BACKGROUND: The problem of transcoding is recurrent when researchers wish to link occupational data from cohorts to Job-Exposure Matrices (JEMs) which were not set up in the same classifications. The Occupational Asthma-specific JEM (OAsJEM) is a JEM developed for assessing exposure to agents known at risk for asthma for jobs coded with ISCO88 occupation classification. To apply the OAsJEM in the CONSTANCES cohort, in which jobs and industries were coded with French PCS2003 and NAF2008 classifications respectively, we developed a crosswalk to convert jobs from PCS2003 into ISCO88 classification.
METHODS: This work was carried out by 2 skilled coders and 1 novice coder who have worked independently by using different tools and transcoding strategies defined a priori. Consensus meetings were organized with skilled coders to define the final crosswalk. This work was elaborated in 2 steps: (i) for 38 ISCO88 codes classified as potentially exposed to cleaning and disinfection products by the OAsJEM, and (ii) for all jobs from the ISCO88 classification. A comparison between the 3 initial coder\'s crosswalk proposals and the final crosswalk was made for the 38 ISCO codes in step (i).
RESULTS: The final crosswalk provided 998 matches between the 482 4-digit PCS2003 codes, 308 4-digit ISCO88 codes, and 31 3-digit ISCO88 codes. Information regarding the NAF2008 industry classification was also used in some cases to improve the final crosswalk. For the selected 38 ISCO88 codes, the final crosswalk provided 110 combinations, but the number of proposed ISCO88-PCS2003 couples by each of the 3 coders varied greatly from 68 to 153. In addition, an important variability between the 3 coders were observed among the number of common combinations between the initial coder\'s proposals and the final crosswalk (from 47% to 78%).
CONCLUSIONS: We have developed a crosswalk specifically for an application of the OAsJEM in population-based surveys using the PCS2003 occupation classification. The development of this crosswalk is of great interest for the use of OAsJEM on the data of the CONSTANCES cohort and on any other survey with occupational data coded according to the French classifications. This OAsJEM crosswalk could be used by other teams to evaluate occupational exposures known to be at risk of asthma from occupational calendars coded with French classifications.

BACKGROUND: The Sheehan Disability Scale (SDS) and the World Health Organization Disability Assessment Scale (WHODAS 2.0) have been widely used to measure functional impairment and disability. To ensure that the scores from these two scales are practically exchangeable across diseases, therapies, and care programmes, the current study aimed to examine the linkage of the WHODAS 2.0 with the SDS and develop a simple and reliable conversion table for the two scales in people with mental disorders.
METHODS: A total of 798 patients (mean age = 36.1, SD = 12.7) were recruited from outpatient clinics of the Institute of Mental Health, and the Community Wellness Clinic in Singapore. Using a single-group design, an equipercentile equating method with log-linear smoothing was used to establish a conversion table from the SDS to the WHODAS 2.0 and vice versa.
RESULTS: The conversion table showed that the scores were consistent for the entire range of scores when the scores were converted either from the SDS to the WHODAS 2.0 or from the WHODAS 2.0 to the SDS. The agreement between the WHODAS 2.0\'s raw and converted scores and SDS\'s raw and converted scores were interpreted as good with intraclass correlation coefficient of 0.711 and 0.725, respectively.
CONCLUSIONS: This study presents a simple and reliable method for converting the SDS scores to the WHODAS 2.0 scores and vice versa, enabling interchangeable use of data across these two disability measures.

UNASSIGNED: The recent COVID-19 (Corona Virus Disease 2019) pandemic dramatically underlined the multi-faceted nature of health research, requiring input from basic biological sciences, pharmaceutical technologies, clinical research), social sciences and public health and social engineering. Systems that could work across different disciplines would therefore seem to be a useful idea to explore. In this study we investigated whether metadata schemas and vocabularies used for discovering scientific studies and resources in the social sciences and in clinical research are similar enough to allow information from different source disciplines to be easily retrieved and presented together.
UNASSIGNED: As a first step a literature search was performed, exemplarily identifying studies and resources, in which data from social sciences have been usefully employed or integrated with that from clinical research and clinical trials. In a second step a comparison of metadata schemas and related resource catalogues in ECRIN (European Clinical Research Infrastructure Network) and CESSDA (Consortium of European Social Science Data Archives) was performed. The focus was on discovery metadata, here defined as the metadata elements used to identify and locate scientific resources.
UNASSIGNED: A close view at the metadata schemas of CESSDA and ECRIN and the basic discovery metadata as well as a crosswalk between ECRIN and CESSDA metadata schemas have shown that there is considerable resemblance between them.
UNASSIGNED: The resemblance could serve as a promising starting point to implement a common search mechanism for ECRIN and CESSDA metadata. In the paper four different options for how to proceed with implementation issues are presented.

Aligned with the increasing need for standardized assessment of physical function in older individuals with cancer and other conditions, several patient-reported outcome measures (PROMs) have been developed and published. The aim of this study is to link the Strength, Assistance with walking, Rising from a chair, Climbing stairs, and Falls questionnaire (SARC-F), and the Patient-Reported Outcomes Measurement Information System® (PROMIS®) Physical Function Short Form 8c (PROMIS PF 8c), and make their scores convertible, in order to expand the use of both instruments in research and inform clinicians and researchers about the interchangeability of critical cut-off scores.
The sample included 300 participants recruited from an online panel. Participants were included if they had received a cancer diagnosis from a clinician and reported receiving anti-cancer treatment. We conducted five linking procedures and selected an optimal one to generate the crosswalk table between the two measures.
The linked T scores of all five methods showed acceptably small mean differences from the observed T scores, and the standard deviation (SD), and root-mean-squared deviation (RMSD) of the differences were generally similar across all methods. After comparing across all statistics, the Stocking-Lord approach was considered as the optimal approach to compute the crosswalk table for converting SARC-F raw scores to PROMIS PF 8c scores. The crosswalk table shows that the SARC-F cut-off value of 4 between healthy versus symptomatic with a corresponding score of 37 fell in the range of moderate physical function limitation from 30 to 39 on the PROMI PF 8c T score metric.
The linkage in this study has potential for improving clinical and research activities for people with cancer and perhaps others with a similar range of physical function. It facilitates the interpretability in scores of both measures on a common metric anchored on general population for further group-level analysis. Researchers can use this crosswalk to harmonize data collected from either instrument without requiring all cohorts to administer the same instrument for a prospective data collection or retrospective data analysis purpose or for a cross-study effectiveness study.

Periodic revisions of the international classification of diseases (ICD) ensure that the classification reflects new practices and knowledge; however, this complicates retrospective research as diagnoses are coded in different versions. For longitudinal disease trajectory studies, a crosswalk is an essential tool and a comprehensive mapping between ICD-8 and ICD-10 has until now been lacking. In this study, we map all ICD-8 morbidity codes to ICD-10 in the expanded Danish ICD version. We mapped ICD-8 codes to ICD-10, using a many-to-one system inspired by general equivalence mappings such that each ICD-8 code maps to a single ICD-10 code. Each ICD-8 code was manually and unidirectionally mapped to a single ICD-10 code based on medical setting and context. Each match was assigned a score (1 of 4 levels) reflecting the quality of the match and, if applicable, a \"flag\" signalling choices made in the mapping. We provide the first complete mapping of the 8596 ICD-8 morbidity codes to ICD-10 codes. All Danish ICD-8 codes representing diseases were mapped and 5106 (59.4%) achieved the highest consistency score. Only 334 (3.9%) of the ICD-8 codes received the lowest mapping consistency score. The mapping provides a scaffold for translation of ICD-8 to ICD-10, which enable longitudinal disease studies back to and 1969 in Denmark and to 1965 internationally with further adaption.

Vehicles tend to produce more pollutants especially particles at an urban intersection than other segments. Meanwhile, pedestrians at an intersection are inevitably exposed to high particle level and suffered from the health problem. Especially, some particles can deposit in different thoracic areas of the respiratory system and cause serious health problems. Hence, in this paper, the particles from 0.3 to 10 μm in 16 channels were measured to compare the spatio-temporal characteristics of them on the crosswalk and the roadside. Based on the roadside of fixed measurements, submicron particles (< 1 μm) are discovered to have a high relation with traffic signal and exhibit a bimodal distribution pattern in the green phase. On the crosswalk of mobile measurements, submicron particles present decreasing trend along the crosswalk while crossing. Additionally, mobile measurements were conducted across six time intervals that correspond to different pedestrian\'s journey when passing the crosswalk. The results showed that all size particles in the first three journeys present high concentrations than that in other journeys. Furthermore, pedestrian exposure to all 16 channel particles was assessed. The total and regional deposition fractions of these particles in different sizes and age groups are determined. What ought to be paid attention to is that these real-world measurement results contribute to advancing the understanding of pedestrian exposure to size-fractionated particles on crosswalk and assisting the pedestrian to make better informed choice so as to limit particle exposure in these pollution hotspots.

UNASSIGNED: The current study aimed to map the disease-specific Schizophrenia Quality of Life Scale (SQLS) onto the three- and five-level EuroQol five-dimension (EQ-5D-3 L and EQ-5D-5 L), Health Utility Index Mark 3 (HUI3) and Short Form six-dimensional (SF-6D) preference-based instruments to inform future cost-utility analyses for treatment of patients with schizophrenia.
UNASSIGNED: Data from 251 outpatients with schizophrenia spectrum disorders was included for analysis. Ordinary least square (OLS), Tobit and beta regression mixture models were employed to estimate the utility scores. Three regression models with a total of 66 specifications were determined by goodness of fit and predictive indices. Distribution of the original data to the distributions of the data generated using the preferred estimated models were then compared.
UNASSIGNED: EQ-5D-3 L and EQ-5D-5 L were best predicted by the OLS model, including SQLS domain scores, domain-squared scores, age, and gender as explanatory predictors. The models produced the best performance index and resembled most closely with the observed EQ-5D data. HUI3 and SF-6D were best predicted by the OLS and Tobit model respectively.
UNASSIGNED: The current study developed mapping models for converting SQLS scores into generic utility scores, which can be used for economic evaluation among patients with schizophrenia.

The American Society of Anesthesiologists Physical Status (ASA-PS) grade better risk stratifies geriatric trauma patients, but it is only reported in patients scheduled for surgery. The Charlson Comorbidity Index (CCI), however, is available for all patients. This study aims to create a crosswalk from the CCI to ASA-PS. Geriatric trauma cases, aged 55 years and older with both ASA-PS and CCI values (N = 4223), were used for the analysis. We assessed the relationship between CCI and ASA-PS, adjusting for age, sex, marital status, and body mass index. We reported the predicted probabilities and the receiver operating characteristics. A CCI of zero was highly predictive of ASA-PS grade 1 or 2, and a CCI of 1 or higher was highly predictive of ASA-PS grade 3 or 4. Additionally, while a CCI of 3 predicted ASA-PS grade 4, a CCI of 4 and higher exhibited greater accuracy in predicting ASA-PS grade 4. We created a formula that may accurately situate a geriatric trauma patient in the appropriate ASA-PS grade after adjusting for age, sex, marital status, and body mass index. In conclusion, ASA-PS grades can be predicted from CCI, and this may aid in generating more predictive trauma models.

Pedestrian accidents contribute significantly to the high number of annual traffic casualties. It is therefore crucial for pedestrians to use safety measures, such as a crosswalk, and to activate pedestrian signals. However, people often fail to activate the signal or are unable to do so - those who are visually impaired or have occupied hands may be unable to activate the system. Failure to activate the signal can result in an accident. This paper proposes an improvement to crosswalk safety by designing a system that can detect pedestrians and trigger the pedestrian signal automatically when necessary.
In this study, a dataset of images was collected in order to train a Convolutional Neural Network (CNN) to distinguish between pedestrians (including bicycle riders) when crossing the street. The resulting system can capture and evaluate images in real-time, and the result can be used to automatically activate a system such as a pedestrian signal. A threshold system is also implemented that triggers the crosswalk only when the positive predictions pass the threshold. This system was tested by deploying it at three real-world environments and comparing the results with a recorded video of the camera\'s view.
The CNN prediction model is able to correctly predict pedestrian and cyclist intentions with an average accuracy of 84.96% and had an absence trigger rate of 0.037%. The prediction accuracy varies based on the location and whether a cyclist or pedestrian is in front of the camera. Pedestrians crossing the street were correctly predicted more accurately than cyclists crossing the street by up to 11.61%, while passing (i.e., non-crossing) cyclists were correctly ignored more than passing pedestrians, by up to 18.75%.
Based on the testing of the system in real-world environments, the authors conclude that it is feasible as a back-up system that can complement existing pedestrian signal buttons, and thereby improve the overall safety of crossing the street. Further improvements to the accuracy can be achieved with a more comprehensive dataset for a specific location where the system is deployed. Implementing different computer vision techniques optimized for tracking objects should also increase the accuracy.

UNASSIGNED: The choice of patient-reported outcome measure (PROM) used in shoulder studies varies based on clinician\'s preference and location. This creates difficulties when attempting to compare studies which have used different PROMs as their outcome measure. This study aims to assess the agreement between the American Shoulder and Elbow Surgeons score (ASES) and the Oxford Shoulder Score (OSS), and identify factors associated with agreement.
UNASSIGNED: Patients with shoulder pathology were identified from a multi-cohort observational practice registry. 1050 paired ASES and OSS pre-treatment scores were prospectively collected. Linear regression was performed to assess the agreement between the PROMs. Mixed-effects analysis of variance was performed to assess the influence of factors associated with agreement.
UNASSIGNED: Regression for mean total and mean function ASES and OSS demonstrated good fit (adjusted R2 57.7%, P < 0.001; and 63.9%, P < 0.001). Mean pain subscore demonstrated a poorer fit (adjusted R2 39.4%, P < 0.001). Crosswalks to convert between mean scores were produced with reasonable precision. Veterans RAND 12-Item Health Survey score, age and diagnosis cohort influenced agreement.
UNASSIGNED: Mean total and mean function ASES and OSS scores agree well with each other. This allows for a more informed comparison of studies using either PROMs as their outcome measure.