Optimal Scaling transformations to model non-linear relations in GLMs with ordered and unordered predictors

In Generalized Linear Models (GLMs) it is assumed that there is a linear effect of the predictor variables on the outcome. However, this assumption is often too strict, because in many applications predictors have a nonlinear relation with the outcome. Optimal Scaling (OS) transformations combined with GLMs can deal with this type of relations. Transformations of the predictors have been integrated in GLMs before, e.g. in Generalized Additive Models. However, the OS methodology has several benefits. For example, the levels of categorical predictors are quantified directly, such that they can be included in the model without defining dummy variables. This approach enhances the interpretation and visualization of the effect of different levels on the outcome. Furthermore, monotonicity restrictions can be applied to the OS transformations such that the original ordering of the category values is preserved. This improves the interpretation of the effect and may prevent overfitting. The scaling level can be chosen for each individual predictor such that models can include mixed scaling levels. In this way, a suitable transformation can be found for each predictor in the model. The implementation of OS in logistic regression is demonstrated using three datasets that contain a binary outcome variable and a set of categorical and/or continuous predictor variables.Comment: 35 pages, 4 figure

From test to rest:Evaluating socioeconomic differences along the COVID-19 care pathway in the Netherlands

IntroductionThe COVID-19 pandemic exacerbated healthcare needs and caused excess mortality, especially among lower socioeconomic groups. This study describes the emergence of socioeconomic differences along the COVID-19 pathway of testing, healthcare use and mortality in the Netherlands.MethodologyThis retrospective observational Dutch population-based study combined individual-level registry data from June 2020 to December 2020 on personal socioeconomic characteristics, COVID-19 administered tests, test results, general practitioner (GP) consultations, hospital admissions, Intensive Care Unit (ICU) admissions and mortality. For each outcome measure, relative differences between income groups were estimated using log-link binomial regression models. Furthermore, regression models explained socioeconomic differences in COVID-19 mortality by differences in ICU/hospital admissions, test administration and test results.ResultsAmong the Dutch population, the lowest income group had a lower test probability (RR = 0.61) and lower risk of testing positive (RR = 0.77) compared to the highest income group. However, among individuals with at least one administered COVID-19 test, the lowest income group had a higher risk of testing positive (RR = 1.40). The likelihood of hospital admissions and ICU admissions were higher for low income groups (RR = 2.11 and RR = 2.46, respectively). The lowest income group had an almost four times higher risk of dying from COVID-19 (RR = 3.85), which could partly be explained by a higher risk of hospitalization and ICU admission, rather than differences in test administration or result.DiscussionOur findings indicated that socioeconomic differences became more pronounced at each step of the care pathway, culminating to a large gap in mortality. This underlines the need for enhancing social security and well-being policies and incorporation of health equity in pandemic preparedness plans

Systematic review of individual-level, community-level, and healthcare system-level factors contributing to socioeconomic differences in healthcare utilisation in OECD countries with universal health coverage

Objectives Countries with universal health coverage (UHC) strive for equal access for equal needs without users getting into financial distress. However, differences in healthcare utilisation (HCU) between socioeconomic groups have been reported in countries with UHC. This systematic review provides an overview individual-level, community-level, and system-level factors contributing to socioeconomic status-related differences in HCU (SES differences in HCU).Design Systematic review following the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines. The review protocol was published in advance.Data sources Embase, PubMed, Web of Science, Scopus, Econlit, and PsycInfo were searched on 9 March 2021 and 9 November 2022.Eligibility criteria Studies that quantified the contribution of one or more factors to SES difference in HCU in OECD countries with UHC.Data extraction and synthesis Studies were screened for eligibility by two independent reviewers. Data were extracted using a predeveloped data-extraction form. Risk of bias (ROB) was assessed using a tailored version of Hoy’s ROB-tool. Findings were categorised according to level and a framework describing the pathway of HCU.Results Of the 7172 articles screened, 314 were included in the review. 64% of the studies adjusted for differences in health needs between socioeconomic groups. The contribution of sex (53%), age (48%), financial situation (25%), and education (22%) to SES differences in HCU were studied most frequently. For most factors, mixed results were found regarding the direction of the contribution to SES differences in HCU.Conclusions SES differences in HCU extensively correlated to factors besides health needs, suggesting that equal access for equal needs is not consistently accomplished. The contribution of factors seemed highly context dependent as no unequivocal patterns were found of how they contributed to SES differences in HCU. Most studies examined the contribution of individual-level factors to SES differences in HCU, leaving the influence of healthcare system-level characteristics relatively unexplored

Improving evaluation of NO2 emission from ships using spatial association on TROPOMI satellite data

As of 2021, more demanding NOx emission requirements entered into force for newly built ships operating on the North and Baltic Sea. Even though various methods are used to assess ships’ pollution in ports and off the coastal areas, monitoring over the open sea has been infeasible until now. In this work, we present a novel automated method for evaluation of NO2 emissions produced by individual seagoing ships. We use the spatial association statistic local Moran’s in order to improve the distinguishability between the plume and the background. Using the Automatic Identification Signal (AIS) data of ship locations as well as incorporated uncertainties in wind speed and wind direction, we automatically associate the detected plumes with individual ships. We evaluate the quality of ship-plume matching by calculating the Pearson correlation coefficient between the values of a model-based emission proxy and the estimated NO2 concentrations. For five of the six analyzed areas, our method yields results that are an improvement over the baseline approach used in a previous study.Computer Systems, Imagery and Medi

Projected Images of Major Chinese Outbound Destinations

This study aimed to explore the projected images of major outbound destinations based on popular travel magazines in China. Travel articles on Hong Kong, Macau, Japan, South Korea, Vietnam and Taiwan from 2006 to 2008 were content analyzed. Japan was reported on most, and the projected images of the six destinations are dominated by leisure and recreation, and culture, history and art. Correspondence analysis was used to examine relationships between destinations and popular image attributes. The results showed that South Korea and Macau had distinct projected images, whereas Japan, Taiwan, Hong Kong and Vietnam shared many similar image attributes. Practical implications for destination marketing organizations are provided

Sub-Typing of Rheumatic Diseases Based on a Systems Diagnosis Questionnaire

The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups.49 patients with a rheumatic disease were characterized using a systems diagnosis questionnaire containing 106 questions based on Chinese and Western medicine symptoms. Categorical principal component analysis (CATPCA) was used to discover differences in symptom patterns between the patients. Two Chinese medicine experts where subsequently asked to rank the Cold and Heat status of all the patients based on the questionnaires. These rankings were used to study the Cold and Heat symptoms used by these practitioners.The CATPCA analysis results in three dimensions. The first dimension is a general factor (40.2% explained variance). In the second dimension (12.5% explained variance) 'anxious', 'worrying', 'uneasy feeling' and 'distressed' were interpreted as the Internal disease stage, and 'aggravate in wind', 'fear of wind' and 'aversion to cold' as the External disease stage. In the third dimension (10.4% explained variance) 'panting s', 'superficial breathing', 'shortness of breath s', 'shortness of breath f' and 'aversion to cold' were interpreted as Cold and 'restless', 'nervous', 'warm feeling', 'dry mouth s' and 'thirst' as Heat related. 'Aversion to cold', 'fear of wind' and 'pain aggravates with cold' are most related to the experts Cold rankings and 'aversion to heat', 'fullness of chest' and 'dry mouth' to the Heat rankings.This study shows that the presented systems diagnosis questionnaire is able to identify groups of symptoms that are relevant for sub-typing patients with a rheumatic disease

Negative Body Image Is Not Related to Spontaneous Body-Scaled Motoric Behavior in Undergraduate Women

Body image disturbance is a core characteristic of anorexia nervosa, which might be grounded in distortions in schematic body representations. In line with this, several studies showed that when walking through door-like apertures of different widths, individuals with anorexia nervosa move as if their bodies are larger than they actually are. They turn their body at a higher aperture/shoulder width ratio than healthy individuals. We examined whether oversized body-scaled motoric behaviors may not be restricted to anorexia nervosa but concern a general feature of negative body image attitudes. Therefore, we investigated the relation between negative body image as assessed with shape and weight concerns subscales of the Eating Disorder Examination Questionnaire and aperture/shoulder width turning ratios in women with a healthy weight (n = 62). We found that a more negative body image was unrelated to higher aperture/shoulder width turning ratios. Bayes analysis provided moderate evidence for the null hypothesis that spontaneous body-scaled motoric behaviors are not involved in negative body image. Future studies should disentangle whether being underweight per se is related to distinctive spontaneous body-scaled motoric behaviors or whether an “oversized” body schema is a unique characteristic of anorexia nervosa, potentially contributing to the persistence of this disorder

How small and medium enterprises are using social networks? Evidence from the Algarve region

The evolution of internet created new opportunities for small and medium enterprises (SME), among which are social networks. This work aims at analyzing the potential of these networks for the SME in Algarve, creating a questionnaire for the purpose. The empirical study revealed that some firms have already an integrated business strategy with social networks, as well as a group in the firm responsible for it. Most of their managers consider that social networks enhance performance, but few really measure these results. A categorical principal component analysis identified two dimensions of social networks’ use: social networks for product-client interaction and knowledge; and social networks with potential for marketing. A supplementary analysis (hierarchical clustering) identified three patterns of SME’s involvement in social networks: cluster Social Net Level 1, cluster Social Net Level 2 and cluster Social Net Level 3. These groups validated the results described above, indicating a sustainable methodological approach

Broadband Meter-Wavelength Observations of Ionospheric Scintillation

Intensity scintillations of cosmic radio sources are used to study astrophysical plasmas like the ionosphere, the solar wind, and the interstellar medium. Normally these observations are relatively narrow band. With Low Frequency Array (LOFAR) technology at the Kilpisj\"arvi Atmospheric Imaging Receiver Array (KAIRA) station in northern Finland we have observed scintillations over a 3 octave bandwidth. ``Parabolic arcs'', which were discovered in interstellar scintillations of pulsars, can provide precise estimates of the distance and velocity of the scattering plasma. Here we report the first observations of such arcs in the ionosphere and the first broad-band observations of arcs anywhere, raising hopes that study of the phenomenon may similarly improve the analysis of ionospheric scintillations. These observations were made of the strong natural radio source Cygnus-A and covered the entire 30-250\,MHz band of KAIRA. Well-defined parabolic arcs were seen early in the observations, before transit, and disappeared after transit although scintillations continued to be obvious during the entire observation. We show that this can be attributed to the structure of Cygnus-A. Initial results from modeling these scintillation arcs are consistent with simultaneous ionospheric soundings taken with other instruments, and indicate that scattering is most likely to be associated more with the topside ionosphere than the F-region peak altitude. Further modeling and possible extension to interferometric observations, using international LOFAR stations, are discussed.Comment: 11 pages, 17 figure