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Drawing on student approaches to learning research, this study combined both self-reported and process data to examine: (1) the extent of the alignment between the self-reported and process data of the profiles of 179 Chinese university students' learning experience in the flipped classrooms and (2) the contributions of the self-reported and process data of students' learning experience to their academic performance. Two groups of students with contrasting learning experiences were identified. One group reported a more desirable learning experience (cohesive conceptions of learning theoretical mechanics, deep approaches in both face-to-face and online learning, and positive perceptions of both the human and non-human elements of the learning environments in the course). Another group had a poorer learning experience (fragmented conceptions, using surface approaches, and perceiving a lack of peer online interaction and not appraising online design of the course site). The students self-reporting better and poorer learning experiences, however, only differed in frequency of participation in the pre-lecture quizzes, demonstrating weak alignment between the self-reported and process data. The hierarchical regression analyses revealed that including both self-reported and process data significantly increased the variance explained in students' academic performance. [ABSTRACT FROM AUTHOR]

When self-reported data are used in statistical analysis to estimate the mean and variance, as well as the regression parameters, the estimates tend, in many cases, to be biased. This is because interviewees have a tendency to heap their answers to certain values. The aim of the paper is to examine the bias-inducing effect of the heaping error in self-reported data, and study the effect on the heaping error on the mean and variance of a distribution as well as the regression parameters. As a result a new method is introduced to correct the effects of bias due to the heaping error using validation data. Using publicly available data and simulation studies, it can be shown that the newly developed method is practical and can easily be applied to correct the bias in the estimated mean and variance, as well as in the estimated regression parameters computed from self-reported data. Hence, using the method of correction presented in this paper allows researchers to draw accurate conclusions leading to the right decisions, e.g. regarding health care planning and delivery. [ABSTRACT FROM AUTHOR]

Background Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self‐reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. Methods and Results We used data from the population‐based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self‐report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self‐report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self‐report tool (n=14 variables) and the clinical tool (n=23 variables) showed high‐to‐excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P

Researchers use often self-reported grades as indicators of students' educational achievement because of difficulties in obtaining the official records. However, the results of such studies are questionable due to doubts regarding the reliability of self-reported grade data and factors that may lead to distortion. In this study, we help to resolve this issue by comparing self-reported grade point averages (GPAs) of 926 eighth grade Romanian students from the previous academic year with their official GPAs. At the descriptive and practical levels, we conclude that the distortions are positive--overreporting is more frequent than underreporting--and that their limited magnitude allows for the use of self-reported grades for the usually description i.e. of achievements in a population of students but not for individual students' diagnostics. At the explanatory level, the distortions are non-random, and there are two main causal mechanisms. First, overreporting is a cognitive and commitment-based error. Second, overreporting is an effect of social desirability, according to which students are expected to perform well based on their social characteristics. However, this second causal channel is not visible in bivariate tests because it is statistically suppressed by the main mechanism and the correlation of actual GPA with gender and socioeconomic status (SES -- parents' education and class) variables. [ABSTRACT FROM AUTHOR]

From the initial COVID-19 outbreak, Italy was the first Western country to be seriously affected by the pandemic. Understanding vaccine hesitancy can help efforts to achieve broad vaccination coverage. The objectives of this research were to determine the extent of vaccine hesitancy in Italy and to understand the characteristics of those segments of the population with some hesitancy. Between January and February 2021, 41,473 subjects answered the second questionnaire delivered in phase II of the web-based EPICOVID19 survey. Among the included adult volunteers living in Italy, 4653 (11.2%) reported having previously received at least one dose of the COVID-19 vaccine. In the sample of 36,820 respondents, all not vaccinated (age 51.1 ± 13.5; 59.7% female; 63.6% high level of education), the comparison between hesitant and inclined participants was accompanied by percentages and odds ratios. A total of 2449 individuals were hesitant (6.7% of the unvaccinated ones). Hesitancy was higher among women (OR = 1.48; 95%CI: 1.36–1.62); it was highest in the 50–59 and 40–49 age groups and among those with a lower educational level. A higher level of education was associated with a lower proportion of hesitancy (5.54%) compared with 9.44% among respondents with a low level of education (OR = 0.56; 95%CI: 0.46–0.68). Hesitancy was most common in subjects who did not report fear of infection (12.4%, OR = 4.0; 95%CI: 3.46–4.61). The results can guide the design of tailored information and communication campaigns through considering objective and subjective characteristics. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to examine the factors that contribute to the early prediction of Massive Open Online Courses (MOOCs) dropouts in order to identify and support at-risk students. We utilize MOOC data of specific duration, with a guided study pace. The dataset exhibits class imbalance, and we apply oversampling techniques to ensure data balancing and unbiased prediction. We examine the predictive performance of five classic classification machine learning (ML) algorithms under four different oversampling techniques and various evaluation metrics. Additionally, we explore the influence of self-reported self-regulated learning (SRL) data provided by students and various other prominent features of MOOCs as potential indicators of early stage dropout prediction. The research questions focus on (1) the performance of the classic classification ML models using various evaluation metrics before and after different methods of oversampling, (2) which self-reported data may constitute crucial predictors for dropout propensity, and (3) the effect of the SRL factor on the dropout prediction performance. The main conclusions are: (1) prominent predictors, including employment status, frequency of chat tool usage, prior subject-related experiences, gender, education, and willingness to participate, exhibit remarkable efficacy in achieving high to excellent recall performance, particularly when specific combinations of algorithms and oversampling methods are applied, (2) self-reported SRL factor, combined with easily provided/self-reported features, performed well as a predictor in terms of recall when LR and SVM algorithms were employed, (3) it is crucial to test diverse machine learning algorithms and oversampling methods in predictive modeling. [ABSTRACT FROM AUTHOR]

We have developed several algorithms to identify nonspecific low back pain in medical-administrative databases using International Classification of Diseases , Ninth Revision and International Classification of Diseases, Tenth Revision. Identifying nonspecific low back pain (LBP) in medico-administrative databases is a major challenge because of the number and heterogeneity of existing diagnostic codes and the absence of standard definitions to use as reference. The objective of this study was to evaluate the sensitivity and specificity of algorithms for the identification of nonspecific LBP from medico-administrative data using self-report information as the reference standard. Self-report data came from the PROspective Québec Study on Work and Health , a 24-year prospective cohort study of white-collar workers. All diagnostic codes that could be associated with nonspecific LBP were identified from the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) in physician and hospital claims. Seven algorithms for identifying nonspecific LBP were built and compared with self-report information. Sensitivity analyses were also conducted using more stringent definitions of LBP. There were 5980 study participants with (n = 2847) and without (n = 3133) LBP included in the analyses. An algorithm that included at least 1 diagnostic code for nonspecific LBP was best to identify cases of LBP in medico-administrative data with sensitivity varying between 8.9% (95% confidence interval [CI] 7.9-10.0) for a 1-year window and 21.5% (95% CI 20.0-23.0) for a 3-year window. Specificity varied from 97.1% (95% CI 96.5-97.7) for a 1-year window to 90.4% (95% CI 89.4-91.5) for a 3-year window. The low sensitivity we found reveals that the identification of nonspecific cases of LBP in administrative data is limited, possibly due to the lack of traditional medical consultation. [ABSTRACT FROM AUTHOR]

