Immune correlates analysis using vaccinees from test negative designs

Biostatistics ◽  
2020 ◽  
Author(s):  
Dean A Follmann ◽  
Lori Dodd
Keyword(s):  
Immune Response ◽  
Logistic Regression ◽  
Disease Risk ◽  
Disease Status ◽  
Vaccination Status ◽  
Important Goal ◽  
Retrospective Design ◽  
Immune Correlates ◽  
Response Slope ◽  
Ring Vaccination

Summary Determining the effect of vaccine-induced immune response on disease risk is an important goal of vaccinology. Typically, immune correlates analyses are conducted prospectively with immune response measured shortly after vaccination and subsequent disease status regressed on immune response. In outbreaks and rare disease settings, collecting samples from all vaccinees is not feasible. The test negative design is a retrospective design used to measure vaccine efficacy where symptomatic individuals who present at a clinic are assessed for relevant disease (cases) or some other disease (controls) and vaccination status ascertained. This article proposes that test negative vaccinees have immune response to vaccine assessed both for relevant (e.g., Ebola) and irrelevant (e.g., vector) proteins. If the latter immune response is unaffected by active (Ebola) infection, and is correlated with the relevant immune response, it can serve as a proxy for the immune response of interest proximal to infection. We show that logistic regression using imputed immune response as the covariate and case disease as outcome can estimate the prospective immune response slope and detail the assumptions needed for unbiased inference. The method is evaluated by simulation under various scenarios including constant and decaying immune response. A simulated dataset motivated by ring vaccination for an ongoing Ebola outbreak is analyzed.

Vaccination status and immune response to 13-valent pneumococcal conjugate vaccine in asplenic individuals

Vaccine ◽  
2015 ◽  
Vol 33 (14) ◽  
pp. 1688-1694 ◽  
Author(s):  
Per Nived ◽  
Charlotte Sværke Jørgensen ◽  
Bo Settergren
Keyword(s):  
Immune Response ◽  
Conjugate Vaccine ◽  
Pneumococcal Conjugate Vaccine ◽  
Vaccination Status

scholarly journals Pathogen Dose in Animal Models of Hemorrhagic Fever Virus Infections and the Potential Impact on Studies of the Immune Response

Pathogens ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 275
Author(s):  
Bryce M. Warner
Keyword(s):  
Immune Response ◽  
Animal Models ◽  
Hemorrhagic Fever ◽  
Virus Infections ◽  
Critical Determinant ◽  
Animal Models Of Disease ◽  
Hemorrhagic Fever Virus ◽  
Immune Correlates ◽  
Clinical Drug Development ◽  
Wide Range

Viral hemorrhagic fever viruses come from a wide range of virus families and are a significant cause of morbidity and mortality worldwide each year. Animal models of infection with a number of these viruses have contributed to our knowledge of their pathogenesis and have been crucial for the development of therapeutics and vaccines that have been approved for human use. Most of these models use artificially high doses of virus, ensuring lethality in pre-clinical drug development studies. However, this can have a significant effect on the immune response generated. Here I discuss how the dose of antigen or pathogen is a critical determinant of immune responses and suggest that the current study of viruses in animal models should take this into account when developing and studying animal models of disease. This can have implications for determination of immune correlates of protection against disease as well as informing relevant vaccination and therapeutic strategies.

scholarly journals Future Oceanic Warming and Acidification Alter Immune Response and Disease Status in a Commercial Shellfish Species, Mytilus edulis L.

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e99712 ◽  
Author(s):  
Clara L. Mackenzie ◽  
Sharon A. Lynch ◽  
Sarah C. Culloty ◽  
Shelagh K. Malham
Keyword(s):  
Immune Response ◽  
Mytilus Edulis ◽  
Disease Status

scholarly journals Applications of Bayesian Shrinkage Prior Models in Clinical Research with Categorical Responses

2021 ◽  
Author(s):  
Arinjita Bhattacharyya ◽  
Subhadip Pal ◽  
Riten Mitra ◽  
Shesh Rai
Keyword(s):  
Colon Cancer ◽  
Logistic Regression ◽  
Variable Selection ◽  
Clinical Research ◽  
Disease Risk ◽  
Brier Score ◽  
Data Sets ◽  
Pima Indians ◽  
Bayesian Shrinkage ◽  
Shrinkage Priors

