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The colorectal cancer screening programme was introduced gradually from 2014. Since 2019, all people aged 55 to 75 have been invited to participate once every two years. In 2021, more than 1.6 million people took part in the screening programme, and colorectal cancer was detected in 2,700 participants, with an early stage of colorectal cancer being detected in 17,000 participants. At this stage, the screening programme has been running for too short a time to be able to scientifically demonstrate that it leads to less mortality from colorectal cancer; however, there are clear indications in that direction. Under the current circumstances, the benefits (prevention of colorectal cancer and associated mortality) outweigh the risks of the screening programme (screening test that detects no abnormalities but is taxing and causes concern, and cases of cancer that are missed). The Council therefore recommends that no modifications be made to the regular programme at this time.The Council does, however, recommend conducting a regional pilot population screening programme with one-off screening of people around 50 years of age. This will allow a review of to what extent such a programme would provide health benefits and whether the benefits would outweigh the risks. Based on the results, a decision can be made as to whether the age limit of the programme should be lowered. One promising development that may improve the screening programme in the long term is customised screening. As such, the Council recommends that this be reviewed. In addition, the Council recommends continuing investment in increasing participation rates.

The colorectal cancer screening programme was introduced gradually from 2014. Since 2019, all people aged 55 to 75 have been invited to participate once every two years. In 2021, more than 1.6 million people took part in the screening programme, and colorectal cancer was detected in 2,700 participants, with an early stage of colorectal cancer being detected in 17,000 participants. At this stage, the screening programme has been running for too short a time to be able to scientifically demonstrate that it leads to less mortality from colorectal cancer; however, there are clear indications in that direction. Under the current circumstances, the benefits (prevention of colorectal cancer and associated mortality) outweigh the risks of the screening programme (screening test that detects no abnormalities but is taxing and causes concern, and cases of cancer that are missed). The Council therefore recommends that no modifications be made to the regular programme at this time. The Council does, however, recommend conducting a regional pilot population screening programme with one-off screening of people around 50 years of age. This will allow a review of to what extent such a programme would provide health benefits and whether the benefits would outweigh the risks. Based on the results, a decision can be made as to whether the age limit of the programme should be lowered. One promising development that may improve the screening programme in the long term is customised screening. As such, the Council recommends that this be reviewed. In addition, the Council recommends continuing investment in increasing participation rates.

In this study, we describe a protocol to assess relations between organization of the primary motor cortex of several trunk muscles with motor and sensory tests during the clinical course of low back pain. We used a cross-sectional and five-week longitudinal case-control design.

Due to the prevalence of skin cancer, the State Secretary for Health, Welfare and Sport requested that the Health Council of the Netherlands review whether introducing a screening programme for skin cancer would be expedient. A key requirement underpinning the introduction of a screening programme is that its usefulness has been established and that its benefits outweigh any disadvantages, which, the Council has determined, is not the case for skin cancer. There is no scientific evidence that a programme of this type would be beneficial. Any added value offered by a screening programme is expected to be low, given that many cases are already detected at an early stage and the mortality rate is low.Care for people with a suspicious lesion on their skin usually starts at the GP, who carries out a risk assessment and will either treat the lesion themselves or refer the patient to a dermatologist. People suspected to have a familial or hereditary predisposition to melanoma are offered a periodic skin examination and genetic testing. Survival rates for skin cancer are high with the current detection and treatment strategies for suspicious lesions.Excessive exposure to sunlight and sunburn in combination with a light skin tone are the principal risk factors for skin cancer. Behaviour can therefore significantly reduce the risk of skin cancer (e.g. not staying in the sun for long periods of time, covering the skin and using a high factor sun cream). The Steering Committee for Skin Cancer Care in the Netherlands (Stuurgroep Huidkankerzorg Nederland) and RIVM are working on more intensive information provision. The Health Council of the Netherlands emphasises the importance of behavioural change and information provision. In the future, apps for suspicious skin lesions may play a role in the detection of skin cancer, however, this requires more development and research.

