In this study, based on data from the Kurdistan HIV/AIDS surveillance system, from September 1988 to April 2020, there were 597 cases have been diagnosed and registered with HIV. We excluding 6 cases, because they were not Kurdistan dweller. The mean (SD) age of patients was 45.3 (0.4), varied from 4 to 74.
The distribution of the investigated characteristics is shown in Supplementary table 1. From 591 infected-patients 522 (88.3%) of them were not entered to the AIDS stage, 69 (11.9) have been diagnosed with HIV/AIDS, 278 (47%) patients died and 98 (16.6%) patients had been lost to follow up. More than 50% of the patient were aged 45–74, 76% had an education level less than a diploma, 63.6% were unemployed and 50.6% were widows. The majority of the HIV positive patients had a history of drug abuse, drug injection and prison. About half of HIV positive patients had used antiretroviral and just 1.4% of them were co-infected with TB.
Table 1
The Survival time for progression from HIV to AIDS, HIV to death and AIDS to death
Survival time (year) | Total | Event | Censored | Survival probability | SE | 95% CI |
From HIV to AIDS |
1 | 557 | 5 | 18 | 0.96 | 0.007 | 0.94–0.97 |
2 | 534 | 4 | 21 | 0.95 | 0.008 | 0.93–0.97 |
3 | 509 | 6 | 14 | 0.94 | 0.009 | 0.92–0.95 |
4 | 489 | 2 | 18 | 0.94 | 0.009 | 0.92–0.95 |
5 | 469 | 7 | 16 | 0.92 | 0.011 | 0.90–0.94 |
6 | 446 | 7 | 16 | 0.91 | 0.012 | 0.88–0.93 |
7 | 423 | 3 | 13 | 0.90 | 0.01 | 0.88–0.93 |
8 | 407 | 4 | 11 | 0.89 | 0.01 | 0.87–0.92 |
9 | 392 | 3 | 16 | 0.89 | 0.01 | 0.86–0.91 |
10 | 373 | 4 | 23 | 0.88 | 0.01 | 0.85–0.90 |
From HIV to Death |
1 | 501 | 47 | 5 | 0.77 | 0.01 | 0.082–0.88 |
2 | 454 | 46 | 0 | 0.69 | 0.02 | 0.65–0.73 |
3 | 408 | 48 | 0 | 0.61 | 0.02 | 0.57–0.65 |
4 | 360 | 31 | 0 | 0.56 | 0.02 | 0.52–0.6 |
5 | 329 | 32 | 0 | 0.5 | 0.02 | 0.46–0.54 |
6 | 297 | 29 | 0 | 0.45 | 0.02 | 0.41–0.49 |
7 | 268 | 21 | 0 | 0.42 | 0.02 | 0..38-0.46 |
8 | 247 | 22 | 0 | 0.38 | 0.02 | 0.34–0.42 |
9 | 225 | 30 | 0 | 0.33 | 0.01 | 0.29–0.37 |
10 | 195 | 24 | 0 | 0.25 | 0.01 | 0.22–0.29 |
From AIDS to Death |
1 | 43 | 1 | 6 | 0.69 | 0.05 | 0.58–0.8 |
2 | 36 | 0 | 2 | 0.69 | 0.05 | 0.56–0.78 |
3 | 34 | 3 | 3 | 0.62 | 0.02 | 0.49–0.7 |
4 | 28 | 3 | 15 | 0.53 | 0.07 | 0.38–0.66 |
5 | 10 | 1 | 2 | 0.47 | 0.08 | 0.3–0.63 |
6 | 7 | 0 | 2 | 0.47 | 0.08 | 0.3–0.63 |
7 | 5 | 0 | 1 | 0.47 | 0.08 | 0.3–0.63 |
8 | 4 | 0 | 3 | | | |
The survival rate for progression from HIV to AIDS, HIV to death and AIDS to death are presented in Table 1. Based on the results, the one-year, 5-year, and 10-year survival rates from HIV to AIDS were 96%, 92%, and 88%, respectively. Moreover, the survival rate from HIV to death in one-year, 5-year, and 10-year were 77%, 50%, and 25%, respectively. From AIDS onset to death, the one-year, and 5-year survival rates were 69% and 47%. Supplementary Fig. 1 is shown the impact of gender and history of drug abuse from the diagnosis of HIV to AIDS, and from AIDS to death.
