Appendix: Expand Wikipedia stub on supplier - induced demand

Appendix for article that has been accepted for Journal of Economic Education. Abstract for article: The authors describe a Wikipedia-based project designed for a graduate course introducing health economics to experienced healthcare professionals. The project allows such students to successfully write articles on niche topics in rapidly evolving health economics subspecialties. These students are given the opportunity to publish their completed projects in Wikipedia. Despite the lack of conventional classroom incentives, the authors have found that the students generally choose to enter their final projects intoWikipedia. The authors explore the motivators for this behavior from the perspective of human capital development and reflect on the implications for enhancing economics education. Finally, they comment more generally on the value of assignments within graduate education that allow adult students to determine their degree of effort and reward along the intensive margin. Keywords: economics education, human capital, supplier-induced demand, Wikipedi

Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis

Background: Contemporary data on causes of vision impairment and blindness form an important basis for recommendations in public health policies. Refreshment of the Global Vision Database with recently published data sources permitted modeling of cause of vision loss data from 1990 to 2015, further disaggregation by cause, and forecasts to 2020. Methods: Published and unpublished population-based data on the causes of vision impairment and blindness from 1980 to 2015 were systematically analysed. A series of regression models were fit to estimate the proportion of moderate and severe vision impairment (MSVI; defined as presenting visual acuity <6/18 but ≥3/60 in the better eye) and blindness (presenting visual acuity <3/60 in the better eye) by cause by age, region, and year. Findings: Among the projected global population with MSVI (216.6 million; 80% uncertainty intervals [UI] 98.5-359.1), in 2015 the leading causes thereof are uncorrected refractive error (116.3 million; UI 49.4-202.1), cataract (52.6 million; UI 18.2-109.6), age-related macular degeneration (AMD; 8.4 million; UI 0.9-29.5), glaucoma (4.0 million; UI 0.6-13.3) and diabetic retinopathy (2.6 million; UI 0.2-9.9). In 2015, the leading global causes of blindness were cataract (12.6 million; UI 3.4-28.7) followed by uncorrected refractive error (7.4 million; UI 2.4-14.8) and glaucoma (2.9 million; UI 0.4-9.9), while by 2020, these numbers affected are anticipated to rise to 13.4 million, 8.0 million and 3.2 million, respectively. Cataract and uncorrected refractive error combined contributed to 55% of blindness and 77% of MSVI in adults aged 50 years and older in 2015. World regions varied markedly in the causes of blindness, with a relatively low prevalence of cataract and a relatively high prevalence of AMD as causes for vision loss in the High-income subregions. Blindness due to cataract and diabetic retinopathy was more common among women, while blindness due to glaucoma and corneal opacity was more common among men, with no gender difference related to AMD. Conclusions: The numbers of people affected by the common causes of vision loss have increased substantially as the population increases and ages. Preventable vision loss due to cataract and refractive error (reversible with surgery and spectacle correction respectively), continue to cause the majority of blindness and MSVI in adults aged 50+ years. A massive scale up of eye care provision to cope with the increasing numbers is needed if one is to address avoidable vision loss

Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis

Background: Global and regional prevalence estimates for blindness and vision impairment are important for the development of public health policies. We aimed to provide global estimates, trends, and projections of global blindness and vision impairment. Methods: We did a systematic review and meta-analysis of population-based datasets relevant to global vision impairment and blindness that were published between 1980 and 2015. We fitted hierarchical models to estimate the prevalence (by age, country, and sex), in 2015, of mild visual impairment (presenting visual acuity worse than 6/12 to 6/18 inclusive), moderate to severe visual impairment (presenting visual acuity worse than 6/18 to 3/60 inclusive), blindness (presenting visual acuity worse than 3/60), and functional presbyopia (defined as presenting near vision worse than N6 or N8 at 40 cm when best-corrected distance visual acuity was better than 6/12). Findings: Globally, of the 7·33 billion people alive in 2015, an estimated 36·0 million (80% uncertainty interval [UI] 12·9–65·4) were blind (crude prevalence 0·48%; 80% UI 0·17–0·87; 56% female), 216·6 million (80% UI 98·5–359·1) people had moderate to severe visual impairment (2·95%, 80% UI 1·34–4·89; 55% female), and 188·5 million (80% UI 64·5–350·2) had mild visual impairment (2·57%, 80% UI 0·88–4·77; 54% female). Functional presbyopia affected an estimated 1094·7 million (80% UI 581·1–1686·5) people aged 35 years and older, with 666·7 million (80% UI 364·9–997·6) being aged 50 years or older. The estimated number of blind people increased by 17·6%, from 30·6 million (80% UI 9·9–57·3) in 1990 to 36·0 million (80% UI 12·9–65·4) in 2015. This change was attributable to three factors, namely an increase because of population growth (38·4%), population ageing after accounting for population growth (34·6%), and reduction in age-specific prevalence (–36·7%). The number of people with moderate and severe visual impairment also increased, from 159·9 million (80% UI 68·3–270·0) in 1990 to 216·6 million (80% UI 98·5–359·1) in 2015. Interpretation: There is an ongoing reduction in the age-standardised prevalence of blindness and visual impairment, yet the growth and ageing of the world’s population is causing a substantial increase in number of people affected. These observations, plus a very large contribution from uncorrected presbyopia, highlight the need to scale up vision impairment alleviation efforts at all levels

Identification of candidate Parkinson disease genes by integrating genome-wide association study, expression, and epigenetic data sets

Importance Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. Objective To investigate what genes and genomic processes underlie the risk of sporadic PD. Design and Setting This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. Main Outcomes and Measures It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. Results Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. Conclusions and Relevance Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies

e-Portfolios in Practice

This presentation focuses on the practical application and implementation of e-Portfolios in the health sciences discipline. The presentation discusses objectives of developing student e-portfolios, portfolio integration into the curriculum across the health sciences professions, implementation and challenges. It demystifies the varied definitions of e-Portfolios and different types of portfolio software. There will be a showcase of e-Portfolios and portfolio templates used in the Doctor of Nursing Program, and the Master of Public Health (MPH) program, and the Doctoral Capstone Course in Occupational Therapy. Objectives: After the presentation, participants should be able to: 1. Describe objectives for the development and use of student e-Portfolios and their potential applications for student professional development and career opportunities. 2. Discuss the various components of e-Portfolios based on the different health professions in the health sciences. 3. Describe the various implementation strategies programs have used and how they have addressed implementation challenges. Presentation: 57 minutes (Note: Audio was cut-off, but entire PowerPoint slide deck is located at bottom of this page