Abstract Background: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions like diabetes, colon cancer, and Alzheimer’s disease. Developing accurate prediction and classification methods have implications for personalized medicine. Building an excellent predictive model involves selecting features that are most significantly associated with the response at hand. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. The article discusses variable selection with three shrinkage priors and illustrates its application to clinical data sets such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer’s data sets. Methods: We present a unified Bayesian hierarchical framework that implements and compares shrinkage priors in binary and multinomial logistic regression models. The key feature is the representation of the likelihood by a Polya-Gamma data augmentation, which admits a natural integration with a family of shrinkage priors. We specifically focus on the Horseshoe, Dirichlet Laplace, and Double Pareto priors. Extensive simulation studies are conducted to assess the performances under different data dimensions and parameter settings. Measures of accuracy, AUC, brier score, L1 error, cross-entropy, ROC surface plots are used as evaluation criteria comparing the priors to frequentist methods like Lasso, Elastic-Net, and Ridge regression. Results: All three priors can be used for robust prediction with significant metrics, irrespective of their categorical response model choices. Simulation study could achieve the mean prediction accuracy of 91% (95% CI: 90.7, 91.2) and 74% (95% CI: 73.8,74.1) for logistic regression and multinomial logistic models, respectively. The model can identify significant variables for disease risk prediction and is computationally efficient. Conclusions: The models are robust enough to conduct both variable selection and future prediction because of their high shrinkage property and applicability to a broad range of classification problems.

scholarly journals Characterization of the skin microbiota in bullous pemphigoid patients and controls reveals novel microbial indicators of disease

2021 ◽  
Author(s):  
Meriem Belheouane ◽  
Britt M Hermes ◽  
Nina Van Beek ◽  
Sandrine Benoit ◽  
Philippe Bernard ◽  
...  
Keyword(s):  
Bullous Pemphigoid ◽  
Disease Risk ◽  
Disease Status ◽  
The Elderly ◽  
Future Research ◽  
Sampling Effort ◽  
Skin Microbiota ◽  
Important Indicator ◽  
Blistering Disease ◽  
Microbe Interactions

Bullous pemphigoid (BP) is an autoimmune skin blistering disease afflicting mostly the elderly and is associated with significantly increased mortality. Here, we conducted the most extensive sampling effort of skin microbiota in BP to date to analyze whether intra-individual, body-site-specific, and/or geographical variation contributes to the emergence of BP. We find marked differences in the skin microbiota of BP patients compared to that of control subjects, and moreover that disease status rather than skin biogeography governs the skin microbiota composition in BP. Our data reveal a discernible transitional stage between normal and diseased skin in BP characterized by a loss of protective microbiota and an increase in sequences matching Staphylococcus aureus, a known inflammation-promoting species. Notably, S. aureus is ubiquitously associated with disease status, suggesting that this taxon is an important indicator of BP. Importantly, differences in a few key indicator taxa are able to reliably discriminate between BP patients and controls, characterized by their opposing abundance patterns. This may serve as valuable information for assessing disease risk and treatment outcomes. Future research will focus on the functional analysis of host-microbe and microbe-microbe interactions and the relevance of the host genome for microbiota abundances to identify novel BP treatment approaches.

scholarly journals COVID-19 risk factors amongst 14,786 care home residents: An observational longitudinal analysis including daily community positive test rates of COVID-19, hospital stays, and vaccination status in Wales (UK) between 1st September 2020 and 1st May 2021.

2021 ◽  
Author(s):  
Joe Hollinghurst ◽  
Robyn Hollinghurst ◽  
Laura North ◽  
Amy Mizen ◽  
Ashley Akbari ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Hospital Admissions ◽  
Care Home ◽  
Vaccination Status ◽  
Care Homes ◽  
Positive Test ◽  
Care Providers ◽  
Specialist Care ◽  
Individual Level

Objectives: Determine individual level risk factors for care home residents testing positive for SARS-CoV-2. Study Design: Longitudinal observational cohort study using individual-level linked data. Setting: Care home residents in Wales (United Kingdom) between 1st September 2020 and 1st May 2021. Participants: 14,786 older care home residents (aged 65+). Our dataset consisted of 2,613,341 individual-level daily observations within 697 care homes. Methods: We estimated odds ratios (ORs [95% confidence interval]) using multilevel logistic regression models. Our outcome of interest was a positive SARS-CoV-2 polymerase chain reaction (PCR) test. We included time dependent covariates for the estimated community positive test rate of COVID-19, hospital admissions, and vaccination status. Additional covariates were included for age, positive PCR tests prior to the study, sex, frailty (using the hospital frailty risk score), and specialist care home services. Results: The multivariable logistic regression model indicated an increase in age (OR 1.01 [1.00,1.01] per year of age), community positive test rate (OR 1.13 [1.12,1.13] per percent increase in positive test rate), hospital inpatients (OR 7.40 [6.54,8.36]), and residents in care homes with non-specialist dementia care (OR 1.42 [1.01,1.99]) had an increased odds of a positive test. Having a positive test prior to the observation period (OR 0.58 [0.49,0.68]) and either one or two doses of a vaccine (0.21 [0.17,0.25] and 0.05 [0.02,0.09] respectively) were associated with a decreased odds of a positive test. Conclusions: Our findings suggest care providers need to stay vigilant despite the vaccination rollout, and extra precautions should be taken when caring for the most vulnerable. Furthermore, minimising potential COVID-19 infection for care home residents admitted to hospital should be prioritised.

scholarly journals Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Huanhuan Zhao ◽  
Xiaoyu Zhang ◽  
Yang Xu ◽  
Lisheng Gao ◽  
Zuchang Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Risk Prediction ◽  
Disease Risk ◽  
Learning Algorithms ◽  
Large Population ◽  
Machine Learning Algorithms ◽  
Hypertension Risk ◽  
Model Training

