The Lancet Countdown South America: increasing health opportunities by identifying the gaps in health and climate change research.

South America is experiencing the effects of climate change, including extreme weather events and changes in temperature and precipitation patterns. These effects interact with existing social vulnerabilities, exacerbating their impact on the health and wellbeing of populations. This viewpoint highlights four main messages from the series, which presented key gaps from five different perspectives of health and climate. First, there is an overall need for local analyses of priority topics to inform public policy, which include national and sub-national evidence to adequately strengthen responses and preparedness for climate change hazards and address relevant social vulnerabilities in South American countries. Second, research in health and climate is done in silos and the intersection is not clear in terms of responsibility and leadership; therefore, transdisciplinary research and action are key. Third, climate research, policies, and action need to be reflected in effective funding schemes, which until now are very limited. For adaptation and mitigation policies to be effective, they need a robust and long-term funding scheme. Finally, climate action is a big opportunity for healthier and more prosperous societies in South America, taking the advantage of strategic climate policies to face the challenges of climate change and tackle existing social inequities.

Multidrug-resistant and methicillin-resistant Staphylococcus aureus (MRSA) in hog slaughter and processing plant workers and their community in North Carolina (USA).

BACKGROUND: Use of antimicrobials in industrial food-animal production is associated with the presence of antimicrobial-resistant Staphylococcus aureus (S. aureus) among animals and humans. Hog slaughter/processing plants process large numbers of animals from industrial animal operations and are environments conducive to the exchange of bacteria between animals and workers. OBJECTIVES: We compared the prevalence of multidrug-resistant S. aureus (MDRSA) and methicillin-resistant S. aureus (MRSA) carriage among processing plant workers, their household members, and community residents. METHODS: We conducted a cross-sectional study of hog slaughter/processing plant workers, their household members, and community residents in North Carolina. Participants responded to a questionnaire and provided a nasal swab. Swabs were tested for S. aureus, and isolates were tested for antimicrobial susceptibility and subjected to multilocus sequence typing. RESULTS: The prevalence of S. aureus was 21.6%, 30.2%, and 22.5% among 162 workers, 63 household members, and 111 community residents, respectively. The overall prevalence of MDRSA and MRSA tested by disk diffusion was 6.9% and 4.8%, respectively. The adjusted prevalence of MDRSA among workers was 1.96 times (95% CI: 0.71, 5.45) the prevalence in community residents. The adjusted average number of antimicrobial classes to which S. aureus isolates from workers were resistant was 2.54 times (95% CI: 1.16, 5.56) the number among isolates from community residents. We identified two MDRSA isolates and one MRSA isolate from workers as sequence type 398, a type associated with exposure to livestock. CONCLUSIONS: Although the prevalence of S. aureus and MRSA was similar in hog slaughter/processing plant workers and their household and community members, S. aureus isolates from workers were resistant to a greater number of antimicrobial classes. These findings may be related to the nontherapeutic use of antimicrobials in food-animal production.

Use of Individual-level Covariates to Improve Latent Class Analysis of Trypanosoma Cruzi Diagnostic Tests.

Statistical methods such as latent class analysis can estimate the sensitivity and specificity of diagnostic tests when no perfect reference test exists. Traditional latent class methods assume a constant disease prevalence in one or more tested populations. When the risk of disease varies in a known way, these models fail to take advantage of additional information that can be obtained by measuring risk factors at the level of the individual. We show that by incorporating complex field-based epidemiologic data, in which the disease prevalence varies as a continuous function of individual-level covariates, our model produces more accurate sensitivity and specificity estimates than previous methods. We apply this technique to a simulated population and to actual Chagas disease test data from a community near Arequipa, Peru. Results from our model estimate that the first-line enzyme-linked immunosorbent assay has a sensitivity of 78% (95% CI: 62-100%) and a specificity of 100% (95% CI: 99-100%). The confirmatory immunofluorescence assay is estimated to be 73% sensitive (95% CI: 65-81%) and 99% specific (95% CI: 96-100%).