Background Commonly used computed tomography (CT) staging systems for chronic rhinosinusitis (CRS) focus on the sinuses and do not quantify disease in the olfactory cleft. The goal of the current study was to determine whether precise measurements of olfactory cleft opacification better correlate with olfaction in patients with CRS. Methods Olfaction was assessed using the 40-item Smell Identification Test (SIT-40) before and after sinus surgery in adult patients. Olfactory cleft opacification was quantified precisely using three-dimensional, computerized volumetric analysis, as well as via a semi-quantitative Likert scale estimations at predetermined anatomic sites. Sinus opacification was also quantified using the Lund-Mackay staging system. Results The overall cohort (n=199) included 89 (44.7%) patients with CRS with nasal polyposis (CRSwNP) and 110 (55.3%) with CRS without nasal polyposis (CRSsNP). The olfactory cleft opacified volume correlated with objective olfaction as determined by the SIT-40 (Rs= −0.461; p<0.001). The correlation was significantly stronger in the CRSwNP subgroup (Rs= −0.573; p<0.001), whereas no appreciable correlation was found in the CRSsNP group (Rs= −0.141; p=0.141). Correlations between sinus-specific Lund-Mackay CT scoring and SIT-40 scores were weaker in the CRSwNP (Rs= −0.377; p<0.001) subgroup but stronger in the CRSsNP (Rs= −0.225; p=0.018) group when compared to olfactory cleft correlations. Greater intra-class correlations (ICC) were found between quantitative volumetric measures of olfactory cleft opacification (ICC=0.844; p<0.001) as compared with semi-quantitative Likert grading (ICC=0.627; p<0.001). Conclusions Quantitative measures of olfactory cleft opacification correlate with objective olfaction, with the strongest correlations seen in patients with nasal polyps.

Background Since the novel coronavirus emerged in late 2019, the scientific and public health community around the world have sought to better understand, surveil, treat, and prevent the disease, COVID-19. In sub-Saharan Africa (SSA), many countries responded aggressively and decisively with lockdown measures and border closures. Such actions may have helped prevent large outbreaks throughout much of the region, though there is substantial variation in caseloads and mortality between nations. Additionally, the health system infrastructure remains a concern throughout much of SSA, and the lockdown measures threaten to increase poverty and food insecurity for the subcontinent’s poorest residents. The lack of sufficient testing, asymptomatic infections, and poor reporting practices in many countries limit our understanding of the virus’s impact, creating a need for better and more accurate surveillance metrics that account for underreporting and data contamination. Objective The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with new and decomposable surveillance metrics of COVID-19 that overcome data limitations and contamination inherent in public health surveillance systems. In addition to prevalence of observed daily and cumulative testing, testing positivity rates, morbidity, and mortality, we derived COVID-19 transmission in terms of speed, acceleration or deceleration, change in acceleration or deceleration (jerk), and 7-day transmission rate persistence, which explains where and how rapidly COVID-19 is transmitting and quantifies shifts in the rate of acceleration or deceleration to inform policies to mitigate and prevent COVID-19 and food insecurity in SSA. Methods We extracted 60 days of COVID-19 data from public health registries and employed an empirical difference equation to measure daily case numbers in 47 sub-Saharan countries as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results Kenya, Ghana, Nigeria, Ethiopia, and South Africa have the most observed cases of COVID-19, and the Seychelles, Eritrea, Mauritius, Comoros, and Burundi have the fewest. In contrast, the speed, acceleration, jerk, and 7-day persistence indicate rates of COVID-19 transmissions differ from observed cases. In September 2020, Cape Verde, Namibia, Eswatini, and South Africa had the highest speed of COVID-19 transmissions at 13.1, 7.1, 3.6, and 3 infections per 100,0000, respectively; Zimbabwe had an acceleration rate of transmission, while Zambia had the largest rate of deceleration this week compared to last week, referred to as a jerk. Finally, the 7-day persistence rate indicates the number of cases on September 15, 2020, which are a function of new infections from September 8, 2020, decreased in South Africa from 216.7 to 173.2 and Ethiopia from 136.7 to 106.3 per 100,000. The statistical approach was validated based on the regression results; they determined recent changes in the pattern of infection, and during the weeks of September 1-8 and September 9-15, there were substantial country differences in the evolution of the SSA pandemic. This change represents a decrease in the transmission model R value for that week and is consistent with a de-escalation in the pandemic for the sub-Saharan African continent in general. Conclusions Standard surveillance metrics such as daily observed new COVID-19 cases or deaths are necessary but insufficient to mitigate and prevent COVID-19 transmission. Public health leaders also need to know where COVID-19 transmission rates are accelerating or decelerating, whether those rates increase or decrease over short time frames because the pandemic can quickly escalate, and how many cases today are a function of new infections 7 days ago. Even though SSA is home to some of the poorest countries in the world, development and population size are not necessarily predictive of COVID-19 transmission, meaning higher income countries like the United States can learn from African countries on how best to implement mitigation and prevention efforts. International Registered Report Identifier (IRRID) RR2-10.2196/21955

