A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers

Performance of binary prediction models in high-correlation low-dimensional settings: a comparison of methods

BACKGROUND

Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate.

METHODS

We compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations.

RESULTS

In the conducted simulations, no effect of collinearity was observed on predictive outcomes (AUC, R², Intercept, Slope) across methods. However, a negative effect of collinearity on the stability of predictor selection was found, affecting all compared methods, but in particular methods that perform strong predictor selection (e.g., Lasso). Methods for which the included set of predictors remained most stable under increased collinearity were Ridge, PCLR, LAELR, and Dropout.

CONCLUSIONS

Based on the results, we would recommend refraining from data-driven predictor selection approaches in the presence of high collinearity, because of the increased instability of predictor selection, even in relatively high events-per-variable settings. The selection of certain predictors over others may disproportionally give the impression that included predictors have a stronger association with the outcome than excluded predictors.

Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain

BACKGROUND

Identification of sciatica may assist timely management but can be challenging in clinical practice. Diagnostic models to identify sciatica have mainly been developed in secondary care settings with conflicting reference standard selection. This study explores the challenges of reference standard selection and aims to ascertain which combination of clinical assessment items best identify sciatica in people seeking primary healthcare.

METHODS

Data on 394 low back-related leg pain consulters were analysed. Potential sciatica indicators were seven clinical assessment items. Two reference standards were used: (i) high confidence sciatica clinical diagnosis; (ii) high confidence sciatica clinical diagnosis with confirmatory magnetic resonance imaging findings. Multivariable logistic regression models were produced for both reference standards. A tool predicting sciatica diagnosis in low back-related leg pain was derived. Latent class modelling explored the validity of the reference standard.

RESULTS

Model (i) retained five items; model (ii) retained six items. Four items remained in both models: below knee pain, leg pain worse than back pain, positive neural tension tests and neurological deficit. Model (i) was well calibrated (p = 0.18), discrimination was area under the receiver operating characteristic curve (AUC) 0.95 (95% CI 0.93, 0.98). Model (ii) showed good discrimination (AUC 0.82; 0.78, 0.86) but poor calibration (p = 0.004). Bootstrapping revealed minimal overfitting in both models. Agreement between the two latent classes and clinical diagnosis groups defined by model (i) was substantial, and fair for model (ii).

CONCLUSION

Four clinical assessment items were common in both reference standard definitions of sciatica. A simple scoring tool for identifying sciatica was developed. These criteria could be used clinically and in research to improve accuracy of identification of this subgroup of back pain patients.

Predicting occupational asthma and rhinitis in bakery workers referred for clinical evaluation

BACKGROUND

Occupational allergic diseases are a major problem in some workplaces like in the baking industry. Diagnostic rules have been used in surveillance but not yet in the occupational respiratory clinic.

OBJECTIVE

To develop diagnostic models predicting baker's asthma and rhinitis among bakery workers at high risk of sensitisation to bakery allergens referred to a specialised clinic.

METHODS

As part of a medical surveillance programme, clinical evaluation was performed on 436 referred Dutch bakery workers at high risk for sensitisation to bakery allergens. Multivariable logistic regression analyses were developed to identify the predictors of onset of baker's asthma and rhinitis using a self-administered questionnaire and compared using a structured medical history. Performance of models was assessed by discrimination (area under the receiver operating characteristics curve) and calibration (Hosmer-Lemeshow test). Internal validity of the models was assessed by a bootstrapping procedure.

RESULTS

The prediction models included the predictors of work-related upper and lower respiratory symptoms, the presence of allergy and allergic symptoms, use of medication (last year), type of job, type of shift and working years with symptoms (≥10 years). The developed models derived from both self-administered questionnaire and the medical history showed a relatively good discrimination and calibration. The internal validity showed that the models developed had satisfactory discrimination. To improve calibrations of models, shrinkage factors were applied to model coefficients.

CONCLUSION

The probability of allergic asthma and rhinitis in referred bakers could be estimated by diagnostic models based on both a self-administered questionnaire and by taking a structured medical history.

Predicting Directly Measured Trunk and Upper Arm Postures in Paper Mill Work From Administrative Data, Workers' Ratings and Posture Observations

Objectives

A cost-efficient approach for assessing working postures could be to build statistical models for predicting results of direct measurements from cheaper data, and apply these models to samples in which only the latter data are available. The present study aimed to build and assess the performance of statistical models predicting inclinometer-assessed trunk and arm posture among paper mill workers. Separate models were built using administrative data, workers' ratings of their exposure, and observations of the work from video recordings as predictors.

Methods

Trunk and upper arm postures were measured using inclinometry on 28 paper mill workers during three work shifts each. Simultaneously, the workers were video filmed, and their postures were assessed by observation of the videos afterwards. Workers' ratings of exposure, and administrative data on staff and production during the shifts were also collected. Linear mixed models were fitted for predicting inclinometer-assessed exposure variables (median trunk and upper arm angle, proportion of time with neutral trunk and upper arm posture, and frequency of periods in neutral trunk and upper arm inclination) from administrative data, workers' ratings, and observations, respectively. Performance was evaluated in terms of Akaike information criterion, proportion of variance explained (R2), and standard error (SE) of the model estimate. For models performing well, validity was assessed by bootstrap resampling.

Results

Models based on administrative data performed poorly (R2 ≤ 15%) and would not be useful for assessing posture in this population. Models using workers' ratings of exposure performed slightly better (8% ≤ R2 ≤ 27% for trunk posture; 14% ≤ R2 ≤ 36% for arm posture). The best model was obtained when using observational data for predicting frequency of periods with neutral arm inclination. It explained 56% of the variance in the postural exposure, and its SE was 5.6. Bootstrap validation of this model showed similar expected performance in other samples (5th-95th percentile: R2 = 45-63%; SE = 5.1-6.2).

Conclusions

Observational data had a better ability to predict inclinometer-assessed upper arm exposures than workers' ratings or administrative data. However, observational measurements are typically more expensive to obtain. The results encourage analyses of the cost-efficiency of modeling based on administrative data, workers' ratings, and observation, compared to the performance and cost of measuring exposure directly.

A Comparison of Two Strategies for Building an Exposure Prediction Model

Cost-efficient assessments of job exposures in large populations may be obtained from models in which 'true' exposures assessed by expensive measurement methods are estimated from easily accessible and cheap predictors. Typically, the models are built on the basis of a validation study comprising 'true' exposure data as well as an extensive collection of candidate predictors from questionnaires or company data, which cannot all be included in the models due to restrictions in the degrees of freedom available for modeling. In these situations, predictors need to be selected using procedures that can identify the best possible subset of predictors among the candidates. The present study compares two strategies for selecting a set of predictor variables. One strategy relies on stepwise hypothesis testing of associations between predictors and exposure, while the other uses cluster analysis to reduce the number of predictors without relying on empirical information about the measured exposure. Both strategies were applied to the same dataset on biomechanical exposure and candidate predictors among computer users, and they were compared in terms of identified predictors of exposure as well as the resulting model fit using bootstrapped resamples of the original data. The identified predictors were, to a large part, different between the two strategies, and the initial model fit was better for the stepwise testing strategy than for the clustering approach. Internal validation of the models using bootstrap resampling with fixed predictors revealed an equally reduced model fit in resampled datasets for both strategies. However, when predictor selection was incorporated in the validation procedure for the stepwise testing strategy, the model fit was reduced to the extent that both strategies showed similar model fit. Thus, the two strategies would both be expected to perform poorly with respect to predicting biomechanical exposure in other samples of computer users.

Prediction models and risk assessment for silicosis using a retrospective cohort study among workers exposed to silica in China

This study aims to develop a prognostic risk prediction model for the development of silicosis among workers exposed to silica dust in China. The prediction model was performed by using retrospective cohort of 3,492 workers exposed to silica in an iron ore, with 33 years of follow-up. We developed a risk score system using a linear combination of the predictors weighted by the LASSO penalized Cox regression coefficients. The model's predictive accuracy was evaluated using time-dependent ROC curves. Six predictors were selected into the final prediction model (age at entry of the cohort, mean concentration of respirable silica, net years of dust exposure, smoking, illiteracy, and no. of jobs). We classified workers into three risk groups according to the quartile (Q1, Q3) of risk score; 203 (23.28%) incident silicosis cases were derived from the high risk group (risk score ≥ 5.91), whilst only 4 (0.46%) cases were from the low risk group (risk score < 3.97). The score system was regarded as accurate given the range of AUCs (83-96%). This study developed a unique score system with a good internal validity, which provides scientific guidance to the clinicians to identify high-risk workers, thus has important cost efficient implications.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

Occupational asthma

Occupational asthma has been defined as asthma due to conditions attributable to work exposures, not to causes outside the workplace. This review focuses on current data on pathogenesis, evaluation, and management.

Medical surveillance for prevention of occupational asthma

PURPOSE OF REVIEW

Because there is sufficient knowledge of its environmental determinants, occupational asthma is a disease that ought to be largely preventable; yet its incidence in many settings remains unacceptably high. Here we review one approach to prevention: the routine use of health surveillance in exposed workforces.

RECENT FINDINGS

Health surveillance is widely practised but there is little evidence that it is used strategically to reduce disease incidence. There are several barriers to the effective use of its various components, chiefly symptoms questionnaires and spirometry. Cost-benefit analyses may help to increase the uptake of industry-wide workplace interventions.

SUMMARY

The effective use of health surveillance for occupational asthma continues to be challenging and there remains relatively little published evidence that will encourage those involved to use it more efficiently. Useful advances could be made by greater collaboration between employers, employee organizations, legislators and researchers.

Investigating factors associated with adherence behaviour in patients with chronic myeloid leukemia: an observational patient-centered outcome study

Background:

Optimal adherence to imatinib therapy is of paramount importance to maximise treatment effectiveness in patients with chronic myeloid leukaemia (CML). The main objective of this study was to investigate patient-reported personal factors associated with adherence behaviour.

Methods:

Analysis was conducted on 413 CML patients receiving long-term therapy with imatinib. Adherence behaviour was measured with the Morisky Medication Adherence Scale and personal factors investigated included: quality of life, perceived social support, fatigue, symptom burden, psychological wellbeing and desire for additional information. Key socio-demographic and treatment-related factors were also taken into account. Univariate and multivariate logistic regression analyses were used to investigate factors associated with optimal adherence to therapy.

Results:

In all, 53% of patients reported an optimal adherence behaviour. The final multivariate model retained the following variables as independent predictors of optimal adherence to therapy: desire for more information (ref. no), odds ratio (OR)=0.43 (95% confidence interval (CI), 0.29–0.66; P<0.001), social support (higher score representing greater support), OR=1.29 (95% CI, 1.11–1.49; P<0.001) and concomitant drug burden (ref. no), OR=1.82 (95% CI, 1.18–2.80; P=0.006).

Conclusion:

This study suggests that a higher level of social support, satisfaction with information received and concomitant drug burden are the main factors associated with greater adherence to long-term imatinib therapy.