There is an urgent need for improved and timely health and nutrition data. We developed and tested a smartphone application that caregivers from a pastoral population used to measure, record and submit high‐frequency and longitudinal health and nutrition information on themselves and their children. The data were assessed by comparing caregiver‐submitted measurements of mid–upper arm circumference (MUAC) to several benchmark data sets, including data collected by community health volunteers from the participating caregivers during the project period and data generated by interpreting photographs of MUAC measurements submitted by all participants. We found that the caregivers participated frequently and consistently over the 12‐month period of the project; most of them made several measurements and submissions in at least 48 of the 52 weeks of the project. The evaluation of data quality was sensitive to which data set was used as the benchmark, but the results indicate that the errors in the caregivers' submissions were similar to that of enumerators in other studies. We then compare the costs of this alternative approach to data collection through more conventional methods, concluding that conventional methods can be more cost‐effective for large socioeconomic surveys that value the breadth of the survey over the frequency of data, while the alternative we tested is favoured for those with objectives that are better met by high‐frequency observations of a smaller number of well‐defined outcomes. Key messages: Caregivers, even those that are illiterate and with little smartphone experience, can and will use a simple icon‐based smartphone application to measure, record and submit health and nutrition information on themselves and their children consistently and at high frequency.The errors in the data submitted by the caregivers are similar to those of data submitted by enumerators in similar settings.The cost per data point of submissions by caregivers is much lower than data collected using conventional approaches but the simple icon‐ and audio‐based approach to data collection may not be appropriate for all data needs. [ABSTRACT FROM AUTHOR]

Drawing from a larger CIPP program evaluation (Context, Input, Processes, Products) comprised of 11 analyses of the ETS online teacher training course titled Teaching Academic English with the TOEFL iBT® Test (TAE), this paper triangulates three qualitatively different data sets and seeks to understand how participants responded to the TAE course. The research team considered self-reported data (closed- and open-answer survey questions), engagement data, and learning data. The self-reported data were strongly positive and illustrated the value participants reported having about the course content, especially ETS-created materials. Some gaps were indicated: calls were made for more materials--especially additional resources participants could use when teaching--and for increased interactions during the course workshop. In partial juxtaposition, the engagement data indicated that participants were engaging the course less than anticipated. The learning data simultaneously indicated that some assessments were quite easy for participants while elsewhere participants did not have the desired uptake of some topics. Viewing the three data sets together, the research team arrived at a more nuanced conclusion: despite participant satisfaction, development processes should continue, to ensure the highest quality teacher training program possible, in terms of its content, resources, and interactions. [ABSTRACT FROM AUTHOR]

From the initial COVID-19 outbreak, Italy was the first Western country to be seriously affected by the pandemic. Understanding vaccine hesitancy can help efforts to achieve broad vaccination coverage. The objectives of this research were to determine the extent of vaccine hesitancy in Italy and to understand the characteristics of those segments of the population with some hesitancy. Between January and February 2021, 41,473 subjects answered the second questionnaire delivered in phase II of the web-based EPICOVID19 survey. Among the included adult volunteers living in Italy, 4653 (11.2%) reported having previously received at least one dose of the COVID-19 vaccine. In the sample of 36,820 respondents, all not vaccinated (age 51.1 ± 13.5; 59.7% female; 63.6% high level of education), the comparison between hesitant and inclined participants was accompanied by percentages and odds ratios. A total of 2449 individuals were hesitant (6.7% of the unvaccinated ones). Hesitancy was higher among women (OR = 1.48; 95%CI: 1.36–1.62); it was highest in the 50–59 and 40–49 age groups and among those with a lower educational level. A higher level of education was associated with a lower proportion of hesitancy (5.54%) compared with 9.44% among respondents with a low level of education (OR = 0.56; 95%CI: 0.46–0.68). Hesitancy was most common in subjects who did not report fear of infection (12.4%, OR = 4.0; 95%CI: 3.46–4.61). The results can guide the design of tailored information and communication campaigns through considering objective and subjective characteristics.

The primary objective of this study is to examine the factors that contribute to the early prediction of Massive Open Online Courses (MOOCs) dropouts in order to identify and support at-risk students. We utilize MOOC data of specific duration, with a guided study pace. The dataset exhibits class imbalance, and we apply oversampling techniques to ensure data balancing and unbiased prediction. We examine the predictive performance of five classic classification machine learning (ML) algorithms under four different oversampling techniques and various evaluation metrics. Additionally, we explore the influence of self-reported self-regulated learning (SRL) data provided by students and various other prominent features of MOOCs as potential indicators of early stage dropout prediction. The research questions focus on (1) the performance of the classic classification ML models using various evaluation metrics before and after different methods of oversampling, (2) which self-reported data may constitute crucial predictors for dropout propensity, and (3) the effect of the SRL factor on the dropout prediction performance. The main conclusions are: (1) prominent predictors, including employment status, frequency of chat tool usage, prior subject-related experiences, gender, education, and willingness to participate, exhibit remarkable efficacy in achieving high to excellent recall performance, particularly when specific combinations of algorithms and oversampling methods are applied, (2) self-reported SRL factor, combined with easily provided/self-reported features, performed well as a predictor in terms of recall when LR and SVM algorithms were employed, (3) it is crucial to test diverse machine learning algorithms and oversampling methods in predictive modeling.

