The malaria burden of Amerindian groups of three Venezuelan states: a descriptive study based on programmatic data.

BACKGROUND: Fifty-three percent of all cases of malaria in the Americas in 2019 came from Venezuela, where the epidemic is heavily focused south of the Orinoco river, and where most of the country's Amerindian groups live. Although the disease is known to represent a significant public health problem among these populations, little epidemiological data exists on the subject. This study aims to provide information on malaria incidence, geospatial clustering, and risk factors associated to Plasmodium falciparum infection among these groups. METHODS: This is a descriptive study based on the analysis of published and unpublished programmatic data collected by Venezuelan health authorities and non-government organizations between 2014 and 2018. The Annual Parasite Index among indigenous groups (API-i) in municipalities of three states (Amazonas, Bolivar, and Sucre) were calculated and compared using the Kruskal Wallis test, risk factors for Plasmodium falciparum infection were identified via binomial logistic regression and maps were constructed to identify clusters of malaria cases among indigenous patients via Moran's I and Getis-Ord's hot spot analysis. RESULTS: 116,097 cases of malaria in Amerindian groups were registered during the study period. An increasing trend was observed between 2014 and 2016 but reverted in 2018. Malaria incidence remains higher than in 2014 and hot spots were identified in the three states, although more importantly in the south of Bolivar. Most cases (73.3%) were caused by Plasmodium vivax, but the Hoti, Yanomami, and Eñepa indigenous groups presented higher odds for infection with Plasmodium falciparum. CONCLUSION: Malaria cases among Amerindian populations increased between 2014 and 2018 and seem to have a different geographic distribution than those among the general population. These findings suggest that tailored interventions will be necessary to curb the impact of malaria transmission in these groups.

Longitudinal passive cough monitoring and its implications for detecting changes in clinical status.

Research question: What is the impact of the duration of cough monitoring on its accuracy in detecting changes in the cough frequency? Materials and methods: This is a statistical analysis of a prospective cohort study. Participants were recruited in the city of Pamplona (Northern Spain), and their cough frequency was passively monitored using smartphone-based acoustic artificial intelligence software. Differences in cough frequency were compared using a one-tailed Mann-Whitney U test and a randomisation routine to simulate 24-h monitoring. Results: 616 participants were monitored for an aggregated duration of over 9 person-years and registered 62 325 coughs. This empiric analysis found that an individual's cough patterns are stochastic, following a binomial distribution. When compared to continuous monitoring, limiting observation to 24 h can lead to inaccurate estimates of change in cough frequency, particularly in persons with low or small changes in rate. Interpretation: Detecting changes in an individual's rate of coughing is complicated by significant stochastic variability within and between days. Assessing change based solely on intermittent sampling, including 24-h, can be misleading. This is particularly problematic in detecting small changes in individuals who have a low rate and/or high variance in cough pattern.

Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence.

Research question: Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods: This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results: We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs·h-1; p=0.00001). There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF 0.43). Technical issues affected uptake and regular use of the system. Interpretation: Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring.

The effect of early treatment with ivermectin on viral load, symptoms and humoral response in patients with non-severe COVID-19: A pilot, double-blind, placebo-controlled, randomized clinical trial.

BACKGROUND: Ivermectin inhibits the replication of SARS-CoV-2 in vitro at concentrations not readily achievable with currently approved doses. There is limited evidence to support its clinical use in COVID-19 patients. We conducted a Pilot, randomized, double-blind, placebo-controlled trial to evaluate the efficacy of a single dose of ivermectin reduce the transmission of SARS-CoV-2 when administered early after disease onset. METHODS: Consecutive patients with non-severe COVID-19 and no risk factors for complicated disease attending the emergency room of the Clínica Universidad de Navarra between July 31, 2020 and September 11, 2020 were enrolled. All enrollments occurred within 72 h of onset of fever or cough. Patients were randomized 1:1 to receive ivermectin, 400 mcg/kg, single dose (n = 12) or placebo (n = 12). The primary outcome measure was the proportion of patients with detectable SARS-CoV-2 RNA by PCR from nasopharyngeal swab at day 7 post-treatment. The primary outcome was supported by determination of the viral load and infectivity of each sample. The differences between ivermectin and placebo were calculated using Fisher's exact test and presented as a relative risk ratio. This study is registered at ClinicalTrials.gov: NCT04390022. FINDINGS: All patients recruited completed the trial (median age, 26 [IQR 19-36 in the ivermectin and 21-44 in the controls] years; 12 [50%] women; 100% had symptoms at recruitment, 70% reported headache, 62% reported fever, 50% reported general malaise and 25% reported cough). At day 7, there was no difference in the proportion of PCR positive patients (RR 0·92, 95% CI: 0·77-1·09, p = 1·0). The ivermectin group had non-statistically significant lower viral loads at day 4 (p = 0·24 for gene E; p = 0·18 for gene N) and day 7 (p = 0·16 for gene E; p = 0·18 for gene N) post treatment as well as lower IgG titers at day 21 post treatment (p = 0·24). Patients in the ivermectin group recovered earlier from hyposmia/anosmia (76 vs 158 patient-days; p < 0.001). INTERPRETATION: Among patients with non-severe COVID-19 and no risk factors for severe disease receiving a single 400 mcg/kg dose of ivermectin within 72 h of fever or cough onset there was no difference in the proportion of PCR positives. There was however a marked reduction of self-reported anosmia/hyposmia, a reduction of cough and a tendency to lower viral loads and lower IgG titers which warrants assessment in larger trials. FUNDING: ISGlobal, Barcelona Institute for Global Health and Clínica Universidad de Navarra.

Epidemiological and clinical characteristics of patients with malaria admitted to a hospital in Mérida, Venezuela.

BACKGROUND: Malaria is expanding rapidly across Venezuela, spreading outwards from traditional high transmission regions in the southeast of the country, but the lack of official data make it impossible to understand the reasons for this expansion and to estimate its real magnitude. This study aims to evaluate the epidemiological characteristics driving the re-emergence of malaria in Mérida, a state in the west of Venezuela, where no cases have been reported since 2003, and also to study the clinical presentation of the disease in patients presenting with malaria. METHODS: Thirty-three patients who presented with anemia and fever and with a microscopic diagnosis of malaria were examined and interviewed. Data were collected in standardized forms and analyzed. One-way analysis of variance was used to study differences among patients infected with different parasites. RESULTS: Twenty-two patients were from the Zulia state and eleven were from the Mérida state, mainly from the lowlands south of Lake Maracaibo. Six of these patients traveled to the Bolívar state between 2017 and 2019. Thirteen patients presented with the WHO criteria for severe malaria.Conclusions:Domestic migration to the southeast of Venezuela may have played an important role in the expansion of malaria in previously existing endemic areas of transmission and also in the increase in the number of cases of severe malaria.