Children with a low level of neuromotor fitness are less skilled to participate in sports activities. Moreover, lower levels of neuromotor fitness are related to adiposity, lower cardiovascular health, and poor self-esteem in children. The aim of this paper was to determine neuromotor fitness in 10–12-year-old Dutch children over a 10-year period. Test scores measured in 2015/2017 (N = 533 in 2015, N = 941 in 2017) were compared with scores of same-aged children measured in 2006 (N = 1986). Neuromotor fitness was assessed using theMOPER fitness test battery, including speed and agility, strength, flexibility, and coordination and upper-limb speed. Data were analyzed using multilevellinear regression models and tobit regression analyses in case of skewed distributions with an excess of zeros. Analyses were stratified by age and gender, and adjusted for level of urbanization. Children in 2015/2017 performed significantly worse on speed and agility (b = 0.8 to 1.1 s), significantly better on coordination/upper-limb speed (b = −1.0 to −0.6 s), and–except for 12-year-old girls–significantly worse on flexibility vs. childrenin 2006 (b = −3.4 to −1.8 cm). Additionally, upper-body strength was significantly worse among 10-year olds (b = −3.2 to −2.5 s) while leg strength was significantly worse among 11-year-olds in 2015/2017 vs. 2006 (b = −1.8 to −1.7 cm). Trunk strength was worse among 11- and 12-year old boys (b = 1.1 to 1.2 s). In line with a previously observed downward trend in neuromotor fitness among children (1980–2006), we found worse scores on speed and agility, and flexibility in 2015/2017 vs. 2006, stressing the need for interventions aimed at improving neuromotor fitness in order to promote physical activity and future health.

Purpose: Diffuse intrinsic pontine glioma (DIPG) is a highly aggressive pediatric brain tumor, for which no effective therapeutic options currently exist. We here determined the potential of inhibition of the maternal embryonic leucine zipper kinase (MELK) for the treatment of DIPG.Experimental Design: We evaluated the antitumor efficacy of the small-molecule MELK inhibitor OTSSP167 in vitro in patient-derived DIPG cultures, and identified the mechanism of action of MELK inhibition in DIPG by RNA sequencing of treated cells. In addition, we determined the blood-brain barrier (BBB) penetration of OTSSP167 and evaluated its translational potential by treating mice bearing patient-derived DIPG xenografts.Results: This study shows that MELK is highly expressed in DIPG cells, both in patient samples and in relevant in vitro and in vivo models, and that treatment with OTSSP167 strongly decreases proliferation of patient-derived DIPG cultures. Inhibition of MELK in DIPG cells functions through reducing inhibitory phosphorylation of PPAR?, resulting in an increase in nuclear translocation and consequent transcriptional activity. Brain pharmacokinetic analyses show that OTSSP167 is a strong substrate for both MDR1 and BCRP, limiting its BBB penetration. Nonetheless, treatment of Mdr1a/b;Bcrp1 knockout mice carrying patient-derived DIPG xenografts with OTSSP167 decreased tumor growth, induced remissions, and resulted in improved survival.Conclusions: We show a strong preclinical effect of the kinase inhibitor OTSSP167 in the treatment of DIPG and identify the MELK-PPAR? signaling axis as a putative therapeutic target in this disease. Clin Cancer Res; 24(22); 5645-57. ©2018 AACR.

Low cardiorespiratory fitness and inactivity are strong health predictors associated with excessive fatness. The objective of this study was to determine the associations between physical activity (PA) and cardiorespiratory fitness and body fatness in South African adolescents. A cross-sectional study was performed with a subsample of 149 school-going adolescents (58 boys and 91 girls) with a mean age of 15.7 ± 0.8 years whom were participating in the Physical Activity and Health Longitudinal Study (PAHLS). Body mass, height, and skinfolds were assessed according to the standards of the International Society for the Advancement of Kinanthropometry (ISAK). Subsequently, body mass index (BMI) and percentage of body fat (%BF) were calculated. Cardiorespiratory fitness was derived from predicted VO2max, and PA assessed by combined heart rate and accelerometry (ActiHeart®, CamNTech). Physical activity was classified according to activity categories. Out of 149 participants, 100 (67.1%) had normal body mass; 26 (17.5%) were underweight and 23 (15.4%) were overweight. Seventeen (18.7%) girls and six (10%) boys were overweight or obese. The prevalence of underweight in boys was 29.3% and 9.9% in girls. A total of 102 (68.5%) participants were sedentary and 47 (31.5%) were active. The overweight group was significantly (p

The purpose of this study was to measure the relationship between socio-economic status (SES) and educational achievements (EA) of rural South African children. The data come from a sample of 238 children (118 boys and 120 girls) from the ongoing Ellisras Longitudinal Study. A questionnaire was used to gather information on six SES measures: Availability of TV and radio at home; type of dwelling; educational level of the father; number of people sharing a bedroom; cooking mode used; and household income. EA was measured using the Educational Achievement Tests. In this sample, with a very low average SES, no strong relationships were found between SES and EA. The general absence of significant relationships between SES and EA may be due to difficulties in accurately and adequately measuring SES and EA, and the little variations in SES. The ‘type of dwelling' and ‘cooking mode' however, was positively associated with EA. Based on this result, improvement of housing and cooking facilities may lead to improvement in the educational achievement of children in low SES rural communities. Keywords: socio-economic status, educational achievements, rural South African children African Journal for Physical, Health Education, Recreation and Dance Vol. 12 (3) 2006: 298-309

Background Epidemiologists are generally interested in the effect of an exposure on an outcome. This so-called exposure effect is often estimated using regression analysis, in which the outcome is regressed on the exposure. The distribution of the outcome determines which regression technique is most appropriate to estimate the exposure effect. In epidemiological research, linear- (for continuous outcomes), logistic- (for binary outcomes) and Cox regression (for survival outcomes) are most commonly applied. In general, the aim is to isolate the true effect of the exposure on the outcome. However, often the association between an exposure and an outcome is not entirely attributable to the exposure, i.e., the effect is biased. If this bias is not accounted for, then the estimated effect is not a good representation of the true underlying effect. This could, for instance, result in under- or overtreatment in patients and influence clinical decision making. Aim In this thesis, I provide non-technical and non-mathematical descriptions of various situations in which bias can occur in regression analysis and propose solutions where possible. I focus on four potential sources of bias: the estimation of non-linear effects, noncollapsibility, causal mediation analysis and competing risks. In each chapter the theory is illustrated using an empirical data example from the Longitudinal Aging Study Amsterdam or the Amsterdam Growth and Health Longitudinal Study. Some chapters additionally contain a simulation study to evaluate model performance and compare methods. Conclusion Although regression models are commonly used in epidemiological research to estimate exposure effects, researchers do often not consider the many different ways in which bias can occur. In this thesis, I reviewed four different potential sources of bias in regression analysis, and proposed solutions where possible. To avoid bias, it is recommended that researchers consider the potential sources in the pre-analysis phase and adapt their analysis if necessary. In addition, it is recommended to transparently report the measures taken to reduce bias and to carefully interpret the results, taking any remaining bias into consideration. Finally, I believe that the field of epidemiology would benefit from more non-technical and non-mathematical papers on advanced topics, as I aimed to contribute to with this thesis.

