Category: Statistics

Scand J Work Environ Health. 2025 Apr 24:4226. doi: 10.5271/sjweh.4226. Online ahead of print.

ABSTRACT

We were delighted by the publication in your journal of the results of a validation study on self-reported night shift work by Vestergaard et al (1). Such exquisite validation studies that compare self-report to employment records are rare and sorely needed if we are to draw appropriate inferences from epidemiologic studies, both in characterization the degree of risk and – as recently argued by IARC – hazard identification (2). However, we have strong reasons to believe that the validation data which Vestergaard et al obtained could (and should) have been better used to “correct” odds ratios (OR) for differential exposure misclassifications. [NB: Our use of the Excel spreadsheet of Lash et al (3) cited in Vestergaard et al (1) leads to the same “corrected” point estimate but a different, wider, 95% confidence interval (CI) 0.88-1.27. The corrected 95% CI reported in table 3 of (1) is obtained if we use rounded-up counts after adjustment. This is incorrect because expected counts “do not have to be integers”, as stated for the Excel spreadsheet that Vestergaard et al used. This illustrates the importance of the use of tools as intended and the unexpected impact on their results of apparently small changes to the input values for their calculations.] First, we must note that quantitative bias analysis does not correct for exposure misclassification in general. In the case of using fixed values of sensitivities and specificities, it provides a corrected estimate only under the assumption that misclassification probabilities are known with absolute certainty. However, it is obvious from table 2 of Vestergaard et al (1) that misclassification probabilities are estimated with uncertainty. When there is uncertainty about sensitivities and specificities, the textbook they quote recommends (urges!) that probabilistic bias analysis should be carried out to account simultaneously for uncertainty in misclassification probabilities and random sampling errors (3). When this is done, probabilistic bias analysis does not guarantee the correction or adjustment for misclassification of exposure, but it merely produces a collection of alternative estimates via a Monte-Carlo simulation. An alternative adjustment approach for this case of uncertain exposure probabilities, which does involve theoretical assurance of correcting the OR for misclassification of exposure, is a Bayesian methodology (4, 5). Probabilistic bias analysis and Bayesian methods are not guaranteed to produce identical numerical results, and only Bayesian methods produce results that can be interpreted as distributions of true values given data, model and priors (6). Second, it is known to be risky to adjust for exposure misclassification using fixed values of sensitivities and specificities if these are not known exactly (4). Small deviations from true misclassification probabilities can have a dramatic impact on the resulting adjustment. Thus, the corrected OR in Vestergaard et al (1) of 1.05 (95% CI 0.95-1.16) is just one of many such adjusted estimates that is consistent with the presented validation data as we show below. Bayesian methods yet again come to the rescue here because they are designed to account for uncertainty in misclassification parameters by using prior probability distributions. Third, we are puzzled by Vestergaard et al`s choice of using the bootstrap to estimate distributions of sensitivities and specificities when there is a far simpler accepted approach to expressing uncertainty about proportions in quantitative bias analyses (Bayesian or probabilistic). When the validation study estimates a proportion k/N, the uncertainty about the true value of the proportion is typically expressed by using a Beta distribution, defined on [0,1] and is a conjugate prior of the Bernoulli distribution. For an observed proportion k/N, given that before performing the validation study we were completely ignorant about the value of the proportion, the Beta(α,β) distribution that captures this information has shape parameters α=k+1 and β=N-k+1, eg, see (7). We calculated these shape parameters for the misclassification probabilities from table 2 of Vestergaard et al (1) (this is partially reproduced in table 1) and presented them in our table 2, which also shows the corresponding means and variances. Fourth, we observe that the Bayesian adjustment for differential exposure misclassification yields what may be considered as qualitatively different results compared to Vestergaard et al`s adjustment of using fixed values. We followed the implementation from Singer et al (8). The Bayesian approach imposed no correlation between the misclassification parameters. We used a vague prior on the OR, null