American journal of preventive medicine
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The Respond pillar of the Ending the HIV Epidemic in the U. S. initiative, which consists of activities also known as cluster and outbreak detection and response, offers a framework to guide tailored implementation of proven HIV prevention strategies where transmission is occurring most rapidly. Cluster and outbreak response involves understanding the networks in which rapid transmission is occurring; linking people in the network to essential services; and identifying and addressing gaps in programs and services such as testing, HIV and other medical care, pre-exposure prophylaxis, and syringe services programs. ⋯ Efforts to address important gaps in service delivery revealed by cluster and outbreak detection and response can strengthen prevention efforts broadly through multidisciplinary, multisector collaboration. In this way, the Respond pillar embodies the collaborative, data-guided approach that is critical to the overall success of the Ending the HIV Epidemic in the U. S. initiative.
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An important goal of the Ending the HIV Epidemic in the U. S. initiative is the timely diagnosis of all people with HIV as early as possible after infection. To end the HIV epidemic, health departments were encouraged to propose new and innovative HIV testing strategies and improve the reach of existing programs. ⋯ There are both proven and emerging approaches to increasing HIV screening and increasing the frequency of HIV screening available. The Ending the HIV Epidemic in the U. S. initiative provides the motivation, the resources, and a coordinated plan to bring them to scale.
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Socioeconomic disadvantage in childhood is strongly associated with a higher risk of cardiovascular disease in high-income countries. However, the association in low- and middle-income countries, where childhood poverty remains prevalent, has not been reviewed. ⋯ Current evidence from middle-income countries provides little support for an association between childhood socioeconomic position and risk of cardiovascular disease, and evidence from low-income countries is lacking. It would be premature to consider childhood poverty as a target for cardiovascular disease prevention in these settings.
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Socioeconomic disadvantage in childhood is strongly associated with a higher risk of cardiovascular disease in high-income countries. However, the association in low- and middle-income countries, where childhood poverty remains prevalent, has not been reviewed. ⋯ Current evidence from middle-income countries provides little support for an association between childhood socioeconomic position and risk of cardiovascular disease, and evidence from low-income countries is lacking. It would be premature to consider childhood poverty as a target for cardiovascular disease prevention in these settings.
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Cardiovascular disease is the leading cause of death worldwide, and cardiovascular disease burden is increasing in low-resource settings and for lower socioeconomic groups. Machine learning algorithms are being developed rapidly and incorporated into clinical practice for cardiovascular disease prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with accounting for the social determinants of cardiovascular outcomes. This study reviews how social determinants of health are being included in machine learning algorithms to inform best practices for the development of algorithms that account for social determinants. ⋯ Given their flexibility, machine learning approaches may provide an opportunity to incorporate the complex nature of social determinants of health. The limited variety of sources and data in the reviewed studies emphasize that there is an opportunity to include more social determinants of health variables, especially environmental ones, that are known to impact cardiovascular disease risk and that recording such data in electronic databases will enable their use.