Misclassification Bias Research Articles

Socio-Demographic, Clinical, and Mortality Differences between HIV-Infected and HIV/HTLV-1 Co-Infected Patients in Peru.

In Peru, the estimated prevalence of human immunodeficiency virus (HIV) and human T-lymphotropic virus-1 (HTLV-1) co-infection has been reported to be as high as 18%. Despite the endemicity of HTLV-1 in Peru, few studies have assessed the impact of HIV/HTLV-1 co-infection. Our study compared socio-demographic and clinical characteristics, and mortality rates between HIV-infected and HIV/HTLV-1 co-infected patients. We reviewed the medical records of patients aged 18 years and older belonging to the HIV and HTLV-1 cohorts in Lima during a 30-year period: 1989-2019. Each HIV/HTLV-1 co-infected patient was randomly matched with two HIV-infected patients with similar characteristics (same sex, age ± 5 years, and same year of HIV diagnosis). Allegedly co-infected patients without a confirmatory diagnosis of HIV and HTLV-1 were excluded. Most of the patients in the HIV-infected group did not have a negative test result for HTLV-1 infection, so we used two probabilistic sensitivity analysis models to correct for potential HTLV-1 exposure misclassification bias in the group of HIV-infected patients. Of 162 patients enrolled, 54 were HIV/HTLV-1 co-infected and 108 were HIV-infected. The median age was 42 years (IQR = 34-51 years) and the majority were male (61.1%), single (44.4%), heterosexual (71%), born in Lima (58%), educated at the secondary school level (55.6%), and receiving antiretroviral treatment (91.4%). HIV/HTLV-1 co-infection was associated with an increased risk of death (HR: 11.8; 95% CI: 1.55-89.00; p = 0.017) while antiretroviral treatment was associated with a decreased risk of death (HR: 0.03; 95% CI: 0.003-0.25; p = 0.001). The overall mortality rate was 13.6 per 100 persons and the survival time for co-infected patients (median = 14.19 years) was significantly shorter than that of HIV-infected patients (median = 23.83 years) (p < 0.001). HIV/HTLV-1 co-infected patients had a significantly shorter survival time compared to HIV-infected patients, suggesting that the immune alterations caused by HTLV-1 in CD4 cell count may have contributed to late initiation of antiretroviral treatment and prophylaxis against opportunistic infections over the decades, and thus reducing their benefits in these patients.

Pulmonary embolism in hospitalized COVID-19 patients: Short- and long-term clinical outcomes

IntroductionPulmonary embolism (PE) is a frequent complication in COVID-19. However, the influence of PE on the prognosis of COVID-19 remains unclear as previous studies were affected by misclassification bias. Therefore, we evaluated a cohort of COVID-19 patients whom all underwent systematic screening for PE (thereby avoiding misclassification) and compared clinical outcomes between patients with and without PE. Materials and methodsWe included all COVID-19 patients who were admitted through the ED between April 2020 and February 2021. All patients underwent systematic work-up for PE in the ED using the YEARS-algorithm. The primary outcome was a composite of in-hospital mortality and ICU admission. We also evaluated long-term outcomes including PE occurrence within 90 days after discharge and one-year all-cause mortality. Results637 ED patients were included in the analysis. PE was diagnosed in 46 of them (7.2%). The occurrence of the primary outcome did not differ between patients with PE and those without (28.3% vs. 26.9%, p = 0.68). The overall rate of PE diagnosed in-hospital (after an initial negative PE screening in the ED) and in the first 90 days after discharge was 3.9% and 1.2% respectively. One-year all-cause mortality was similar between patients with and without PE (26.1% vs. 24.4%, p = 0.83). ConclusionsIn a cohort of COVID-19 patients who underwent systematic PE screening in the ED, we found no differences in mortality rate and ICU admissions between patients with and without PE. This may indicate that proactive PE screening, and thus timely diagnosis and treatment of PE, may limit further clinical deterioration and associated mortality in COVID-19 patients.