The effect of the predictors of progression from HIV to AIDS and from AIDS to death is shown in Tables 2 and 3, respectively. According to the multivariate analysis using Cox's proportional hazard model, there was a statistically significant association between duration from HIV diagnosis to AIDS and the first CD4 count under 500, using ART, and history of drug use with the hazard ratio of 1.93, 4.53, and 0.76, respectively. Furthermore, there was a statistically significant association between death resulted from HIV and some investigated factors such as unemployment, using ART with HR of 3.4 and 0.07, respectively.
Table 2
Effect of predictors on survival times from HIV diagnosis to AIDS using the Cox regression
Variable | Unadjusted HR (95% CI) | P-value | Adjusted HR (95% CI) | P-value |
Gender |
Female | 1 | | | |
Male | 1.15 (0.4–2.9) | 0.7 | | |
Age Group (year) |
0–44 | 1 | | | |
45–74 | 0.6(0.4–1.01) | 0.05 | | |
Education |
Illiterate | 1 | | | |
Under diploma | 2.92(0.7–11.9) | 0.1 | | |
Academic | 3.11(0.4–22.1) | 0.2 | | |
Occupation |
Employed | 1 | | | |
Unemployed | 0.7(0.4–1.2) | 0.2 | | |
History of marriage |
No | 1 | | | |
Yes | 1.17(0.6–2.2) | 0.6 | | |
Modes of HIV transmission |
Injection drug users | 1 | | | |
Sexual | 1.98(1.06–3.7) | 0.03 | | |
Mother to child/Blood | 0.99(0.14–7.2) | 0.9 | | |
First CD4 |
500+ | 1 | | 1 | |
0-500 | 2.01(1.2–3.4) | 0.009 | 1.93(1.1–3.3) | 0.01 |
Antiretroviral therapy |
No | 1 | | 1 | |
Yes | 14.4(6.2–33.4) | 0.000 | 4.53(1.6–12.6) | 0.004 |
History of drug use |
No | 1 | | 1 | |
Yes | 0.46(0.2–0.8) | 0.009 | 0.76(0.4–1.3) | 0.03 |
History of injection |
No | 1 | | | |
Yes | 0.4(0.09–1.7) | 0.2 | | |
History of prison |
No | 1 | | | |
Yes | 0.6(0.3–1.1) | 0.1 | | |
The study aimed was to investigate the relationship between demographic, social, epidemiological and clinical features of HIV-infected patients with disease progression and mortality. The results of the multivariate analysis showed that lower CD4 at the beginning of HIV diagnosis, sexual transmission of HIV, drug use, use and non-adherence to ART can shorten the duration between HIV diagnosis and disease progression [2]. The results also indicated that unemployment, sexual transmission and non-adherence to ART can increase the risk of death in HIV/AIDS patients.
Table 3
Effect of predictors on survival times from AIDS diagnosis to Death using the Cox regression
Variable | Unadjusted HR (95% CI) | P-value | Adjusted HP (95% CI) | P-value |
Gender |
Female | 1 | | | |
Male | 1.07 (0.25–4.5) | 0.9 | | |
Age Group (year) |
0–44 | 1 | | | |
45–74 | 1.2(0.57–2.6) | 0.6 | | |
Education |
Illiterate | 1 | | | |
Under diploma | 0.13(0.02–0.6) | 0.01 | | |
Academic | 0.25(0.03-2) | 0.19 | | |
Occupation |
Employed | 1 | | 1 | |
Unemployed | 3.8(1.32–11.11) | 0.01 | 3.4(1.16–9.9) | 0.02 |
History of marriage |
No | 1 | | | |
Yes | 2.15(0.6–7.4) | 0.2 | | |
Modes of HIV transmission |
Injection drug users | 1 | | | |
Sexual | 0.47(0.14–1.6) | 0.2 | | |
Mother to child/Blood | 1.27(0.17–9.5) | 0.8 | | |
First CD4 |
500+ | 1 | | | |
0-500 | 1.5(0.6–3.9) | 0.3 | | |
Antiretroviral therapy |
No | 1 | | 1 | |
Yes | 0.06(0.02–0.2) | 0.000 | 0.07 (0.02–0.22) | 0.000 |
History of drug use |
No | 1 | | | |
Yes | 1.67(0.63–4.4) | 0.3 | | |
History of injection |
No | 1 | | | |
Yes | 21.6(0.002-Inf+) | 0.5 | | |
History of prison |
No | 1 | | | |
Yes | 1.8(0.6–5.4) | 0.2 | | |
In our study, the survival rate from HIV to death in one-year, 5-year, and 10-year were 77%, 50%, and 25%, respectively. Liu Z Q et al. in their conducted study on HIV positive patients in China reached the 91%, 86%, and 79% in one- year, five- year and ten- years survival rates, respectively [13]. In Italy, the 10-year survival rate of HIV infected patients in 2012 were 44.7% [14]. It seems that the survival rate of HIV positive patients in developing countries is lower than more developed countries.
