Big Data analytics of HIV treatment gaps in South Carolina: Identification and prediction

项目摘要/摘要。艾滋病毒的早期诊断以及与艾滋病毒医疗保健的联系和保留 HIV+患者对患者的生存和治疗非常重要。错过了早期诊断艾滋病毒的机会 即使是推荐的常规艾滋病毒检测仍在继续。全国和南卡罗来纳州(SC)估计 艾滋病毒医疗保留率略高于50%，这表明艾滋病毒治疗方面存在缺口。具有重要意义 未接受艾滋病毒医疗护理、改善护理结果和艾滋病毒预防的艾滋病毒+个人比例 作为国家艾滋病毒/艾滋病战略的一部分，很难实现。这项研究的目的是用小说 机器学习算法，以进一步探索、识别、表征和解释遗漏的预测因素 在南卡罗来纳州所有活着的艾滋病毒+个人中利用艾滋病毒医疗服务的机会。HIV+的概况 基于艾滋病毒就医行为模式的个人将在伴随而来的情况下发展 查明艾滋病毒护理方面的差距和重新参与艾滋病毒护理的错失机会。健康状况 还将研究HIV+个体的艾滋病毒前诊断的使用行为，以确定遗漏的地方 艾滋病毒检测的机会出现了。调查结果将与自然科学部门正在进行的努力相结合 健康和环境控制(DHEC)的S数据到护理(DTC)计划以及Ryan White Care 程序。艾滋病毒治疗带来的公共健康价值包括改善护理的生存结果 减少艾滋病毒携带者的感染，减少艾滋病毒的传播。这些重要组件构成了 美国抗击和控制艾滋病毒流行的总体战略，并与 减少新的艾滋病毒感染的战略目标。使用现有的州级CD4和病毒载量(VL)检测数据 该州表示，对于2004年以来所有SC HIV+患者，这项研究将把住院和门诊索赔数据来源联系起来 艾滋病毒/艾滋病电子报告系统、地区卫生资源档案和国家惩戒数据 数据库，以创建一个跨越10年(2004-2013年)的独特的基于人口的数据集。高级大数据 分析算法将被用来创建艾滋病毒诊断前和诊断后的个人水平的健康概况模式 用于确定艾滋病毒医疗保健中关联和保留的最佳预测因素。这些 算法将有助于挖掘艾滋病毒医疗保健利用的隐藏特征/预测因素。预测性的 可用于预测未接受护理的HIV+患者将在何处获得常规医疗服务的模型 (错失的机会)也将被开发。研究结果将为公共卫生提供新的指导 针对早期HIV检测的干预措施以及与SC HIV感染者的HIV医疗护理的联系和保留 个人。

Project Summary/Abstract. Early HIV diagnosis as well as linkage into and retention in HIV medical care for HIV+ individuals is important for patient survival and treatment. Missed opportunities for early HIV diagnosis continues even with recommended routine HIV testing. National and South Carolina (SC) estimates of retention in HIV medical care are slightly above fifty percent, indicating a gap in HIV treatment. With significant proportions of HIV+ individuals not receiving HIV medical care, improved outcomes of care and HIV prevention as part of national HIV/AIDs strategies are difficult to achieve. The purpose of this study is to use novel machine learning algorithms to further explore, identify, characterize, and explain predictors of missed opportunities for HIV medical care utilization among all living HIV+ individuals in SC. Profiles of HIV+ individuals based on their patterns of HIV medical care seeking behavior will be developed with concomitant identification of both gaps in HIV care and missed opportunities for reengagement into HIV care. Health utilization behavior for HIV+ individuals' pre-HIV diagnosis also will be studied to identify where missed opportunities for HIV testing occurs. Findings will be integrated with the ongoing effort of the SC Department of Health and Environmental Control (DHEC)'s Data-to-Care (DTC) program as well as the Ryan White Care Program. The public health value that HIV treatment brings includes improved survival outcomes of care among HIV+ individuals as well as reduced HIV transmission. These important components form part of the overall strategy for fighting and controlling the HIV epidemic in the United States and aligns closely with the strategic goals of reducing new HIV infections. Using state-level CD4 and Viral Load (VL) testing data available for all SC HIV+ individuals since 2004, the study will link inpatient and outpatient claims data sources, the state electronic HIV/AIDS reporting system, Area Health Resource Files, and data from the state corrections database to create a unique population based dataset spanning 10 years (2004-2013). Advanced Big Data analytical algorithms will be used to create person-level profile patterns of pre- and post- HIV diagnosis health utilization behaviors and for identifying best predictors of linkage and retention in HIV medical care. These algorithms will be useful in unearthing hidden features/predictors of HIV medical care utilization. A predictive model useful for predicting where HIV+ individuals who are not in care will access routine medical care (missed opportunities) also will be developed. Findings will provide fresh guidance for public health interventions targeting early HIV testing and linkage to and retention in HIV medical care for SC HIV-infected individuals.