically ill patients were 60 or older. Furthermore, rapid disease progression was noted in elderly patients. Therefore, close monitoring and timely treatment should be performed in elderly COVID-19 patients.


Subject(s)
COVID-19/epidemiology , Age Factors , Aged , CD4-CD8 Ratio , COVID-19/blood , COVID-19/diagnosis , COVID-19/immunology , Critical Illness , Female , Humans , Immunity , Lymphocyte Count , Male , Middle Aged , Preliminary Data , Retrospective Studies , SARS-CoV-2/isolation & purification , Severity of Illness Index
8.
J Med Internet Res ; 23(2): e26257, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1574035

ABSTRACT

BACKGROUND: As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. OBJECTIVE: In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. METHODS: We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. RESULTS: Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days). CONCLUSIONS: The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Age Factors , Aged , Critical Care/statistics & numerical data , Dementia/epidemiology , Female , Humans , Kidney Failure, Chronic/epidemiology , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Prognosis , Proportional Hazards Models , ROC Curve , Republic of Korea/epidemiology , Retrospective Studies , Risk Factors , Severity of Illness Index , Survival Rate
9.
PLoS Comput Biol ; 17(12): e1009697, 2021 12.
Article in English | MEDLINE | ID: covidwho-1571974

ABSTRACT

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.


Subject(s)
Algorithms , COVID-19 Vaccines/therapeutic use , COVID-19/prevention & control , SARS-CoV-2 , Vaccination/methods , Age Factors , COVID-19/epidemiology , COVID-19/immunology , COVID-19 Vaccines/supply & distribution , Computational Biology , Computer Simulation , Health Care Rationing/methods , Health Care Rationing/statistics & numerical data , Humans , Mass Vaccination/methods , Mass Vaccination/statistics & numerical data , Netherlands/epidemiology , Pandemics/prevention & control , Pandemics/statistics & numerical data , SARS-CoV-2/immunology , Vaccination/statistics & numerical data
10.
Nat Commun ; 12(1): 7251, 2021 12 13.
Article in English | MEDLINE | ID: covidwho-1569250

ABSTRACT

New lineages of SARS-CoV-2 are of potential concern due to higher transmissibility, risk of severe outcomes, and/or escape from neutralizing antibodies. Lineage B.1.1.7 (the Alpha variant) became dominant in early 2021, but the association between transmissibility and risk factors, such as age of primary case and viral load remains poorly understood. Here, we used comprehensive administrative data from Denmark, comprising the full population (January 11 to February 7, 2021), to estimate household transmissibility. This study included 5,241 households with primary cases; 808 were infected with lineage B.1.1.7 and 4,433 with other lineages. Here, we report an attack rate of 38% in households with a primary case infected with B.1.1.7 and 27% in households with other lineages. Primary cases infected with B.1.1.7 had an increased transmissibility of 1.5-1.7 times that of primary cases infected with other lineages. The increased transmissibility of B.1.1.7 was multiplicative across age and viral load.


Subject(s)
Age Factors , COVID-19/transmission , SARS-CoV-2 , Viral Load , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , Child , Child, Preschool , Denmark/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Young Adult
13.
J Clin Invest ; 131(13)2021 07 01.
Article in English | MEDLINE | ID: covidwho-1556620

ABSTRACT

Seasonal influenza vaccination elicits a diminished adaptive immune response in the elderly, and the mechanisms of immunosenescence are not fully understood. Using Ig-Seq, we found a marked increase with age in the prevalence of cross-reactive (CR) serum antibodies that recognize both the H1N1 (vaccine-H1) and H3N2 (vaccine-H3) components of an egg-produced split influenza vaccine. CR antibodies accounted for 73% ± 18% of the serum vaccine responses in a cohort of elderly donors, 65% ± 15% in late middle-aged donors, and only 13% ± 5% in persons under 35 years of age. The antibody response to non-HA antigens was boosted by vaccination. Recombinant expression of 19 vaccine-H1+H3 CR serum monoclonal antibodies (s-mAbs) revealed that they predominantly bound to non-HA influenza proteins. A sizable fraction of vaccine-H1+H3 CR s-mAbs recognized with high affinity the sulfated glycans, in particular sulfated type 2 N-acetyllactosamine (Galß1-4GalNAcß), which is found on egg-produced proteins and thus unlikely to contribute to protection against influenza infection in humans. Antibodies against sulfated glycans in egg-produced vaccine had been identified in animals but were not previously characterized in humans. Collectively, our results provide a quantitative basis for how repeated exposure to split influenza vaccine correlates with unintended focusing of serum antibody responses to non-HA antigens that may result in suboptimal immunity against influenza.