This thesis focuses on the development and evaluation of prediction models for knee and low back pain in primary care. Knee complaints as well as low back pain are common musculoskeletal disorders in the western countries and patients with these complaints are frequently treated by physical therapists in primary care. It is known that persistent complaints, in particular, put considerable pressure on society, as well in terms of costs for the employer and employee as in terms of quality of life for patients and their families. Conservative treatment by physical therapists can possibly prevent or delay surgery or other interventions, which may reduce health care costs. Neither physical therapy nor other treatment strategies are standardized, and at the same time, the effects of various treatment strategies are small. Several researchers examined the effects of physical therapy treatment and concluded that the evidence for effects in patients with knee or back complaints is small. This is probably due to the large heterogeneity in patients, type of interventions and different outcome measures used. Prediction models are needed to distinguish between high and low risk patients and can be helpful for physical therapists in providing a correct prognosis and an individual treatment plan. Knowledge about the clinical course and possible prognostic factors is essential in order to make individualized treatment decisions. Therefore, more insight is needed in which prediction models for musculoskeletal complaints (and especially for knee and back complaints) are usable yet in primary care, which models can be useful after validation and what methods can be used to make developing high quality prediction models easier in clinical research. Therefore, in chapter two a systematic review has been carried out according to existing prediction models for patients with Anterior Knee Pain in primary care to examine if there are models that are directly usable in daily practice or that some existing models can be updated. In chapter three a systematic review has been carried out according to existing prediction models for patients with nontraumatic knee pain in primary care with the same objective and to examen if new summary prediction models can be developed based on the existing models and internally and externally validated. To gain a well-defined outcome measure, in chapter four, LCGA has been used to define a dichotomous outcome measure that leads to a stable and validated prediction model for LBP. In chapter five of this thesis, we evaluated if a former predictor variable for chronic LBP is still clinically relevant in the light of the novel method for evaluating the discriminative performance of a model, the Decision Curve Analysis (DCA) using the Net Benefit (NB). This method is able to identify the number of patients that are better classified and permits an understanding of the clinical value of methods for patient selection by prediction models. Last but not least, a common problem in developing prediction models is the amount of missing data in the available data sets. Multiple Imputation (MI) is the recommended method for processing these incomplete data. If a dataset contains missing values, it is recommended to apply MI before excluding variables in logistic regression with backward selection (BWS) from the pooled model. In chapter six, the objective was to evaluate the results of four different pooling methods (D1, D2, D3 and the Median-P-Rule) in Multiply Imputed datasets after a BWS-procedure in simulated datasets and compare these with the results in the complete dataset (without missing data). All analyzes were repeated in real-world datasets to be sure of the usability in daily practice. Especially, the stability of the developed prediction models was evaluated.

Summary Chapter 1 introduces the context and importance of our topic. Mental disorders are the leading cause of sickness absence and disability pensions in European countries. We need more knowledge of predictors of mental sickness absence to identify those workers at high risk of mental long-term sickness absence (LTSA) before they report sick. In this thesis, prediction models for mental LTSA will be developed and validated using variables that are commonly addressed in occupational health surveys. This thesis also investigates causal mechanisms of mental long-term sickness absence. Chapter 2 describes the predictive performance of the Maslach Burnout Inventory (MBI-GS) and Utrecht Work Engagement Scale (UWES) for identifying workers at increased risk of mental LTSA. It was concluded that the MBI—GS and UWES predicted future mental LTSA in non-sicklisted employees, but discrimination was not practically useful. Chapter 3 investigates the ability of mental health symptoms to identify workers at risk of mental LTSA. Mental health symptoms were measured at baseline with the 4-DSQ (distress and depressed mood) and MBI–GS (fatigue). The symptom scores were analyzed against incident mental LTSA. Distress fairly discriminated between workers with and without mental LTSA, whereas the discriminative ability of both depressed mood and fatigue was poor. It was concluded that the 4-DSQ distress scale may be a promising tool to screen working populations for mental LTSA. Chapter 4 compares the discrimination by the 16-item 4-DSQ distress scale with discrimination by a distress screener with items on worrying, listlessness, and feeling tense, derived from the full 16-item distress scale. Discrimination between non-sicklisted workers with and without mental LTSA was found to be similar for the 16-item distress scale and the three-item screener. Thus, it is more convenient for healthcare providers to use the three key questions of the 16-item 4-DSQ distress scale to identify non-sicklisted employees at risk of future mental SA. Chapter 5 investigates psychosocial job demands and job resources for their predictions of mental LTSA among Norwegian nurses. Job demands and job resources were measured at baseline and linked to mental LTSA during 2-year follow-up. Harassment and social support were associated with mental LTSA, but the Cox regression model did not discriminate between nurses with and without mental LTSA. Chapter 6 describes predictions of mental LTSA by psychosocial job demands and job resources. Only performance feedback was associated with mental LTSA. A prediction model including psychosocial work characteristics poorly discriminated between workers with and without mental LTSA. Chapter 7 investigates the mediational effect of distress, burnout, work satisfaction, engagement, and work ability for the relation between psychosocial working conditions and mental health-related LTSA. Role clarity, cognitive demands, emotional demands, work variety, learning opportunities, social support from colleagues, and social support from family and friends were related to mental LTSA. Emotional demands had the strongest direct effect on mental LTSA. The relation between psychosocial working conditions and mental LTSA was mediated by distress, work satisfaction, and work ability. Distress was the most important mediator between psychosocial working conditions and mental LTSA. Chapter 8 presents the development of a multivariable prediction model for mental LTSA using distress together with other occupational health survey variables. An 11-predictor logistic regression model discriminated between workers with and without mental LTSA during 1-year follow-up. A 3-node decision tree equally well discriminated between participants with and without mental LTSA at follow-up. Chapter 9 describes the external validation of the logistic regression and decision tree prediction models. Both models fairly discriminated between participants with and without mental LTSA during follow-up. We recommend to use the decision tree based on distress, gender and work satisfaction in preventive consultations following occupational health surveys.