centered with 95% CI 0.02-50, as recommended for a sparse data problem (9). We also specified a uniform prior (0-1) on the exposure prevalence among controls. The Bayesian model converged and none of its diagnostics appear anomalous; implementation details that center around R (10) packages rjags (11) can be found in the supplementary material (www.sjweh.fi/article/4226) appendix A. Summaries of the posterior distributions are presented in table 3. The posterior OR adjusted for recall bias had a mean of 0.98, median of 0.97 and a credible interval of 0.30-1.71. As an added benefit, we have learned about the distributions of misclassification parameters and true prevalences, which can be used further if one is to update the study in question or use similar exposure assessment tools in a setting where similar exposure misclassification is suspected. Lastly, we carried out our probabilistic bias analysis using the same Beta distributions as in table 2, assuming that the correlation of sensitivities and specificities is weak (ie, 0.1). Details of the implementation of probabilistic bias analysis using the R package episensor (12) are available in supplementary appendix B. The resulting simulated OR had a median of 1.00 and a 95% simulation interval of 0.48-1.31. Thus, Vestergaard et al (1) is an example of a study where using fixed values of misclassification probabilities leads to a rather different estimate of 1.05 (and corresponding 95% CI 0.95-1.16) compared to both probabilistic bias analysis and Bayesian adjustment method that use the same validation data. Distributions of OR obtained after probabilistic and Bayesian adjustments are illustrated in figure 1, which shows that the Bayesian method (in red) favors lower true values of the OR compared to the probabilistic one (in gray). When faced with numerically different results of adjustment for exposure misclassification, we advise our colleagues to rely on the results that arise from the more theoretically justified methodology. In the case of adjustment from Vestergaard et al (1), we think that the Bayesian results are more defensible, yielding an adjusted OR centered around 1.0 (95% credible interval 0.3-1.7). This result appears to us to be a rather more convincing estimate for the association of breast cancer with report of ever having worked night shifts than Vestergaard et al`s “corrected” estimate. We urge epidemiologists who collect precious validation data to collaborate with statisticians who can help them fully utilize it, arriving at more defensible effect estimates and, ultimately, better risk assessments. References 1. Vestergaard JM, Haug JN, Dalbøge A, Bonde JP, Garde AH, Hansen J et al. Validity of self-reported night shift work among women with and without breast cancer. Scand J Work Environ Health 2024 Apr;50(3):152-7. https://doi.org/10.5271/sjweh.4142. 2. IARC. Statistical Methods in Cancer Research Volume V: Bias Assessment in Case-Control and Cohort Studies for Hazard Identification. IARC Scientific Publication No. 171. 1 ed. Lyon, France: International Agency for Research on Cancer; 2024. 3. Lash TL, Fox MP, Fink AK. Applying Quantitative Bias Analysis to Epidemiologic Data: Springer; 2021. 4. Gustafson P, Le ND, Saskin R. Case-control analysis with partial knowledge of exposure misclassification probabilities. Biometrics 2001 Jun;57(2):598-609. https://doi.org/10.1111/j.0006-341X.2001.00598.x. 5. Gustafson P. Measurement Error and Misclassification in Statistics and Epidemiology: Chapman & Hall/CRC Press; 2004. 6. MacLehose RF, Gustafson P. Is probabilistic bias analysis approximately Bayesian? Epidemiology 2012 Jan;23(1):151-8. https://doi.org/10.1097/EDE.0b013e31823b539c. 7. Luta G, Ford MB, Bondy M, Shields PG, Stamey JD. Bayesian sensitivity analysis methods to evaluate bias due to misclassification and missing data using informative priors and external validation data. Cancer Epidemiol 2013 Apr;37(2):121-6. https://doi.org/10.1016/j.canep.2012.11.006. 8. Singer AB, Daniele Fallin M, Burstyn I. Bayesian Correction for Exposure Misclassification and Evolution of Evidence in Two Studies of the Association Between Maternal Occupational Exposure to Asthmagens and Risk of Autism Spectrum Disorder. Curr Environ Health Rep 2018 Sep;5(3):338-50. https://doi.org/10.1007/s40572-018-0205-0. 9. Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ 2016 Apr;352:i1981. https://doi.org/10.1136/bmj.i1981. 10. Team RD. A language and environment for statistical computing. ISBN 3-900051-07-0. Vienna, Austria: R Foundation for Statistical Computing; 2006. 11. Plummer M. rjags: Bayesian Graphical Models using MCMC. R package version 4-16 ed2024. 12. Haine D. The episensr package: basic sensitivity analysis of epidemiological results. R package version 1.3.0. 2023 Available from: https://dhaine.github.io/episensr/.