Our results indicated that the level of CD4 cells count in the time of HIV diagnosis is an independent predictor of poor survival. This finding is also supported by several previous studies [15, 16]. Lower CD4 cells count can be the main reason for a higher risk of opportunistic complications (infections or malignancies) in patients with HIV/AIDS.
Based on the evidence, until the introduction of Highly Active Antiretroviral Therapy (HAART), age was a strong predictor of HIV progression and death risk [17]. In this study, we did not observe a statistically significant relationship between age group and disease progression or mortality.
The results revealed that poor social situations or unemployment is an independent predictor for mortality in patients with HIV/AIDS. In fact, unemployment is a major problem of HIV infected patients in Iran [18]. This finding is also supported by a study of DeSilva in Jos, Nigeria [19] and Delpierre et al. in France [20].
According to the results, non-adherence to ART is the main risk factor for HIV progression and mortality. The role of ARTs as a therapeutic/preventive effective strategy in the control of HIV/AIDS in many different countries is undeniable [6]. ART improves the immune system, quality of life, life expectancy and is a highly effective protective factor to progression from AIDS to death in AIDS patients [16, 21, 22]. So that promoting ART coverage in countries involving the HIV/AIDS epidemic is one of the most important goals of world health organization. In fact, effective ART is the main intervention to improve longevity and prevent opportunistic infections in patients with HIV/AIDS. This strategy is now a critical component of HIV prevention in the world [23]. Fortunately, today there are many ART drugs approved for HIV-infected adult patients and are gaining Food and Drug Administration (FDA) approval for use in children.
Another risk factors for HIV progression that found in our study was drug use. Injecting drug use is a barrier to receive ARV [24]. Raposeiras et.al. showed that Tobacco, illicit drugs use and risk of cardiovascular disease in patients living with HIV [25]. The study conducted by Parashar et al. is confirmed by our findings [26]. The results also showed that sexual transmission of HIV is significantly associated with the survival of HIV infected patients. This finding is consistent with other studies that conducted by Maracy et al. in Iran [27].
The main strength of our study was using the registry-based database of all HIV/AIDS patients in the Kurdistan province combined with a long and nearly complete follow-up of patients. Additionally, access to comprehensive Iran death registry database allowed us to specify all deaths which have been occurred for HIV patients because some patients occasionally did not seek routine care, and may have died so that to obtain accurate data, all of the patients who did not seek routine health services were followed up using the national death registry software.
The research findings illustrated that non-adherence to ART is the main risk factors in the incidence of AIDS and HIV-related mortality. Unemployed AIDS-patients are also more likely to die; this, which needs more attention in Iran.
Limitation
Our study has some limitations. In this study, we assessed the effect of some predicting factors for the progression of HIV and may there are several other factors that we did not evaluate. For example, in the previous studies have been shown that TB co-infection and low body max index (BMI) correlated with mortality in HIV infected patients [28, 29], while we could not assess the effect of these main factors in mortality and disease progression of HIV infected persons due to lack of required data. Second, left censoring in our data is undeniable, because we don’t know the date of HIV onset, therefore the date of HIV diagnosis was considered as HIV onset and this issue might lead to underestimation of the actual time of duration from HIV to AIDS stage.