BACKGROUND: The largest case-control study (Interphone study) investigating glioma risk related to mobile phone use showed a J-shaped relationship with reduced relative risks for moderate use and a 40% increased relative risk among the 10% heaviest regular mobile phone users, using a categorical risk model based on deciles of lifetime duration of use among ever regular users.
METHODS: We conducted Monte Carlo simulations examining whether the reported estimates are compatible with an assumption of no effect of mobile phone use on glioma risk when the various forms of biases present in the Interphone study are accounted for. Four scenarios of sources of error in self-reported mobile phone use were considered, along with selection bias. Input parameters used for simulations were those obtained from Interphone validation studies on reporting accuracy and from using a nonresponse questionnaire.
RESULTS: We found that the scenario simultaneously modeling systematic and random reporting errors produced a J-shaped relationship perfectly compatible with the observed relationship from the main Interphone study with a simulated spurious increased relative risk among heaviest users (odds ratio = 1.91) compared with never regular users. The main determinant for producing this J shape was higher reporting error variance in cases compared with controls, as observed in the validation studies. Selection bias contributed to the reduced risks as well.
CONCLUSIONS: Some uncertainty remains, but the evidence from the present simulation study shifts the overall assessment to making it less likely that heavy mobile phone use is causally related to an increased glioma risk.

BACKGROUND: Certain associations observed in the National Birth Defects Prevention Study (NBDPS) contrasted with other research or were from areas with mixed findings, including no decrease in odds of spina bifida with periconceptional folic acid supplementation, moderately increased cleft palate odds with ondansetron use and reduced hypospadias odds with maternal smoking.
OBJECTIVE: To investigate the plausibility and extent of differential participation to produce effect estimates observed in NBDPS.
METHODS: We searched the literature for factors related to these exposures and participation and conducted deterministic quantitative bias analyses. We estimated case-control participation and expected exposure prevalence based on internal and external reports, respectively. For the folic acid-spina bifida and ondansetron-cleft palate analyses, we hypothesized the true odds ratio (OR) based on prior studies and quantified the degree of exposure over- (or under-) representation to produce the crude OR (cOR) in NBDPS. For the smoking-hypospadias analysis, we estimated the extent of selection bias needed to nullify the association as well as the maximum potential harmful OR.
RESULTS: Under our assumptions (participation, exposure prevalence, true OR), there was overrepresentation of folic acid use and underrepresentation of ondansetron use and smoking among participants. Folic acid-exposed spina bifida cases would need to have been ≥1.2× more likely to participate than exposed controls to yield the observed null cOR. Ondansetron-exposed cleft palate cases would need to have been 1.6× more likely to participate than exposed controls if the true OR is null. Smoking-exposed hypospadias cases would need to have been ≥1.2 times less likely to participate than exposed controls for the association to falsely appear protective (upper bound of selection bias adjusted smoking-hypospadias OR = 2.02).
CONCLUSIONS: Differential participation could partly explain certain associations observed in NBDPS, but questions remain about why. Potential impacts of other systematic errors (e.g. exposure misclassification) could be informed by additional research.

Vaccine effectiveness (VE) studies consolidate knowledge of real-world effectiveness in different contexts. However, methodological issues may undermine their conclusions: to assess the VE against COVID-19 within the Italian population, a specific threat to validity is related to the consequences of divergent compliance to the Green Pass policy.
To address this challenge we conducted a test negative case-control (TNCC) study and multiple sensitivity analysis among residents aged ≥ 12 in Friuli Venezia Giulia Region (FVG), North-east Italy, from February 1, 2021 to March 31, 2022. Information regarding 211,437 cases of COVID-19 infection and 845,748 matched controls was obtained from the regional computerized health database. The investigation considered: COVID-19 infection, hospitalization, and death. Multiple conditional logistic regressions adjusted for covariates were performed and VE was estimated as (1-OR COVID-19vaccinated vs. unvaccinated)x100. Mediation analyses were carried out to offset potential collider variables, particularly, the number of swabs performed after the introduction of pandemic restrictions.
Full-cycle VE against infection decreased from 96% (95% CI: 96, 97) in the Alpha period to 43% (95% CI: 42, 45) in the Omicron period. Booster dose raised the protection in Omicron period to 67% (95% CI: 66, 67). Against the evasive Omicron variant, the protection of the booster dose was 87% (95% CI: 83, 90) for hospitalization and 90% (95% CI: 82, 95) for death. The number of swabs performed was included as a covariate in the adjustments, and the mediation analysis confirmed that it was a strong mediator between vaccination and COVID-19-related outcomes.
The study suggests that, under similar TNCC settings, mediation analysis and adjustment for number of diagnostic tests should be included, as an effective approach to the challenge of differential testing behavior that may determine substantial selection bias. This correction allowed us to align with results from other studies that show how full-cycle VE against infection was initially high but decreased over time by variant circulation, counterbalanced by booster dose that raised protection across variants and outcome severity.