Background: The first imperative in producing the relevant and needed knowledge about major neurocognitive disorder (MNCD) is to identify people presenting with the condition adequately. To document potential disparities between administrative health databases and population-based surveys could help identify specific challenges in this population and methodological shortfalls.Objective: To describe and compare the characteristics of community-dwelling older adults according to four groups: 1) No MNCD; 2) Self-reported MNCD only; 3) MNCD in administrative health data only; 4) MNCD in both self-reported and administrative health data.Methods: This retrospective cohort study used the Care Trajectories-Enriched Data (TorSaDE) cohort, a linkage between five waves of the Canadian Community Health Survey (CCHS) and health administrative health data. We included older adults living in the community who participated in at least one cycle of the CCHS. We reported on positive and negative MNCD in self-reported versus administrative health data. We then compared groups' characteristics using chi-square tests and ANOVA.Results: The study cohort was composed of 25,125 older adults, of which 784 (3.1%) had MNCD. About 70% of people with an MNCD identified in administrative health data did not report it in the CCHS. The four groups present specific challenges related to the importance of perception, timely diagnosis, and the caregivers' roles in reporting health information.Conclusion: To a certain degree, both data sources fail to consider subgroups experiencing issues related to MNCD; studies like ours provide insight to understand their characteristics and needs better. [ABSTRACT FROM AUTHOR]

Aim: To examine self‐reported opioid prevalence at a dental school clinic for patients ≥65 years old as compared to national data, comparing gender, ethnicity/race and older adult age groupings. Methods and results: Self‐reported prescription opioid medication use was extracted from the medical record for dental patients ≥65 years old who visited the school's general dental clinic (GDC) in 2012 or 2017. This data was compared to the National Health and Nutrition Examination Survey (NHANES) data for 2011–2012 and 2017–18. There was a significant increase in prevalence of opioid use in adults ≥65 between 2012 (4.5%) and 2017 (6.5%) and for ages 65–79 (from 4.7% to 6.3%) and ≥80 (3.4% to 7.9%), women (4.8% to 7.0%), and African Americans (4.7% to 8.4%) in the GDC. Older adults at the GDC reported less opioid use than the NHANES national average for both periods no matter the gender or the age with variable results for race/ethnicity. Conclusion: The prevalence of older adults taking opioids in our general dental school clinic population increased significantly in 2017 as compared to 2012 but was lower than the national average for the respective periods. Awareness of existing opioid usage in older adult patients and its higher adverse risk potential is critical when prescribing analgesics for dental pain for this age group. [ABSTRACT FROM AUTHOR]

Background: Producing relevant knowledge on the prevalence of mood disorders (MDs) requires a clear identification of people living with the condition. Analyzing this multifaceted disease from the perspective of health administrative data and population-based surveys could contribute to document inconsistencies between these data sources and highlight the strengths and limitations of each methodological approaches., Objectives: The aim of this study was to estimate the prevalence of MD disease, assess concordance of MD patterns in population-based surveys versus health administrative data, and investigate statistical differences in characteristics between individuals presenting the disease in each data sources., Methods: This study used the Care Trajectories-Enriched Data (TorSaDE) cohort. The TorSaDE cohort is built by merging five waves of the Canadian Community Health Survey (CCHS) with health administrative data of the province of Quebec, Canada. The sample includes individuals who participated in at least one round of CCHS and for whom evidence of use of health services in the year of CCHS completion and the year before were present in health administrative data. The cohort was split into four groups based on the presence and absence of MD in self-reported versus health administrative data. Groups' characteristics were compared using chi-square tests and ANOVA., Results: The study cohort was composed of 96,079 individuals, of which 10,418 (10.8%) had MD, regardless of the data sources. Self-reported prevalence of MD was 6.03%, while the prevalence from health administrative data was about 7.79%. Estimates showed a low level of concordance between the two measures, as only 27.4% of people presenting this medical condition were identified in both data sources. Furthermore, individuals identified with MD only in survey data had poorer socioeconomic outcomes but better health outcomes than those from the concordant group (i.e., identified in both data sources). In addition, people presenting MD in health administrative data only had better socioeconomic and health outcomes than those who reported MD diagnosis only in survey data., Conclusion: Findings suggest that each measure capture different specific subpopulations. Estimates obtained from each source should thus be contextualized and interpreted with caution., (© 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.)

Background: Social inclusion is a context for both risk and protective factors of migrant youth delinquency. This study aims to shed light on the issue by comparing delinquency amongst native, first-generation, and second-generation immigrant youths in Portugal, a country located in the south of Europe, an area where research in this field is still scarce. Methods: The research is based on the International Self-Reported Delinquency (ISRD-3) dataset, which includes information on over 4,000 adolescents, who self-reported on their socio-demographic status, leisure activities, school and neighbourhood environment, family bonds, and self-control. Results: Nested Logistic Regression analyses showed that a young first-generation immigrant is twice as likely to commit a crime, with or without violence, as a young native born in Portugal. However, no differences were found regarding the prevalence of delinquency amongst second-generation immigrants and natives, which is likely due to the integration and cultural assimilation of the immigrant over time. Regarding the analysed risk factors, it was found that both structural and individual factors, identified by the theories of control, stress, as well as situational action theory, have a direct effect on the commission of juvenile crimes (both non-violent and violent). Moreover, this effect is significant in adolescents living in Portugal in general, both immigrants and natives. The most influential variable for both types of delinquent behaviour, with and without violence, is peer delinquency, followed by low morality and self-control. Conclusion: These findings have relevant policy implications and are useful for evidence-based interventions aimed at promoting migrant adolescent well-being and targeting host countries' performance. [ABSTRACT FROM AUTHOR]

BackgroundSocial inclusion is a context for both risk and protective factors of migrant youth delinquency. This study aims to shed light on the issue by comparing delinquency amongst native, first-generation, and second-generation immigrant youths in Portugal, a country located in the south of Europe, an area where research in this field is still scarce.MethodsThe research is based on the International Self-Reported Delinquency (ISRD-3) dataset, which includes information on over 4,000 adolescents, who self-reported on their socio-demographic status, leisure activities, school and neighbourhood environment, family bonds, and self-control.ResultsNested Logistic Regression analyses showed that a young first-generation immigrant is twice as likely to commit a crime, with or without violence, as a young native born in Portugal. However, no differences were found regarding the prevalence of delinquency amongst second-generation immigrants and natives, which is likely due to the integration and cultural assimilation of the immigrant over time. Regarding the analysed risk factors, it was found that both structural and individual factors, identified by the theories of control, stress, as well as situational action theory, have a direct effect on the commission of juvenile crimes (both non-violent and violent). Moreover, this effect is significant in adolescents living in Portugal in general, both immigrants and natives. The most influential variable for both types of delinquent behaviour, with and without violence, is peer delinquency, followed by low morality and self-control.ConclusionThese findings have relevant policy implications and are useful for evidence-based interventions aimed at promoting migrant adolescent well-being and targeting host countries’ performance.

