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As with other fields, medical sciences are subject to different sources of bias. While understanding sources of bias is a key element for drawing valid conclusions, bias in health research continues to be a very sensitive issue that can affect the focus and outcome of investigations. Information bias, otherwise known as misclassification, is one of the most common sources of bias that affects the validity of health research. It originates from the approach that is utilized to obtain or confirm study measurements. This paper seeks to raise awareness of information bias in observational and experimental research study designs as well as to enrich discussions concerning bias problems. Specifying the types of bias can be essential to limit its effects and, the use of adjustment methods might serve to improve clinical evaluation and health care practice.

Background Failing to integrate all sources of a ubiquitous hazard candidate may explain inconsistent and/or null, and overall misleading, results in epidemiological studies such as those related to shift-work. Methods We explore this rationale on the assumption that Doll and Hill had confined their 1950 landmark study to smoking at workplaces alone. We assess how non-differential, or how differential, underestimation of exposure could have biased computed risks. Results Systematically unappreciated exposures at play could have led to substantial information bias. Beyond affecting the magnitude of risks, not even the direction of risk distortion would have been predictable. Conclusions Disturbed chronobiology research should consider cumulative doses from all walks of life. This is a conditio sine qua non to avoid potentially biased and uninterpretable risk estimates when assessing effects of a ubiquitous hazard candidate.

Quantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of QBA methods for summary-level data published in the peer-reviewed literature. We searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was preregistered on the Open Science Framework (https://osf.io/ue6vm/). Our search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary-level data. Of the 57 QBA methods, 53 (93%) were explicitly designed for observational studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 19 (33%) misclassification bias, 6 (11%) selection bias, and 3 (5%) multiple biases. Thirty-eight (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. Twenty-two (39%) articles provided code or online tools to implement the QBA methods. In this systematic review, we identified a total of 57 QBA methods for summary-level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary-level epidemiologic data. [Display omitted]

The concept of bias is the lack of internal validity or incorrect assessment of the association between an exposure and an effect in the target population in which the statistic estimated has an expectation that does not equal the true value. Biases can be classified by the research stage in which they occur or by the direction of change in a estimate. The most important biases are those produced in the definition and selection of the study population, data collection, and the association between different determinants of an effect in the population. A definition of the most common biases occurring in these stages is given.

Because of a range of constraints, the availability of biodiversity-related information varies considerably over space, time, taxa, and types of data, thereby causing gaps in knowledge. Despite growing awareness of this issue among scientists, it is still poorly known how—and whether—scientific efforts have contributed to overcoming these information gaps. Focusing on spatial gaps in global biodiversity data, we show that the accumulation rates of nonbird species occurrence records stored in the Global Biodiversity Information Facility have not improved—and have even potentially declined—over the past three decades in data-poor, often biodiversity-rich regions. Meanwhile, one citizen-science project, eBird, has been making a considerable contribution to the collection and sharing of bird records, even in the data-poorest countries, and is accelerating the accumulation of bird records globally. We discuss the potentials and limitations of citizen-science projects for tackling gaps in biodiversity information, particularly from the perspective of biodiversity conservation.

The internal validity of an epidemiological study can be affected by random error and systematic error. Random error reflects a problem of precision in assessing a given exposure-disease relationship and can be reduced by increasing the sample size. On the other hand, systematic error or bias reflects a problem of validity of the study and arises because of any error resulting from methods used by the investigator when recruiting individuals for the study, from factors affecting the study participation (selection bias) or from systematic distortions when collecting information about exposures and outcomes (information bias). Another important factor which may affect the internal validity of a clinical study is confounding. In this article, we focus on two categories of bias: selection bias and information bias. Confounding will be described in a future article of this series. Copyright © 2010 S. Karger AG, Basel [PUBLICATION ABSTRACT]

OBJECTIVE:Diagnostic codes used in administrative database research can create bias due to misclassification. Quantitative bias analysis (QBA) can correct for this bias, requires only code sensitivity and specificity, but may return invalid results. Bootstrap imputation (BI) can also address misclassification bias but traditionally requires multivariate models to accurately estimate disease probability. This study compared misclassification bias correction using QBA and BI. STUDY DESIGN:Serum creatinine measures were used to determine severe renal failure status in 100,000 hospitalized patients. Prevalence of severe renal failure in 86 patient strata and its association with 43 covariates was determined and compared with results in which renal failure status was determined using diagnostic codes (sensitivity 71.3%, specificity 96.2%). Differences in results (misclassification bias) were then corrected with QBA or BI (using progressively more complex methods to estimate disease probability). RESULTS:In total, 7.4% of patients had severe renal failure. Imputing disease status with diagnostic codes exaggerated prevalence estimates [median relative change (range), 16.6% (0.8%–74.5%)] and its association with covariates [median (range) exponentiated absolute parameter estimate difference, 1.16 (1.01–2.04)]. QBA produced invalid results 9.3% of the time and increased bias in estimates of both disease prevalence and covariate associations. BI decreased misclassification bias with increasingly accurate disease probability estimates. CONCLUSIONS:QBA can produce invalid results and increase misclassification bias. BI avoids invalid results and can importantly decrease misclassification bias when accurate disease probability estimates are used.