P-328 Endometrial receptivity in women with endometriosis undergoing Assisted Reproductive Technology (ART) after frozen embryo transfer: a matched pair case-control study

Abstract Study question Does endometriosis affect endometrial receptivity in frozen/thawed embryo transfer (FET) cycles? Summary answer Stage III/IV endometriosis-patients undergoing ART submitted to FET cycles have implantation, pregnancy and live-birth rates, as well as pregnancy outcomes, similar to control women. What is known already The increased infertility rate observed in women with endometriosis is supposed to be linked, among other factors, to molecular alterations already documented in the endometrial tissue of these affected women. These alterations might influence the interaction between the embryo and the endometrium, leading to a compromised implantation; however, data from the literature are controversial and do not always corroborate the hypothesis of a lower implantation rate in women with endometriosis. Study design, size, duration This is a retrospective matched case-control study including 101 women diagnosed with stage III/IV endometriosis. They were matched in a 1:1 ratio with patients undergoing frozen embryo transfers after ART treatments for other infertility-related indications at the Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico di Milano, between 2015 and 2022. Participants/materials, setting, methods Patients (n = 202) were submitted to ovarian stimulation according to standard protocols and only single embryo transfer cases and the 1st ART cycle with freeze-all protocol were included. To match, it was considered the women's age ( ± 1 year), and the number ( = ) and quality ( ± 1 top vs low) of blastocysts obtained in each cycle, considering the Istanbul consensus as the embryo standard assessment protocol. The main outcome evaluated in this study was implantation rate. Main results and the role of chance Baseline characteristics did not significantly vary between the two study groups, except for AMH levels, which was considerably lower in the endometriosis group compared to control (4.0 [2.7 - 5.4] vs 4.6 [3.7 - 6.5], respectively; p = 0.03). Considering the treatment outcomes, the number of oocytes inseminated/injected in each cycle was similar between endometriosis and control group (8 [7 - 10] vs 9 [7 - 11], respectively; p = 0.26). In line with the matching performed, the number of blastocysts generated did not differ between women with and without endometriosis (3 [2 - 4] vs 3 [2 - 4], respectively; p = 1.00), as well as the number of top-quality blastocysts (2 [1 - 3] vs 2 [1 - 3], respectively; p = 0.76). In regard to our main outcome evaluated, implantation rate in women with endometriosis and their controls were similar (36% vs 40%, respectively; p = 0.52). There was also no difference between the endometriosis group and controls considering cumulative pregnancy outcomes, such as pregnancy rate (58% vs 65%, respectively; p = 0.39), cumulative live birth rate (51% vs 59%, respectively; p = 0.26), pregnancy complications (6% vs 14%, respectively; p = 0.34), and pregnancy loss (16% vs 19%, respectively; p = 0.70). Limitations, reasons for caution As it is a retrospective study, our reports might be subjected to misclassification bias. Also, some of the women in the control group may have asymptomatic undiagnosed endometriosis; however, considering the incidence of this disease, it should not compromise our evaluation. Wider implications of the findings Our present findings give support to the idea that endometriosis does not influence the implantation rate in women with endometriosis undergoing ART submitted to FET. Considering fertility, this finding raises doubts about the clinical relevance of the molecular aberrations detected in the endometrium of women affected by endometriosis. Trial registration number not applicable

Preconception Periodontitis and Risk of Spontaneous Abortion in a Prospective Cohort Study.

Few studies have evaluated the association between periodontitis and spontaneous abortion (SAB), and all had limitations. We used data from the Pregnancy Study Online (PRESTO), a prospective preconception cohort study of 3,444 pregnancy planners in the United States and Canada (2019-2022), to address this question. Participants provided self-reported data on periodontitis diagnosis, treatment, and symptoms of severity (i.e., loose teeth) via the enrollment questionnaire. SAB (pregnancy loss at <20 weeks' gestation) was assessed via bimonthly follow-up questionnaires. Participants contributed person-time from the date of a positive pregnancy test to the gestational week of SAB, loss to follow-up, or 20 weeks' gestation, whichever came first. We fitted Cox regression models with weeks of gestation as the time scale to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs), and we used inverse probability of treatment weighting to account for differential loss to follow-up. We used probabilistic quantitative bias analysis to estimate the magnitude and direction of the effect of exposure misclassification bias on results. In weighted multivariable models, we saw no appreciable association between preconception periodontitis diagnosis (HR=0.97, 95% CI: 0.76, 1.23) or treatment (HR=1.01, 95% CI: 0.79, 1.27) and SAB. A history of loose teeth was positively associated with SAB (HR=1.38, 95% CI: 0.88, 2.14). Quantitative bias analysis indicated that our findings were biased towards the null but with considerable uncertainty in the bias-adjusted results.

