Sub-Saharan Africa (SSA) countries are faced with the challenge of educating a critical mass of occupational therapists (OTs) and physiotherapists (PTs) to meet the growing demand for health and rehabilitation services. The World Federation of Occupational Therapy (WFOT) and World Confederation of Physical Therapy (WCPT) have argued for the need of graduate-level training for OTs and PTs for decades. However, very few studies have been conducted to determine the availability of OT and PT training programs and practitioners in SSA countries. Initial data were collected and compiled from an extensive literature search conducted using MEDLINE and PubMed to examine the availability of OT and PT education and training programs in SSA countries. Additional data were collected, compiled, and collated from academic institutions, ministries of health, health professions associations, and licensing authorities in SSA countries. Secondary data were also collected from the websites of organizations such as the World Bank, World Health Organization (WHO), WFOT, and WCPT. This investigation revealed that there are limited number of OT and PT training programs and that these training programs in Anglophone SSA countries are offered at or below the bachelor’s level. More than half of the countries do not have OT or PT training programs. The number of qualified OTs and PTs appears to be insufficient to meet the demand for rehabilitation services. Nigeria and South Africa are the only countries offering post-entry-level masters and doctoral-level training programs in physiotherapy and occupational therapy. Higher learning institutions in SSA countries need to collaborate and partner with other regional and foreign universities to elevate the educational training and increase the supply of PTs and OTs in the region.

Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation.

U.S. non-citizen residents are burdened by inequitable access to socioeconomic resources, potentially placing them at heightened risk of COVID-19-related disparities. However, COVID-19 impacts on non-citizens are not well understood. Accordingly, the current study investigated COVID-19 mortality disparities within New York (NYC) and Los Angeles (LAC) to test our hypothesis that areas with large proportions of non-citizens will have disproportionately high COVID-19 mortality rates. We examined ecological associations between March 2020–January 2021 COVID-19 mortality rates (per 100,000 residents) and percent non-citizens (using ZIP Code Tabulation Areas (ZCTA) for NYC and City/Community units of analysis for LAC) while controlling for sociodemographic factors. Multiple linear regression analyses revealed significant positive associations between the percentage of non-citizen residents and COVID-19 mortality rates in NYC (95% CI 0.309, 5.181) and LAC (95% CI 0.498, 8.720). Despite NYC and LAC policies intended to provide sanctuary and improve healthcare access for non-citizen residents, communities with larger proportions of non-citizens appear to endure higher COVID-19 mortality rates. The challenges that non-citizens endure—e.g., inequitable access to public benefits—may discourage help-seeking behaviors. Thus, improved health surveillance, public health messaging, and sanctuary policies will be essential for reducing COVID-19 mortality disparities in communities with large shares of non-citizens.

Introduction: Surgical skill evaluation while performing minimally invasive surgeries is a highly complex task. It is important to objectively assess an individual's technical skills throughout surgical training to monitor progress and to intervene when skills are not commensurate with the year of training. The miniaturization of wireless wearable platforms integrated with sensor technology has made it possible to noninvasively assess muscle activations and movement variability during performance of minimally invasive surgical tasks. Our objective was to use electromyography (EMG) to deconstruct the motions of a surgeon during robotic suturing (RS) and distinguish quantifiable movements that characterize the skill of an experienced expert urologic surgeon from trainees. Methods: Three skill groups of participants, novice ( n = 11), intermediate ( n = 12), and expert ( n = 3), were enrolled in the study. A total of 12 wireless wearable sensors consisting of surface EMGs and accelerometers were placed along upper extremity muscles to assess muscle activations and movement variability, respectively. Participants then performed a RS task. Results: EMG-based parameters, total time, dominant frequency, and cumulative muscular workload, were significantly different across the three skill groups. We also found nonlinear movement variability parameters such as correlation dimension, Lyapunov exponent trended differently across the three skill groups. Conclusions: These findings suggest that economy of motion variables and nonlinear movement variabilities are affected by surgical experience level. Wearable sensor signal analysis could make it possible to objectively evaluate surgical skill level periodically throughout the residency training experience. Clinical Trial Registration Number: HS# 2018-4407.