Identification of factors affecting the survival lifetime of HIV+ terminal patients in Albert Luthuli municipality of South Africa
Identification of factors affecting the survival lifetime of HIV+ terminal patients in Albert Luthuli municipality of South Africa
复制标题
南非阿尔伯特·卢图利市艾滋病毒末期患者生存期影响因素的识别
DOI：
10.21203/rs.3.rs-328558/v1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Annah Managa
中科院分区：
文献类型：
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作者：
Pepukai Bengura;P. Ndlovu;Annah Managa
背景：南非是世界上艾滋病疫情最严重的国家，阿尔伯特·卢图利市所在的姆普马兰加省的艾滋病毒感染率仅次于夸祖鲁-纳塔尔省。本研究的目的是确定影响阿尔伯特·卢图利社区农村医院HIV+晚期患者生存时间的因素。方法：这是一项典型的回溯性队列纵向设计研究，从2010年到2017年对HIV+晚期患者进行回顾性跟踪，直到一名患者死亡、转移到另一家医院、失去随访或在观察期结束时仍活着。每个患者的随访时间从患者开始参加医院健康中心的ART计划时开始。结果：通过COX比例风险回归模型分析，发现ART依从性(差、可、好)、年龄、随访量、基线钠、基线病毒载量、治疗后随访的CD4计数(方案1)交互作用和随访淋巴细胞史(是、否)交互作用对HIV+晚期患者的生存时间有显著影响(p值均为0.1)。此外，通过分位数回归模型发现，分别由0.1、0.5和0.9个分位数表示的HIV+患者的短、中、长生存期不一定受到相同因素的显著影响。讨论：Cox PH模型和分位数回归分析在回答本研究问题时相辅相成。结论：本研究通过Logistic回归、Cox PH回归和分位数回归模型对影响HIV+晚期患者生存的因素进行了识别和建模。。COX回归分析显示，影响HIV+晚期患者生存时间的因素依次为：抗逆转录病毒治疗依从性、年龄、随访量、基线钠、基线病毒载量、随访淋巴细胞与结核病病史的相互作用和随访的治疗(方案1)。
Background: South Africa has the biggest HIV epidemic in the world, with Mpumalanga province in which Albert Luthuli municipality is located having the second highest HIV prevalence rate after KwaZulu-Natal province. The objective of the study was to identify the factors that affect the survival lifetime of HIV+ terminal patients in rural district hospitals of Albert Luthuli municipality.Methods: This is a typical retrospective cohort longitudinal design study whereby cohort of HIV+ terminal patients was retrospectively followed from 2010 to 2017 until a patient died, transferred to another hospital, lost to follow-up or was still alive at the end of the observation period. The follow-up time for each patient started at the time the patient got initiated to the ART programme at the hospital’s wellness centre. Nonparametric survival analysis and semiparametric survival analysis methods were used to analyse the data.Results: Through Cox proportional hazards regression modelling, it was found that ART adherence (poor, fair, good), Age, Follow-up mass, Baseline sodium, Baseline viral load, Follow CD4 count by Treatment (Regimen 1) interaction and Follow-up lymphocyte by TB history (yes, no) interaction had significant effects on survival lifetime of HIV+ terminal patients (p-values < 0.1). Furthermore, through quantile regression modelling, it was found that short, medium and long survival times of HIV+ patients, respectively represented by the 0.1, 0.5 and 0.9 quantiles, were not necessarily significantly affected by the same factors.Discussion: The Cox PH modelling and the quantile regression analysis complemented each other in answering the research question. However, although the Cox PH modelling was the main approach in this study, the quantile regression analysis results are more informative than the Cox PH modelling results.Conclusion: The study identified and modelled the factors affecting the survival of HIV+ terminal patients in Albert Luthuli Municipality by using Logistic regression, Cox PH regression, and Quantile regression modelling. . Cox regression modelled the factors affecting the survival lifetime of HIV+ terminal patients as: ART adherence, Age, Follow-up mass, Baseline sodium, Baseline viral load and interactions of Follow-up lymphocyte by TB history and Follow-up CD4 by Treatment (Regimen 1).