Low-level occupational exposure to BTEX and dyschromatopsia: a systematic review and meta-analysis

Objectives. The present study aimed to assess whether occupational exposure to low concentrations of benzene, toluene, ethylbenzene and xylene (BTEX) is associated with color vision impairment. Methods. We queried PubMed, Scopus, Embase, Web of Science and ProQuest as the main databases, as well as gray literature such as Google Scholar. A random-effects model was used to assess relative risk. A funnel plot was created to assess publication bias. Meta-regression analysis was applied to identify variables that explain the between-study variation in the reported risk estimate. Results. An overall standardized mean difference of 0.529 (95% confidence interval [0.269, 0.788]; p < 0.0001) was obtained in the random-effects model, which corresponded to a medium-size effect. Duration and the levels of exposure to benzene, toluene and xylene were the significant predictors of the magnitude of the combined risk estimate. Chronic exposure to low levels of BTEX was associated with dyschromatopsia determined by the color confusion index. Conclusions. The impairments can occur even at exposures lower than the occupational exposure limits of BTEX. However, there are several flaws in the determination of workers' exposure, which did not allow to establish how low a level of these chemicals can cause color vision impairment.

Trajectories of mental health outcomes following COVID-19 infection: a prospective longitudinal study

BACKGROUND

The COVID-19 pandemic has triggered a global mental health crisis. Yet, we know little about the lasting effects of COVID-19 infection on mental health. This prospective longitudinal study aimed to investigate the trajectories of mental health changes in individuals infected with COVID-19 and to identify potential predictors that may influence these changes.

METHODS

A web-survey that targeted individuals that had been infected with COVID-19 was used at three time-points: T0 (baseline), T1 (six months), and T2 (twelve months). The survey included demographics, questions related to COVID-19 status, previous psychiatric diagnosis, post-COVID impairments, fatigue, and standardized measures of depression, anxiety, insomnia. Linear mixed models were used to examine changes in depression, anxiety, and insomnia over time and identify factors that impacted trajectories of mental health outcomes.

RESULTS

A total of 236 individuals completed assessments and was included in the longitudinal sample. The participants' age ranged between 19 and 81 years old (M = 48.71, SD = 10.74). The results revealed notable changes in mental health outcomes over time. The trajectory of depression showed significant improvement over time while the trends in anxiety and insomnia did not exhibit significant changes over time. Younger participants and individuals who experienced severe COVID-19 infection in the acute phase were identified as high-risk groups with worst mental ill-health. The main predictors of the changes in the mental health outcomes were fatigue and post-COVID impairments.

CONCLUSIONS

The findings of our study suggest that mental health outcomes following COVID-19 infection exhibit a dynamic pattern over time. The study provides valuable insights into the mental health trajectory following COVID-19 infection, emphasizing the need for ongoing assessment, support, and interventions tailored to the evolving mental health needs of this population.

Pulmonary function and respiratory symptoms in workers exposed to respirable silica dust: A historical cohort study

Background

The adverse health effects of silica are still a major concern in some industries. The purpose of this study was to evaluate pulmonary function in a group of sub-radiological silicotic workers after 11 years of silica dust exposure.

Methods

The study sample consisted of 381 exposed and 254 non-exposed workers. The history of pulmonary function parameters was obtained from workers' medical records. The data were collected through interviews with employees and completing questionnaires on demographic variables, detailed occupational and medical history, and respiratory symptoms. Workers' exposure to silica dust was also determined.

Results

The mean frequency of workers’ exposure to silica dust was 6.3 times greater than its exposure limit. All pulmonary function parameters were significantly lower in the silica-exposed workers, and the difference between the two groups was still statistically significant after adjusting the potential confounding variables. FEV1 showed the greatest reduction, and FVC and FEV1 showed a significant decreasing trend. Also the prevalence of respiratory symptoms was significantly higher in smokers than in nonsmokers among silica-exposed workers.

Conclusions

Even in the absence of radiographic evidence of silicosis, exposure to high levels of silica dust is associated with reductions in pulmonary function. In the absence of radiological evidence of silicosis, progressive deterioration of FEV1 over time most likely indicates sub-radiological silicosis. The effects were associated with the severity and duration of exposure. Exposure to sub-TLV levels of silica dust may not affect pulmonary function. Smoking appears to have a synergistic effect in relatively high silica exposures.

