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. Author manuscript; available in PMC: 2024 May 1.
Published in final edited form as: AIDS Care. 2022 May 16;35(5):753–763. doi: 10.1080/09540121.2022.2074957

Prevalence and Trend of AIDS-defining cancers and non-AIDS-defining cancers and their Association with Antiretroviral Therapy among People Living with HIV in South Carolina: A Population-based Cohort Study

Chigozie A Nkwonta 1,*, Jiajia Zhang 2,4, Shujie Chen 2,4, Sharon Weissman 2,5, Bankole Olatosi 2,6, Xiaoming Li 2,3
PMCID: PMC9666704  NIHMSID: NIHMS1806994  PMID: 35578401

Abstract

Monitoring cancer trends and risk is critical as cancer remains a growing problem in persons living with HIV (PLWH). Recent population-based data are limited regarding the cancer trends among PLWH. Our study examined the prevalence and trends in the rate of AIDS-defining cancers (ADC) and non-AIDS-defining cancers (NADC) and their risk factors in PLWH in South Carolina. Utilizing linked population-based HIV data (2005–2020), time dependent proportional hazards model was used to identify associated risk predictors of developing cancer in PLWH. Among 11,238 PLWH, 250 individuals developed ADC and 454 developed NADC. The median time from HIV diagnosis to cancer diagnosis was 1.9 years for ADC and 3.8 years for NADC. Individuals who developed ADC or NADC were more likely to be older, male, use substances, have hepatitis infection, hypothyroidism, hypertension and renal disease. Individuals with viral load >100,000 copies/ml were more likely to develop ADC while those with CD4 count >350 cells/mm3 were less likely to develop ADC or NADC. Our findings suggest that long-term viral suppression may contributes to risk reduction for cancer in PLWH. Early HIV diagnosis along with viral load suppression should be a part of ongoing cancer prevention efforts.

Keywords: AIDS-defining cancers, non-AIDS-defining cancers, viral load suppression, tobacco use

Introduction

The advent of antiretroviral therapy (ART) has prolonged the lifespan of HIV-infected individuals. Mortality among people living with HIV (PLWH) has decreased by 45% from 1999–2011, but PLWH are experiencing a higher incidence of comorbid conditions, including cancer (Smith, et al., 2014; Shiels, et al., 2011). PLWH have a substantially higher risk of cancer compared to the general population (Rubinstein, et al., 2014). Although the availability of ART has lowered the incidence of AIDS-defining cancers (ADC), there has been an increase in non-AIDS defining cancer (NADC) (Shiels, & Engels, 2017; Yarchoan, & Uldrick, 2018). The burden of cancer in PLWH in the United States is projected to increase, with the aging HIV population (Hernández-Ramírez, et al., 2020). Coinfection with oncogenic viruses such as human papillomavirus, viral hepatitis, and Epstein-Barr virus contributes to the higher risk of cancers (Robbins, et al.,2014), and is common among PLWH. On the other hand, cancer is strongly associated with tobacco use, which is a frequent risk factor in PLWH (Park, et al.,2016).

This persistently high risk of cancer among PLWH may be due to multiple cumulative factors such as (1) an aging population, (2) delayed HIV diagnosis and awareness of HIV status, (3) difficulty in accessing general medical care, and (4) poor retention in care and inadequate viral load control with ART. These risk factors are common in South Carolina (SC), a southern state with a high HIV burden. The HIV epidemic in SC is a complex unevenly distributed epidemics, affecting mostly African Americans, males and men who have sex with men (MSM) (South Carolina Department of Health and Environmental Control, [SC DHEC] 2017). In addition, in SC only 66% of the PLWH are in care; 54% are retained in continuous care; and 53% are virally suppressed (SC DHEC, 2017).

The cancer burden among PLWH in the US will continue to evolve as the size and demographics of the PLWH population changes. Understanding the association between the degree of immune suppression and the risk of cancer is vital especially in a state like SC where almost half (47%) of the PLWH are not virally suppressed. Past study has found higher than expected cancer incidence among PLWH (Robbins, et al., 2015).. There are limited current data, evaluating the prevalence, trends, and predictors of cancer among HIV infected persons in SC. In addition, few studies comprehensively included or examined longitudinal measures such as CD4 counts, HIV RNA measures and other common comorbidities at baseline and time of cancer diagnosis (Long, et al., 2008; Powles, et al., 2009). The aim of this study is to examine the prevalence of cancer, trends in the rate of cancer, and the predictors of developing cancer among PLWH in SC.

Method

This is a retrospective analysis of PLWH in SC. We used linked data from the SC Revenue and Fiscal Affairs Office Integrated Data System, SC DHEC enhanced HIV/AIDS Surveillance data system (e-HARS), Ryan White Service Reports, and Health Sciences South Carolina. The data sources and linkages are described in detail elsewhere (Olatosi, et al., 2019). Since this study used de-identified data, the study protocol received an exempt review from the Institutional Review Board of University of South Carolina.

For this study, we included 12,170 HIV-infected persons diagnosed in SC from January 2005 to December 2020. However, we excluded 196 who were <18 years at time of HIV diagnosis and 736 individuals who do not have CD4/VL test records before their cancer diagnosis in ADC cohort and excluded 741 in NADC cohort. Analysis for ADC and NADC was based on the remaining 11,238 and 11,233 PLWH, respectively.

Outcome variables

The cancer types were identified by their ICD9/10 code. The ADC included Kaposi sarcoma, non-Hodgkin lymphoma, and invasive cervical cancer. The NADC were other cancers: anal cancer, breast cancer, bladder cancer, cancer of the bronchus or lung, colon cancer, rectal cancer, head and neck cancer, Hodgkin’s lymphoma, kidney cancer, liver or bile duct cancer, leukemias, melanoma, myeloma, ovary cancer, prostate cancer, stomach cancer, testicular cancer, thyroid cancer, and uterine cancer.