PMID:40273363 | DOI:10.5271/sjweh.4226

Gigascience. 2025 Jan 6;14:giaf028. doi: 10.1093/gigascience/giaf028.

ABSTRACT

We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

PMID:40272882 | DOI:10.1093/gigascience/giaf028

Biostatistics. 2024 Dec 31;26(1):kxaf002. doi: 10.1093/biostatistics/kxaf002.

ABSTRACT

Assessing how brain functional connectivity networks vary across individuals promises to uncover important scientific questions such as patterns of healthy brain aging through the lifespan or dysconnectivity associated with disease. In this article, we introduce a general regression framework, Connectivity Regression (ConnReg), for regressing subject-specific functional connectivity networks on covariates while accounting for within-network inter-edge dependence. ConnReg utilizes a multivariate generalization of Fisher’s transformation to project network objects into an alternative space where Gaussian assumptions are justified and positive semidefinite constraints are automatically satisfied. Penalized multivariate regression is fit in the transformed space to simultaneously induce sparsity in regression coefficients and in covariance elements, which capture within network inter-edge dependence. We use permutation tests to perform multiplicity-adjusted inference to identify covariates associated with connectivity, and stability selection scores to identify network edges that vary with selected covariates. Simulation studies validate the inferential properties of our proposed method and demonstrate how estimating and accounting for within-network inter-edge dependence leads to more efficient estimation, more powerful inference, and more accurate selection of covariate-dependent network edges. We apply ConnReg to the Human Connectome Project Young Adult study, revealing insights into how connectivity varies with language processing covariates and structural brain features.

PMID:40272849 | DOI:10.1093/biostatistics/kxaf002

Cutan Ocul Toxicol. 2025 Apr 24:1-6. doi: 10.1080/15569527.2025.2496640. Online ahead of print.

ABSTRACT

PURPOSE: Hyperbaric oxygen therapy (HBOT) is a treatment modality commonly used for various medical conditions, such as diabetic foot ulcers and sudden hearing loss. This study aims to evaluate HBOT’s effects on central corneal thickness (CCT) and other corneal topographic parameters through comprehensive ophthalmic assessment.

MATERIALS AND METHODS: Detailed ophthalmologic examinations and corneal topography measurements were performed on 92 patients with various non-ophthalmologic diseases, both before and immediately after undergoing HBOT. Corneal topography was measured before and after the therapy. The recorded parameters included central corneal thickness, anterior chamber depth, anterior chamber volume, and corneal volume. The patients were also categorised into two groups: diabetic (n = 22) and non-diabetic (n = 70).

RESULTS: Following treatment, statistically significant reductions were observed in CCT (529.69 ± 31.7 μm vs. 526.63 ± 33 μm, p = 0.002) and corneal volume (58.63 ± 3.71 mm³ vs. 58.21 ± 3.58 mm³, p = 0.016). Conversely, anterior chamber volume significantly increased (124.38 ± 30 mm³ vs. 126.42 ± 30.7 mm³, p = 0.003). Comparative analysis between diabetic and non-diabetic groups revealed no substantial differences in CCT and corneal volume changes following HBOT. However, the diabetic group exhibited significantly lower baseline anterior chamber volume before treatment (p = 0.01 and p = 0.042).

CONCLUSIONS: HBOT administration resulted in measurable reductions in CCT and corneal volume, along with an increase in anterior chamber volume, in all treated eyes. The observed decrease in corneal thickness manifested less prominently in diabetic patients compared to their non-diabetic counterparts, suggesting potential metabolic influences on corneal response to hyperoxic conditions.

PMID:40272842 | DOI:10.1080/15569527.2025.2496640

Fam Med. 2025 Apr;57(4):286-291. doi: 10.22454/FamMed.2025.925429.

