RESEARCH PAPER
Identifying important risk factors for survival of HIV-infected patients using censored quantile regression
 
More details
Hide details
1
Department of Science, Hamedan University of Technology, Hamedan, 65155, Iran
 
2
School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
 
3
Research Center for Health Sciences and Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, 65175-4171, Iran
 
4
School of Public Health, University of Alberta, Edmonton, Alberta, Canada
 
5
Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
 
 
Submission date: 2021-07-24
 
 
Final revision date: 2021-08-11
 
 
Acceptance date: 2021-08-11
 
 
Publication date: 2023-01-28
 
 
HIV & AIDS Review 2023;22(1):19-24
 
KEYWORDS
TOPICS
ABSTRACT
Introduction:
This study aimed to estimate the effect of potential risk factors on survival of human immunodeficiency virus/acquired immunodeficiency syndrome (AIDS) patients using censored quintile regression model.

Material and methods:
We used a dataset from a (registry-based) retrospective cohort study conducted in Tehran (from April, 2004 to March, 2018). Demographic information, such as age, sex, marital status, and educational level as well as behavioral information, including being-in-prison, drug/alcohol abuse and smoking, antiretroviral therapy, co-infection with tuberculosis (TB), and CD4+ cell count, were investigated as potential risk factors for AIDS progression. Censored quintile regression was used to estimate and investigate these factors for AIDS progression.

Results:
Mean age of patients was 33.93 years. Time to progression ranged from 0.01 to 223.17 months, and mean of time to progression was 40.55 months. A total of 1,249 (50.5%) patients experienced an event by end of the study. Impact of age, gender, prison, being addicted, being infected with tuberculosis, and using highly active antiretroviral therapy (HAART) were significant for most of quintiles (p < 0.05).

Conclusions:
It was shown that age, being prisoned, TB infection, and HAART were significantly associated with a lower time in AIDS progression. Censored quintile regression could be an appropriate choice for considering time-varying effects and easy interpretation of regression coefficients in analyzing AIDS progression data.

 
REFERENCES (30)
1.
Dean HD, Fenton KA. Addressing social determinants of health in the prevention and control of HIV/AIDS, viral hepatitis, sexually transmitted infections, and tuberculosis. Los Angeles: SAGE Publications Sage CA; 2010.
 
2.
Poorolajal J, Molaeipoor L, Mohraz M, et al. Predictors of progression to AIDS and mortality post-HIV infection: a long-term retrospective cohort study. AIDS Care 2015; 27: 1205-1212.
 
3.
World Health Organization. HIV/AIDS. 2017. Available from: http://www.who.int/mediacentre... (Accessed: 17.02.2017).
 
4.
Hamidi O, Tapak M, Poorolajal J, Amini P, Tapak L. Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS. Epidemiology, Biostatistics and Public Health 2017; 14: e12663-1-e12663-10. doi: 10.2427/12663.
 
5.
Poorolajal J, Hooshmand E, Mahjub H, Esmailnasab N, Jenabi E. Survival rate of AIDS disease and mortality in HIV-infected patients: a meta-analysis. Public Health 2016; 139: 3-12.
 
6.
Hamidi O, Tapak L, Poorolajal J, Amini P. Identifying risk factors for progression to AIDS and mortality post-HIV infection using illness-death multistate model. Clin Epidemiol Global Health 2017; 5: 163-168.
 
7.
Langford SE, Ananworanich J, Cooper DA. Predictors of disease progression in HIV infection: a review. AIDS Res Ther 2007; 4: 11. doi: 10.1186/1742-6405-4-11.
 
8.
de Oliveira Silva M, Bastos M, Martins Netto E, de Lima Gouvea NA, Leite Torres AJ, Kallas E, Watkins DI, et al. Acute HIV infection with rapid progression to AIDS. Braz J Infect Dis 2010; 14: 291-293.
 
9.
Hamidi O, Poorolajal J, Tapak L. Identifying predictors of progression to AIDS and mortality post-HIV infection using parametric multistate model. Epidemiology, Biostatistics and Public Health 2017; 14: e12438-1-e12438-9. doi: 10.2427/12438.
 
10.
Koenker R, Geling O. Reappraising medfly longevity: a quantile regression survival analysis. J Am Stat Assoc 2001; 96: 458-468.
 
