American journal of preventive medicine
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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.
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Individuals who have diabetes or hypertension established before pregnancy are at increased risk for maternal and infant health complications. Guidelines recommend that providers deliver prepregnancy counseling, but little is known about the receipt of those services among patients with chronic conditions. ⋯ Women with prepregnancy diabetes, hypertension, or both reported low levels of the recommended prepregnancy counseling, suggesting an evidence-practice gap that should be addressed to optimize maternal and infant health outcomes. There is a need for evidence-based and patient-centered models of prepregnancy counseling for those with diabetes and hypertension.
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Proactive, electronic referral of primary care patients to quitlines has great potential to provide evidence-based tobacco-cessation assistance to tobacco users. However, the quitline contact rates and engagement of individuals beyond 1 counseling call are poor. This study examines the characteristics of electronically referred patients who engage with the quitline. ⋯ The current quitline phone-based approach is less likely to engage younger adults and those with Medicaid coverage; however, there is a need to improve quitline engagement across all patients. Identification and testing of alternative engagement approaches are needed.
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Childhood declines in cardiovascular health have been linked to the development of subclinical atherosclerosis; however, less is known about the timing and sequence of the decline of the specific cardiovascular health components. The study objective is to identify the patterns of decline and associations with adulthood subclinical atherosclerosis. ⋯ Those who lost the ideal status of cardiovascular health in childhood and early adulthood had more subclinical atherosclerosis than those who retained the ideal cardiovascular health across the life course, underscoring the importance of preserving the ideal cardiovascular health beginning in childhood and continued into adulthood.
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Numerous environmental factors within supermarkets can influence the healthfulness of food purchases. This research aims to identify the changes in store healthfulness scores and assess the variations by store type and neighborhood deprivation using an adapted Consumer Nutrition Environment tool. ⋯ The adapted Consumer Nutrition Environment tool is sensitive to longitudinal changes in environmental variables that contribute to store healthfulness. A wider application of this tool could be used to map in-store environments to identify targets for interventions to encourage healthier food purchasing.