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implement hypertension risk prediction model. This study evaluated and compared the performance of four machine learning algorithms on predicting the risk of hypertension based on easy-to-collect risk factors. A dataset of 29,700 samples collected through a physical examination was used for model training and testing. Firstly, we identified easy-to-collect risk factors of hypertension, through univariate logistic regression analysis. Then, based on the selected features, 10-fold cross-validation was utilized to optimize four models, random forest (RF), CatBoost, MLP neural network and logistic regression (LR), to find the best hyper-parameters on the training set. Finally, the performance of models was evaluated by AUC, accuracy, sensitivity and specificity on the test set. The experimental results showed that the RF model outperformed the other three models, and achieved an AUC of 0.92, an accuracy of 0.82, a sensitivity of 0.83 and a specificity of 0.81. In addition, Body Mass Index (BMI), age, family history and waist circumference (WC) are the four primary risk factors of hypertension. These findings reveal that it is feasible to use machine learning algorithms, especially RF, to predict hypertension risk without clinical or genetic data. The technique can provide a non-invasive and economical way for the prevention and management of hypertension in a large population.

scholarly journals Boosting and lassoing new prostate cancer SNP risk factors and their connection to selenium

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David E. Booth ◽  
Venugopal Gopalakrishna-Remani ◽  
Matthew L. Cooper ◽  
Fiona R. Green ◽  
Margaret P. Rayman
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Logistic Regression ◽  
Variable Selection ◽  
Disease Risk ◽  
Gradient Boosting ◽  
Stepwise Logistic Regression ◽  
Anticancer Effect ◽  
Cancer Data ◽  
Snp Interaction

AbstractWe begin by arguing that the often used algorithm for the discovery and use of disease risk factors, stepwise logistic regression, is unstable. We then argue that there are other algorithms available that are much more stable and reliable (e.g. the lasso and gradient boosting). We then propose a protocol for the discovery and use of risk factors using lasso or boosting variable selection. We then illustrate the use of the protocol with a set of prostate cancer data and show that it recovers known risk factors. Finally, we use the protocol to identify new and important SNP based risk factors for prostate cancer and further seek evidence for or against the hypothesis of an anticancer function for Selenium in prostate cancer. We find that the anticancer effect may depend on the SNP-SNP interaction and, in particular, which alleles are present.

scholarly journals Hepatitis B vaccination status and vaccine immune response among children in rural Senegal

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
L Perieres ◽  
M Coste ◽  
S Ndiour ◽  
P Halfon ◽  
C Sokhna ◽  
...  
Keyword(s):  
Immune Response ◽  
Hepatitis B ◽  
Chronic Infection ◽  
Vaccination Status ◽  
Hbv Infection ◽  
Hepatitis B Vaccination ◽  
Chronic Hbv Infection ◽  
Hbv Vaccination ◽  
Chronic Hbv ◽  
Vaccine Immunity

Abstract Background Hepatitis B vaccination during childhood is key to reduce the prevalence of Hepatitis B virus (HBV) infection. In Senegal, a highly endemic country, the three-dose hepatitis B vaccine and the birth dose vaccine were introduced in the Expanded Programme on Immunization (EPI) in 2004 and 2016 respectively. This study aimed to determine chronic HBV infection prevalence, hepatitis B vaccination status and vaccine immunity among children in Senegal. Methods A cross-sectional study including HBV screening was conducted at home among children aged 6 months to 15 years (i.e. born after the introduction of the HBV vaccine in the EPI) in the rural zone of Niakhar. Dried Blood Spot (DBS) samples were collected for the detection of HBsAg, anti-HBc Ab and anti-HBs Ab using chemoluminescence. Vaccination status was assessed using information on vaccination cards. Detectable vaccine immunity was defined with an adjusted DBS threshold of DOI≥0.36 IU/mL (corresponding to 10 IU/mL in venous blood sampling). Results Between October and December 2018, 455 children were enrolled. Preliminary results show that 7/455 (1.5%) had been in contact with HBV (positive anti-HBc Ab) and 5/455 (1.1%) had chronic HBV infection (positive HBsAg). Only 161/455 (35.4%) children had a vaccination card available. Among those, 150/161 (93.2%) received at least 3 doses of hepatitis B vaccine, of which 83/150 (55.3%) had detectable vaccine immunity. The proportion of children with detectable vaccine immunity was significantly higher in children <5 years than in children aged 5-9 and 10-15 (72.3% versus 47.3%, p = 0.006 and 72.3% versus 14.3%, p < 0.001). Conclusions Preliminary results suggest a low prevalence of HBV chronic infection among children born after the introduction of HBV vaccination in Senegal. However, detectable vaccine immunity rapidly decreases with age among vaccinated children, signalling a need for further studies on the immune response to HBV vaccination in this context. Key messages HBV chronic infection is low among children born after the introduction of HBV vaccination in Senegal. Further studies on the immune response to HBV vaccination in this context are needed.

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