11.
Peng L, Huang Y. Survival analysis with quantile regression models. J Am Stat Assoc 2008; 103: 637-649.
 
12.
Fitzenberger B. 15 A guide to censored quantile regressions. Handbook of Statistics 1997; 15: 405-437.
 
13.
Portnoy S. Censored regression quantiles. J Am Stat Assoc 2003; 98: 1001-1012.
 
14.
Wang HJ, Wang L. Locally weighted censored quantile regression. J Am Stat Assoc 2009; 104: 1117-1128.
 
15.
Portnoy S. The jackknife’s edge: Inference for censored regression quantiles. Comput Stat Data Analysis 2014; 72: 273-281.
 
16.
Graham MH. Confronting multicollinearity in ecological multiple regression. Ecology 2003; 84: 2809-2815.
 
17.
Antiretroviral Therapy Cohort Collaboration Survival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies. Lancet HIV 2017; 4: e349-e356. doi: 10.1016/S2352-3018(17)30066-8.
 
18.
Chen L, Yang J, Zhang R, et al. Rates and risk factors associated with the progression of HIV to AIDS among HIV patients from Zhejiang, China between 2008 and 2012. AIDS Res Ther 2015; 12: 32. doi: 10.1186/s12981-015-0074-7.
 
19.
Prejean J, Song R, Hernandez A, et al. Estimated HIV incidence in the United States, 2006-2009. PLoS One 2011; 6: e17502. doi: 10.1371/journal.pone.0017502.
 
20.
Deeks SG, Lewin SR, Havlir DV. The end of AIDS: HIV infection as a chronic disease. Lancet 2013; 382: 1525-1533.
 
21.
Althoff KN, Gebo KA, Gange SJ, et al. CD4 count at presentation for HIV care in the United States and Canada: are those over 50 years more likely to have a delayed presentation? AIDS Res Ther 2010; 7: 45. doi: 10.1186/1742-6405-7-45.
 
22.
Abioye AI, Soipe AI, Salako AA, et al. Are there differences in disease progression and mortality among male and female HIV patients on antiretroviral therapy? A meta-analysis of observational cohorts. AIDS Care 2015; 27: 1468-1486.
 
23.
Jarrin I, Geskus R, Bhaskaran K, et al.; CASCADE Collaboration. Gender differences in HIV progression to AIDS and death in industrialized countries: slower disease progression following HIV seroconversion in women. Am J Epidemiol 2008; 168: 532-540.
 
24.
Djachenko A, St John W, Mitchell C. Smoking cessation in male prisoners: a literature review. Int J Prisoner Health 2015; 11: 39-48.
 
25.
Nijhawan A, Kim S, Rich JD. Management of HIV infection in patients with substance use problems. Curr Infect Dis Rep 2008; 10: 432-438.
 
26.
Socio-economic Inequalities and HIV Writing Group for Collaboration of Observational HIV Epidemiological Research in Europe (COHERE) in EuroCoord; Lodi S, Dray-Spira R, Touloumi G, et al. Delayed HIV diagnosis and initiation of antiretroviral therapy: inequalities by educational level, COHERE in EuroCoord. AIDS 2014; 28: 2297-2306.
 
27.
Blanc FX, Sok T, Laureillard D, et al. Earlier versus later start of anti­retroviral therapy in HIV-infected adults with tuberculosis. N Engl J Med 2011; 365: 1471-1481.
 
28.
Hwang JH, Choe PG, Kim NH, et al. Incidence and risk factors of tuberculosis in patients with human immunodeficiency virus infection. J Korean Med Sci 2013; 28: 374-377.
 
29.
Opportunistic Infections Project Team of the Collaboration of Observational HIV Epidemiological Research in Europe (COHERE); Mocroft A, Reiss P, Kirk O, et al. Is it safe to discontinue primary Pneumocystis jiroveci pneumonia prophylaxis in patients with virologically suppressed HIV infection and a CD4 cell count < 200 cells/µL? Clin Infect Dis 2010; 51: 611-619.
 
30.
Wey A, Wang L, Rudser K. Censored quantile regression with recursive partitioning-based weights. Biostatistics 2014; 15: 170-181.
 
eISSN:1732-2707
ISSN:1730-1270
Journals System - logo
Scroll to top