Background: Adolescents living with HIV (ALWH) who transition from pediatric to adult care face several challenges that increase their risk of experiencing treatment interruptions and being lost to HIV care with resultant increased morbidity and mortality. To date, few studies have examined their outcomes post-healthcare transition (HCT), precluding the development and dissemination of evidence-based interventions aimed at retaining ALWH in HIV care both during and after HCT. We conducted a systematic review to synthesize the outcomes of ALWH post-HCT to provide suggestions for future directions. Methods: We systematically searched several electronic databases through October 2019 using keywords for HIV, HCT and ALWH. We categorized studies by target population, country (i.e., upper-high income and low-middle income), study design (i.e., descriptive, mixed methods, quantitative), outcomes measured, and follow-up period. Results: A total of 24 studies met inclusion criteria. Studies were categorized according to the following HCT outcomes: retention in HIV care post-HCT (n = 13), changes in CD4+ count and viral load post-HCT (n = 16), and mortality among ALWH post-HCT (n = 7). Most studies (n = 11) examining retention in HIV care indicated that more than 70% of ALWH were retained in care 1-2 years post-HCT while the remaining studies (n = 2) reported retention rates less than 55%. While studies indicated that CD4+ counts and viral loads tended to worsen during the first few years post-HCT, these differences were often not statistically significant. Among all ALWH who transitioned to adult care, a small proportion died within their first seven years post-HCT. Among qualitative studies, common themes included transition readiness (n = 6), provider-patient relationship in the adult clinic setting (n = 6), and concern about the adult clinic setting (n = 4). Conclusions: Transition outcomes were poorest for ALWH with unsuppressed viremia pre-HCT, suggesting that this subgroup of ALWH may need greater support from their treatment teams and caregivers during and post-HCT to improve clinical outcomes.

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BACKGROUND Since the novel coronavirus emerged in late 2019, the scientific and public health community around the world have sought to better understand, surveil, treat, and prevent the disease, COVID-19. In sub-Saharan Africa (SSA), many countries responded aggressively and decisively with lockdown measures and border closures. Such actions may have helped prevent large outbreaks throughout much of the region, though there is substantial variation in caseloads and mortality between nations. Additionally, the health system infrastructure remains a concern throughout much of sub-Saharan Africa, and the lockdown measures threaten to increase poverty and food insecurity for the subcontinent’s poorest residents. The lack of sufficient testing, asymptomatic infections, and poor reporting practices in many countries limit our understanding of the virus’s impact, creating a need for better and more accurate surveillance metrics that account for under-reporting and data contamination. OBJECTIVE The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with new and decomposable surveillance metrics of COVID-19 that overcome data limitations and contamination inherent in public health surveillance systems. In addition to prevalence of observed daily and cumulative testing, testing positivity rates, morbidity and mortality, we derive COVID transmission in terms of: 1) speed, 2) acceleration or deceleration, 3) change in acceleration or deceleration (jerk), and 4) 7-day transmission rate lag which explains where and how rapidly COVID is transmitting, and quantifies shifts in the rate of acceleration or deceleration in order to inform policies to mitigate and prevent COVID and food insecurity in sub-Saharan Africa. METHODS Kenya, Ghana, Nigeria, Ethiopia and South Africa have the most observed cases of COVID and the Seychelles, Eritrea, Mauritius, Comoros, and Burundi have the fewest. In contrast, the speed, acceleration, jerk, and 7-Day Lag indicate rates of COVID transmissions differ from observed cases. In September 2020, Cape Verde, Namibia, Eswatini, and South Africa had highest speed of COVID transmissions at 13.1, 7.1, 3.6, and 3 infections per 100,0000; Zimbabwe has an acceleration rate of transmission while Zambia has the largest rate of deceleration this week compared to last week referred to as a jerk. Finally, the 7 Day Lag or persistence rate indicates the number of cases on Sept 15, 2020 that are a function of new infections from September 8, 2020 decreased in South Africa from 216.7 to 173.2 and Ethiopia from 136.7 to 106.3 per 100,000. The statistical approach is validated based on the regression results; they determine recent changes in the pattern of infection; and during the weeks of September 1-8 and September 9-15 there were substantial country differences in the evolution of the SSA pandemic. This change represents a decrease in the transmission model R value for that week, and is consistent with a de-escalation in the pandemic for Sub-Saharan African continent in general. RESULTS - CONCLUSIONS 1) Standard surveillance metrics such as daily observed new COVID cases or deaths are necessary but insufficient to mitigate and prevent COVID transmission. Public Health leaders also need to know where COVID transmission rates are accelerating or decelerating, whether those rates increase or decrease over short time frames because the pandemic can quickly escalate, and how many cases today are a function of new infections 7 days ago; and 2) Even though SSA is home to some of the poorest countries on the globe, development and population size are not necessarily predictive of COVID transmission, meaning higher income countries like the USA, can learn from African countries on how best to implement mitigation and prevention efforts. INTERNATIONAL REGISTERED REPORT RR2-21955