Registration in the Dutch national COVID-19 vaccination register requires consent from the vaccinee. This causes misclassification of nonconsenting vaccinated persons as being unvaccinated. We quantified and corrected the resulting information bias in vaccine effectiveness (VE) estimates. National data were used for the period dominated by the SARS-CoV-2 Delta variant (July 11 to November 15, 2021). VE ((1-relative risk)∗100%) against COVID-19 hospitalization and intensive care unit (ICU) admission was estimated for individuals 12 to 49, 50 to 69, and ≥70 years of age using negative binomial regression. Anonymous data on vaccinations administered by the Municipal Health Services were used to determine informed consent percentages and estimate corrected VEs by iteratively imputing corrected vaccination status. Absolute bias was calculated as the absolute change in VE; relative bias as uncorrected/corrected relative risk. A total of 8804 COVID-19 hospitalizations and 1692 COVID-19 ICU admissions were observed. The bias was largest in the 70+ age group where the nonconsent proportion was 7.0% and observed vaccination coverage was 87%: VE of primary vaccination against hospitalization changed from 75.5% (95% CI 73.5–77.4) before to 85.9% (95% CI 84.7–87.1) after correction (absolute bias −10.4 percentage point, relative bias 1.74). VE against ICU admission in this group was 88.7% (95% CI 86.2–90.8) before and 93.7% (95% CI 92.2–94.9) after correction (absolute bias −5.0 percentage point, relative bias 1.79). VE estimates can be substantially biased with modest nonconsent percentages for vaccination data registration. Data on covariate-specific nonconsent percentages should be available to correct this bias. [Display omitted]

Algal blooms produced by cyanobacteria liberate microcystins and other toxins that create a public health hazard. During the 2018 bloom of Microcystis aeruginosa in Florida, USA, residential and recreational exposures were associated with an increased risk of self-reporting respiratory, gastrointestinal, or ocular symptoms for 125 participants. Subsequently, 207 persons were interviewed between 2019 and 2024 in the absence of large-scale algal blooms and were considered non-exposed. Analyses of cyanotoxins and brevetoxins in water and air showed only intermittent, background levels of toxins during the non-bloom period. The purpose of this report was to compare symptom reporting between active bloom and non-bloom periods. The assessment of information bias from self-reported symptoms is an important issue in epidemiologic studies of harmful algal blooms. During the non-bloom period, no statistically significant associations with residential, recreational, or occupational exposures were found for any symptom group. Estimated risks for respiratory, gastrointestinal, and ocular symptoms, headache, and skin rash were significantly higher for persons sampled during the bloom than the non-bloom period with odds ratios (ORs) of 2.3 to 8.3. ORs for specific respiratory symptoms were also significantly elevated. After adjustment for confounders and multiple exposures in multivariable analyses, the differences in symptom reporting between bloom and non-bloom periods remained statistically significant. In summary, the use of self-reported symptoms in this epidemiologic study of exposure to a cyanobacterial algal bloom did not appear to introduce substantial information bias.

This paper investigates three controversies involving potential causes and consequences of information bias in case and death definitions during the coronavirus disease (COVID-19) pandemic. First, evidence suggests China’s surveillance data were biased and misinterpreted by the World Health Organization (WHO), prompting the WHO to advise nations to copy China’s lockdowns. China appeared to use narrow diagnostic definitions that undercounted cases and deaths. Second, novel genomic data disseminated during the pandemic without adequate guidance from rigorous epidemiologic studies biased infection control policies in many countries. A novel genomic sequence of a virus is insufficient to declare new cases of a novel disease. Third, media reports of COVID-19 surveillance data in many nations appeared to be biased. Broadened surveillance definitions captured additional information, but unadjusted surveillance data disseminated to the public are not true cases and deaths. Recommendations include clarification of the proper use of diagnostic and surveillance case and death definitions to avoid information bias.