Selection of Ordinally Scaled Independent Variables

Ordinal categorial variables are a common case in regression modeling. Although the case of ordinal response variables has been well investigated, less work has been done concerning ordinal predictors. This article deals with the selection of ordinally scaled independent variables in the classical linear model, where the ordinal structure is taken into account by use of a difference penalty on adjacent dummy coefficients. It is shown how the Group Lasso can be used for the selection of ordinal predictors, and an alternative blockwise Boosting procedure is proposed. Emphasis is placed on the application of the presented methods to the (Comprehensive) ICF Core Set for chronic widespread pain. The paper is a preprint of an article accepted for publication in the Journal of the Royal Statistical Society Series C (Applied Statistics). Please use the journal version for citation

Addressing the challenge of health measurement

Assessing the health of populations is important for various reasons, especially for health policy purposes. Therefore, there exists a substantial need for health comparisons between populations, including the comparison of individuals, groups of persons, or even populations from different countries, at one point in time and over time. Two fundamentally different approaches exist to assess the health of populations. The first approach relies on indirect measures of health, which are based on mortality and morbidity statistics, and which are therefore only available at the population level. The second approach relies on direct measures of health, which are collected – based on health surveys – at the individual level. Based on the needs for comparisons, indirect measures appear to be less appropriate, as they are only available at the population level, but not at the individual or group level. Direct measures, however, are originally obtained at the individual level, and can then be aggregated to any group level, even to the population level. Therefore, direct measures seem to be more appropriate for these comparison purposes. The open question is then how to compare overall health based on data collected within health surveys. At first glance, a single general health question seems to be appealing. However, studies have shown that this kind of question is not appropriate to compare health over time, nor across populations. Qualitative studies found that respondents even consider very different aspects of health when responding to such a question. A more appropriate approach seems to be the use of data on several domains of health, as for example mobility, self-care and pain. Anyway, measuring health based on a set of domains is an extremely frequent approach. It provides more comprehensive information and can therefore be used for a wider range of possible applications. However, three open questions must be addressed when measuring health based on a set of domains. First, a parsimonious set of domains must be selected. Second, health measurement based on this set of domains must be operationalized in a standardized way. Third, this information must be aggregated into a summary measure of health, thereby taking into account that categorical responses to survey questions could be differently interpreted by respondents, and are not necessarily directly comparable. These open questions are addressed in this doctoral thesis. The overall objective of this doctoral thesis is to develop a valid, reliable and sensitive metric of health – based on data collected on a set of domains – that permits to monitor the health of populations over time, and which provides the basis for the comparisons of health across different populations. To achieve this aim two psychometric studies were carried out, entitled “Towards a Minimal Generic Set of Domains” and “Development of a metric of health”. In the first study a minimal generic set of domains suitable for measuring health both in the general population and in clinical populations was identified, and contrasted to the domains of the World Health Survey (WHS). The eight domains of the WHS – mobility, self-care, pain and discomfort, cognition, interpersonal activities, vision, sleep and energy, and affect – were used as a reference, as this set – developed by the World Health Organization (WHO) – so far constitutes the most advanced proposal of what to measure for international health comparisons. To propose the domains for the minimal generic set, two different regression methodologies – Random Forest and Group Lasso – were applied for the sake of robustness to three different data sources, two national general population surveys and one large international clinical study: the German National Health Interview and Examination Survey 1998, the United States National Health and Nutrition Examination Survey 2007/2008, and the ICF Core Set studies. A domain was selected when it was sufficiently explanatory for self-perceived health. Based on the analyses the following set of domains, systematically named based on their respective categories within the International Classification of Functioning, Disability and Health (ICF), was proposed as a minimal generic set: b130 Energy and drive functions b152 Emotional functions b280 Sensation of pain d230 Carrying out daily routine d450 Walking d455 Moving around d850 Remunerative employment Based on this set, four of the eight domains of the WHS were confirmed both in the general and in clinical populations: mobility, pain and discomfort, sleep and energy, and affect. The other WHS domains not represented in the proposed minimal generic set are vision, which was only confirmed with data of the general population, self-care and interpersonal activities, which were only confirmed with data of the clinical population and cognition, which could not be confirmed at all. The ICF categories of `carrying out daily routine´ and `remunerative employment´ also fulfilled the inclusion criteria, though not directly related to any of the eight WHS domains. This minimal generic set can be used as the starting point to address one of the most important challenges in health measurement, namely the comparability of data across studies and countries. It also represents the first step for developing a common metric of health to link information from the general population to information about sub-populations, such as clinical and institutional populations, e.g. persons living in nursing homes. In the second study a sound psychometric measure was developed based on information collected on the domains of the minimal generic set: energy and drive functions, emotional functions, sensation of pain, carrying out daily routine, mobility and remunerative employment. It was demonstrated that this metric can be used to assess the health of populations and also to monitor health over time. To develop this metric of health, data from two successive waves of the English Longitudinal Study of Ageing (ELSA) was used. A specific Item Response Theory (IRT) model, the Partial Credit Model (PCM), was applied on 12 items representing the 6 domains from the minimal generic set. All three IRT model assumptions – unidimensionality, local independency and monotonicity – were examined and found to be fulfilled. The developed metric showed sound psychometric properties: high internal consistency reliability, high construct validity and high sensitivity to change. Therefore, it can be considered an appropriate measure of population health. Furthermore, it was demonstrated how the health of populations can be compared based on this metric, for subgroups of populations, and over time. Finally, it was outlined how this metric can be used as the basis for comparing health across different populations, as for example from two different countries. The developed health metric can be seen as the starting point for a wide range of health comparisons, between individuals, groups of persons and populations as a whole, and both at one point in time and over time. It opens up a wide range of possible applications for both health care providers and health policy, and both in clinical settings and in the general population

