Indigenous communities worldwide share common features that make them especially vulnerable to the complications of and mortality from COVID-19. They also possess resilient attributes that can be leveraged to promote prevention efforts. How can indigenous communities best mitigate potential devastating effects of COVID-19? In Bolivia, where nearly half of all citizens claim indigenous origins, no specific guidelines have been outlined for indigenous communities inhabiting native communal territories. In this Public Health article, we describe collaborative efforts, as anthropologists, physicians, tribal leaders, and local officials, to develop and implement a multiphase COVID-19 prevention and containment plan focused on voluntary collective isolation and contact-tracing among Tsimane forager-horticulturalists in the Bolivian Amazon. Phase 1 involves education, outreach, and preparation, and phase 2 focuses on containment, patient management, and quarantine. Features of this plan might be exported and adapted to local circumstances elsewhere to prevent widespread mortality in indigenous communities.

Concerns have been raised about the possibility that inhibitors of the renin-angiotensin-aldosterone system (RAAS) could predispose individuals to severe COVID-19; however, epidemiological evidence is lacking. We report the results of a case-population study done in Madrid, Spain, since the outbreak of COVID-19.

Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

Nivolumab and ipilimumab, alone or in combination, are widely used immunotherapeutic treatment options for patients with advanced-ie, unresectable or metastatic-melanoma. This criterion, however, excludes patients with stage IV melanoma with no evidence of disease. We therefore aimed to evaluate the safety and efficacy of adjuvant nivolumab plus ipilimumab or nivolumab monotherapy versus a placebo in this patient population.

Atezolizumab can induce sustained responses in metastatic urothelial carcinoma. We report the results of IMvigor130, a phase 3 trial that compared atezolizumab with or without platinum-based chemotherapy versus placebo plus platinum-based chemotherapy in first-line metastatic urothelial carcinoma.