Prosperity Now

BackgroundThe COVID-19 pandemic not only disproportionately impacted the health of immigrants in the US, but led to financial disparities as well. However, few studies to date capture the experience of asylum-seeking and refugee populations.MethodsWe analyzed semi-structured interviews conducted in fall 2021 with immigrants (primarily asylum-seekers and refugees from countries in Central and East Africa) and key informants, and conducted focus group discussions in spring 2023 with employees of a non-profit supporting the financial needs of asylum-seekers and refugees in Portland, ME.ResultsWe identified several ways in which the COVID-19 pandemic negatively impacted the financial stability of asylum-seekers and refugees, including: job loss, work permit backlog, lack of sick leave benefits, childcare expenses and related job strain, rising cost of basic goods, issues of housing availability/affordability, and healthcare expenses.ConclusionPolicy changes that address structural factors and support financial stability among asylum-seekers and refugees and other low-income populations could help mitigate the impact of future public health crises and ultimately improve health equity.

BackgroundThe World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients’ health and well-being. Therefore, making it easier and more accurate to understand and diagnose patients with skeletal malocclusions is necessary.ObjectivesThe main aim of this research was the establishment of machine learning models to correctly classify individual Arab patients, being citizens of Israel, as skeletal class II or III. Secondary outcomes of the study included comparing cephalometric parameters between patients with skeletal class II and III and between age and gender-specific subgroups, an analysis of the correlation of various cephalometric variables, and principal component analysis in skeletal class diagnosis.MethodsThis quantitative, observational study is based on data from the Orthodontic Center, Jatt, Israel. The experimental data consisted of the coded records of 502 Arab patients diagnosed as Class II or III according to the Calculated_ANB. This parameter was defined as the difference between the measured ANB angle and the individualized ANB of Panagiotidis and Witt. In this observational study, we focused on the primary aim, i.e., the establishment of machine learning models for the correct classification of skeletal class II and III in a group of Arab orthodontic patients. For this purpose, various ML models and input data was tested after identifying the most relevant parameters by conducting a principal component analysis. As secondary outcomes this study compared the cephalometric parameters and analyzed their correlations between skeletal class II and III as well as between gender and age specific subgroups.ResultsComparison of the two groups demonstrated significant differences between skeletal class II and class III patients. This was shown for the parameters NL-NSL angle, PFH/AFH ratio, SNA angle, SNB angle, SN-Ba angle. SN-Pg angle, and ML-NSL angle in skeletal class III patients, and for S-N (mm) in skeletal class II patients. In skeletal class II and skeletal class III patients, the results showed that the Calculated_ANB correlated well with many other cephalometric parameters. With the help of the Principal Component Analysis (PCA), it was possible to explain about 71% of the variation between the first two PCs. Finally, applying the stepwise forward Machine Learning models, it could be demonstrated that the model works only with the parameters Wits appraisal and SNB angle was able to predict the allocation of patients to either skeletal class II or III with an accuracy of 0.95, compared to a value of 0.99 when all parameters were used (“general model”).ConclusionThere is a significant relationship between many cephalometric parameters within the different groups of gender and age. This study highlights the high accuracy and power of Wits appraisal and the SNB angle in evaluating the classification of orthodontic malocclusion.