Specificities of the Diabetic Population in French Guiana: The Health Barometer Survey.

AIM: This study aims to identify the epidemiological and social characteristics of the diabetic population in French Guiana. BACKGROUND: The prevalence of diabetes is very high in the French overseas departments. French Guiana is, however, a very atypical territory, closer in the epidemiological field to Latin America than European and French standards. OBJECTIVE: To perform a descriptive analysis of variables related to renouncing medical care, social parameters, and use of healthcare services. METHODS: A two-stage random sample of 1390 individuals aged 30 to 75 years was surveyed by telephone, and screening was initially done for diabetic versus non-diabetic individuals. Logistic regression was fitted on the sample to adjust for potential confounding factors. A Kaplan-Meier analysis showed the risk of diabetes as a function of the age of onset. RESULTS: The prevalence rate of diabetes was 9.3%, particularly among women, for whom 20% had a history of gestational diabetes. Excess weight and obesity were found in 60% of people with diabetes. The diabetic individuals in French Guiana were younger than those in mainland France, and 30% of people with diabetes were on insulin. They often reported sleep problems, and their health status was described as poor. People with diabetes did not regularly consult a doctor and were very rarely followed up by a specialist. CONCLUSIONS: The prevalence rate of diabetes and obesity in French Guiana was one of the highest among the French territories, with specific vulnerabilities requiring to be addressed by local health policies.

Evaluating the impact of curfews and other measures on SARS-CoV-2 transmission in French Guiana

While general lockdowns have proven effective to control SARS-CoV-2 epidemics, they come with enormous costs for society. It is therefore essential to identify control strategies with lower social and economic impact. Here, we report and evaluate the control strategy implemented during a large SARS-CoV-2 epidemic in June-July 2020 in French Guiana that relied on curfews, targeted lockdowns and other measures. We find that the combination of these interventions reduced the basic reproduction number of SARS-CoV-2 from 1.7 to 1.1, which was sufficient to avoid saturation of hospitals. We estimate that thanks to the young demographics across the territory, the risk of hospitalisation following infection was 0.3 times that of metropolitan France and that about 20% of the population was infected by July. Our model projections are consistent with a recent seroprevalence study. The study showcases how mathematical modeling can be used to support healthcare planning and decision making in a context of high uncertainty.

Prospective comparison of saliva and nasopharyngeal swab sampling for mass screening for COVID-19

Current testing for COVID-19 relies on quantitative reverse-transcriptase polymerase chain reaction from a nasopharyngeal swab specimen. Saliva samples have advantages regarding ease and painlessness of collection, which does not require trained staff and may allow self-sampling. We enrolled 776 persons at various field-testing sites and collected nasopharyngeal and pooled saliva samples. 162 had a positive COVID-19 RT-PCR, 61% were mildly symptomatic and 39% asymptomatic. The sensitivity of RT-PCR on saliva samples versus nasopharygeal swabs varied depending on the patient groups considered or on Ct thresholds. There were 10 (6.2%) patients with a positive saliva sample and a negative nasopharyngeal swab, all of whom had Ct values<25. For symptomatic patients for whom the interval between symptoms onset and sampling was <10 days sensitivity was 77% but when excluding persons with isolated Ngen positivity (54/162), sensitivity was 90%. In asymptomatic patients, the sensitivity was only 24%. When we looked at patients with Cts <30, sensitivity was 83% or 88.9% when considering 2 genes. The relatively good performance for patients with low Cts suggests that Saliva testing could be a useful and acceptable tool to identify infectious persons in mass screening contexts, a strategically important task for contact tracing and isolation in the community.

In search for the hotspots of Disease X: A biogeographic approach to mapping the predictive risk of WHO s Blueprint Priority Diseases

Anthropization of natural habitats including climate change along with overpopulation and global travel have been contributing to emerging infectious diseases outbreaks. The recent COVID-19 outbreak in Wuhan, highlights such threats to human health, social stability and global trade and economy. We used species distribution modelling and environmental data from satellite imagery to model Blueprint Priority Diseases occurrences. We constructed classical regression and Support Vector Machine models based on environmental predictor variables such as landscape, tree cover loss, climatic covariates. Models were evaluated and a weighed mean was used to map the predictive risk of disease emergence. We mapped the predictive risk for filovirus, Nipah, Rift Valley Fever and coronavirus diseases. Elevation, tree cover loss and climatic covariates were found to significant factors influencing disease emergence. We also showed the relevance of disease biogeography and in the identification potential hotspots for Disease X in regions in Uganda and China. Article Summary LineIn our study with the use of a biogeographic approach, we were able to identify Wuhan as a potential hotspot of disease emergence in the absence of COVID-19 data and we confirm that distribution of disease emergence in humans is spatially dependent on environmental factors.