Educational inequalities in self-rated health and emotional exhaustion among workers during the COVID-19 pandemic: a longitudinal study.

OBJECTIVE: This study aimed to investigate trends in educational inequalities in poor health and emotional exhaustion during the pandemic among workers, and differences in trends between men and women. METHODS: Five waves (2019-2021) from the longitudinal study 'the Netherlands Working Conditions Survey COVID-19 study' were used (response rates: 32-38%). Generalized logistic mixed models were used to estimate the changes in absolute and relative educational inequalities in poor health and emotional exhaustion for all workers (n = 12,479) and for men and women, separately. RESULTS: Low and intermediate educated workers reported more often poor health (OR 2.54; 95% CI 1.71-3.77 and OR 2.09; 95% CI 1.68-2.61, respectively) than high educated workers. Intermediate educated women (OR 0.49; 95% CI 0.37-0.64) reported less emotional exhaustion than high educated women, but no differences were observed among men. The prevalence of poor health first decreased across all educational levels until March 2021, and bounced back in November 2021. A similar pattern was found for emotional exhaustion, but for low and intermediate educated workers only. Relative educational inequalities in poor health reduced among men during the pandemic, and absolute differences decreased among men and women by 2.4-2.6%. Relative educational inequalities in emotional exhaustion widened among men only. Absolute differences in emotional exhaustion first increased among both men and women, but narrowed between the last two waves. DISCUSSION: Socioeconomic inequalities for poor self-rated health remained but narrowed in relative and absolute terms during the pandemic. With regard to emotional exhaustion, socioeconomic inequalities returned to pre-COVID-19 levels at the end of 2021.

Interrater and intrarater agreement of confocal microscopy imaging in diagnosing and subtyping basal cell carcinoma.

BACKGROUND: Reflectance confocal microscopy (RCM) imaging can be used to diagnose and subtype basal cell carcinoma (BCC) but relies on individual morphologic pattern recognition that might vary among users. OBJECTIVES: We assessed the inter-rater and intrarater agreement of RCM in correctly diagnosing and subtyping BCC. METHODS: In this prospective study, we evaluated the inter-rater and intrarater agreement of RCM on BCC presence and subtype among three raters with varying experience who independently assessed static images of 48 RCM cases twice with four-week interval (T1 and T2). Histopathologic confirmation of presence and subtype of BCC from surgical excision specimen was defined as the reference standard. RESULTS: The inter-rater agreement of RCM for BCC presence showed an agreement of 82% at T1 and 84% at T2. The agreements for subtyping BCC were lower (52% for T1 and 47% for T2). The intrarater agreement of RCM for BCC presence showed an observed agreement that varied from 79% to 92%. The observed agreements for subtyping varied from 56% to 71%. CONCLUSIONS: In conclusion, our results show that RCM is reliable in correctly diagnosing BCC based on the assessment of static RCM images. RCM could potentially play an important role in BCC management if accurate subtyping will be achieved. Therefore, future clinical studies on reliability and specific RCM features for BCC subtypes are required.

Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score.

OBJECTIVE: In rheumatoid arthritis (RA), it is of major importance to identify non-responders to tumour necrosis factor-α inhibitors (TNFi) before starting treatment, to prevent a delay in effective treatment. We developed a protein score for the response to TNFi treatment in RA and investigated its predictive value. METHOD: In RA patients eligible for biological treatment included in the BiOCURA registry, 53 inflammatory proteins were measured using xMAP® technology. A supervised cluster analysis method, partial least squares (PLS), was used to select the best combination of proteins. Using logistic regression, a predictive model containing readily available clinical parameters was developed and the potential of this model with and without the protein score to predict European League Against Rheumatism (EULAR) response was assessed using the area under the receiving operating characteristics curve (AUC-ROC) and the net reclassification index (NRI). RESULTS: For the development step (n = 65 patient), PLS revealed 12 important proteins: CCL3 (macrophage inflammatory protein, MIP1a), CCL17 (thymus and activation-regulated chemokine), CCL19 (MIP3b), CCL22 (macrophage-derived chemokine), interleukin-4 (IL-4), IL-6, IL-7, IL-15, soluble cluster of differentiation 14 (sCD14), sCD74 (macrophage migration inhibitory factor), soluble IL-1 receptor I, and soluble tumour necrosis factor receptor II. The protein score scarcely improved the AUC-ROC (0.72 to 0.77) and the ability to improve classification and reclassification (NRI = 0.05). In validation (n = 185), the model including protein score did not improve the AUC-ROC (0.71 to 0.67) or the reclassification (NRI = -0.11). CONCLUSION: No proteomic predictors were identified that were more suitable than clinical parameters in distinguishing TNFi non-responders from responders before the start of treatment. As the results of previous studies and this study are disparate, we currently have no proteomic predictors for the response to TNFi.

The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age.

BACKGROUND: The majority of breast cancer patients are postmenopausal women who are increasingly being offered adjuvant chemotherapy. Since the beneficial effect of chemotherapy in postmenopausal patients predominantly occurs in the first 5 years after diagnosis, a prognostic marker for early events can be of use for adjuvant treatment decision making. The aim of this study was to evaluate the prognostic value of the 70-gene prognosis signature for early events in postmenopausal patients. METHODS: Frozen tumor samples from 148 patients aged 55-70 years were selected (T1-2, N0) and classified by the 70-gene prognosis signature (MammaPrint) into good or poor prognosis. Eighteen percent received hormonal therapy. RESULTS: Breast cancer-specific survival (BCSS) at 5 years was 99% for the good-prognosis signature versus 80% for the poor-prognosis signature group (P = 0.036). The 70-gene prognosis signature was a significant and independent predictor of BCCS during the first 5 years of follow-up with an adjusted hazard ratio of 14.4 (95% confidence interval 1.7-122.2; P = 0.01) at 5 years. CONCLUSION: The 70-gene prognosis signature can accurately select postmenopausal patients at low risk of breast cancer-related death within 5 years of diagnosis and can be of clinical use in selecting postmenopausal women for adjuvant chemotherapy.