For many years, epidemiologists were mainly focused on the estimation of exposure-outcome effects. Nowadays, epidemiologists are also interested in assessing the causal mechanisms underlying exposure-outcome effects. Mediation analysis can be used to gain insight in these causal mechanisms, as it decomposes the total exposure-outcome effect into a direct effect and an indirect effect through a mediator. Traditionally, mediation analysis is performed based on a sequence of three linear regression equations. Subsequently, the indirect effect is estimated based on the product-of-coefficients estimator or the difference-in-coefficients estimator. In recent years, causal mediation analysis gained in popularity. Causal mediation analysis is based on the potential outcomes framework and counterfactual framework and defines the direct, indirect, and total effect as the difference between two potential outcomes. There are some important differences between traditional and causal mediation analysis. Traditional mediation analysis does not incorporate exposure-mediator interaction in its effect definitions while causal mediation analysis does incorporate exposure-mediator interaction in its effect definitions. Another important difference between traditional and causal mediation analysis is that the causal effect definitions are not dependent on a specific estimation method, while the traditional effects are defined and estimated based on (linear) regression coefficients. The causal effect definitions can therefore be applied to any mediation model, and do not necessarily depend on parametric assumptions. Based on these differences between traditional and causal mediation analysis, the question arises when and why the effect estimates from these two methods are the same or different. The aim of this thesis is to assess the similarities and differences in the effect estimates yielded by traditional mediation analysis and causal mediation analysis for mediation models frequently encountered in epidemiological research. Simulation studies and empirical data examples were used to compare the traditional and causal effect estimates for mediation models with 1) a continuous mediator and a continuous outcome, 2) a continuous mediator and a binary outcome, and 3) a binary mediator and a binary outcome. This thesis shows that the traditional and causal effect estimates based on linear regression analysis are the same, while this does not necessarily hold when logistic regression is used. When logistic regression analysis is used, the traditional product-of-coefficients estimator and difference-in-coefficients estimator yield different indirect effect estimates. Standardization of the underlying regression coefficients minimizes the difference between these two indirect effect estimates, but does not fully dissolve the difference. The difference between the effect estimates based on these two traditional estimators is caused by non-collapsibility. Furthermore, in the presence of an exposure-mediator interaction, the traditional and causal estimates of the direct and total effect differ. In this situation, the traditional direct effect estimates conditional on the average mediator value under the two exposure levels of interest are similar to the estimate of the controlled direct effect rather than the natural direct effects from causal mediation analysis. Causal mediation analysis is the preferred method for mediation analysis, as this method provides general definitions of causal effects that can be applied to any mediation model. In some situations, traditional mediation analysis can be used to estimate causal effects, for example when the mediator and outcome are both continuous, while in other situations the causal estimators are needed to ensure a causal interpretation of the effect estimates. Epidemiologists use mediation analysis to unravel the causal mechanisms underlying exposure-outcome effects. Causal mediation analysis provides general definitions of the causal effect that constitute these causal mechanisms. These causal effect definitions can be applied to derive the causal effect estimators for any type of mediation model. Therefore, causal mediation analysis should become the default method for mediation analysis in epidemiological research.

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