Aims: To assess the validity of self‐reported continuous medication use with drug metabolites measured in plasma by using untargeted mass spectrometric techniques. Methods: In a population‐based cohort in Bonn, Germany, we compared interview‐based, self‐reported medication intake with drug‐specific metabolites measured in plasma (based on participants who completed their study visits between March 2016 and February 2020). Analyses were done stratified by sex and age (<65 years vs ≥65 years). Cohen's kappa (κ) statistics with 95% confidence intervals (CI) were calculated. Results: A total of 13 drugs used to treat hypertension, gout, diabetes, epilepsy and depression were analysed in a sample of 4386 individuals (mean age 55 years, 56.1% women). Eleven drugs showed almost perfect agreement (κ > 0.8), whereas sitagliptin and hydrochlorothiazide showed substantial (κ = 0.8, 95% CI 0.71–0.90) and moderate agreement (κ = 0.61, 95% CI 0.56–0.66), respectively. Frequency of use allowed sex‐ and age‐stratified analyses for eight and nine drugs, respectively. For five drugs, concordance tended to be higher for women than for men. For most drugs, concordance was higher among individuals aged ≥65 years than among individuals aged <65 years, but these age‐related differences were not statistically significant. Conclusion: High concordance rates between self‐reported drug use and metabolites measured in plasma suggest that self‐reported drug use is reliable and accurate for assessing drug use. [ABSTRACT FROM AUTHOR]

Logbook data from commercial fisheries are a vital component in the machinery of management, including tracking the volume of catches and allocating catch spatially. At the same time, logbooks can provide a unique window into the ecological and sociological conditions in marine fisheries, where fishermen interact with marine species and environments frequently and broadly. Traditional logbooks, however, often are not sufficiently standardized (when personal logs), or lack the detail (when regulatory documents) required to adequately understand fisheries ecosystems. The Study Fleet program, operated by the Northeast Fisheries Science Center’s Cooperative Research Branch, was developed to address these shortfalls by engaging members of the fishing industry in collecting high-resolution catch, effort, and environmental data using electronic logbooks. Since its inception, the Study Fleet has expanded from a small project focused on collecting detailed catch information from the New England multispecies groundfish fishery to a program with a wider scope encompassing a variety of fisheries, gears, and environmental parameters from North Carolina to Maine U.S. Over the years, a number of lessons have been learned about recruiting and supporting industry partners, managing the data, evolving technical specifications, and the challenges associated with analyzing and applying self-reported fisheries data. Here we describe the current state of the program and provide summaries of the Study Fleet program operations and outcomes from 2007-2020, with an eye towards successes, challenges, and applicability of the approach in other regions. We suggest other reference fleet programs, as well as other developing fishery dependent data collections (e.g., electronic monitoring programs), develop detailed roadmaps for each data collection to keep participants engaged as collaborators, target specific fisheries to keep resources from being stretched too thin, and partner with data users early. Additionally, we suggest programs invest in the long-term participation of individual fishermen, carefully weigh the pros and cons of involvement in regulatory reporting, and plan data products and applications well in advance to ensure that the sampling scheme and granularity of the data meet the needs of stock assessment, ecosystem, and oceanographic scientists.

Cocaine addiction is an important public health problem worldwide. Cognitive-behavioral therapy is a counseling intervention for supporting cocaine-dependent individuals through recovery and relapse prevention. It may reduce patients' cocaine uses by improving their motivations and enabling them to recognize risky situations. To study the effect of cognitive behavioral therapy on cocaine dependence, the self-reported cocaine use with urine test data were collected at the Primary Care Center of Yale-New Haven Hospital. Its outcomes are binary, including both the daily self-reported drug uses and weekly urine test results. To date, the generalized estimating equations are widely used to analyze binary data with repeated measures. However, due to the existence of significant self-report bias in the self-reported cocaine use with urine test data, a direct application of the generalized estimating equations approach may not be valid. In this paper, we proposed a novel mean corrected generalized estimating equations approach for analyzing longitudinal binary outcomes subject to reporting bias. The mean corrected generalized estimating equations can provide consistently and asymptotically normally distributed estimators under true contamination probabilities. In the self-reported cocaine use with urine test study, accurate weekly urine test results are used to detect contamination. The superior performances of the proposed method are illustrated by both simulation studies and real data analysis. [ABSTRACT FROM AUTHOR]

Introduction: Driving while intoxicated is a major risk factor for road traffic crashes, and it is important to understand the global developments, differences between countries, and determinants of driving while intoxicated. Because objective data are often missing, it is useful to investigate self-reported data. Method: The E-Survey of Road Users' Attitudes (ESRA2) on attitudes and behaviours in traffic—performed in 21 countries in Europe and 32 countries worldwide—was used to study the prevalence of self-reported impaired driving, national differences, changes over time, and factors that determine impaired driving and multiple traffic offences. Descriptive statistics and regression analyses were used. Results: In the European Union (EU), represented in the survey by 20 member states, 13% of car drivers reported to have driven under the influence of alcohol in the past 30 days, and 5% reported to have driven under the influence of drugs. For the participating countries in Africa, North America, and Asia/Oceania, these percentages were almost the same for drinking and driving (resp. 14%, 11%, 17%) but were clearly higher for drug driving (18%, 12%, 18%). Drivers who reported drinking and driving, drug driving, or multiple traffic offences also reported significantly higher involvement in injury crashes than other drivers. Regression analyses indicated that previous experience with drinking and driving was the strongest predictor for current drinking and driving; additionally, for drug driving, the perceived personal and social acceptance of this risky behaviour were the strongest predictors. Conclusions: Drinking and driving is a common risky behaviour worldwide, with an average of between 11% and 17% of drivers in major regions reporting to have engaged in this behaviour in the last month. Drug driving is less frequently reported in Europe than drinking and driving, but in Africa, North America, and Asia/Oceania, drug driving is reported as much or even more frequently.

Background: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis., Methods and Results: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P <0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly., Conclusions: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.

Purpose: Work disability (WD) is a medico-legal concept that refers to disability benefits (DB) granted due to diseases. We assessed whether subjective cognitive complaints (SCC)—presenting as self-rated difficulties of concentration, memory, clear thinking, and decision making—predict permanent WD in knowledge-intensive occupations. Methods: In this prospective cohort study with up to 7-year follow-up, we combined the SCC questionnaire results with reliable registry data on the DBs of 7161 professional/managerial employees (46% females). We excluded employees who were on long-term sickness absence (SA) or had received a DB at baseline. The exposure variable was the presence of SCC. Age and SA before the questionnaire as a proxy measure of general health were treated as confounders and the analyses were conducted by gender. The outcome variable was a granted DB. The cumulative incidence function illustrates the difference between SCC categories, and the Fine-Gray model estimates the predictors of WD during the 8-year follow-up. Results: The annual incidence of DB was 0.15% in the entire cohort: 0.18% among the females, and 0.12% among the males (p = 0.795). The most common primary reasons for permanent WD were mental (36%) and musculoskeletal (20%) disorders. SCC predicted DB in both genders when controlling for age and prior SA. Hazard ratios were 2.9 with a 95% confidence interval 1.4–6.0 for the females and 3.7 (1.8–7.9) for the males. Conclusion: Subjective cognitive complaints predict permanent WD in knowledge-intensive occupations. This finding has implications for supporting work ability and preventing work disability among employees with cognitively demanding tasks. [ABSTRACT FROM AUTHOR]

Low back pain (LBP) is a high-burden condition that lacks routine surveillance data. Health administrative data may be used for surveillance, but their validity for measuring LBP in the general population has not been established. We aimed to (1) determine the validity of health administrative data to measure LBP compared to self-reported LBP in a population-based sample of Ontario adults; and (2) describe the differences in characteristics of LBP cases based on data sources. Adult respondents (≥18 years) of the Canadian Community Health Survey (CCHS) from 2003 to 2012 were included (N = 150,695). Canadian Community Health Survey data were individually linked to health administrative data, including Ontario Health Insurance Plan and hospitalization data. The reference standard was defined as self-reported back problem diagnosed by a health professional in the CCHS. Measurement of LBP from billing records was defined as ≥1 physician billing or procedural code for LBP during the year preceding CCHS interview date. We measured concurrent validity by comparing prevalence, agreement (kappa), and accuracy (sensitivity, specificity, and positive and negative predictive values [PV]) of administrative data to measure LBP. Prevalence of LBP was higher using self-reported (21.2%) than administrative data (10.2%), and agreement was low (kappa = 0.21). Administrative data had sensitivity 23.9% (95% CI 23.1-24.6), specificity 93.4% (95% CI 93.2-93.7), positive PV 50.4% (95% CI 49.1-51.7), and negative PV 82.0% (95% CI 81.7-82.3). Characteristics of LBP cases based on data sources differed in sex, health/behaviour characteristics, and allied health care utilization. Using health administrative data significantly underestimates the prevalence of LBP. This can lead to misclassification bias that is likely nondifferential in epidemiological studies. [ABSTRACT FROM AUTHOR]

Recent studies show the high prevalence of respondents who perform other activities while responding questionnaires. Due to the scarcity of studies and the possible impact of multitasking on the quality of data, this paper analyses the percentage of respondents who multitask, and examines the most frequent secondary activities engaged in during an online survey conducted with panelists in Spain (N = 1,007). It also analyses a series of variables that are associated with different forms of multitasking and examines the effects of this on the quality of the data. The findings showed a high prevalence of multitaskers (39.7%). In addition, it was found that the profile of the participants varied according to the type of activity they carried out. The effect of multitasking on the quality of the data was limited. [ABSTRACT FROM AUTHOR]

Many industries are required to monitor themselves in meeting regulatory policies intended to protect the environment. Self‐reporting of environmental performance can place the cost of monitoring on companies rather than taxpayers, but there are obvious risks of bias, often addressed through external audits or inspections. Surprisingly, there have been relatively few empirical analyses of bias in industry self‐reported data. Here, we test for bias in reporting of environmental compliance data using a unique data set from Canadian salmon farms, where companies monitor the number of parasitic sea lice on fish in open sea pens, in order to minimize impacts on wild fish in surrounding waters. We fit a hierarchical population‐dynamics model to these sea‐louse count data using a Bayesian approach. We found that the industry's monthly counts of two sea‐louse species, Caligus clemensi and Lepeophtheirus salmonis, increased by a factor of 1.95 (95% credible interval: 1.57, 2.42) and 1.18 (1.06, 1.31), respectively, in months when counts were audited by the federal fisheries department. Consequently, industry sea‐louse counts are less likely to trigger costly but mandated delousing treatments intended to avoid sea‐louse epidemics in wild juvenile salmon. These results highlight the potential for combining external audits of industry self‐reported data with analyses of their reporting to maintain compliance with regulations, achieve intended conservation goals, and build public confidence in the process. [ABSTRACT FROM AUTHOR]

COVID pandemic influence on eating behavior and dietary habits with respect to various plant-based foods in Serbia were estimated by an anonymous questionnaire. Most examinees agree that fruits and vegetables contribute to strengthening immunity and that herbal medicines and natural products have beneficial effects on health. Around 55% of examinees consider their diet balanced, and 4% have started to drink herbal teas more often when the pandemic started. Garlic and ginger were the most frequently reported newly included plants in examinees' diet. The attitudes toward plant-based food products are not strongly dependent on the education level. The age and previous dietary habits of examinees have great influence in the current frequency of consuming fruits, vegetables, herbal teas, spices, and dietary supplements. There was a significant shift toward greater use of herbal teas and dietary supplements, especially among the population that has already consumed them occasionally. Somewhat concerning is the fact that around half of respondents use dietary supplements without the recommendation of an expert. People with good dietary habits and the elderly were most prone to improving their diet. Roughly 15% of all respondents now have the same habits as before the pandemic, although they improved their diets temporarily at the beginning of COVID pandemic. [ABSTRACT FROM AUTHOR]

Knowledge-intensive work requires capabilities like monitoring multiple sources of information, prioritizing between competing tasks, switching between tasks, and resisting distraction from the primary task(s). We assessed whether subjective cognitive complaints (SCC), presenting as self-rated problems with difficulties of concentration, memory, clear thinking and decision making predict sickness absence (SA) in knowledge-intensive occupations. We combined SCC questionnaire results with reliable registry data on SA of 7743 professional/managerial employees (47% female). We excluded employees who were not active in working life, on long-term SA, and those on a work disability benefit at baseline. The exposure variable was the presence of SCC. Age and SA before the questionnaire as a proxy measure of general health were treated as confounders and the analyses were conducted by gender. The outcome measure was the accumulated SA days during a 12-month follow-up. We used a hurdle model to analyse the SA data. SCC predicted the number of SA days during the 12-month follow-up. The ratio of the means of SA days was higher than 2.8 as compared to the reference group, irrespective of gender, with the lowest limit of 95% confidence interval 2.2. In the Hurdle model, SCC, SA days prior to the questionnaire, and age were additive predictors of the likelihood of SA and accumulated SA days, if any. Subjective cognitive complaints predict sickness absence in knowledge-intensive occupations, irrespective of gender, age, or general health. This finding has implications for supporting work ability (productivity) among employees with cognitively demanding tasks.

Background Population health studies often use existing databases that are not necessarily constituted for research purposes. The question arises as to whether different data sources such as in administrative health data (AHD) and self-report questionnaires are equivalent and lead to similar information. Objectives The main objective of this study was to assess the level of agreement between self-reported medical conditions and medical diagnosis captured in AHD. A secondary objective was to identify predictors of agreement among medical conditions between the two data sources. Therefore, the purposes of the study were to explore the extent to which these two methods of commonly used public health data collection provide concordant records and identify the main predictors of statistical variations. Methods Data was extracted from CARTaGENE, a population-based cohort study in Québec, Canada, which was linked to the provincial health insurance records of the same individuals, namely the MED-ÉCHO database from the Régie de l’assurance maladie du Québec (RAMQ) and the fee-for-service billing records provided by the physician, for the time period 1998-2012. Agreement statistics (kappa coefficient) along with sensitivity, specificity and predictive positive value were calculated for 19 chronic conditions and 12 types of cancers. Logistic regressions were used to identify predictors of concordance between self-report and AHD from significant covariates (sex, age groups, education, region, income, heavy utilization of health care system and Charlson comorbidity index). Results Agreement between self-reported data and AHD across diseases ranged from kappa of 0.09 for chronic renal failure to 0.86 for type 2 diabetes. Sensitivity of self-reported data was higher than 50% for 14 out of the 31 medical conditions studied, especially for myocardial infarction (88.62%), breast cancer (86.28%), and diabetes (85.06%). Specificity was generally high with a minimum value of 89.70%. Lower concordance between data sources was observed for higher frequency of health care utilization and higher comorbidity scores. Discussion Overall, there was moderate agreement between the two data sources but important variations were found depending on the type of disease. This suggests that CARTaGENE’s participants were generally able to correctly identify the kind of diseases they suffer from, with some exceptions. These results may help researchers choose adequate data sources according to specific study objectives. These results also suggest that Québec’s AHD seem to underestimate the prevalence of some chronic conditions, which might result in inaccurate estimates of morbidity with consequences for public health surveillance.

OBJECTIVES: The study aim was to examine (i) non-response bias between responders and non-responders, and (ii) whether the association between self-reported sickness absence (SA) and register-based SA differed by gender, age, sector, or physically demanding work. METHODS: The responses of 8110 participants to a question on self-reported SA in past 12 months in the Work Environment and Health in Denmark Survey (2014) was linked to 12 months of SA data from the Danish Register of Work Absence. We used logistic regression for the non-response analysis and Poisson regression to examine associations. RESULTS: Responders had on average 0.5 days less SA per year than non-responders. Public employees had a higher response rate than private employees (approximately five percentage points), women had a higher rate than men (approximately nine percentage points), and older employees a higher rate than younger employees (approximately nine percentage points in ten years). Self-reported SA correlated highly with register-based SA (Spearman’s rank correlation=0.76). In general, responders with few SA days (30) over-reported their SA. Women under-reported significantly more than men (average difference one day); older employees under-reported significantly more than younger employees (difference between age groups 18–29 and 60–64 was 1.7 days). Differences between sectors or levels of physically demanding work were non-significant. CONCLUSIONS: Self-reported SA data may be influenced by non-response bias, and different accuracy in different demographic groups. When available, the use of register-based SA data is recommended.

Rahmat Sediq,1 Jurjen van der Schans,1 Aafje Dotinga,2 Rolinde A Alingh,2 Bob Wilffert,1,3 Jens HJ Bos,1 Catharina CM Schuiling-Veninga,1 Eelko Hak1,4 1Department of Pharmaco-Therapy, Epidemiology & Economics, University of Groningen, Groningen Research Institute of Pharmacy, Groningen, the Netherlands; 2Lifelines Cohort Study, Lifelines Databeheer B.V., Groningen, the Netherlands; 3Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands; 4Department of Epidemiology, University Medical Center Groningen, Groningen, the Netherlands Background: While self-reported data are commonly used as a source of medication use for pharmaco-epidemiological studies, such information is prone to forms of bias. Several previous studies showed that various factors like age, type of drug and data collection method may influence accuracy. We aimed to assess the concordance of the self-reported medication use that was documented at entry to the Lifelines Cohort Study, a three-generation follow-up study in the Netherlands that started in 2006 and included over 167,000 participants. Materials and methods: As part of the PharmLines Initiative, we collected medication data from the Lifelines participants encoded according to the Anatomical Therapeutic Chemical (ATC) coding scheme and linked the data via Statistics Netherlands to the widely used and representative pharmacy prescription database of the University of Groningen, IADB.nl. Analyses were conducted at second level of ATC coding for all recorded medications as well as a top list of most used medications at drug-specific fifth level. Cohen’s kappa statistics were used to measure the concordance for all participants according to sex and age. Results: The level of concordance between the two data sources largely differed according to the therapeutic class. Medication used for the cardiovascular system and diabetes, thyroid therapy, bisphosphonates and anti-thrombotic drugs showed a very good agreement (κ>0.75). Medication as needed or prone to stigmatization bias showed a moderate agreement (κ=0.41–0.60), whereas medications used for short periods of time showed a fair agreement (κ=0.0–0.4). Concordance was similar for males and females, but younger adults tended to have lower concordance rates than older adults. Conclusion: The self-reported method was valid for capturing prevalent chronic medication use at one moment in time, but invalid for medication used for short periods of time. There is no effect of sex on the agreement, and more studies are needed on the influence of age. Future pharmaco-epidemiological studies should preferably combine the two data sources to achieve the highest accuracy of drug exposure rates. Keywords: self-reported data, prescription data, pharmacy records, agreement, questionnaire, medication, interview, validity

Driving while intoxicated is a major risk factor for road traffic crashes, and it is important to understand the global developments, differences between countries, and determinants of driving while intoxicated. Because objective data are often missing, it is useful to investigate self-reported data. The E -Survey of Road Users' Attitudes (ESRA2) on attitudes and behaviours in traffic—performed in 21 countries in Europe and 32 countries worldwide—was used to study the prevalence of self-reported impaired driving, national differences, changes over time, and factors that determine impaired driving and multiple traffic offences. Descriptive statistics and regression analyses were used. In the European Union (EU), represented in the survey by 20 member states, 13% of car drivers reported to have driven under the influence of alcohol in the past 30 days, and 5% reported to have driven under the influence of drugs. For the participating countries in Africa, North America, and Asia/Oceania, these percentages were almost the same for drinking and driving (resp. 14%, 11%, 17%) but were clearly higher for drug driving (18%, 12%, 18%). Drivers who reported drinking and driving, drug driving, or multiple traffic offences also reported significantly higher involvement in injury crashes than other drivers. Regression analyses indicated that previous experience with drinking and driving was the strongest predictor for current drinking and driving; additionally, for drug driving, the perceived personal and social acceptance of this risky behaviour were the strongest predictors. Drinking and driving is a common risky behaviour worldwide, with an average of between 11% and 17% of drivers in major regions reporting to have engaged in this behaviour in the last month. Drug driving is less frequently reported in Europe than drinking and driving, but in Africa, North America, and Asia/Oceania, drug driving is reported as much or even more frequently. • Self-reported driving over the legal limit varies from 11% in the EU to 17% in Asia/Oceania. • Drivers reporting multiple offences vary from 2% (EU) to 7% (Asia/Oceania). • Six countries identified increased odds of self-reported drinking and driving and of drivers engaging in drugged driving. • Multiple offenders have the greatest odds of being involved in (self-reported) injury crashes. • Impaired driving and multiple offences are linked to various violation-related beliefs. [ABSTRACT FROM AUTHOR]

