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Predicting the onset of hypertension for workers: does including work characteristics improve risk predictive accuracy?

ABSTRACT: Despite extensive evidence of work as a key social determinant of hypertension, risk prediction equations incorporating this information are lacking. Such limitations hinder clinicians' ability to tailor patient care and comprehensively address hypertension risk factors. This study examined whether including work characteristics in hypertension risk equations improves their predictive accuracy. Using occupation ratings from the Occupational Information Network database, we measured job demand, job control, and supportiveness of supervisors and coworkers for occupations in the United States economy. We linked these occupation-based measures with the employment status and health data of participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study. We fit logistic regression equations to estimate the probability of hypertension onset in five years among CARDIA participants with and without variables reflecting work characteristics. Based on the Harrell's c- and Hosmer-Lemeshow's goodness-of-fit statistics, we found that our logistic regression models that include work characteristics predict hypertension onset more accurately than those that do not incorporate these variables. We also found that the models that rely on occupation-based measures predict hypertension onset more accurately for White than Black participants, even after accounting for a sample size difference. Including other aspects of work, such as workers' experience in the workplace, and other social determinants of health in risk equations may eliminate this discrepancy. Overall, our study showed that clinicians should examine workers' work-related characteristics to tailor hypertension care plans appropriately.

SUBMITTER: Chantarat T

PROVIDER: S-EPMC8914447 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundElevated blood pressure (BP) is the leading global risk factor for mortality. Delay in seeking hypertension care is associated with increased mortality.ObjectivesThis study investigated whether community health workers, equipped with behavioral communication strategies and smartphone technology, can increase linkage of individuals with elevated BP to a hypertension care program in western Kenya and significantly reduce BP.MethodsThe study was a cluster randomized trial with 3 arms: 1) usual care (standard training); 2) "paper-based" (tailored behavioral communication, using paper-based tools); and 3) "smartphone" (tailored behavioral communication, using smartphone technology). The co-primary outcomes were: 1) linkage to care; and 2) change in systolic BP (SBP). A covariate-adjusted mixed-effects model was used, adjusting for differential time to follow-up. Bootstrap and multiple imputation were used to handle missing data.ResultsA total of 1,460 individuals (58% women) were enrolled (491 usual care, 500 paper-based, 469 smartphone). Average baseline SBP was 159.4 mm Hg. Follow-up measures of linkage were available for 1,128 (77%) and BP for 1,106 (76%). Linkage to care was 49% overall, with significantly greater linkage in the usual care and smartphone arms of the trial. Average overall follow-up SBP was 149.9 mm Hg. Participants in the smartphone arm experienced a modestly greater reduction in SBP versus usual care (-13.1 mm Hg vs. -9.7 mm Hg), but this difference was not statistically significant. Mediation analysis revealed that linkage to care contributed to SBP change.ConclusionsA strategy combining tailored behavioral communication and mobile health (mHealth) for community health workers led to improved linkage to care, but not statistically significant improvement in SBP reduction. Further innovations to improve hypertension control are needed. (Optimizing Linkage and Retention to Hypertension Care in Rural Kenya [LARK]; NCT01844596).

Project description:ObjectivesThis study aims to assess the heterogeneity of psychosocial working conditions of young workers by identifying subgroups of work characteristic configurations within young workers and to assess these subgroups' associations with emotional exhaustion.DesignLatent class analysis. Groups were formed based on 12 work characteristics (8 job demands and 4 job resources), educational level and sex. Differences in emotional exhaustion between subgroups were analysed using analysis of variance and post hoc comparisons.SettingData from the 2019 wave of the Netherlands Working Conditions Survey.Participants7301 individuals between the age of 18 and 30 years, who worked more than 16 hours per week.Main outcome measureEmotional exhaustion.ResultsFive subgroups of work characteristics could be identified and were labelled as: (1) 'low-complexity work' (24.4%), (2) 'office work' (32.3%), (3) 'manual and non-interpersonal work' (12.4%), (4) 'non-manual and interpersonal work' (21.0%), and (5) 'manual and interpersonal work' (9.9%). Mean scores for emotional exhaustion in the two interpersonal work groups (M=3.11, SD=1.4; M=3.45, SD=1.6) were significantly higher than in the first three groups (M=2.05, SD=1.1; M=1.98, SD=1.0; M=2.05, SD=1.1) (all 95% CIs excluding 0). Further, mean scores for emotional exhaustion were significantly higher in the 'manual and interpersonal work' group than in the 'non-manual and interpersonal work' group (95% CI 0.24, 0.45). All results could be replicated in the 2017 and 2021 waves of the Netherlands Working Conditions Survey.ConclusionsYoung workers reported heterogeneous work characteristic configurations with substantial differences in degrees of emotional exhaustion between the identified subgroups. Preventing emotional exhaustion should focus on the two interpersonal work subgroups, which showed a high degree of emotional exhaustion. In prevention efforts, these groups' configurations of work characteristics should be taken into account.

Project description:Abstract Objective Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs. Materials and Methods We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date. To ensure generalization to unseen data, we trained our models on the first dataset (dataset A “train and validation”) using cross-validation, and then applied the models to a second dataset (dataset B “test”) to assess their performance. We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance. Results With the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the “train and validation” dataset A and 0.94 in the “test” dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension. Conclusion These findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.

Project description:BackgroundThere are no accepted universal biomarkers capable to accurately predict response to immuno-checkpoint inhibitors (ICI). Although recent literature has been flooded with studies on ICI predictive biomarkers, available data show that currently approved companion diagnostics either leave out many possible responders, as in the case of PD-L1 testing for first-line metastatic lung cancer, or apply to a small subset of patients, such as the recently approved treatment for microsatellite instability-high or mismatch repair deficiency tumors. In this study, we conducted a survey of the available data on ICI trials with matched genomic or transcriptomic datasets in order to cross-validate the proposed biomarkers, to assess whether their prediction power was confirmed and, mainly, to investigate if their combination was able to generate a better predictive tool.MethodsWe extracted clinical information and sequencing data details from publicly available datasets, along with a list of possible biomarkers obtained from the recent literature. After an operation of data harmonization, we validated the performance of all the biomarkers taken individually. Furthermore, we tested two strategies to combine the best performing biomarkers in order to improve their predictive value.ResultsWhen considered individually, some of the biomarkers, such as the ImmunoPhenoScore, and the IFN-? signature, did not confirm their originally proposed predictive power. The best absolute scoring biomarkers are TIDE, one of the ICB resistance signatures and CTLA4 with a mean AUC?>?0.66. Among the combinations tested, generalized linear models showed the best performance with an AUC of 0.78.ConclusionsWe confirmed that the available biomarkers, taken individually, fail to provide a satisfactory predictive value. Unfortunately, also combination of some of them only provides marginal improvements. Hence, in order to generate a more robust way to predict ICI efficacy it is necessary to analyze and combine additional biomarkers and interrogate a wider set of clinical data.

Project description:It is important to understand which components of successful multifaceted interventions are responsible for study outcomes, since some components may be more important contributors to the intervention effect than others.We conducted a mediation analysis to determine which of seven factors had the greatest effect on change in systolic blood pressure (BP) after 6 months in a trial to improve hypertension control.The study was a preplanned secondary analysis of a cluster-randomized clinical trial. Eight clinics in an integrated health system were randomized to provide usual care to their patients (n?=?222), and eight were randomized to provide a telemonitoring intervention (n?=?228).Four hundred three of 450 trial participants completing the 6-month follow-up visit were included.Intervention group participants received home BP telemonitors and transmitted measurements to pharmacists, who adjusted medications and provided advice to improve adherence to medications and lifestyle modification via telephone visits.Path analytic models estimated indirect effects of the seven potential mediators of intervention effect (defined as the difference between the intervention and usual care groups in change in systolic BP from baseline to 6 months). The potential mediators were change in home BP monitor use, number of BP medication classes, adherence to BP medications, physical activity, salt intake, alcohol use, and weight.The difference in change in systolic BP was 11.3 mmHg. The multivariable mediation model explained 47 % (5.3 mmHg) of the intervention effect. Nearly all of this was mediated by two factors: an increase in medication treatment intensity (24 %) and increased home BP monitor use (19 %). The other five factors were not significant mediators, although medication adherence and salt intake improved more in the intervention group than in the usual care group.Most of the explained intervention effect was attributable to the combination of self-monitoring and medication intensification. High adherence at baseline and the relatively low intensity of resources directed toward lifestyle change may explain why these factors did not contribute to the improvement in BP.

Project description:BackgroundAnxiety disorders are associated with functional disability, sickness absence, and decreased productivity. Effective treatments of anxiety disorders can result in remission of symptoms. However the effects on work related outcomes are largely unknown. Exposure in vivo is potentially well fit to improve work-related outcomes. This study systematically reviews the effectiveness of exposure-in-vivo containing interventions in reducing work-related adverse outcomes in workers with anxiety disorders.MethodsA systematic study search was conducted in Medline, Cinahl, Embase and Psycinfo. Two reviewers independently extracted data and from each study assessed the quality of evidence by using the GRADE approach. We performed a meta-analysis if data showed sufficient clinical homogeneity.ResultsSeven studies containing 11 exposure-in-vivo interventions were included. Four studies were focused on Obsessive Compulsive Disorder (OCD), two on Post Traumatic Stress Disorder (PTSD), and one on a mixed group of OCD and severe phobias. The studies were grouped according to type of anxiety disorder and subsequently according to type of comparisons. For OCD, exposure-in-vivo containing interventions can yield better work-related outcomes compared to medication (SSRIs) and relaxation but not better compared to response prevention. The results on anxiety outcomes were similar. The net contribution of exposure in vivo in two OCD intervention programs is also presented as a meta-analysis and shows significant positive results on work role limitations. The calculated pooled effect size with 95% confidence interval was 0.72 (0.28, 1.15). For PTSD, exposure-in-vivo containing interventions can yield better work-related and anxiety-related outcomes compared to a waiting-list but not better compared to imaginal exposure.ConclusionsExposure in vivo as part of an anxiety treatment can reduce work-related adverse outcomes in workers with OCD and PTSD better than various other anxiety treatments or a waiting-list. We recommend that it should be studied how the results of these studies can be transferred to the practice of occupational health professionals and how clinicians can make better use of them to improve work-related outcomes. In future research, priority should be given to high-quality randomised controlled trials (RCTs) in which exposure-in-vivo containing interventions are applied to a variety of anxiety disorders and compared with other clinical anxiety treatments such as SSRIs. Work-related outcomes, in particular work functioning and sickness absence, need to be assessed with reliable and valid measures.

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Predicting the onset of hypertension for workers: does including work characteristics improve risk predictive accuracy?

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