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The Manual Ability Classification System (MACS) for children with cerebral palsy: scale development and evidence of validity and reliability
The Manual Ability Classification System (MACS) has been developed to classify how children with cerebral palsy (CP) use their hands when handling objects in daily activities. The classification is designed to reflect the child's typical manual performance, not the child's maximal capacity. It classifies the collaborative use of both hands together. Validation was based on the experience within an expert group, a review of the literature, and thorough analysis of children across a spectrum of function. Discussions continued until consensus was reached, first about the constructs, then about the content of the five levels. Parents and therapists were interviewed about the content and the description of levels. Reliability was tested between pairs of therapists for 168 children (70 females, 98 males; with hemiplegia [n=52], diplegia [n=70], tetraplegia [n=19], ataxia [n=6], dyskinesia [n=19], and unspecified CP [n=2]) between 4 and 18 years and between 25 parents and their children's therapists. The results demonstrated that MACS has good validity and reliability. The intraclass correlation coefficient between therapists was 0.97 (95% confidence interval 0.96–0.98), and between parents and therapist was 0.96 (0.89–0.98), indicating excellent agreement.
Journal Article
Are manual workers at higher risk of death than non-manual employees when living in Swedish municipalities with higher income inequality?
Objectives: To test the hypothesis that manual workers are at higher risk of death than are non-manual employees when living in municipalities with higher income inequality. Design: Hierarchical regression was used for the analysis were individuals were nested within municipalities according to the 1990 Swedish census. The outcome was all-cause mortality 1992–1998. The income measure at the individual level was disposable family income weighted against composition of family; the income inequality measure used at the municipality level was the Gini coefficient. Participants: The study population consisted of 1 578 186 people aged 40–64 years in the 1990 Swedish census, who were being reported as unskilled or skilled manual workers, lower-, intermediate-, or high-level non-manual employees. Results: There was no significant association between income inequality at the municipality level and risk of death, but an expected gradient with unskilled manual workers having the highest risk and high-level non-manual employees having the lowest. However, in the interaction models the relative risk (RR) of death for high-level non-manual employees was decreasing with increasing income inequality (RR = 0.77; 95% CI, 0.63–0.93), whereas the corresponding risk for unskilled manual workers increased with increasing income inequality (RR = 1.24; 95% CI, 1.06–1.46). The RRs for skilled manual, low- and medium- level non-manual employees were not significant. Controlling for income at the individual level did not substantially alter these findings, neither did potential confounders at the municipality level. Conclusions: The findings suggest that there could be a differential impact from income inequality on risk of death, dependent on individuals' social position.
Journal Article
Object classification with aggregating multiple spatial views using a machine-learning approach
The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For presentation purposes and easier verification of the solution, an application was developed to create views of 3D objects, classify them using the selected CNN, and evaluate the performance of the CNN. The evaluation is based on metrics and characteristics described in the article. Seven testing objects were used to verify the proposed solution; five CNNs were tested for each.
Journal Article
A Modification of the Elixhauser Comorbidity Measures into a Point System for Hospital Death Using Administrative Data
Background: Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the Elixhauser classification system requires 30 binary variables, making its use for reporting and analysis of comorbidity cumbersome. Objective: Modify the Elixhauser classification system into a single numeric score for administrative data. Methods: For all hospitalizations at the Ottawa Hospital, Canada, between 1996 and 2008, we determined if International Classification of Disease codes for chronic diagnoses were in any of the 30 Elixhauser comorbidity groups. We then used backward stepwise multivariate logistic regression to determine the independent association of each comorbidity group with death in hospital. Regression coefficients were modified into a scoring system that reflected the strength of each comorbidity group's independent association with hospital death. Results: Hospitalizations that were included were 345,795 (derivation: 228,565; validation 117,230). Twenty-one of the 30 groups were independently associated with hospital mortality. The resulting comorbidity score had an equivalent discrimination in the derivation and validation groups (overall c-statistic 0.763, 95% CI: 0.759-0.766). This was similar to models having all Elixhauser groups (0.760, 95% CI: 0.756-0.764) or significant groups only (0.759, 95% CI: 0.754-0.762), but significantly exceeded discrimination when comorbidity was expressed using the Charlson score (0.745, 95% CI: 0.742-0.749). Conclusion: When analyzing administrative data, the Elixhauser comorbidity system can be condensed to a single numeric score that summarizes disease burden and is adequately discriminative for death in hospital.
Journal Article
Validity of Myocardial Infarction Diagnoses in Administrative Databases: A Systematic Review
Though administrative databases are increasingly being used for research related to myocardial infarction (MI), the validity of MI diagnoses in these databases has never been synthesized on a large scale.
To conduct the first systematic review of studies reporting on the validity of diagnostic codes for identifying MI in administrative data.
MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to identify MI; or (b) Evaluating the validity of MI codes in administrative data; and (c) Reporting validation statistics (sensitivity, specificity, positive predictive value (PPV), negative predictive value, or Kappa scores) for MI, or data sufficient for their calculation. Additonal articles were located by handsearch (up to February 2011) of original papers. Data were extracted by two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.
Thirty studies published from 1984-2010 were included; most assessed codes from the International Classification of Diseases (ICD)-9th revision. Sensitivity and specificity of hospitalization data for identifying MI in most [≥50%] studies was ≥86%, and PPV in most studies was ≥93%. The PPV was higher in the more-recent studies, and lower when criteria that do not incorporate cardiac troponin levels (such as the MONICA) were employed as the gold standard. MI as a cause-of-death on death certificates also demonstrated lower accuracy, with maximum PPV of 60% (for definite MI).
Hospitalization data has higher validity and hence can be used to identify MI, but the accuracy of MI as a cause-of-death on death certificates is suboptimal, and more studies are needed on the validity of ICD-10 codes. When using administrative data for research purposes, authors should recognize these factors and avoid using vital statistics data if hospitalization data is not available to confirm deaths from MI.
Journal Article
Macromorphoscopic trait expression in a cranial sample from Medellín, Colombia
•Cranial macromorphoscopic trait variation is explored in a sample from Colombia.•US-based ancestry assessment methods can classify unknown individuals from Colombia.•Colombian phylogeographic social distinctions are not usefully discernible.•Pooled Colombian samples are more useful in the assessment of ancestry at this time.•Artificial neural networks may be useful for ancestry assessment in some cases.
Adjusting existing methods of human identification developed by forensic anthropologists in the United States for use with populations not included in the original development of an analytical method requires data collection using contemporary osteological collections from those populations, and an assessment of the within-group variation present. The primary purpose of this research is to document cranial macromorphoscopic trait variation using methods previously developed in the United States in a sample of 244 individuals from Antioquia, Medellín, Colombia. All individuals are of known age, sex, and birth region. The complex population and demographic history of Colombia makes ancestry assessment particularly difficult in that country. To that end, we explore inter-regional variation throughout Antioquia using birthplace to determine whether forensic anthropologists can provide finer levels of detail beyond identifying an unknown set of human remains as ‘Colombian’ or, more generally, Hispanic. State and local levels of identification resulting from the varied population histories of each state within Antioquia permit finer resolution, but only to a degree of certainty. Artificial neural networks (aNN) correctly classified only 18.6% of a validation sample, following modest classification accuracies of test/tuning (11.6%) and training (82.8%) samples to original birthplace. As with most neural networks, overfitting is an issue with these analyses. To remedy this overfitting and to document the applicability of aNNs to the assessment of ancestry in Colombia, we pooled the sample of Colombian data and compared that to modern American samples. In those analyses, the best aNN model correctly classified 48.4% (validation) of the sample. Given these results, finer levels of analysis in Colombia are not yet possible using only macromorphoscopic trait data.
Journal Article
Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank
Gil McVean and colleagues present a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Their method displays increased power to detect genetic effects over other approaches and identifies novel associations between classical HLA alleles and common immune-mediated diseases.
Genetic discovery from the multitude of phenotypes extractable from routine healthcare data can transform understanding of the human phenome and accelerate progress toward precision medicine. However, a critical question when analyzing high-dimensional and heterogeneous data is how best to interrogate increasingly specific subphenotypes while retaining statistical power to detect genetic associations. Here we develop and employ a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Our method displays a more than 20% increase in power to detect genetic effects over other approaches and identifies new associations between classical human leukocyte antigen (HLA) alleles and common immune-mediated diseases (IMDs). By applying the approach to genetic risk scores (GRSs), we show the extent of genetic sharing among IMDs and expose differences in disease perception or diagnosis with potential clinical implications.
Journal Article
Farm outcomes based on cluster analysis of compound farm evaluation
The purpose of this paper is to examine the internal structure of Czech agricultural holdings based on a multicriteria evaluation of the five dimensions representing the main functions of agriculture including production, economicfactors, financial stability, environmental, and social and other factors. A cluster analysis was performed to identify two clusters of farms. The first cluster consists of smaller holdings that specialize in livestock production and achieve poorer financial results compared to the second cluster, which includes a larger share of large holdings that focus on crop production. The first cluster exhibited better performance as regards environmental protection and financial stability. In contrast, the second cluster achieved better scores regarding production and economic factors. However, an evaluation of all dimensions showed that the second cluster of farms obtained slightly better ratings (2.7% above the overall average) then the first cluster (3.1% below the overall average score). It is up to policy makers to decide which group of farmers, is more approaching the aim of the new agricultural policy. Policy makers can consider the results of this study to find the areas where the sustainability rate should be increased and purposefully promote that by specific measures to achieve balanced farming system.
Journal Article