Abstract Background The health of people who use drugs (PWUD) is characterized by multimorbidity and chronicity of health conditions, necessitating an understanding of their health care utilization. The objective of this study was to evaluate emergency department (ED) visits and hospital admissions among a cohort of PWUD. Methods We used a retrospective observational design between 2012 and 2013. The population was a marginalized cohort of PWUD (the PROUD study) for whom survey data was linked (n = 663) to provincial health administrative data housed at the Institute for Clinical Evaluative Sciences. We constructed a 5:1 comparison group matched by age, sex, income quintile, and region. The main outcomes were defined as having two or more ED visits, or one or more hospital admissions, in the year prior to survey completion. We used multivariable logistic regression analyses to identify factors associated with these outcomes. Results Compared to the matched cohort, PWUD had higher rates of ED visits (rate ratio [RR] 7.0; 95% confidence interval [95% CI] 6.5–7.6) and hospitalization (RR 7.7; 95% CI 5.9–10.0). After adjustment, factors predicting more ED visits were receiving disability (adjusted odds ratio [AOR] 3.0; 95% CI 1.7–5.5) or income assistance (AOR 2.7; 95% CI 1.5–5.0), injection drug use (AOR 2.1; 95% CI 1.3–3.4), incarceration within 12 months (AOR 1.6; 95% CI 1.1–2.4), mental health comorbidity (AOR 2.1; 95% CI 1.4–3.1), and a suicide attempt within 12 months (AOR 2.1; 95% CI 1.1–3.4). Receiving methadone (AOR 0.5; 95% CI 0.3–0.9) and having a regular family physician (AOR 0.5; 95% CI 0.2–0.9) were associated with lower odds of having more ED visits. Factors associated with more hospital admissions included Aboriginal identity (AOR 2.4; 95% CI 1.4–4.1), receiving disability (AOR 2.4; 95% CI 1.1–5.4), non-injection drug use (opioids and non-opioids) (AOR 2.2; 95% CI 1.1–4.4), comorbid HIV (AOR 2.4; 95% CI 1.2–5.6), mental health comorbidity (AOR 2.4; 95% CI 1.3–4.2), and unstable housing (AOR 1.9; 95% CI 1.0–3.4); there were no protective factors for hospitalization. Conclusions Improved post-incarceration support, housing services, and access to integrated primary care services including opioid replacement therapy may be effective interventions to decrease acute care use among PWUD, including targeted approaches for people receiving social assistance or with mental health concerns.

Objective: Maternal recall of birthweight is a convenient and cost-effective way to obtain birthweight measurements when official records are unavailable. It is important to assess the validity of maternal recall of birthweight before using these measurements to draw conclusions about a population. Methods: This is secondary analysis of data from a previous cohort study. We analyzed actual and reported birthweights of 200 mother-and-child pairs from Southern India. We validated maternal report of birthweight by generating correlation coefficients, summary statistics, and Bland-Altman plots. We ran simulations to evaluate how misclassification as low or normal birthweight changed with the mean birthweight of the cohort. Results: Reported birthweight was strongly correlated with actual birthweight (r=0.80, P<0.001); 55%, 78.5%, and 93% of subjects reported values within 50 g, 250 g, and 500 g, respectively of actual birthweight. None of sociodemographic covariates was significantly associated with the accuracy of maternal recall of birthweight. 7.5% of children were misclassified as either low or normal birthweight by reported birthweight. Simulations revealed that increasing the reported and actual birthweights by 500g reduces the misclassification rate from 7.5% to 1.5%. Conclusion: Maternal recall is a sufficiently accurate measure of actual birthweight. However, the distribution of actual birthweight in the population must be taken into consideration when classifying babies as low or normal birthweight, especially in populations where mean birthweight is close to 2500g. [ABSTRACT FROM AUTHOR]

Criminologists are increasingly interested in the effects of life-course dynamics on criminological development. However, detailed longitudinal data are difficult to obtain and possibly confounded due to recall errors. Life Event Calendars (LECs) are designed to reduce recall errors and are increasingly used as a method for obtaining valid retrospective data in criminological studies. Yet few studies exist that assess the accuracy of LEC data in offender samples. This study aims to fill this void. We compare data regarding the prevalence and timing of marriage, divorce, and childbirth obtained through an LEC to official registry data in a sample of convicted offenders. We examine whether the accuracy of the data vary by event or respondent specific characteristics. We conclude that the LEC data are quite accurate regarding the prevalence of marriage, divorce, and childbirth. The data are less accurate regarding the timing of these life events. [ABSTRACT FROM AUTHOR]

Storm runoff pollutants are among the major sources of surface water impairments, globally. Despite several monitoring programs and guidance on stormwater management practices, there are many streams still impaired by urban runoff. This study evaluates an industry sector’s pollutant discharge characteristics using the self-reported data collected under Tennessee Multi Sector Permit program. The stormwater pollutant discharge characteristics were analyzed from 2014 to 2018 for an industry sector involving twelve facilities in West Tennessee, USA. The data analysis revealed the presence of both organic and inorganic contaminants in stormwater samples collected at all twelve industrial facilities, with the most common metals being magnesium, copper, and aluminum. The principal component analysis (PCA) was applied to better understand the correlation between water quality parameters, their origins, and seasonal variations. Furthermore, the water quality indexes (WQIs) were calculated to evaluate the stormwater quality variations among studied facilities and seasons. The results demonstrated slight variations in stormwater WQIs among the studied facilities ranging from “Bad” to “Medium” quality. The lowest seasonal average WQI was found for spring compared to the other seasons. Certain limitations associated with the self-reported nature of data were identified to inform the decision makers regarding the required future changes.

I read with great interest the paper by Hanvold et al (1) about the construction and evaluation of a gender-specific job exposure matrix (JEM) of mechanical and psychosocial work exposures using a Norwegian national survey data. I have found one significant methodological issue in the paper. In addition, I would like to discuss an important knowledge gap in the literature that the paper by Hanvold et al exposes. The authors said that “…Earlier studies recommend 10 subjects in each group to achieve a reliable estimation of exposures, however, we used a minimum of 19 in each occupational group to increase the reliability of the estimates.” The earlier study referred to here is that of Le Moual et al (2). However, like other investigators (3), the authors have misunderstood this paper. Le Moual et al did not make any recommendation for a minimum number of subjects in each occupation for creating a JEM. In fact, Le Moural et al (2) constructed their JEMs with 2, not 10 subjects, as the minimum number of subjects in each occupation (see a note in table 1: one exclusion criterion for their JEM was “occupations