Humans are exposed to diverse communities of microbes every day. With more time spent indoors by humans, investigations into the communities of microbes inhabiting occupied spaces have become important to deduce the impacts of these microbes on human health and building health. Studies so far have given considerable insight into the communities of the indoor microbiota humans interact with, but mainly focus on sampling surfaces or indoor dust from filters. Beneath the surfaces though, building envelopes have the potential to contain environments that would support the growth of microbial communities. But due to design choices and distance from ground moisture, for example, the temperature and humidity across a building will vary and cause environmental gradients. These microenvironments could then influence the composition of the microbial communities within the walls. Here we present a case study designed to quantify any patterns in the compositions of fungal and bacterial communities existing in a building envelope and determine some of the key variables, such as cardinal direction, distance from floor or distance from wall joinings, that may influence any microbial community composition variation. By drilling small holes across walls of a house, we extracted microbes onto air filters and conducted amplicon sequencing. We found sampling height (distance from the floor) and cardinal direction the wall was facing caused differences in the diversity of the microbial communities, showing that patterns in the microbial composition will be dependent on sampling location within the building. By sampling beneath the surfaces, our approach provides a more complete picture of the microbial condition of a building environment, with the significant variation in community composition demonstrating a potential sampling bias if multiple sampling locations across a building are not considered. By identifying features of the built environment that promote/retard microbial growth, improvements to building designs can be made to achieve overall healthier occupied spaces.

In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias, regardless of the conditioning methods employed. In this commentary, we distinguish between two types of collider stratification bias: collider restriction bias due to restricting to one level of a collider (or a descendant of a collider) and collider adjustment bias through inclusion of a collider (or a descendant of a collider) in a regression model. We argue that categorizing collider adjustment bias as a form of selection bias may lead to semantic confusion, as adjustment for a collider in a regression model does not involve selecting a sample for analysis. Instead, we propose that collider adjustment bias can be better viewed as a type of overadjustment bias. We further provide two distinct causal diagram structures to distinguish collider restriction bias and collider adjustment bias. We hope that such a terminological distinction can facilitate easier and clearer communication.

Matching by a confounder in a case-control study nearly always produces a control-selection bias that mixes with the confounding to produce a net bias. Previous theoretical work has assumed that control for a single confounder, the matching factor, is sufficient to remove all the confounding and that the confounder-exposure, confounder-outcome and exposure-outcome associations are monotonic. Under these conditions: (a) The net bias is toward the null if the exposure affects the outcome and nil if it does not. (b) If the confounding is away from the null, the selection bias is toward the null. (c) If the confounding is toward the null, the selection bias can be in any direction or even nil. If more than one confounder needs to be controlled to remove all the confounding, the net bias from matching by one of them can be away from the null, whether the exposure affects the outcome or not. An influential heuristic, that matching controls to cases by a variable associated with exposure always brings the marginal exposure distributions of the case and control groups closer together, turns out to be faulty. The implications of matching by confounders in case-control studies are less straightforward than previously thought. Suggestions are offered for advancing the methodologic literature on this topic.

