Self-selection biases in psychological studies: Personality and affective disorders are prevalent among participants.

Respondents select the type of psychological studies that they want to participate in consistence with their needs and individual characteristics, which creates an unintentional self-selection bias. The question remains whether participants attracted by psychological studies may have more psychological dysfunctions related to personality and affective disorders compared to the general population. We investigated (N = 947; 62% women) whether the type of the invitation (to talk about recent critical or regular life events) or the source of the data (either face-to-face or online) attracts people with different psychopathology. Most importantly, participants who alone applied to take part in paid psychological studies had more symptoms of personality disorders than those who had never before applied to take part in psychological studies. The current results strongly translate into a recommendation for either the modification of recruitment strategies or much greater caution when generalizing results for this methodological reason.

Improving the representativeness of the tribal behavioral risk factor surveillance system through data integration.

BACKGROUND: Previous literature showed significant health disparities between Native American population and other populations such as Non-Hispanic White. Most existing studies for Native American Health were based on non-probability samples which suffer with selection bias. In this paper, we are the first to evaluate the effectiveness of data integration methods, including calibration and sequential mass imputation, to improve the representativeness of the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) in terms of reducing the biases of the raw estimates. METHODS: We evaluated the benefits of our proposed data integration methods, including calibration and sequential mass imputation, by using the 2019 TBRFSS and the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). We combined the data from the 2018 and 2019 BRFSS by composite weighting. Demographic variables and general health variables were used as predictors for data integration. The following health-related variables were used for evaluation in terms of biases: Smoking status, Arthritis status, Cardiovascular Disease status, Chronic Obstructive Pulmonary Disease status, Asthma status, Cancer status, Stroke status, Diabetes status, and Health Coverage status. RESULTS: For most health-related variables, data integration methods showed smaller biases compared with unadjusted TBRFSS estimates. After calibration, the demographic and general health variables benchmarked with those for the BRFSS. CONCLUSION: Data integration procedures, including calibration and sequential mass imputation methods, hold promise for improving the representativeness of the TBRFSS.

A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors.

We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.

Prospective sampling bias in COVID-19 recruitment methods: experimental evidence from a national randomized survey testing recruitment materials.

BACKGROUND: In the context of the COVID-19 pandemic, social science research has required recruiting many prospective participants. Many researchers have explicitly taken advantage of widespread public interest in COVID-19 to advertise their studies. Leveraging this interest, however, risks creating unrepresentative samples due to differential interest in the topic. In this study, we investigate the design of survey recruitment materials with respect to the views of resultant participants. METHODS: Within a pan-Canadian survey (stratified random mail sampling, n = 1969), the design of recruitment invitations to prospective respondents was experimentally varied, with some prospective respondents receiving COVID-specific recruitment messages and others receiving more general recruitment messages (described as research about health and health policy). All respondents participated, however, in the same survey, allowing comparison of both demographic and attitudinal features between these groups. RESULTS: Respondents recruited via COVID-19 specific postcards were more likely to agree that COVID-19 is serious and believe that they were likely to contract COVID-19 compared to non-COVID respondents (odds = 0.71, p = 0.04; odds = 0.74, p = 0.03 respectively; comparing health to COVID-19 framed respondents). COVID-19 specific respondents were more likely to disagree that the COVID-19 threat was exaggerated compared to the non-COVID survey respondents (odds = 1.44, p = 0.02). CONCLUSIONS: COVID-19 recruitment framing garnered a higher response rate, as well as a sample with greater concern about coronavirus risks and impacts than respondents who received more neutrally framed recruitment materials.

Re-analysis on the statistical sampling biases of a mask promotion trial in Bangladesh: a statistical replication.

A recent randomized trial evaluated the impact of mask promotion on COVID-19-related outcomes. We find that staff behavior in both unblinded and supposedly blinded steps caused large and statistically significant imbalances in population sizes. These denominator differences constitute the rate differences observed in the trial, complicating inferences of causality.

Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation.

