Effects of Political Instability in Venezuela on Malaria Resurgence at Ecuador-Peru Border, 2018.

Mass migration from Venezuela has increased malaria resurgence risk across South America. During 2018, migrants from Venezuela constituted 96% of imported malaria cases along the Ecuador-Peru border. Plasmodium vivax predominated (96%). Autochthonous malaria cases emerged in areas previously malaria-free. Heightened malaria control and a response to this humanitarian crisis are imperative.

A Cross-Sectional Study Evaluating the Use of Free Clinics in Syracuse, NY: Patient Demographics and Barriers to Accessing Healthcare in Traditional Settings.

Free clinics provide healthcare to underserved patient populations, playing a critical role in the medical safety-net. Syracuse, New York has notable racial, socioeconomic, and educational disparities and is home to four free clinics. Little is known about these clinics' patient population. This study attempts to better define this population and the barriers they face accessing traditional care. We developed a 27-question survey investigating patient demographics, barriers to traditional healthcare, and experience at local free clinics. Our analysis included descriptive statistics, t-tests, one-way ANOVA and Chi square testing. Of 287 patients surveyed, 55% of patients were employed, 78% were uninsured, and 43% cited cost as their primary barrier to insurance. 29% rated their health as fair or poor. 21% had been to the Emergency Room (ER) in the past six months. 38% stated they would go to the ER if free clinics did not exist. Insurance coverage was unrelated to education or employment status (p = .52 and .81, respectively), but differed significantly between racial and ethnic groups (p < .007). Insured patients were more likely to have visited an ER in the past 6 months (p = .01), received preventive health services (p = .02), and seen a provider outside of the free clinic as compared to patients without insurance (p < .001). Free clinic patients represent a heterogeneous population with poor health indicators and several barriers to traditional care, especially cost. This information may aid public health agencies in developing policies to increase access to medical care and decrease morbidity and mortality among this population.

Getting PrEP to the patients who need it.

A fraction of those eligible for PrEP to prevent HIV infection receive a prescription. Newer drug regimens and updated recommendations can help you reduce that gap.

Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection.

BACKGROUND: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. METHODOLOGY/PRINCIPAL FINDINGS: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. CONCLUSIONS/SIGNIFICANCE: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.

Case Report: An Acute Chikungunya Infection and a Recent Secondary Dengue Infection in a Peripartum Case in Ecuador.

Dengue virus (DENV) and chikungunya virus (CHIKV) are transmitted by the same mosquito vectors and now co-circulate in many parts of the world; however, coinfections and serial infections are not often diagnosed or reported. A 38-week pregnant woman was admitted to the hospital with a diagnosis of suspected DENV and CHIKV in southern coastal Ecuador. The pregnancy was complicated by mild polyhydramnios and fetal tachycardia, and a healthy newborn was born. The patient was positive for a recent secondary DENV infection (Immunoglobulin M and Immunoglobulin G positive) and an acute CHIKV infection (real-time reverse transcriptase polymerase chain reaction positive) (Asian genotype). The newborn was not tested for either virus. This case resulted in a benign clinical course with a favorable pregnancy outcome.