Feasibility and acceptability of social determinants of health data collection in the context of a Children’s Oncology Group trial.

10010 Background: Children who are Black, Hispanic or of proxied low socioeconomic status experience inferior survival outcomes despite receipt of uniform treatment on Children’s Oncology Group (COG) trials. Investigation of mechanisms underlying these disparities and evaluation of interventions to address them has been limited by inadequate data. Prior COG trials have collected child race, ethnicity, insurance and zip code—non-modifiable data elements that are prone to misclassification bias—as proxies for exposure to adverse social determinants of health (SDOH). Collection of parent-reported SDOH data provides key information not previously assessed in COG trials, including modifiable exposures such as household material hardship (HMH): food, housing, utility or transportation insecurity. We report the preliminary feasibility and acceptability of the first COG trial to prospectively embed SDOH data collection. Methods: ANBL1531 (NCT03126916), the current frontline, groupwide COG trial for patients with high-risk neuroblastoma, was amended in September 2019 to include an opt-in, correlative aim to evaluate parent-reported SDOH. Parents of patients &lt; 18 years could opt-in during initial ANBL1531 consent to complete a brief, single-timepoint survey at their enrolling site during Induction Cycle 1. Survey domains included parent-reported demographics, HMH, household income, and social support. Paper/pencil surveys available in English, Spanish or French were self-completed or read-aloud by site personnel and could be administered in any language with an interpreter. Surveys were submitted to a central disparities study team for data cleaning and entry into an electronic database. Results: As of December 31, 2022, 360/413 eligible participants opted-in to the embedded SDOH aim across 101 COG sites (87.2% consent rate). Among participants, 316 surveys (87.8%) were completed a median of 11 days post-enrollment. Four (1.1%) parents declined survey completion (3 withdrew consent). Reasons for non-completed surveys included: survey still pending within protocol-specified window (n = 10), institutional COVID-19 restrictions (n = 6), site scheduling issues (n = 6), patient came off ANBL1531 protocol (n = 5) and unknown (n = 13). Data missingness for HMH, the primary SDOH of interest, was 0.8%. Surveys were administered in English, Spanish, French, Vietnamese, Arabic, Mandarin and Tamil. Conclusions: Collection of parent-reported SDOH data as an optional, embedded component of a groupwide COG trial is feasible and acceptable, as demonstrated by remarkable willingness to participate and infrequency of missing data. Incorporation of this approach in future National Clinical Trials Network trials is essential to inform impactful health equity research, including the development and evaluation of targeted interventions to address outcome disparities.

Misclassification Bias in the Assessment of Gene-by-Environment Interactions.

Misclassification bias is a common concern in epidemiologic studies. Despite strong bias on main effects, gene-environment interactions have been shown to be biased towards the null under gene-environment independence. In the context of a recent article examining the interaction between nerve agent exposure and paraoxonase-1 gene on Gulf War Illness, we aimed to assess the impact of recall bias-a common misclassfication bias-on the identification of gene-environment interactions when the independence assumption is violated. We derive equations to quantify the bias of the interaction, and numerically illustrate these results by simulating a case-control study of 1000 cases and 1000 controls. Simulation input parameters included exposure prevalence, strength of gene-environment dependence, strength of the main effect, exposure specificity among cases, and strength of the gene-environment interaction. We show that, even if gene-environment independence is violated, we can bound possible gene-environment interactions by knowing the strength and direction of the gene-environment dependence ( ) and the observed gene-environment interaction ( )-thus often still allowing for the identification of such interactions. Depending on whether is larger or smaller than the inverse of , is a lower (if ) or upper (if ) bound for the true interaction. In addition, the bias magnitude is somewhat predictable by examining other characteristics such as exposure prevalence, the strength of the exposure main effect, and directions of the recall bias and gene-environment dependence. Even if gene-environment dependence exists, we may still be able to identify gene-environment interactions even when misclassification bias is present.

Multi-Cause Calibration of Verbal Autopsy-Based Cause-Specific Mortality Estimates of Children and Neonates in Mozambique.

The Countrywide Mortality Surveillance for Action platform is collecting verbal autopsy (VA) records from a nationally representative sample in Mozambique. These records are used to estimate the national and subnational cause-specific mortality fractions (CSMFs) for children (1-59 months) and neonates (1-28 days). Cross-tabulation of VA-based cause-of-death (COD) determination against that from the minimally invasive tissue sampling (MITS) from the Child Health and Mortality Prevention project revealed important misclassification errors for all the VA algorithms, which if not accounted for will lead to bias in the estimates of CSMF from VA. A recently proposed Bayesian VA-calibration method is used that accounts for this misclassification bias and produces calibrated estimates of CSMF. Both the VA-COD and the MITS-COD can be multi-cause (i.e., suggest more than one probable COD for some of the records). To fully use this probabilistic COD data, we use the multi-cause VA calibration. Two different computer-coded VA algorithms are considered-InSilicoVA and EAVA-and the final CSMF estimates are obtained using an ensemble calibration that uses data from both the algorithms. The calibrated estimates consistently offer a better fit to the data and reveal important changes in the CSMF for both children and neonates in Mozambique after accounting for VA misclassification bias.

Misclassification Error, Binary Regression Bias, and Reliability in Multidimensional Poverty Measurement: An Estimation Approach Based on Bayesian Modelling

ABSTRACT Measurement error affects the quality of population orderings of an index and, hence, increases the misclassification of the poor and the non-poor groups and affects statistical inferences from binary regression models. Hence, the conclusions about the extent, profile, and distribution of poverty are likely to be misleading. However, the size and type (false positive/negatives) of classification error have remained untraceable in poverty research. This paper draws upon previous theoretical literature to develop a Bayesian-based estimator of population misclassification and binary-regression coefficient bias. The study uses the reliability values of existing poverty indices to set up a Monte Carlo study based on factor mixture models to illustrate the connections between measurement error, misclassification, and bias and evaluate the procedure and discusses its importance for real-world applications.

What could explain the high prevalence of progressive fibrosing interstitial lung disease in a patient registry?

<b>It is important to standardise diagnostic protocols and data collection processes across centres participating in registries, to avoid or reduce the risk of diagnostic misclassification bias and other related types of biases in registry-based studies</b>http://bit.ly/3D7lvIA

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

Non-Parametric MCMC Gibbs Sampler Approach and Misclassification Assessment of Estimating Haplotype Frequencies among Related Statistical Approaches

Introduction: Haplotype analysis allows higher resolution analysis in genetic association studies and is used as a reference panel for genotype imputation in genome-wide association studies. Haplotypes estimates from genotypes among unrelated individuals but misclassification of the haplotype reconstruction will directly affect the accuracy of the results.&#x0D; Methods: This study proposes a novel statistical method Gibbs sampler algorithm to estimate haplotype frequency and quantify the influence of misclassification bias of the estimate haplotype. The performance of the algorithm is evaluated on simulated datasets assuming that linkage phase unknown. The simulation used different minor allele frequencies at each single nucleotide polymorphism (SNP) and different linkagedisequilibrium between the SNPs.&#x0D; Results: The Gibbs sampler algorithm presents higher accuracy among over seven SNPs or less, validated, and deals with missing genotype compared to previous related statistical approaches. Misclassification of estimated haplotypes leads to non-differential bias in exposure and affects haplotype estimates in haplotype analysis. The observed odds ratio underestimates the association between haplotype and phenotype by 36% to 99%.&#x0D; Conclusion: The Gibbs sampler algorithm provides higher accuracy and robust effectiveness performance, handles missing genotypes and provides uncertain probabilities of haplotype frequencies. The misclassification bias of the estimate haplotype underestimates the genetic association by more than forty percent.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability.

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Sociodemographic characteristics differ across routine adult vaccine cohorts: An All of Us descriptive study.

The National Institutes of Health All of Us (AoU) Research Program is currently building a database of 1million+ adult subjects. With it, we describe the characteristics of those with documented vaccinations. To describe the sociodemographic, health status, and lifestyle factors associated with vaccinations. This is a retrospective study involving data from the AoU program (R2020Q4R2, N= 315,297). Five vaccine cohorts [influenza, hepatitis B (HBV), pneumococcal <65 years old, pneumococcal ≥65 years old, and human papillomavirus (HPV)] were generated based on vaccination history. The influenza cohort comprised participants with documented influenza vaccinations in electronic health records (EHRs) from September 2017 to May 2018. Other vaccine cohorts comprised participants with ≥1 lifetime record(s) of vaccination documented in the EHR by December 2018. The vaccine cohorts were compared to the overall AoU cohort. Descriptive statistics were generated using EHR- and survey-based sociodemographic, health, and lifestyle information. The SAMBA (0.9.0) R package was utilized to adjust for EHR selection and outcome misclassification biases to infer sources of disparity for pneumococcal vaccinations in older adults. Cohort counts were as follows: influenza (n= 15,346), HBV (n= 6323), pneumococcal <65 (n= 15,217), pneumococcal ≥65 (n= 15,100), and HPV (n= 2125). All vaccine cohorts had higher proportions of White and non-Hispanic/Latino participants compared to the overall AoU cohort. The largest differences were found in pneumococcal age ≥65, with 80.2% White participants compared to 52.9% in the overall study population. Multivariable analysis revealed that race/ethnic disparities in pneumococcal vaccination among older adults were explained by biological sex, income, health insurance, and education-related variables. Racial, ethnic, education, and income characteristics differ across the vaccine cohorts among AoU participants. These findings inform future utilization of large health databases in vaccine epidemiology research and emphasize the need for more targeted interventions that address differences in vaccine uptake.

Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19

Safe and effective vaccines are crucial for the control of Covid-19 and to protect individuals at higher risk of severe disease. The test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines. However, the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for diagnosing the SARS-Cov-2 infection. We propose a simple Bayesian modeling approach for estimating vaccine effectiveness that is robust even when the diagnostic test is imperfect. We use simulation studies to demonstrate the robustness of our method to misclassification bias and illustrate the utility of our approach using real-world examples.

Validation to correct for outcome misclassification bias.

Validation to correct for outcome misclassification bias.

Association between folate and non-alcoholic fatty liver disease among US adults: a nationwide cross-sectional analysis.

Association between folate and non-alcoholic fatty liver disease among US adults: a nationwide cross-sectional analysis.

Cigarette smoking and prostate cancer: A systematic review and meta-analysis of prospective cohort studies.

Cigarette smoking is a well-known cancer-causing behavior and a leading cause of death from cancer. However, according to previously published research and meta-analyses, cigarette smoking has a significant inverse association with prostate cancer incidence. Therefore, this study aims to investigate this association based on updated evidence by conducting a systematic review and meta-analysis. A search for relevant articles was performed in PubMed and Scopus databases to obtain the pooled relative risk (RR) and the corresponding 95% confidence intervals (CIs) for the risk of prostate cancer incidence among smokers compared to non-smokers. Our search was limited to prospective cohort studies. A total of 17 cohort studies were included in the systematic review. Fifteen studies were included in the meta-analysis and showed that cigarette smoking has an inverse association with prostate cancer incidence with a relative risk of 0.84 (95% CI: 0.78-0.91). From all cohorts included in this systematic review, five studies examined the association between current smokers and the risk of death from prostate cancer. Therefore, a meta-analysis of these cohort studies was performed and showed that current smokers had a 42% higher risk of death from prostate cancer when compared to non-smokers with a relative risk of 1.42 (95% CI: 1.20-1.68). Data from observational studies suggest that cigarette smoking has an inverse association with prostate cancer incidence. However, smokers have an increased risk of death from prostate cancer. Important to realize that this lower risk for smokers might be attributed to low prostate cancer screening uptake among smokers, misclassification bias, or selection bias from the included original studies. In summary, our results indicate that the incidence of prostate cancer is lower among smokers. Nevertheless, smokers who develop the disease have a significantly worse prognosis.

Accuracy of International Classification of Diseases Codes for Identifying Acute Optic Neuritis.

The accuracy of International Classification of Diseases (ICD) codes for identifying cases of acute optic neuritis (aON) is not known. A prior study reported 61% accuracy for ICD code plus MRI consistent with aON within 2 months. This study determined accuracy for ICD code plus MRI within 2 months regardless of results. Retrospective chart review was conducted using a medical record research repository of a tertiary care institution from 1998 to 2019. Subjects with ICD-9/10 codes for ON and an MRI brain and/or orbits within 2 months of earliest (initial) ICD code were included. MRI was classified as positive or negative for aON based on report noting gadolinium-contrast enhancement. Clinical diagnosis at the time of initial code was classified as aON, prior ON, considered ON, alternative diagnosis, or unknown based on review of physician authored clinical notes within 7 days of the initial code. Accuracy of ICD code for aON, acute or prior ON, and acute, prior, or considered ON were calculated for all subjects and stratified based on MRI result. Two hundred fifty-one subjects had MRI results within 2 months of their initial ON ICD code (49 positive MRI [previously reported]; 202 negative MRI). Among those with negative MRI, 32 (16%) had aON, 40 (20%) had prior ON, 19 (9%) considered ON as a diagnosis, 92 (46%) had other confirmed diagnoses, and 19 (9%) had unknown diagnosis at time of code. Considering all subjects, accuracy for ICD code was 25% for acute ON, 41% for acute or prior ON, and 48% for acute, prior, or considered ON. Positive MRI, increased number of ON ICD codes, a code given by an ophthalmologist or neurologist within 2 months, and the presence of a neurology encounter within 2 months were associated with an increased accuracy for clinical aON diagnosis. In the setting of an MRI within 2 months, ICD codes for ON have low accuracy for acute ON and only slightly better accuracy for acute or prior ON. Accuracy is higher for cases with a positive MRI than those with a negative MRI, suggesting positive MRI in conjunction with ICD codes may help more accurately identify cases. Reliance on ICD and Current Procedural Terminology codes alone to identify aON cases may introduce substantial misclassification bias in claims-based research.

A propensity score matched cohort study identifying an association of acne, but not oral antibiotic or isotretinoin use, with risk of incident inflammatory bowel disease

Concerns remain regarding whether oral antibiotic or isotretinoin use for acne is associated with increased risk of inflammatory bowel disease (IBD); little is known about whether acne itself is associated with IBD. To determine whether isotretinoin exposure, oral tetracycline-class antibiotic exposure, and/or acne itself are associated with IBD. A propensity score matched cohort study was performed using TriNetX between 2001 and 2022 to compare the 1-year incidence of IBD between those without acne compared to those with acne managed without systemic medications, acne managed with oral tetracycline-class antibiotics, and acne managed with isotretinoin. There was a statistically significant association between acne and risk of incident IBD (odds ratio: 1.42; 95% confidence interval: 1.23-1.65). There was no statistically significant association between oral tetracycline-class antibiotic or isotretinoin exposure and IBD. Use of electronic health data; potential for misclassification bias. This matched cohort study identifies an association between acne and IBD. These data provide further reassurance regarding the use of isotretinoin in the treatment of acne.

Quantitative bias analysis of prevalence under misclassification: evaluation indicators, calculation method and case analysis.

Prevalence estimates are fundamental to epidemiological studies. Although they are highly vulnerable to misclassification bias, the risk of bias assessment of prevalence estimates is often neglected. Quantitative bias analysis (QBA) can effectively estimate misclassification bias in epidemiological studies; however, relatively few applications are identified. One reason for its low usage is the lack of knowledge and tools for these methods among researchers. To expand existing evaluation methods, based on the QBA principles, three indicators are proposed. One is the relative bias that quantifies the bias direction through its signs and the bias magnitude through its quantity. The second is the critical point of positive test proportion in case of a misclassification bias that is equal to zero. The third is the bound of positive test proportion equal to adjusted prevalence at misclassification bias level α. These indicators express the magnitude, direction and uncertainty of the misclassification bias of prevalence estimates, respectively. Using these indicators, it was found that slight oscillations of the positive test proportion within a certain range can lead to substantial increases in the misclassification bias. Hence, researchers should account for misclassification error analytically when interpreting the significance of adjusted prevalence for epidemiological decision making. This highlights the importance of applying QBA to these analyses. In this article, we have used three real-world cases to illustrate the characteristics and calculation methods of presented indicators. To facilitate application, an Excel-based calculation tool is provided.