BACKGROUND: Selection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly dangerous in internet-based public health surveillance, where traditional mitigation and adjustment methods are inapplicable, unavailable, or out of date. Recent theoretical advances in causal modeling can mitigate these threats, but these innovations have not been widely deployed in the epidemiological community. OBJECTIVE: The purpose of our paper is to demonstrate the practical utility of causal modeling to both detect unmeasured confounding and selection bias and guide model selection to minimize bias. We implemented this approach in an applied epidemiological study of the COVID-19 cumulative infection rate in the New York City (NYC) spring 2020 epidemic. METHODS: We collected primary data from Qualtrics surveys of Amazon Mechanical Turk (MTurk) crowd workers residing in New Jersey and New York State across 2 sampling periods: April 11-14 and May 8-11, 2020. The surveys queried the subjects on household health status and demographic characteristics. We constructed a set of possible causal models of household infection and survey selection mechanisms and ranked them by compatibility with the collected survey data. The most compatible causal model was then used to estimate the cumulative infection rate in each survey period. RESULTS: There were 527 and 513 responses collected for the 2 periods, respectively. Response demographics were highly skewed toward a younger age in both survey periods. Despite the extremely strong relationship between age and COVID-19 symptoms, we recovered minimally biased estimates of the cumulative infection rate using only primary data and the most compatible causal model, with a relative bias of +3.8% and -1.9% from the reported cumulative infection rate for the first and second survey periods, respectively. CONCLUSIONS: We successfully recovered accurate estimates of the cumulative infection rate from an internet-based crowdsourced sample despite considerable selection bias and unmeasured confounding in the primary data. This implementation demonstrates how simple applications of structural causal modeling can be effectively used to determine falsifiable model conditions, detect selection bias and confounding factors, and minimize estimate bias through model selection in a novel epidemiological context. As the disease and social dynamics of COVID-19 continue to evolve, public health surveillance protocols must continue to adapt; the emergence of Omicron variants and shift to at-home testing as recent challenges. Rigorous and transparent methods to develop, deploy, and diagnosis adapted surveillance protocols will be critical to their success.

Experience and lessons learned from multi-modal internet-based recruitment of U.S. Vietnamese into research.

BACKGROUND: Asian-Americans are one of the most understudied racial/ethnic minority populations. To increase representation of Asian subgroups, researchers have traditionally relied on data collection at community venues and events. However, the COVID-19 pandemic has created serious challenges for in-person data collection. In this case study, we describe multi-modal strategies for online recruitment of U.S. Vietnamese parents, compare response rates and participant characteristics among strategies, and discuss lessons learned. METHODS: We recruited 408 participants from community-based organizations (CBOs) (n = 68), Facebook groups (n = 97), listservs (n = 4), personal network (n = 42), and snowball sampling (n = 197). Using chi-square tests and one-way analyses of variance, we compared participants recruited through different strategies regarding sociodemographic characteristics, acculturation-related characteristics, and mobile health usage. RESULTS: The overall response rate was 71.8% (range: 51.5% for Vietnamese CBOs to 86.6% for Facebook groups). Significant differences exist for all sociodemographic and almost all acculturation-related characteristics among recruitment strategies. Notably, CBO-recruited participants were the oldest, had lived in the U.S. for the longest duration, and had the lowest Vietnamese language ability. We found some similarities between Facebook-recruited participants and those referred by Facebook-recruited participants. Mobile health usage was high and did not vary based on recruitment strategies. Challenges included encountering fraudulent responses (e.g., non-Vietnamese). Perceived benefits and trust appeared to facilitate recruitment. CONCLUSIONS: Facebook and snowball sampling may be feasible strategies to recruit U.S. Vietnamese. Findings suggest the potential for mobile-based research implementation. Perceived benefits and trust could encourage participation and may be related to cultural ties. Attention should be paid to recruitment with CBOs and handling fraudulent responses.

Recruiting Cancer Survivors to a Mobile Mindfulness Intervention in the United States: Exploring Online and Face-to-Face Recruitment Strategies.