Subject(s)
Antibodies, Viral/biosynthesis , Influenza Vaccines/immunology , Influenza, Human/immunology , Viral Proteins/immunology , Adult , Age Factors , Aged , Animals , Antibodies, Monoclonal/immunology , Antibodies, Viral/blood , Cohort Studies , Cross Reactions , Eggs/analysis , Humans , Immunoglobulin G/biosynthesis , Immunoglobulin G/blood , Influenza A Virus, H1N1 Subtype/immunology , Influenza A Virus, H3N2 Subtype/immunology , Influenza, Human/prevention & control , Influenza, Human/virology , Middle Aged , Polysaccharides/immunology , Vaccination
14.
Pan Afr Med J ; 38: 93, 2021.
Article in French | MEDLINE | ID: covidwho-1547720

ABSTRACT

Introduction: SARS-CoV-2 serology tests could play a crucial role in estimating the prevalence of COVID-19. The purpose of this study was to estimate the prevalence of COVID-19 among travellers and workers in Bukavu, a city in eastern Democratic Republic of the Congo. Methods: between May and August 2020, the Cellex qSARS-CoV-2 IgG/IgM Rapid Test (Cellex, Inc., USA), lateral flow immunoassay was used to rapidly detect and differentiate antibodies against SARS-CoV-2 among travellers and workers seeking medical certification. Results: among the 684 residents of the city of Bukavu screened for COVID-19 (4.2% Hispanic, 2.8% other African, 0.9% Asian), the seroprevalence anti-SARS-CoV-2 antibodies was 40.8% (IgG+/IgM+: 34.6%; IgG+/IgM-: 0.5%; IgG-/IgM+: 5.4%). Cumulative seroprevalence of anti-SARS-CoV-2 IgG antibodies increased from 24.5% to 35.2% from May to August 2020. Independent predictors of SARS-CoV-2 antibodies were age > 60 years [adjusted OR = 2.07(1.26-3.38)] and non-membership of the medical staff [adjusted OR = 2.28 (1.22-4.26)]. Thirteen point nine percent of patients seropositive for SARS-CoV-2 antibodies were symptomatic and hospitalized. Conclusion: this study shows a very high seroprevalence of SARS-CoV-2 antibodies among travellers and workers in Bukavu, a city in eastern Democratic Republic of the Congo, which may positively affect community immunity in the study population. Thus, the management of COVID-19 should be contextualized according to local realities.


Subject(s)
Antibodies, Viral/blood , COVID-19/epidemiology , SARS-CoV-2/isolation & purification , Travel , Adult , Age Factors , Aged , COVID-19/diagnosis , Democratic Republic of the Congo/epidemiology , Female , Hospitalization/statistics & numerical data , Humans , Immunoassay , Male , Middle Aged , SARS-CoV-2/immunology , Seroepidemiologic Studies
15.
J Med Virol ; 93(12): 6703-6713, 2021 12.
Article in English | MEDLINE | ID: covidwho-1544323

ABSTRACT

Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77-0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77-0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the "first wave" of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78-0.87]; for full follow-up: 0.82 [95% CI: 0.78-0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.


Subject(s)
COVID-19/diagnosis , Adult , Age Factors , Aged , Aged, 80 and over , C-Reactive Protein/analysis , COVID-19/mortality , COVID-19/pathology , Female , Fibrin Fibrinogen Degradation Products/analysis , Humans , Male , Middle Aged , Reproducibility of Results , Risk Assessment , Risk Factors , Severity of Illness Index , Urea/blood , Young Adult
16.
J Med Virol ; 93(12): 6660-6670, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1544317

ABSTRACT

With the wide spread of Coronavirus, most people who infected with the COVID-19, will recover without requiring special treatment. Whereas, elders and those with underlying medical problems are more likely to have serious illnesses, even be threatened with death. Many more disciplines try to find solutions and drive master plan to this global trouble. Consequently, by taking one particular population, Hungary, this study aims to explore a pattern of COVID-19 victims, who suffered from some underlying conditions. Age, gender, and underlying medical problems form the structure of the clustering. K-Means and two step clustering methods were applied for age-based and age-independent analysis. Grouping of the deaths in the form of two different scenarios may highlight some concepts of this deadly disease for public health professionals. Our result for clustering can forecast similar cases which are assigned to any cluster that it will be a serious cautious for the population.


Subject(s)
COVID-19/epidemiology , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Asthma/complications , COVID-19/etiology , Diabetes Complications/epidemiology , Female , Humans , Hungary/epidemiology , Lung Diseases/complications , Male , Middle Aged , Neoplasms/complications , Obesity/complications , Risk Factors , Schizophrenia/complications , Sex Factors , Young Adult
17.
J Med Virol ; 93(12): 6506-6511, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1544294