ABSTRACT

BACKGROUND AND OBJECTIVES: The Council of Academic Family Medicine Educational Research Alliance (CERA) is a unique collaboration of academic family medicine organizations (Society of Teachers of Family Medicine [STFM], Association of Family Medicine Residency Directors, North American Primary Care Research Group, Association of Departments of Family Medicine) that facilitates and improves educational research in family medicine. CERA conducts approximately five surveys per year, including residency program directors, clerkship directors, department chairs, and general membership. Members of these organizations propose modules of 10 questions for these surveys. Proposals are peer-reviewed, and the top proposals are incorporated, along with standardized demographic questions, into an omnibus survey. We sought to determine the impact of self-reported gender of the primary submitter on survey module acceptance rates.

METHODS: We conducted a bibliometric analysis to explore author characteristics and quantify dissemination efforts. We conducted ꭓ2 analyses to determine gender differences in proposal acceptance. We used the exact binomial test to compare proportions of women authors to the benchmark proportion of women in STFM.

RESULTS: Overall, women submitted 66% (460/699) of CERA survey module proposals and authored 65% of accepted CERA modules (157/241) with the highest proportion concentrated among Clerkship Surveys (73%, 40/55). The acceptance rate did not differ significantly by gender (χ2=0.07, df=1, P=.80). A total of 73.4% (177/241) of module authors went on to present or publish their findings; we found no significant differences in scholarly output by gender (χ2=0.70, df=1, P=.41).

CONCLUSIONS: These findings indicate that the CERA module submission process has been successful in achieving comparable acceptance rates for men and women submitters. Other specialties should consider a similar model as a means to support early career educational researchers, including women.

PMID:40272837 | DOI:10.22454/FamMed.2025.925429

JAMA Ophthalmol. 2025 Apr 24. doi: 10.1001/jamaophthalmol.2025.0834. Online ahead of print.

ABSTRACT

IMPORTANCE: Recently, it was proved that the large language model Generative Pre-trained Transformer 4 (GPT-4; OpenAI) can fabricate synthetic medical datasets designed to support false scientific evidence.

OBJECTIVE: To uncover statistical patterns that may suggest fabrication in datasets produced by large language models and to improve these synthetic datasets by attempting to remove detectable marks of nonauthenticity, investigating the limits of generative artificial intelligence.

DESIGN, SETTING, AND PARTICIPANTS: In this quality improvement study, synthetic datasets were produced for 3 fictional clinical studies designed to compare the outcomes of 2 alternative treatments for specific ocular diseases. Synthetic datasets were produced using the default GPT-4o model and a custom GPT. Data fabrication was conducted in November 2024.

EXPOSURE: Prompts were submitted to GPT-4o to produce 12 “unrefined” datasets, which underwent forensic examination. Based on the outcomes of this analysis, the custom GPT Synthetic Data Creator was built with detailed instructions to generate 12 “refined” datasets designed to evade authenticity checks. Then, forensic analysis was repeated on these enhanced datasets.

MAIN OUTCOMES AND MEASURES: Forensic analysis was performed to identify statistical anomalies in demographic data, distribution uniformity, and repetitive patterns of last digits, as well as linear correlations, distribution shape, and outliers of study variables. Datasets were also qualitatively assessed for the presence of unrealistic clinical records.

RESULTS: Forensic analysis identified 103 fabrication marks among 304 tests (33.9%) in unrefined datasets. Notable flaws included mismatch between patient names and gender (n = 12), baseline visits occurring during weekends (n = 12), age calculation errors (n = 9), lack of uniformity (n = 4), and repetitive numerical patterns in last digits (n = 7). Very weak correlations (r < 0.1) were observed between study variables (n = 12). In addition, variables showed a suspicious distribution shape (n = 6). Compared with unrefined datasets, refined ones showed 29.3% (95% CI, 23.5%-35.1%) fewer signs of fabrication (14 of 304 statistical tests performed [4.6%]). Four refined datasets passed forensic analysis as authentic; however, suspicious distribution shape or other issues were found in others.

CONCLUSIONS AND RELEVANCE: Sufficiently sophisticated custom GPTs can perform complex statistical tasks and may be abused to fabricate synthetic datasets that can pass forensic analysis as authentic.

PMID:40272814 | DOI:10.1001/jamaophthalmol.2025.0834

JAMA. 2025 Apr 24. doi: 10.1001/jama.2025.4605. Online ahead of print.

ABSTRACT

IMPORTANCE: Children living in poverty are at increased risk of mental health and neurodevelopmental disorders. Little is known about the trends in diagnoses of these disorders among children enrolled in public insurance programs, such as Medicaid, which insure more than 1 in 3 US children.

OBJECTIVE: To provide comprehensive, multistate estimates of changes in the percentage of publicly insured children with mental health and/or neurodevelopmental disorder diagnoses.

DESIGN, SETTING, AND PARTICIPANTS: This serial, cross-sectional study used administrative claims data from 22 states to test trends from 2010 to 2019 in the percentage of publicly insured children aged 3 to 17 years with mental health or neurodevelopmental disorder diagnoses. Regression models included a dummy variable for each year, controlled for child demographics, county-level metropolitan status, median household income, and US Census region. Adjusted risk differences were estimated, with standard errors clustered at the state level.

EXPOSURE: Calendar year.

MAIN OUTCOMES: Any mental health or neurodevelopmental disorder diagnosis in the calendar year, and any diagnosis in 1 of 13 specific diagnostic categories.

RESULTS: A total of 129 306 637 child-year observations (29 925 633 unique publicly insured children) were included. The percentage of publicly insured children with any diagnosed mental health or neurodevelopmental disorder increased from 10.7% in 2010 to 16.5% in 2019; this change remained significant after adjustment for covariates (adjusted risk difference [aRD], 6.7 percentage points [95% CI, 5.0-8.4]). Statistically significant increases were also observed in 9 of the 13 diagnostic categories examined. The largest absolute increases were observed for attention-deficit/hyperactivity disorder (aRD, 2.3 percentage points [95% CI, 1.4-3.3]), trauma- and stressor-related disorders (aRD, 1.7 percentage points [95% CI, 0.9-2.5]), anxiety disorders (aRD, 1.6 percentage points [95% CI, 1.2-2.1]), autism spectrum disorders (aRD, 1.1 percentage points [95% CI, 0.9-1.4]), depressive disorders (aRD, 0.9 percentage points [95% CI, 0.6-1.3]), and other neurodevelopmental disorders (aRD, 2.6 percentage points [95% CI, 1.8-3.5]).

CONCLUSIONS AND RELEVANCE: The percentage of publicly insured children receiving any mental health or neurodevelopmental disorder diagnosis significantly increased between 2010 and 2019, with increases observed for most diagnostic categories examined. These findings highlight the need for access to appropriate services in safety net systems and other settings that serve this population.

PMID:40272810 | DOI:10.1001/jama.2025.4605

JAMA Netw Open. 2025 Apr 1;8(4):e256852. doi: 10.1001/jamanetworkopen.2025.6852.

ABSTRACT

IMPORTANCE: Guidelines recommend annual imaging surveillance after endovascular abdominal aortic aneurysm repair (EVAR). How these guidelines translate into practice among veterans remains poorly described.

OBJECTIVE: To characterize post-EVAR surveillance among veterans.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study evaluated veterans who underwent EVAR between January 1, 2000, and December 31, 2023, at US Department of Veterans Affairs (VA) hospitals and received follow-up care at VA and non-VA hospitals and imaging centers with payment via Medicare or the VA. Veterans treated with EVAR in VA hospitals during the study period were included.

EXPOSURE: Years after EVAR.

MAIN OUTCOMES AND MEASURES: The primary outcome was annual surveillance adherence, measured as 1 or more imaging studies in the abdomen or pelvis each year after EVAR. Stepwise logistic regression modeling was used to determine factors associated with poor adherence. Secondary outcomes were imaging type (cross-sectional, ultrasonography, or ultrasonography followed by cross-sectional imaging) and factors associated with lower adherence.

RESULTS: The cohort included 27 792 veterans (27 624 male [99.4%]; 22 521 aged ≥65 years [81.0%]). Mean (SD) follow-up was 6.0 (4.0) years. The mean (SD) proportion of time that veterans were surveillance adherent was 71.1% (28.5%). Surveillance was initially high, with 25 026 of 27 792 veterans (90.0%) undergoing surveillance imaging in year 1 after EVAR. However, this proportion decreased further out from EVAR, with 12 401 of 21 384 veterans (58.0%) undergoing surveillance imaging by year 4 after EVAR. Veterans were most likely to undergo imaging with computed tomography scans (21 911 veterans [78.8%]). However, the proportion with surveillance via ultrasonography alone increased from 823 of 25 026 veterans (3.3%) in year 1 after EVAR to 2567 of 12 401 veterans (20.7%) in year 4 after EVAR. White race (odds ratio [OR] vs all other racial groups, 0.84; 95% CI, 0.72-0.98), married status (OR vs all other social status categories, 0.80; 95% CI, 0.71-0.89), having a service-connected disability (OR, 0.69; 95% CI, 0.62-0.77), and a higher Charlson Comorbidity Index score (OR per 1-unit increase, 0.93; 95% CI, 0.91-0.95) were associated with lower odds of poor surveillance adherence.

CONCLUSIONS AND RELEVANCE: In this study, post-EVAR imaging surveillance was high, although surveillance lapses were more likely further out from EVAR and for patients with certain characteristics. This information may inform future patient-centered efforts to improve post-EVAR imaging adherence.

PMID:40272801 | DOI:10.1001/jamanetworkopen.2025.6852

JAMA Netw Open. 2025 Apr 1;8(4):e257115. doi: 10.1001/jamanetworkopen.2025.7115.

ABSTRACT

IMPORTANCE: Mental and somatic health is often impaired among transgender persons. Studies regarding coexisting mental and somatic health outcomes among transgender persons are limited.

OBJECTIVE: To assess health diagnoses and medicine use among transgender persons compared with cisgender controls.

DESIGN, SETTING, AND PARTICIPANTS: This register-based national cohort study included data from January 1, 2000, to December 31, 2021. Transgender persons were included on the first date of receipt of a transgender identity contact code. Controls included 10 age-matched cisgender men (n = 5) and women (n = 5) for each transgender person. Statistical analyses were conducted from September to December 2024.

MAIN OUTCOMES AND MEASURES: The main outcomes were International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis codes and medicine use in a 5-year period up to the first date of transgender contact code for most commonly occurring mental and physical illnesses. The main outcomes were determined after data collection.

RESULTS: The cohort included 3812 transgender persons (1993 transmasculine persons [52.3%] with a median age of 19 years [IQR, 15-24 years] and 1819 transfeminine persons [47.7%] with a median age of 23 years [IQR, 19-33 years]) and 38 120 cisgender controls. The odds for a mental health diagnosis was up to 12 times higher among transgender persons compared with cisgender controls. Among transmasculine and transfeminine persons, neurotic, stress-related disorders (transmasculine: adjusted odds ratio [AOR], 4.70 [95% CI, 4.02-5.50]; transfeminine: AOR, 5.27 [95% CI, 4.28-6.49]); developmental disorders, including autism (transmasculine: AOR, 11.67 [95% CI, 8.85-15.39]; transfeminine: AOR, 9.39 [95% CI, 7.05-12.50]); mood (affective) disorders (transmasculine: AOR, 5.41 [95% CI, 4.32-6.77]; transfeminine: AOR, 5.61 [95% CI, 4.16-7.57]); and behavioral disorders (transmasculine: AOR, 4.50 [95% CI, 3.61-5.62]; transfeminine: AOR, 4.15 [95% CI, 3.19-5.39]) were the most frequent mental health diagnoses compared with cisgender controls of the opposite sex at birth. Transmasculine persons had higher odds for somatic diagnosis codes of diabetes (AOR, 2.00 [95% CI, 1.12-3.56]), asthma (including chronic obstructive lung disease; AOR, 1.40 [95% CI, 1.06-1.85]), injury and poisoning (AOR, 1.28 [95% CI, 1.15-1.41]), and pain (AOR, 1.29 [95% CI, 1.12-1.49]) compared with control cisgender women. Among transfeminine persons, somatic diagnosis codes of infection (AOR, 1.68 [95% CI, 1.33-2.13]), anemia (AOR, 3.08 [95% CI, 1.36-6.97]), diabetes (AOR, 1.95 [95% CI, 1.25-3.05]), sleep apnea (AOR, 3.41 [95% CI, 1.84-6.31]), and pain (AOR, 1.31 [95% CI, 1.08-1.58]) were more frequent compared with control cisgender men. Transgender persons had higher use of psychopharmacologic medicine, antacids, and laxatives compared with cisgender controls (transmasculine persons vs control cisgender women, antipsychotics: AOR, 6.20 [95% CI, 5.07-7.59]; hypnotics-sedatives: AOR, 4.45 [95% CI, 3.78-5.23]; antacids: AOR, 1.25 [95% CI, 1.07-1.45]; and laxatives: AOR, 1.53 [95% CI, 1.17-1.99]; transfeminine persons vs control cisgender men, antipsychotics: AOR, 4.74 [95% CI, 3.92-5.74]; hypnotics-sedatives: AOR, 3.01 [95% CI, 2.53-3.57]; and antacids: AOR, 1.32 [95% CI, 1.12-1.56]). Mental health diagnoses and use of psychopharmacologic drugs were coexisting with somatic diagnoses and use of drugs for somatic diseases.