Cancer survivorship research faces several recruitment challenges, such as accrual of a representative sample, as well as participant retention. Our study explores patterns in recruited demographics, patient-reported outcomes (PROs), and retention rates for a randomized controlled trial (RCT) utilizing a mobile mindfulness intervention for the well-being of cancer survivors. In total, 123 participants were recruited using traditional and online strategies. Using the chi-square test of independence, recruitment type was compared with demographic and clinical variables, PROs, and retention at Time 2 and Time 3. Online recruitment resulted in almost double the yield compared to traditional recruitment. Online-recruited participants were more often younger, from the continental U.S., Caucasian, diagnosed and treated less recently, at a later stage of diagnosis, diagnosed with blood cancer, without high blood pressure, and with less reported pain. The recruitment method was not significantly associated with retention. Online recruitment may capture a larger, broader survivor sample, but, similar to traditional recruitment, may also lead to selection biases depending on where efforts are focused. Future research should assess the reasons underlying the higher yield and retention rates of online recruitment and should evaluate how to apply a mix of traditional and online recruitment strategies to efficiently accrue samples that are representative of the survivor population.

On selection bias in comparison measures of smartphone-generated population mobility: an illustration of no-bias conditions with a commercial data source.

PURPOSE: Passively generated cell-phone location ("mobility") data originally intended for commercial use has become frequently used in epidemiologic research, notably during the COVID-19 pandemic to study the impact of physical-distancing recommendations on aggregate population behavior (e.g., average daily mobility). Given the opaque nature of how individuals are selected into these datasets, researchers have cautioned that their use may give rise to selection bias, yet little guidance exists for assessing this potential threat to validity in mobility-data research. Through an example analysis of cell-phone-derived mobility data, we present a set of conditions to guide the assessment of selection bias in measures comparing aggregate mobility patterns over time and between groups. METHODS: We specifically consider bias in measures comparing group-level mobility in the same group (difference, ratio, percent difference) and between groups (difference in differences, ratio of ratios, ratio of percent differences). We illustrate no-bias conditions in these measures through an example comparing block-group-level mobility between income groups in United States metro areas before (January 1st-March 10, 2020) and after (March 11th-April 19th, 2020) the day COVID-19 was declared a pandemic. RESULTS: Within-group contrasts describing mobility over time, especially for the higher-income decile, were expected to be most resistant to bias during the example study period. CONCLUSIONS: The presented conditions can be used to assess the susceptibility to selection bias of group-level measures comparing mobility. Importantly, they can be used even without knowledge of the degree of bias in each group at each time point. We further highlight links between no-bias principles originating in epidemiology and economics, showing that certain assumptions (e.g., parallel trends) can apply to biases beyond their original application.

Should samples be weighted to decrease selection bias in online surveys during the COVID-19 pandemic? Data from seven datasets.

BACKGROUND: Online surveys have triggered a heated debate regarding their scientific validity. Many authors have adopted weighting methods to enhance the quality of online survey findings, while others did not find an advantage for this method. This work aims to compare weighted and unweighted association measures after adjustment over potential confounding, taking into account dataset properties such as the initial gap between the population and the selected sample, the sample size, and the variable types. METHODS: This study assessed seven datasets collected between 2019 and 2021 during the COVID-19 pandemic through online cross-sectional surveys using the snowball sampling technique. Weighting methods were applied to adjust the online sample over sociodemographic features of the target population. RESULTS: Despite varying age and gender gaps between weighted and unweighted samples, strong similarities were found for dependent and independent variables. When applied on the same datasets, the regression analysis results showed a high relative difference between methods for some variables, while a low difference was found for others. In terms of absolute impact, the highest impact on the association measure was related to the sample size, followed by the age gap, the gender gap, and finally, the significance of the association between weighted age and the dependent variable. CONCLUSION: The results of this analysis of online surveys indicate that weighting methods should be used cautiously, as weighting did not affect the results in some databases, while it did in others. Further research is necessary to define situations in which weighting would be beneficial.

Evaluation of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection.