ABSTRACT

Anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglouilin G (IgG) and immunoglouilin M (IgM) antibodies have been widely used to assist clinical diagnosis. Our previous study reported a discrepancy in SARS-CoV-2 antibody response between male and female coronavirus disease 2019 (COVID-19) patients. However, the duration and discrepancy between ages as well as sexes of SARS-CoV-2 antibody in convalescent COVID-19 patients have not been clarified. In this study, a total of 538 health-examination individuals who were confirmed with SARS-CoV-2 infection a year ago were enrolled. Blood samples were collected and detected for IgM and IgG antibodies. Among these convalescent patients, 12.80% were detected positive for IgM antibodies. The positive rates for IgM antibody were close between sexes: for males, this is 9.17% and for females 13.75%. However, the IgG antibody was detected positive in as much as 82.90% convalescent patients and the positive rates were nearly the same between males (82.57%) and females (82.98%). Besides this, the level of IgM and IgG antibodies showed no difference between male and female convalescent patients. The level of IgG antibodies showed a significant difference between ages. The elder patients (over 35 years old) maintained a higher level of IgG antibody than the younger patients (under or equal 35 years old) after recovering for 1 year. In addition, IgG antibody was more vulnerable to disappear in younger patients than in elder patients. Overall, our study identified over 1-year duration of SARS-CoV-2 antibody and age difference of IgG antibody response in convalescent COVID-19 patients. These findings may provide new insights into long-term humoral immune response, vaccines efficacy and age-based personalized vaccination strategies.


Subject(s)
Antibodies, Viral/blood , Immunoglobulin G/blood , Immunoglobulin M/blood , SARS-CoV-2/immunology , Adult , Age Factors , Aged , COVID-19/immunology , Coronavirus Nucleocapsid Proteins/immunology , Female , Humans , Male , Middle Aged , Phosphoproteins/immunology , Sex Factors , Spike Glycoprotein, Coronavirus/immunology , Young Adult
18.
J Med Virol ; 94(1): 366-371, 2022 01.
Article in English | MEDLINE | ID: covidwho-1544350

ABSTRACT

Co-epidemics happening simultaneously can generate a burden on healthcare systems. The co-occurrence of SARS-CoV-2 with vector-borne diseases (VBD), such as malaria and dengue in resource-limited settings represents an additional challenge to the healthcare systems. Herein, we assessed the coinfection rate between SARS-CoV-2 and VBD to highlight the need to carry out an accurate diagnosis and promote timely measures for these infections in Luanda, the capital city of Angola. This was a cross-sectional study conducted with 105 subjects tested for the SARS-CoV-2 and VBD with a rapid detection test in April 2021. The participants tested positive for SARS-CoV-2 (3.80%), malaria (13.3%), and dengue (27.6%). Low odds related to testing positivity to SARS-CoV-2 or VBD were observed in participants above or equal to 40 years (odds ratio [OR]: 0.60, p = 0.536), while higher odds were observed in male (OR: 1.44, p = 0.392) and urbanized areas (OR: 3.78, p = 0.223). The overall co-infection rate between SARS-CoV-2 and VBD was 11.4%. Our findings showed a coinfection between SARS-CoV-2 with malaria and dengue, which could indicate the need to integrate the screening for VBD in the SARS-CoV-2 testing algorithm and the adjustment of treatment protocols. Further studies are warranted to better elucidate the relationship between COVID-19 and VBD in Angola.


Subject(s)
COVID-19/epidemiology , Coinfection/epidemiology , Dengue/epidemiology , Malaria/epidemiology , Vector Borne Diseases/epidemiology , Adolescent , Adult , Age Factors , Angola/epidemiology , Antibodies, Protozoan/blood , Antibodies, Viral/blood , COVID-19 Testing , Chikungunya Fever/epidemiology , Child , Child, Preschool , Cross-Sectional Studies , Female , Humans , Infant , Infant, Newborn , Male , Mass Screening , Middle Aged , RNA, Viral/blood , SARS-CoV-2/isolation & purification , Sex Factors , Young Adult , Zika Virus Infection/epidemiology
20.
Gac Med Mex ; 157(3): 263-270, 2021.
Article in English | MEDLINE | ID: covidwho-1535083

ABSTRACT

INTRODUCTION: Historically, pandemics have resulted in higher mortality rates in the most vulnerable populations. Social determinants of health (SDH) have been associated with people morbidity and mortality at different levels. OBJECTIVE: To determine the relationship between SDH and COVID-19 severity and mortality. METHODS: Retrospective study, where data from patients with COVID-19 were collected at a public hospital in Chile. Sociodemographic variables related to structural SDH were classified according to the following categories: gender, age (< 65 years, ≥ 65 years), secondary education (completed or not), work status (active, inactive) and income (< USD 320, ≥ USD 320). RESULTS: A total of 1,012 laboratory-confirmed COVID-19 cases were included. Average age was 64.2 ± 17.5 years. Mortality of the entire sample was 14.5 %. Age, level of education, unemployment and income had a strong association with mortality (p < 0.001). CONCLUSIONS: The findings reinforce the idea that SDH should be considered a public health priority, which is why political efforts should focus on reducing health inequalities for future generations.


Subject(s)
COVID-19/epidemiology , Social Determinants of Health , Age Factors , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/physiopathology , Chile/epidemiology , Educational Status , Female , Hospitals, Public , Humans , Income/statistics & numerical data , Male , Middle Aged , Retrospective Studies , Risk Factors , Severity of Illness Index , Unemployment/statistics & numerical data
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