In this study, we addressed potential biases which can occur when sensorial scores of temperature, wetness and discomfort are repeatedly reported, in transient exercise conditions. We pointed out that, when repeatedly reported, previous sensorial scores can be set by the participants as reference values and the subsequent score may be given based on the previous point of reference, the latter phenomenon leading to a bias which we defined as ‘anchoring bias’. Indeed, the findings shown that subsequent sensorial scores are prone to anchoring biases and that the bias consisted in a systematically higher magnitude of sensation as compared to when reported a single time only. As such, the study allowed recognition, quantification and mitigation of the identified bias which can improve the methodological rigour of research studies involving assessments of sensorial data in transient conditions. [ABSTRACT FROM AUTHOR]

Objectives The study aim was to examine (i) non-response bias between responders and non-responders, and (ii) whether the association between self-reported sickness absence (SA) and register-based SA differed by gender, age, sector, or physically demanding work. Methods The responses of 8110 participants to a question on self-reported SA in the past 12 months in the Work Environment and Health in Denmark Survey (2014) was linked to 12 months of SA data from the Danish Register of Work Absence. We used logistic regression for the non-response analysis and Poisson regression to examine associations. Results Responders had on average 0.5 days less SA per year than non-responders. Public employees had a higher response rate than private employees (approximately five percentage points), women had a higher rate than men (approximately nine percentage points), and older employees a higher rate than younger employees (approximately nine percentage points in ten years). Self-reported SA correlated highly with register-based SA (Spearman's rank correlation=0.76). In general, responders with few SA days (<10) under-reported their SA while responders with many SA days (>30) over-reported their SA. Women under-reported significantly more than men (average difference one day); older employees under-reported significantly more than younger employees (difference between age groups 18-29 and 60-64 was 1.7 days). Differences between sectors or levels of physically demanding work were non-significant. Conclusions Self-reported SA data may be influenced by non-response bias, and different accuracy in different demographic groups. When available, the use of register-based SA data is recommended. [ABSTRACT FROM AUTHOR]

Research has documented that both unipolar and bipolar depression are positively correlated with involvement in delinquency and crime. The present study sought to broaden the understanding of these relationships by looking for links between offending and family histories of depressive symptoms in relationship to offspring delinquency. More than 6,000 college students and their mothers provided self-reported information regarding feelings of depression. Students provided self-reports of involvement in various categories of offending and drug use from ages 10 through 18. Numerous significant positive correlations were found between general feelings of depression and of manic depression and involvement in delinquency. The depression-delinquency relationships were strongest when considering offspring themselves, although maternal depression symptoms were also associated with various forms of offspring delinquency and drug use. To help assess the causal chains that might be involved, multiple regression and mediation analysis revealed that parental depression enhanced the probability of offspring feeling depressed and may have thereby contributed to offspring being delinquent, particularly in the case of manic depression. This study reconfirmed the well-established relationship between depression and involvement in delinquency and drug use, and suggests that it extends back to parental forms of depression, especially by the mother. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the convergent validity of self-reported and objective measures of school music ensemble participation. Self-reported survey responses to a question about high school music ensemble participation and administrative data in the form of high school transcript-indicated ensemble enrollments were compared across N = 62,110 observations. Cohen’s kappa (κ) showed substantial agreement between self-reports and the administrative data. Evidence from the study suggests that self-reports are a valid measure of music ensemble participation, suitable for use in social scientific music education research. [ABSTRACT FROM AUTHOR]

Background: The health of people who use drugs (PWUD) is characterized by multimorbidity and chronicity of health conditions, necessitating an understanding of their health care utilization. The objective of this study was to evaluate emergency department (ED) visits and hospital admissions among a cohort of PWUD. Methods: We used a retrospective observational design between 2012 and 2013. The population was a marginalized cohort of PWUD (the PROUD study) for whom survey data was linked (n = 663) to provincial health administrative data housed at the Institute for Clinical Evaluative Sciences. We constructed a 5:1 comparison group matched by age, sex, income quintile, and region. The main outcomes were defined as having two or more ED visits, or one or more hospital admissions, in the year prior to survey completion. We used multivariable logistic regression analyses to identify factors associated with these outcomes. Results: Compared to the matched cohort, PWUD had higher rates of ED visits (rate ratio [RR] 7.0; 95% confidence interval [95% CI] 6.5-7.6) and hospitalization (RR 7.7; 95% CI 5.9-10.0). After adjustment, factors predicting more ED visits were receiving disability (adjusted odds ratio [AOR] 3.0; 95% CI 1.7-5.5) or income assistance (AOR 2.7; 95% CI 1.5-5.0), injection drug use (AOR 2.1; 95% CI 1.3-3.4), incarceration within 12 months (AOR 1.6; 95% CI 1.1-2.4), mental health comorbidity (AOR 2.1; 95% CI 1.4-3.1), and a suicide attempt within 12 months (AOR 2.1; 95% CI 1.1-3.4). Receiving methadone (AOR 0.5; 95% CI 0.3-0.9) and having a regular family physician (AOR 0.5; 95% CI 0.2-0.9) were associated with lower odds of having more ED visits. Factors associated with more hospital admissions included Aboriginal identity (AOR 2.4; 95% CI 1.4-4.1), receiving disability (AOR 2.4; 95% CI 1.1-5.4), non-injection drug use (opioids and non-opioids) (AOR 2.2; 95% CI 1.1-4.4), comorbid HIV (AOR 2.4; 95% CI 1.2-5.6), mental health comorbidity (AOR 2.4; 95% CI 1.3-4.2), and unstable housing (AOR 1.9; 95% CI 1.0-3.4); there were no protective factors for hospitalization. Conclusions: Improved post-incarceration support, housing services, and access to integrated primary care services including opioid replacement therapy may be effective interventions to decrease acute care use among PWUD, including targeted approaches for people receiving social assistance or with mental health concerns. [ABSTRACT FROM AUTHOR]

Purpose The objective of this article is to describe how self-monitoring contributed to measurement reactivity in a randomized clinical trial (RCT). The implications of measurement reactivity on self-monitoring and suggestions for minimizing its effect will be discussed. Design The study involved a secondary data analysis of responses from 145 participants who completed a 12-month long RCT. The original sample consisted of 202 community-living adults with long-term indwelling urinary catheters. Methods The data analyzed were from the participants' brief responses to the final study question concerning their perceived study participation value. The data were discussed and coded iteratively until three researchers reached consensus. At the end, each item was recoded into eight categories and minor codes. Findings Our belief that the calendar was the probable cause of reactivity was not fully supported as the control group also learned about paying attention to urinary catheter problems by the questions asked through the seven bimonthly interviews. Conclusions The calendar and interview questions were reactive by increasing participants' self-monitoring of catheter problems. Specifically, the frequent interviews exposed participants to catheter-related concerns and provided them with ideas as to how to better manage them. Clinical Relevance This article provides insight related to the internal validity threat of measurement reactivity in RCTs and gives suggestions to reduce its effects. [ABSTRACT FROM AUTHOR]