Development of a metric for tracking and comparing population health based on the minimal generic set of domains of functioning and health

Merged planning photographs recording of Pits 1.4 and 1.5 [196] and [197] to south of building; facing west; linked as external references to excavation plan 'Thwing_4-2_excavation_plan.dwg

Factors Predicting Response to the Recovery-Oriented Cognitive Behavioural Workshop for Persons Diagnosed with Schizophrenia

A recovery-oriented, cognitive behavioural workshop for service users diagnosed with schizophrenia was developed, implemented and evaluated in a pilot study. Further analysis is required regarding factors which contribute to better treatment response, as this will provide useful information for workshop adaptation. Secondary multilevel model analyses were performed to determine whether workshop and booster session attendance, as well as sociodemographic variables such as gender, age, education, and duration of illness, predicted workshop responsiveness. Results showed that completers had lower responsiveness to the workshop in terms of confidence and hope, whereas those who attended an online booster session demonstrated better responsiveness as to psychosocial functioning. Longer duration of illness and older age generally predicted lower intervention responsiveness. In conclusion, adaptations utilising more booster sessions and accommodating older participants with longer duration of illness are required, as is further workshop evaluation in a randomised controlled study

ICF-based prediction of return to work after trauma rehabilitation: Results of the icfPROreha study in patients with severe musculoskeletal injuries