CONCLUSIONS AND RELEVANCE: This cohort study of Danish transgender persons and cisgender controls found significantly higher risks for mental and somatic health diagnoses among transgender persons. Coexistence of mental health outcomes and somatic health outcomes among transgender persons could be associated with stress encountered due to belonging to a gender identity or sexual orientation minority group; mental and physical morbidity should be considered an integrated part of transgender care.

PMID:40272800 | DOI:10.1001/jamanetworkopen.2025.7115

JAMA Netw Open. 2025 Apr 1;8(4):e257202. doi: 10.1001/jamanetworkopen.2025.7202.

ABSTRACT

IMPORTANCE: Bloodstream infections (BSIs) associated with peripheral intravenous catheters (PIVCs) are rare but preventable adverse events. The association of dwell time with the risk of BSIs with PIVCs remains controversial.

OBJECTIVE: To analyze the risk of BSIs during PIVC maintenance therapy.

DESIGN, SETTING, AND PARTICIPANTS: In this observational cohort study, all patients hospitalized at Geneva University Hospitals with at least 1 PIVC insertion on the upper extremity (N = 371 061) between January 1, 2016, and February 29, 2020, were evaluated. Statistical analysis was performed from January 2023 to January 2025.

EXPOSURE: At least 1 PIVC insertion on the upper extremity.

MAIN OUTCOMES AND MEASURES: The primary outcome was BSIs with PIVCs; data were collected by prospective BSI surveillance. The daily risk of BSIs with PIVCs was analyzed using the hazard rate function by kernel-based methods. Multivariable logistic models were performed to evaluate the risk of BSIs with PIVCs comparing different cutoff values of dwell times (>3 vs ≤3 days, >4 vs ≤4 days, >5 vs ≤5 days, and >6 vs ≤6 days).

RESULTS: A total of 371 061 PIVCs (median patient age, 63 years [IQR, 41-79 years]; 187 786 women [51%]) with documented catheter duration were included. A total of 140 178 PIVCs (38%) had a dwell time of 1 to 2 days, 119 252 (32%) had a dwell time of 3 to 4 days, and 111 631 (30%) had a dwell time of more than 4 days. The instantaneous risk of BSIs with PIVCs was low in the first 2 days of dwell time and increased rapidly thereafter. The risk of BSIs was significantly increased after 3 days of catheter maintenance (adjusted odds ratio [AOR], 13.55; 95% CI, 5.44-34.00). This risk was the highest after 3 days and remained increased thereafter (>4 days: AOR, 8.53; 95% CI, 4.47-16.28; >5 days: AOR, 5.38; 95% CI, 3.23-8.96; and >6 days: AOR, 7.63; 95% CI, 4.57-12.74).

CONCLUSIONS AND RELEVANCE: In this cohort study of 371 061 PIVCs, dwell time was associated with the development of BSIs with PIVCs. After day 3, PIVC indication should be reviewed and PIVC replacement considered.

PMID:40272799 | DOI:10.1001/jamanetworkopen.2025.7202