Health Impacts of Catastrophic Climate Change: Expert Workshop. Avoid Dangerous Climate Change (AVOID)

Climate change is likely to have serious and significant impacts on human population health. The mechanisms by which climate change may affect health are becoming better understood. Current quantitative methods of estimating future health impacts rely on disease-specific models that primarily describe relationships between mean values of weather variables and health outcomes and do not address the impacts of extreme events or weather disasters. Extreme events have the potential to disrupt community function, which is of concern for decision-makers. Estimating the magnitude and extent of impacts from low probability high impact events is challenging because there is often no analogue that can provide relevant evidence and that take into account the complexity of factors determining future vulnerability and health impacts (the social determinants of health)

Managing health risks in a changing climate: Red Cross operations in East Africa and Southeast Asia

While climate variability and change affect global patterns of disease, there are few examples of methods that effectively integrate climate into health programming. This study examines a Red Cross Red Crescent pilot project in Kenya, Tanzania, Vietnam, and Indonesia that incorporated climate information and considerations in health operations. Our investigation looks at three elements of programming: baseline community perceptions of climate and health, integration of climate information in operations, and resulting community-level risk reduction behaviour. (1) Through community focus groups, semi-structured interviews, and household surveys, our research reveals that potential health effects of climate variability and change are a key concern at the community level. (2) Project implementors used climate information to design educational materials and health contingency plans to inform when and where disease prevention activities should be concentrated. This climate-based disease anticipation and improved sharing of incidence data aimed to quickly detect and respond to changing disease patterns in a variable climate. (3) Subsequently, community-level risk reduction behaviour significantly increased in project locations. This pilot is evidence that climate information and considerations can be readily integrated into health programming to account for changing risks, using existing disease prevention techniques to address priority concerns of vulnerable communities

Modeling in Real Time During the Ebola Response

To aid decision-making during CDC’s response to the 2014–2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014–July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood

Associations between Sociodemographic Characteristics and Sexual Risk Behaviors among Methamphetamine-using Men who Have Sex with Men

BACKGROUND:Methamphetamine-using men who have sex with men (MSM) exhibit elevated rates of HIV and STI prevalence, indicating increased engagement in sexual risk behaviors. OBJECTIVES:This analysis elucidates associations between participant sociodemographics (i.e., age, racial/ethnic identity, sexual identity, educational attainment, and HIV status) and sexual risk behaviors, particularly substance use before/during sex, and engagement in condomless anal intercourse (CAI) with casual, anonymous, and/or exchange male partners. METHODS:From March 2014 through January 2016, 286 methamphetamine-using MSM enrolled in a technology-based study to reduce methamphetamine use and HIV sexual risk behaviors. A robustly estimated generalized structural equation model employing the negative binomial family and log link function (n = 282) tested the simultaneous associations between participant sociodemographics and engagement in HIV sexual risk behaviors. RESULTS:Participants' racial/ethnic identity (χ2(6) = 43.5; p < 0.0001), HIV status (χ2(6) = 22.0; p = 0.0012), educational attainment level (χ2(6) = 13.8; p = 0.0322), and years of age (χ2(6) = 32.4; p < 0.0001) all influenced participants' engagement in substance use before/during sex and engagement in CAI. Methamphetamine (χ2(2) = 7.0; p = 0.0309) and marijuana (χ2(2) = 9.7; p = 0.0079) use before/during sex influenced participants' engagement in CAI with casual, anonymous, and exchange male partners. CONCLUSION:Results indicate the importance of intervention efforts focused on younger racial/ethnic minority MSM with fewer years of educational attainment, and provides evidence of the specific subpopulations of MSM at greatest risk of detrimental effects of illicit substance use