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首页> 外文期刊>AIDS care. >Implementing data-to-care initiatives for HIV in NewYork state: assessing the value of community health centers identifying persons out of care for health department follow-up
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Implementing data-to-care initiatives for HIV in NewYork state: assessing the value of community health centers identifying persons out of care for health department follow-up

机译：在纽约州实施艾滋病毒委员会的数据 - 关注倡议：评估社区保健中心的价值，确定卫生部门的护理人员

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To end the HIV/AIDS epidemic, innovative strategies are needed to improve outcomes along the HIV care continuum. Data-to-Care is a public health strategy whereby HIV surveillance data are used to identify people living with HIV/AIDS for linkage to, or re-engagement in HIV medical care. Three main approaches to Data-to-Care are defined by where persons out of care are identified and where outreach activities are initiated: the Health Department level, the Healthcare Provider level, or a combination of the two (Combination Model). The purpose of this evaluation was to compare successes and challenges for two Data-to-Care models implemented in NewYork State between 1 January 2015 and 1 September 2016: a Health Department Model, and a Combination Model. The Health Department Model identifies persons presumed to be out of care based on an absence of HIV laboratory tests within the states surveillance system alone, and the Combination Model identifies individuals based on both an absence of a medical provider visit at a partnering health center, and an absence of HIV laboratory tests in the surveillance system. Only counties served by partnering health centers were included in this evaluation. In the Health Department Model, 348 out of 1352 (26%) surveillance identified individuals were truly out of care; of those, re-linkage success was 78%. In the Combination Model, 19 out of 51 (37%) individuals were truly out of care; of those, re-linkage success was 63%. The proportion of cases truly out of care was significantly higher for the Combination Model than the Health Department Model (p-value: 0.08). Both models were successful in re-linking a high proportion of individuals back to care, though the efficiency of identifying individuals who are truly out of care remains an area in need of further refinement for both models.
机译：为了结束艾滋病毒/艾滋病流行病，需要创新的策略来改善艾滋病毒护理连续体的结果。数据进行保健是一种公共卫生策略，艾滋病毒监测数据用于识别艾滋病毒/艾滋病患者的艾滋病毒/艾滋病，以便在艾滋病病毒医疗保健中联系或重新参与。数据以提供的三种主要方法是由核心超出护理人员的地方定义，并启动外联活动：卫生部门一级，医疗保健提供者级别，或两者的组合（组合模型）。该评估的目的是比较2015年1月1日至2016年9月1日至2016年9月1日期间在纽约州实施的两种数据到护理模型的成功和挑战：卫生部门模型和组合模型。卫生部门模式确定了在缺乏艾滋病毒实验室检验的情况下，卫生部门识别出于各种艾滋病毒实验室测试，而组合模型根据缺乏医疗提供者访问的人在合作卫生中心的情况下识别个人，以及监测系统中没有艾滋病毒实验室测试。只有合作伙伴服务的县都包含在此评估中。在卫生部门模式中，1352人中的348人（26％）监督发现的个体真正远离护理;其中，重新联动成功为78％。在组合模型中，51名（37％）的人真正远离护理;其中，重新联动成功为63％。组合模型的案例比例明显高于卫生部门模型（P值：0.08）。这两种模型都成功地重新关联了高比例的个人回归照顾，尽管识别真正远离护理的个人的效率仍然需要进一步改进两种模型的区域。

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