Causes of Childhood Blindness in North Ghana: Results From a School for the Blind in Wa, Upper West Region

Background: Because most developing countries lack sufficient resources and infrastructure to conduct population-based studies on childhood blindness, it can be difficult to obtain epidemiologically reliable data available for planning public health strategies to effectively address the major determinants of childhood blindness. The major etiologies of blindness can differ regionally and intra-regionally. The objective of this retrospective study was to determine (1) the major causes of childhood blindness (BL) and severe visual impairment (SVI) in students who attend Wa Methodist School for the Blind in Upper West Region, North Ghana, and (2) any potential temporal trends in the causes of blindness for this region.

Methods: In this retrospective study, demographic data and clinical information from an eye screening at Wa Methodist School for the Blind were coded according to the World Health Organization/Prevention of Blindness standardized reporting methodology. Causes of BL and SVI were categorized anatomically and etiologically. We determined the major causes of BL/SVI over time using information provided about the age at onset of visual loss for each student.

Results: The major anatomical causes of BL/SVI among the 190 students screened were corneal opacity and phthisis bulbi (n=28, 15%), optic atrophy (n=23, 13%), glaucoma (n=18, 9%), microphthalmos (n=18, 9%), and cataract (n=18, 9%). Within the first year of life, students became blind mainly due to whole globe causes (n=23, 26%), cataract (n=15, 17%), and optic atrophy (n=11, 13%). Those who became blind after age one year had whole globe causes (n=26, 26%), corneal opacity (n=24, 24%), and optic atrophy (n=13, 13%).

Conclusion: At the Wa Methodist School for the Blind, the major anatomical causes of BL/SVI were corneal opacity and phthisis bulbi. About half of all students became blind within the first year of life, and were disproportionately affected by cataract and retinal causes in comparison to the other students who became blind after age one year. While research in blind schools has a number of implicit disadvantages and limitations, considering the temporal trends and other epidemiological factors of blindness may increase the usefulness and/or implications of the data that come from blind school studies in order to improve screening methods for newborns in hospitals and primary care centers, and to help tailor preventative and treatment programs to reduce avoidable childhood blindness in neonates and schoolchildren.

Math 412 - Topology with Applications

Highlights of Data Expedition: • Students explored daily observations of local climate data spanning the past 35 years. • Topological Data Analysis, or TDA for short, provides cutting-edge tools for studying the geometry of data in arbitrarily high dimensions. • Using TDA tools, students discovered intrinsic dynamical features of the data and learned how to quantify periodic phenomenon in a time-series. • Since nature invariably produces noisy data which rarely has exact periodicity, students also considered the theoretical basis of almost-periodicity and even invented and tested new mathematical definitions of almost-periodic functions. Summary The dataset we used for this data expedition comes from the Global Historical Climatology Network. “GHCN (Global Historical Climatology Network)-Daily is an integrated database of daily climate summaries from land surface stations across the globe.” Source: https://www.ncdc.noaa.gov/oa/climate/ghcn-daily/ We focused on the daily maximum and minimum temperatures from January 1, 1980 to April 1, 2015 collected from RDU International Airport. Through a guided series of exercises designed to be performed in Matlab, students explore these time-series, initially by direct visualization and basic statistical techniques. Then students are guided through a special sliding-window construction which transforms a time-series into a high-dimensional geometric curve. These high-dimensional curves can be visualized by projecting down to lower dimensions as in the figure below (Figure 1), however, our focus here was to use persistent homology to directly study the high-dimensional embedding. The shape of these curves has meaningful information but how one describes the “shape” of data depends on which scale the data is being considered. However, choosing the appropriate scale is rarely an obvious choice. Persistent homology overcomes this obstacle by allowing us to quantitatively study geometric features of the data across multiple-scales. Through this data expedition, students are introduced to numerically computing persistent homology using the rips collapse algorithm and interpreting the results. In the specific context of sliding-window constructions, 1-dimensional persistent homology can reveal the nature of periodic structure in the original data. I created a special technique to study how these high-dimensional sliding-window curves form loops in order to quantify the periodicity. Students are guided through this construction and learn how to visualize and interpret this information. Climate data is extremely complex (as anyone who has suffered from a bad weather prediction can attest) and numerous variables play a role in determining our daily weather and temperatures. This complexity coupled with imperfections of measuring devices results in very noisy data. This causes the annual seasonal periodicity to be far from exact. To this end, I have students explore existing theoretical notions of almost-periodicity and test it on the data. They find that some existing definitions are also inadequate in this context. Hence I challenged them to invent new mathematics by proposing and testing their own definition. These students rose to the challenge and suggested a number of creative definitions. While autocorrelation and spectral methods based on Fourier analysis are often used to explore periodicity, the construction here provides an alternative paradigm to quantify periodic structure in almost-periodic signals using tools from topological data analysis.

Community Vulnerability and Adaptation to Climate Change in East Palo Alto

Climate change and sea level rise continue to devastate communities around the globe. The impacts have a disproportionate effect on those of lower socio-economic levels, and the consequences are frequently not borne equally amongst impacted individuals (UNDP, 2013). Community-based adaptation has been widely used to assess vulnerabilities and impacts at the community level, with an inclusive process that addresses root causes of risk. The process provides the opportunity for local government to empower and engaged impacted communities in identifying and prioritizing their urgent adaptation needs. This study aims to understand East Palo Alto community vulnerabilities by assessing local knowledge and perception of risk to climate change. East Palo Alto, an urban city in California with socio-economic challenges, is vulnerable to flooding and coastal inundation. The limited financial and institutional capacity of the local government and community increases vulnerability and risk. Recommendations and steps are presented to guide actions and programs that are crucial in addressing community priorities and concerns.

Community Vulnerability and Adaptation to Climate Change in East Palo Alto

Climate change and sea level rise continue to devastate communities around the globe. The impacts have a disproportionate effect on those of lower socio-economic levels, and the consequences are frequently not borne equally amongst impacted individuals (UNDP, 2013). Community-based adaptation has been widely used to assess vulnerabilities and impacts at the community level, with an inclusive process that addresses root causes of risk. The process provides the opportunity for local government to empower and engaged impacted communities in identifying and prioritising their urgent adaptation needs. This study aims to understand East Palo Alto community vulnerabilities by assessing local knowledge and perception of risk to climate change. East Palo Alto, an urban city in California with socio- economic challenges, is vulnerable to flooding and coastal inundation. The limited financial and institutional capacity of the local government and community increases vulnerability and risk. Recommendations and steps are presented to guide actions and programs that are crucial in addressing community priorities and concerns