A Review of Extraction Methods and Analytical Techniques for Styrene and its Metabolites in Biological Matrices

We reviewed the toxicokinetics of styrene to introduce reliable surrogates for biological monitoring of styrene workers. Also, extraction techniques and analytical methods for styrene and its metabolites have been discussed. Sample preparation is the main bottleneck of the analytical techniques for styrene and its metabolites. While some microextraction methods have been developed to overcome such drawbacks, some still have limitations such as long extraction time, fiber swelling and breakage, and the cost and the limited lifetime of the fiber. Among all, microextraction by packed sorbents coupled with high performance liquid chromatography with ultraviolet detection (MEPS-HPLC-UV) can be the method of choice for determining styrene metabolites. Few studies investigated unchanged styrene in breath samples. Chemical determination in exhaled breath provides new insights into organ toxicity in workers with inhalation exposures and can be considered as a fascinating tool in risk assessment strategies. Taking blood samples is invasive and less accepted by workers than other samples. In contrast, breath analysis is the most attractive method for workers because breath samples are easy to collect and non-invasive, and does not require worker transfer to health facilities. Therefore, developing selective and sensitive methods for determining styrene in breath samples is recommended for future studies.

Elevated blood pressure and risk of aortic valve disease: a cohort analysis of 5.4 million UK adults

Aims

To test two related hypotheses that elevated blood pressure (BP) is a risk factor for aortic valve stenosis (AS) or regurgitation (AR).

Methods and results

In this cohort study of 5.4 million UK patients with no known cardiovascular disease or aortic valve disease at baseline, we investigated the relationship between BP and risk of incident AS and AR using multivariable-adjusted Cox regression models. Over a median follow-up of 9.2 years, 20 680 patients (0.38%) were diagnosed with AS and 6440 (0.12%) patients with AR. Systolic BP (SBP) was continuously related to the risk of AS and AR with no evidence of a nadir down to 115 mmHg. Each 20 mmHg increment in SBP was associated with a 41% higher risk of AS (hazard ratio 1.41, 95% confidence interval 1.38–1.45) and a 38% higher risk of AR (1.38, 1.31–1.45). Associations were stronger in younger patients but with no strong evidence for interaction by gender or body mass index. Each 10 mmHg increment in diastolic BP was associated with a 24% higher risk of AS (1.24, 1.19–1.29) but not AR (1.04, 0.97–1.11). Each 15 mmHg increment in pulse pressure was associated with a 46% greater risk of AS (1.46, 1.42–1.50) and a 53% higher risk of AR (1.53, 1.45–1.62).

Conclusion

Long-term exposure to elevated BP across its whole spectrum was associated with increased risk of AS and AR. The possible causal nature of the observed associations warrants further investigation.

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

BACKGROUND

Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one.

METHODS AND FINDINGS

We used longitudinal data from linked electronic health records of 4.6 million patients aged 18-100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions. We then added 13 more variables (marital status, prior general practice visits, and 11 additional morbidities), and also enriched all variables by incorporating temporal information whenever possible (e.g., time since first diagnosis). We also varied the prediction windows to 12, 36, 48, and 60 months after baseline and compared model performances. For internal validation, we used 5-fold cross-validation. When the initial set of variables was used, GBC outperformed RF and CPH, with an area under the receiver operating characteristic curve (AUC) of 0.779 (95% CI 0.777, 0.781), compared to 0.752 (95% CI 0.751, 0.753) and 0.740 (95% CI 0.739, 0.741), respectively. In external validation, we observed an AUC of 0.796, 0.736, and 0.736 for GBC, RF, and CPH, respectively. The addition of temporal information improved AUC across all models. In internal validation, the AUC rose to 0.848 (95% CI 0.847, 0.849), 0.825 (95% CI 0.824, 0.826), and 0.805 (95% CI 0.804, 0.806) for GBC, RF, and CPH, respectively, while the AUC in external validation rose to 0.826, 0.810, and 0.788, respectively. This enhancement also resulted in robust predictions for longer time horizons, with AUC values remaining at similar levels across all models. Overall, compared to the baseline reference CPH model, the final GBC model showed a 10.8% higher AUC (0.848 compared to 0.740) for prediction of risk of emergency admission within 24 months. GBC also showed the best calibration throughout the risk spectrum. Despite the wide range of variables included in models, our study was still limited by the number of variables included; inclusion of more variables could have further improved model performances.

CONCLUSIONS

The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning.