Income disparity among persons with disabilities assessed by education and sex : findings from a field survey conducted in Metro Manila, the Philippines

How do persons with disabilities (PWDs) earn a living? From the view point of poverty reduction, this question is quite critical in developing countries. This paper presents an investigation of economic activities of PWDs in the Philippines where, among developing countries, disability-related legislation is relatively progressive. In 2008, a field survey was conducted in cooperation with Disability People’s Organizations (DPOs) using a tailor-made questionnaire in four representative cities of Metro Manila. The level and determinants of income of PWDs were examined with Mincer regression. Conclusions are as follows: (1) The incidence and depth of poverty are greater among sample PWDs than that of the total population in Metro Manila. (2) There is remarkable income disparity among PWDs which is associated with education and sex. (3) After controlling individual, parental, and environmental characteristics, it was found that female PWDs are likely to earn less than male PWDs due to fewer opportunities to participate in economic activities. It is suggested that female PWDs are doubly handicapped in earning income

Income disparity among persons with disabilities assessed by education and sex : findings from a field survey conducted in Metro Manila, the Philippines

How do persons with disabilities (PWDs) earn a living? From the view point of poverty reduction, this question is quite critical in developing countries. This paper presents an investigation of economic activities of PWDs in the Philippines where, among developing countries, disability-related legislation is relatively progressive. In 2008, a field survey was conducted in cooperation with Disability People’s Organizations (DPOs) using a tailor-made questionnaire in four representative cities of Metro Manila. The level and determinants of income of PWDs were examined with Mincer regression. Conclusions are as follows: (1) The incidence and depth of poverty are greater among sample PWDs than that of the total population in Metro Manila. (2) There is remarkable income disparity among PWDs which is associated with education and sex. (3) After controlling individual, parental, and environmental characteristics, it was found that female PWDs are likely to earn less than male PWDs due to fewer opportunities to participate in economic activities. It is suggested that female PWDs are doubly handicapped in earning income.Philippines, Income distribution, People with Disabilities, Education, Gender, Poverty, Disability, Mincer regression, Heckman selection model, I12 - Health Production: Nutrition, Mortality, Morbidity, Substance Abuse and Addiction, Disability, and Economic

Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls

Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of minimally or non-invasive screening tests. Urinary polyamines have been reported as potential markers to detect CRC, and an accurate pattern recognition to differentiate CRC with early stage cases from healthy controls are needed. Here, we utilized liquid chromatography triple quadrupole mass spectrometry to profile seven kinds of polyamines, such as spermine and spermidine with their acetylated forms. Urinary samples from 201 CRCs and 31 non-CRCs revealed the N1,N12-diacetylspermine showing the highest area under the receiver operating characteristic curve (AUC), 0.794 (the 95% confidence interval (CI): 0.704–0.885, p < 0.0001), to differentiate CRC from the benign and healthy controls. Overall, 59 samples were analyzed to evaluate the reproducibility of quantified concentrations, acquired by collecting three times on three days each from each healthy control. We confirmed the stability of the observed quantified values. A machine learning method using combinations of polyamines showed a higher AUC value of 0.961 (95% CI: 0.937–0.984, p < 0.0001). Computational validations confirmed the generalization ability of the models. Taken together, polyamines and a machine-learning method showed potential as a screening tool of CRC