INTRODUCTION: Eosinopenia has been observed during infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. This study evaluated the role of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection. METHODS: Information on 429 patients with confirmed COVID-19, admitted to Apollo Hospitals, Chennai, India between 04 June 2020 to 15 August 2020, was retrospectively collected through electronic records and analysed. RESULTS: 79.25% of the patients included in the study had eosinopenia on admission. The median eosinophil count in COVID-19-positive patients was 0.015 × 109 /L, and in negative patients, it was 0.249 × 109 /L. Eighteen per cent of the positive patients presented with 0 eosinophil count. Eosinopenia for early diagnosis of COVID-19 had a sensitivity of 80.68% and specificity of 100% with an accuracy of 85.24. Role of eosinopenia in prognostication of COVID-19 was found to be insignificant. There was no statistically significant difference between the median eosinophil counts in survivors and nonsurvivors. Eosinophil trends during the course of disease were found to be similar between survivors and nonsurvivors. CONCLUSIONS: Eosinopenia on admission is a reliable and convenient early diagnostic marker for COVID-19 infection, helping in early identification, triaging and isolation of the patients till nucleic acid test results are available. Role of eosinopenia as a prognostic indicator is insignificant.

Current Data on COVID-19 mRNA-Vaccine Safety during Pregnancy Might Be Subject to Selection Bias. Reply to Stroobandt, S.; Stroobandt, R. Data of the COVID-19 mRNA-Vaccine V-Safe Surveillance System and Pregnancy Registry Reveals Poor Embryonic and Second Trimester Fetal Survival Rate. Comment on "Stuckelberger et al. SARS-CoV-2 Vaccine Willingness among Pregnant and Breastfeeding Women during the First Pandemic Wave: A Cross-Sectional Study in Switzerland. Viruses 2021, 13, 1199".

We would like to thank Stroobandt, S. and Stroobandt, R. for showing interest in our paper [...].

Obtaining Prevalence Estimates of Coronavirus Disease 2019: A Model to Inform Decision-Making.

We evaluated whether randomly sampling and testing a set number of individuals for coronavirus disease 2019 (COVID-19) while adjusting for misclassification error captures the true prevalence. We also quantified the impact of misclassification error bias on publicly reported case data in Maryland. Using a stratified random sampling approach, 50,000 individuals were selected from a simulated Maryland population to estimate the prevalence of COVID-19. We examined the situation when the true prevalence is low (0.07%-2%), medium (2%-5%), and high (6%-10%). Bayesian models informed by published validity estimates were used to account for misclassification error when estimating COVID-19 prevalence. Adjustment for misclassification error captured the true prevalence 100% of the time, irrespective of the true prevalence level. When adjustment for misclassification error was not done, the results highly varied depending on the population's underlying true prevalence and the type of diagnostic test used. Generally, the prevalence estimates without adjustment for misclassification error worsened as the true prevalence level increased. Adjustment for misclassification error for publicly reported Maryland data led to a minimal but not significant increase in the estimated average daily cases. Random sampling and testing of COVID-19 are needed with adjustment for misclassification error to improve COVID-19 prevalence estimates.

Retest positive for SARS-CoV-2 RNA of "recovered" patients with COVID-19: Persistence, sampling issues, or re-infection?

"Retest Positive" for severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) from "recovered" coronavirus disease-19 (COVID-19) has been reported and raised several important questions for this novel coronavirus and COVID-19 disease. In this commentary, we discussed several questions: (a) Can SARS-CoV-2 re-infect the individuals who recovered from COVID-19? This question is also associated with other questions: whether or not SARS-CoV-2 infection induces protective reaction or neutralized antibody? Will SARS-CoV-2 vaccines work? (b) Why could some recovered patients with COVID-19 be re-tested positive for SARS-CoV-2 RNA? (c) Are some recovered pwith atients COVID-19 with re-testing positive for SARS-CoV-2 RNA infectious? and (d) How should the COVID-19 patients with retest positive for SARS-CoV-2 be managed?