Background Brazil ranks second worldwide in total number of COVID-19 cases and deaths. Understanding the possible socioeconomic and ethnic health inequities is particularly important given the diverse population and fragile political and economic situation. We aimed to characterise the COVID-19 pandemic in Brazil and assess variations in mortality according to region, ethnicity, comorbidities, and symptoms.Methods We conducted a cross-sectional observational study of COVID-19 hospital mortality using data from the SIVEP-Gripe (Sistema de Informação de Vigilância Epidemiológica da Gripe) dataset to characterise the COVID-19 pandemic in Brazil. In the study, we included hospitalised patients who had a positive RT-PCR test for severe acute respiratory syndrome coronavirus 2 and who had ethnicity information in the dataset. Ethnicity of participants was classified according to the five categories used by the Brazilian Institute of Geography and Statistics: Branco (White), Preto (Black), Amarelo (East Asian), Indígeno (Indigenous), or Pardo (mixed ethnicity). We assessed regional variations in patients with COVID-19 admitted to hospital by state and by two socioeconomically grouped regions (north and central-south). We used mixed-effects Cox regression survival analysis to estimate the effects of ethnicity and comorbidity at an individual level in the context of regional variation.Findings Of 99 557 patients in the SIVEP-Gripe dataset, we included 11 321 patients in our study. 9278 (82•0%) of these patients were from the central-south region, and 2043 (18•0%) were from the north region. Compared with White Brazilians, Pardo and Black Brazilians with COVID-19 who were admitted to hospital had significantly higher risk of mortality (hazard ratio [HR] 1•45, 95% CI 1•33-1•58 for Pardo Brazilians; 1•32, 1•15-1•52 for Black Brazilians). Pardo ethnicity was the second most important risk factor (after age) for death. Comorbidities were more common in Brazilians admitted to hospital in the north region than in the central-south, with similar proportions between the various ethnic groups. States in the north had higher HRs compared with those of the central-south, except for Rio de Janeiro, which had a much higher HR than that of the other central-south states.Interpretation We found evidence of two distinct but associated effects: increased mortality in the north region (regional effect) and in the Pardo and Black populations (ethnicity effect). We speculate that the regional effect is driven by increasing comorbidity burden in regions with lower levels of socioeconomic development. The ethnicity effect might be related to differences in susceptibility to COVID-19 and access to health care (including intensive care) across ethnicities. Our analysis supports an urgent effort on the part of Brazilian authorities to consider how the national response to COVID-19 can better protect Pardo and Black Brazilians, as well as the population of poorer states, from their higher risk of dying of COVID-19.

Highly active antiretroviral therapy has decreased human immunodeficiency virus (HIV)-associated mortality; other comorbidities, such as chronic liver disease, are assuming greater importance. We retrospectively examined the causes of death of HIV-seropositive patients at our institution in 1991, 1996, and 1998-1999. In 1998-1999, 11 (50%) of 22 deaths were due to end-stage liver disease, compared with 3 (11.5%) of 26 in 1991 and 5 (13.9%) of 36 in 1996 (P=.003). In 1998-1999, 55% of patients had nondetectable plasma HIV RNA levels and/or CD4 cell counts of >200 cells/mm(3) within the year before death. Most of the patients that were tested had detectable antibodies to hepatitis C virus (75% of patients who died in 1991, 57.7% who died in 1996, and 93.8% who died in 1998-1999; P=NS). In 1998-1999, 7 patients (31.8%) discontinued antiretroviral therapy because of hepatotoxicity, compared with 0 in 1991 and 2 (5.6%) in 1996. End-stage liver disease is now the leading cause of death in our hospitalized HIV-seropositive population.

We describe a patient who developed daptomycin-resistant, methicillin-resistant Staphylococcus aureus (MRSA) during an episode of presumed septic thrombophlebitis of the portal vein. Although daptomycin is an alternative agent for treatment of drug-resistant gram-positive bacterial infections, development of resistance during prolonged use may occur with MRSA bacteremia from a persistent focus.

Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real‐world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state‐of‐the‐art machine learning methods for causal inference, developed for estimating treatment effects both in the cross‐section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.

Schistosomiasis is a major, worldwide cause of morbidity and mortality. Disease from the organism Schistosoma mansoni results from egg deposition in the liver, intestines, and other organs and is associated with an intense, granulomatous response from the human host. Clinical manifestations range from mild to severe intestinal forms, and hepatosplenic schistosomiasis, which is associated with hepatic fibrosis, portal hypertension, esophageal varices, and splenomegaly. This article presents information about the epidemiology, immunopathogenesis and clinical aspects of the disease, the relationship between hepatic schistosomiasis and viral infections, diagnosis, therapy, and control strategies for schistosomiasis.

CAM therapies complement, rather than replace, HAART. Even among poorly educated, nongay male patients with insecure housing, rates of ingested CAM use were substantial. Physicians should routinely ask about ingested CAM therapy use and become knowledgeable about potential hazards.

Incarceration presents a unique opportunity to identify injection drug users with acute HCV infection, to initiate counseling regarding other bloodborne pathogens, and to facilitate immunizations and HCV treatment.

Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods.