A scatter method for data and variable importance evaluation

2012 ◽  
Vol 19 (2) ◽  
pp. 137-149 ◽  
Author(s):  
Martti Juhola ◽  
Markku Siermala
2019 ◽  
Vol 22 (7) ◽  
pp. 502-505
Author(s):  
Ataman Gönel ◽  
Ismail Koyuncu

A 33-month old female child presented at a pediatric clinic with acute tonsillitis, and it was subsequently discovered that she had familial hyperlipidemia. Measurement of the patient’s whole blood tests was performed by a multiparameter automated hematology analyzer, the CELLDYN Ruby System® (Abbott, Lake Forest, USA) using venous blood extracted from a tube containing 3.0 mL of EDTA. Although her hematocrit levels were within normal limits, the hemoglobin (Hgb) level, mean corpuscular volume (MCH) and mean corpuscular Hgb concentration (MCHC) could not be determined using the spectrophotometric method. The results of these tests could not be measured when repeated using dilution. When the sample was left to rest for several minutes, it was observed to be excessively lipemic. The measurements were repeated using the Alinity HQ Analyzer® (Abbott), which determines Hgb concentration using laser scatter and spectrophotometry. Hgb cellular concentration was incorrectly measured as being 21.9 mg/dL using routine spectrophotometry (denoted by a flag indicating Hgb interference) and correctly found to be 10.8 mg/dL. Thus, in samples of excessive lipemia, Hgb, MCH, and MCHC levels cannot be measured accurately using spectrophotometry. Hematology analyzers that can measure cellular hemoglobin (cHGB) and average erythrocyte hemoglobin concentration (cHCM) by laser scatter method may be recommended when analyzing a blood sample that contains excessive lipemia.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Yang ◽  
Julian Hagenauer ◽  
Martin Dijst ◽  
Marco Helbich

Abstract Background Migrants experience substantial changes in their neighborhood physical and social environments along their migration journeys, but little is known about how perceived changes in their neighborhood environment pre- and post-migration correlate with their mental health. Our aim was to examine the associations between recalled changes in the perceived neighborhood physical and social environments and migrants’ mental health in the host city. Methods We used cross-sectional data on 591 migrants in Shenzhen, China. We assessed their risk of mental illness using the General Health Questionnaire (GHQ). Neighborhood perceptions were collected retrospectively pre- and post-migration. We used random forests to analyze possibly non-linear associations between GHQ scores and changes in the neighborhood environment, variable importance, and for exploratory analysis of variable interactions. Results Perceived changes in neighborhood aesthetics, safety, and green space were non-linearly associated with migrants’ mental health: A decline in these characteristics was associated with poor mental health, while improvements in them were unrelated to mental health benefits. Variable importance showed that change in safety was the most influential neighborhood characteristic, although individual-level characteristics—such as self-reported physical health, personal income, and hukou (i.e., the Chinese household registration system)—appeared to be more important to explain GHQ scores and also strongly interacted with other variables. For physical health, we found different associations between changes in the neighborhood provoked by migration and mental health. Conclusion Our findings suggest that perceived degradations in the physical environment are related to poorer post-migration mental health. In addition, it seems that perceived changes in the neighborhood environment play a minor role compared to individual-level characteristics, in particular migrants’ physical health condition. Replication of our findings in longitudinal settings is needed to exclude reverse causality.


Author(s):  
Miyako Sagawa ◽  
Hernan Aguirre ◽  
Fabio Daolio ◽  
Arnaud Liefooghe ◽  
Bilel Derbel ◽  
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Keyword(s):  

2021 ◽  
Author(s):  
Francis Fuller Bbosa ◽  
Ronald Wesonga ◽  
Peter Nabende ◽  
Josephine Nabukenya

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