BACKGROUND: Suicide is a pressing public health problem, and firearm owners are at especially elevated risk. Certain health conditions are markers of suicide risk, but more research is needed on clinical risk markers for suicide among firearm owners specifically. Our goal was to examine associations of emergency department and inpatient hospital visits for behavioral and physical health conditions with firearm suicide among handgun purchasers.
METHODS: This was a case-control study of 5415 legal handgun purchasers in California who died between January 1, 2008, and December 31, 2013. Cases were firearm suicide decedents; controls were motor vehicle crash decedents. Exposures were emergency department and hospital visits for six categories of health diagnoses in the 3 years prior to death. To account for selection bias due to deceased controls, we used probabilistic quantitative bias analysis to generate bias-adjusted estimates.
RESULTS: There were 3862 firearm suicide decedents and 1553 motor vehicle crash decedents. In multivariable models, suicidal ideation/attempt (OR 4.92; 95% CI 3.27-7.40), mental illness (OR 1.97; 95% CI 1.60-2.43), drug use disorder (OR 1.40; 95% CI 1.05-1.88), pain (OR 1.34; 95% CI 1.07-1.69), and alcohol use disorder (OR 1.29; 95% CI 1.01-1.65) were associated with higher odds of firearm suicide. When adjusting for all conditions simultaneously, only the associations for suicidal ideation/attempt and mental illness remained significant. Quantitative bias analysis indicated that observed associations were generally biased downward. For example, the bias-adjusted OR for suicidal ideation/attempt was 8.39 (95% simulation interval 5.46-13.04), almost twice that of the observed OR.
CONCLUSIONS: Diagnoses for behavioral health conditions were markers for firearm suicide risk among handgun purchasers, even for conservative estimates that did not adjust for selection bias. Encounters with the healthcare system may provide opportunities to identify firearm owners at high risk of suicide.

Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self-reported questionnaires, hospital records, etc.) or different levels of granularity (i.e., specificity of inclusion criteria) to define cases and controls. The extent to which this variability in cohort definitions impacts the end-results of a GWAS study is unclear. In this study, we systematically evaluated the effect of the data sources used for case and control definitions on GWAS findings. Using the UK Biobank, we selected three diseases-glaucoma, migraine, and iron-deficiency anemia. For each disease, we designed 13 GWAS, each using different combinations of data sources to define cases and controls, and then calculated the pairwise genetic correlations between all GWAS for each disease. We found that the data sources used to define cases for a given disease can have a significant impact on GWAS end-results, but the extent of this depends heavily on the disease in question. This suggests the need for greater scrutiny on how case cohorts are defined for GWAS.

Real-world evidence has been increasingly used to support evaluations of emerging therapies. These investigations are often conducted in settings that may not be representative of the underlying population. The purpose of this investigation was to empirically quantify the magnitude of this selection bias. Individuals diagnosed with solid metastatic cancer in Alberta, Canada, between 2010-2019 were identified using the provincial cancer registry for 13 common metastatic sites. Two outcomes used to support oncology reimbursement decisions were examined: the proportion of individuals who initiated systemic therapy and median overall survival (OS). These outcomes were assessed in the entire population and in a subset of individuals who were referred to a medical oncologist. Among the 23,152 individuals in the entire population, 40.8% (95% CI: 40.2-41.4) initiated systemic therapy, and the median OS from diagnosis was 5.4 months (95% CI: 5.3-5.6). Among those who were referred to a medical oncologist (n = 13,372; 57.8%), 67.4% (95% CI: 66.6-68.2) initiated systemic therapy, and the median OS from diagnosis was 11.2 months (95% CI: 10.9-11.5). The magnitude of bias varied by cancer site where lower referral rates were associated with greater bias. Non-referral is an important source of selection bias in real-world investigations. Studies that rely on limited-catchment real-world data should be interpreted with caution, particularly in metastatic cancer settings.

Propensity score matching (PSM) is an increasingly applied method of ensuring comparability between groups of interest. However, PSM is often applied unconditionally, without precise considerations. The purpose of this study is to provide a nonmathematical guide for clinicians at the stage of designing a PSM-based study. We provide a seed of thought for considering whether applying PSM would be appropriate and, if so, the scope of the list of variables. Although PSM may be simple, its results could vary substantially according to how the propensity score is constructed. Misleading results can be avoided through a critical review of the process of PSM.