Background: Physical aspects such as the type and severity of an injury are not the only factors contributing to whether or not a person can return to work (RTW) after a serious injury. A more comprehensive, biopsychosocial approach is needed to understand the complexity of RTW fully. The study aims to identify predictors of RTW 78 weeks after discharge from initial inpatient trauma rehabilitation in patients with severe musculoskeletal injuries using a biopsychosocial perspective. Methods: This is a prospective multicenter longitudinal study with a follow-up of up to 78 weeks after discharge from trauma rehabilitation. Data on potential predictors were collected at admission to rehabilitation using a comprehensive assessment tool. The status of RTW (yes vs. no) was assessed 78 weeks after discharge from rehabilitation. The data were randomly divided into a training and a validation data set in a ratio of 9:1. On the training data, we performed bivariate and multiple logistic regression analyses on the association of RTW and potential predictors. The final logit model was selected via stepwise variable selection based on the Akaike information criterion. The final model was validated for the training and the validation data. Results: Data from 761 patients (n = 561 male, 73.7%; mean age: 47.5 years, SD 12.3), primarily suffering from severe injuries to large joints and complex fractures of the large tubular bones, could be considered for analyses. At 78 weeks after discharge, 618 patients (81.2%) had returned to work. Eleven predictors remained in the final logit model: general health, current state of health, sensation of pain, limitations and restrictions in activities and participation (disability), professional sector, ongoing legal disputes, financial concerns (assets), personality traits, life satisfaction preaccident, attitude to life, and demand for pension claim. A predicted probability for RTW based on the multiple logistic regression model of 76.3% was revealed as the optimal cut-off score based on the ROC curve. Conclusion: A holistic biopsychosocial approach is needed to address RTW and strengthen person-centered treatment and rehabilitation. Patients at risk for no RTW in the long term can already be identified at the onset of rehabilitation

ICF-based prediction of return to work after trauma rehabilitation: Results of the icfPROreha study in patients with severe musculoskeletal injuries

BackgroundPhysical aspects such as the type and severity of an injury are not the only factors contributing to whether or not a person can return to work (RTW) after a serious injury. A more comprehensive, biopsychosocial approach is needed to understand the complexity of RTW fully. The study aims to identify predictors of RTW 78 weeks after discharge from initial inpatient trauma rehabilitation in patients with severe musculoskeletal injuries using a biopsychosocial perspective.MethodsThis is a prospective multicenter longitudinal study with a follow-up of up to 78 weeks after discharge from trauma rehabilitation. Data on potential predictors were collected at admission to rehabilitation using a comprehensive assessment tool. The status of RTW (yes vs. no) was assessed 78 weeks after discharge from rehabilitation. The data were randomly divided into a training and a validation data set in a ratio of 9:1. On the training data, we performed bivariate and multiple logistic regression analyses on the association of RTW and potential predictors. The final logit model was selected via stepwise variable selection based on the Akaike information criterion. The final model was validated for the training and the validation data.ResultsData from 761 patients (n = 561 male, 73.7%; mean age: 47.5 years, SD 12.3), primarily suffering from severe injuries to large joints and complex fractures of the large tubular bones, could be considered for analyses. At 78 weeks after discharge, 618 patients (81.2%) had returned to work. Eleven predictors remained in the final logit model: general health, current state of health, sensation of pain, limitations and restrictions in activities and participation (disability), professional sector, ongoing legal disputes, financial concerns (assets), personality traits, life satisfaction preaccident, attitude to life, and demand for pension claim. A predicted probability for RTW based on the multiple logistic regression model of 76.3% was revealed as the optimal cut-off score based on the ROC curve.ConclusionA holistic biopsychosocial approach is needed to address RTW and strengthen person-centered treatment and rehabilitation. Patients at risk for no RTW in the long term can already be identified at the onset of rehabilitation

Which environmental factors are associated with lived health when controlling for biological health? - a multilevel analysis

Background: Lived health and biological health are two different perspectives of health introduced by the International Classification of Functioning, Disability and Health (ICF). Since in the concept of lived health the impact of the environment on biological health is inherently included, it seems intuitive that when identifying the environmental determinants of health, lived health is the appropriate outcome. The Multilevel Item Response Theory (MLIRT) model has proven to be a successful method when dealing with the relation between a latent variable and observed variables. The objective of this study was to identify environmental factors associated with lived health when controlling for biological health by using the MLIRT framework. Methods: We performed a psychometric study using cross-sectional data from the Spanish Survey on Disability, Independence and Dependency Situation. Data were collected from 17,303 adults living in 15,263 dwellings. The MLIRT model was used for each of the two steps of the analysis to: (1) calculate people's biological health abilities and (2) estimate the association between lived health and environmental factors when controlling for biological health. The hierarchical structure of individuals in dwellings was considered in both models. Results: Social support, being able to maintain one's job, the extent to which one's health needs are addressed and being discriminated against due to one's health problems were the environmental factors identified as associated with lived health. Biological health also had a strong positive association with lived health. Conclusions: This study identified environmental factors associated with people's lived health differences within and between dwellings according to the MLIRT-model approach. This study paves the way for the future implementation of the MLIRT model when analysing ICF-based data

Which environmental factors are associated with lived health when controlling for biological health? - a multilevel analysis

Background: Lived health and biological health are two different perspectives of health introduced by the International Classification of Functioning, Disability and Health (ICF). Since in the concept of lived health the impact of the environment on biological health is inherently included, it seems intuitive that when identifying the environmental determinants of health, lived health is the appropriate outcome. The Multilevel Item Response Theory (MLIRT) model has proven to be a successful method when dealing with the relation between a latent variable and observed variables. The objective of this study was to identify environmental factors associated with lived health when controlling for biological health by using the MLIRT framework. Methods: We performed a psychometric study using cross-sectional data from the Spanish Survey on Disability, Independence and Dependency Situation. Data were collected from 17,303 adults living in 15,263 dwellings. The MLIRT model was used for each of the two steps of the analysis to: (1) calculate people's biological health abilities and (2) estimate the association between lived health and environmental factors when controlling for biological health. The hierarchical structure of individuals in dwellings was considered in both models. Results: Social support, being able to maintain one's job, the extent to which one's health needs are addressed and being discriminated against due to one's health problems were the environmental factors identified as associated with lived health. Biological health also had a strong positive association with lived health. Conclusions: This study identified environmental factors associated with people's lived health differences within and between dwellings according to the MLIRT-model approach. This study paves the way for the future implementation of the MLIRT model when analysing ICF-based data

Biological health or lived health: which predicts self-reported general health better?

Background: Lived health is a person's level of functioning in his or her current environment and depends both on the person's environment and biological health. Our study addresses the question whether biological health or lived health is more predictive of self-reported general health (SRGH). Methods: This is a psychometric study using cross-sectional data from the Spanish Survey on Disability, Independence and Dependency Situation. Data was collected from 17,739 people in the community and 9,707 from an institutionalized population. The following analysis steps were performed: (1) a biological health and a lived health score were calculated for each person by constructing a biological health scale and a lived health scale using Samejima's Graded Response Model; and (2) variable importance measures were calculated for each study population using Random Forest, with SRGH as the dependent variable and the biological health and the lived health scores as independent variables. Results: The levels of biological health were higher for the community-dwelling population than for the institutionalized population. When technical assistance, personal assistance or both were received, the difference in lived health between the community-dwelling population and institutionalized population was smaller. According to Random Forest's variable importance measures, for both study populations, lived health is a more important predictor of SRGH than biological health. Conclusions: In general, people base their evaluation of their own health on their lived health experience rather than their experience of biological health. This study also sheds light on the challenges of assessing biological health and lived health at the general population level

Which Environmental Factors Have the Highest Impact on the Performance of People Experiencing Difficulties in Capacity?

Disability is understood by the World Health Organization (WHO) as the outcome of the interaction between a health condition and personal and environmental factors. Comprehensive data about environmental factors is therefore essential to understand and influence disability. We aimed to identify which environmental factors have the highest impact on the performance of people with mild, moderate and severe difficulties in capacity, who are at risk of experiencing disability to different extents, using data from a pilot study of the WHO Model Disability Survey in Cambodia and random forest regression. Hindering or facilitating aspects of places to socialize in community activities, transportation and natural environment as well as use and need of personal assistance and use of medication on a regular basis were the most important environmental factors across groups. Hindering or facilitating aspects of the general environment were the most relevant in persons experiencing mild levels of difficulties in capacity, while social support, attitudes of others and use of medication on a regular basis were highly relevant for the performance of persons experiencing moderate to higher levels of difficulties in capacity. Additionally, we corroborate the high importance of the use and need of assistive devices for people with severe difficulties in capacity