Predictors

The following variables were included as potential predictors: age at HIV diagnosis, gender, race, ethnicity, transmission risk, and place of residence. Tobacco use, alcohol use, illicit drug use, hepatitis infections, and common comorbidities (dyslipidemia, hypothyroidism, obesity, renal disease, diabetes mellitus, hypertension, arthritis, COPD, cardiovascular disease, cerebrovascular disease) were recorded per clinic visit and defined with the corresponding ICD9/10 code. HIV variables assessed were longitudinal HIV viral load and CD4. CD4 counts were grouped into categories as >350 cells/mm3, 201–350 cells/mm3 and <200 cells/mm3. HIV viral loads were categorized as >100,000 copies/ml, 10,001–100,000 copies/ml, 401-<10,000 copies/ml and <400 copies/ml.

Statistical analysis

We presented the percentage and frequency for all categorical variables and median days from HIV diagnosis to first ADC or NADC diagnosis. Initial CD4 count (first CD4 count after HIV diagnosis), recent CD4 count (recent CD4 count before cancer diagnosis), initial VL (first VL after HIV diagnosis) and recent VL (recent VL before cancer diagnosis) were extracted from longitudinal records and reported in frequency table. The median number of CD4/VL observations is 13 (minimum of 1 and maximum of 85). Trend of incidence of ADC and NADC from 2005 to 2020 is presented in a line chart. Prevalence of most common cancers are drawn in a bar chart. To study the relationship between ADC/NADC and main predictor, time dependent proportional hazards (PH) models were used to calculate multivariable adjusted hazards ratio (HR) and 95% confidence intervals (CI). All patients were considered under follow-up from date of HIV diagnosis until the end of December 2020. Multivariable models included the following variables: gender, race, age at HIV diagnosis and lifestyle factors and presented as hazard ratios and confidence interval (CI). Kaplan-Meier survival plots were used to present probability of cancer free over the time across categories of predictors. All analyses were performed with SAS version 9.4 (Cary, North Carolina, USA).

Results

Characteristics of study population and patterns of cancer

A total of 250 (2.22%) individuals developed ADC with 1.95 years median time from HIV diagnosis to developing ADC. A comparison of baseline demographics and clinical characteristics for those who developed ADC and those who did not develop ADC is shown in Table 1 and Table 2. Significant differences exist by age, tobacco use, hepatitis infection, and Hypothyroidism and renal diseases. In addition, individuals who developed ADC were less likely to have an initial and recent VL count <400 copies/ml and initial and recent CD4 count greater than 200 cells/ml.

Table 1a.

Demographic and clinical characteristics of the study population, with subjects with ADC.

Characteristics Overall (N=11238) No ADC (N=10988) ADC (N=250) p-value*
Age group <.0001
18–29 4585 (40.8) 4512 (98.41) 73 (1.59)
30–39 2501 (22.25) 2447 (97.84) 54 (2.16)
40–49 2264 (20.15) 2186 (96.55) 78 (3.45)
50+ 1888 (16.8) 1843 (97.62) 45 (2.38)
Sex 0.7648
Female 2697 (24) 2639 (97.85) 58 (2.15)
Male 8541 (76) 8349 (97.75) 192 (2.25)
Race 0.0666
White 2504 (22.28) 2432 (97.12) 72 (2.88)
Black 7751 (68.97) 7589 (97.91) 162 (2.09)
Hispanic 648 (5.77) 637 (98.3) 11 (1.7)
Other/unknown 335 (2.98) 330 (98.51) 5 (1.49)
Transmission risk 0.0843
Heterosexual 2091 (18.61) 2031 (97.13) 60 (2.87)
MSM 5696 (50.69) 5582 (98) 114 (2)
IDU/MSM 600 (5.34) 583 (97.17) 17 (2.83)
Other/unknown 2851 (25.37) 2792 (97.93) 59 (2.07)
Residency 0.1053
Rural 1910 (17) 1858 (97.28) 52 (2.72)
Urban 9328 (83) 9130 (97.88) 198 (2.12)
Alcohol Use 0.8914
No 9564 (85.1) 9352 (97.78) 212 (2.22)
Yes 1674 (14.9) 1636 (97.73) 38 (2.27)
Tobacco Use 0.0191
No 5453 (48.52) 5350 (98.11) 103 (1.89)
Yes 5785 (51.48) 5638 (97.46) 147 (2.54)
Illicit_Drug_use 0.9558
No 9110 (81.06) 8907 (97.77) 203 (2.23)
Yes 2128 (18.94) 2081 (97.79) 47 (2.21)
Hepatitis B or C 0.0002
No 10531 (93.71) 10311 (97.91) 220 (2.09)
Yes 707 (6.29) 677 (95.76) 30 (4.24)
Hypothyroidism <.0001
No 8732 (77.7) 8598 (98.47) 134 (1.53)
Yes 2506 (22.3) 2390 (95.37) 116 (4.63)
Renal disease <.0001
No 9245 (82.27) 9072 (98.13) 173 (1.87)
Yes 1993 (17.73) 1916 (96.14) 77 (3.86)
Diabetes 0.5315
No 9881 (87.92) 9658 (97.74) 223 (2.26)
Yes 1357 (12.08) 1330 (98.01) 27 (1.99)
Obesity 0.9706
No 9853 (87.68) 9634 (97.78) 219 (2.22)
Yes 1385 (12.32) 1354 (97.76) 31 (2.24)
Dyslipidemia 0.7433
No 9951 (88.55) 9728 (97.76) 223 (2.24)
Yes 1287 (11.45) 1260 (97.9) 27 (2.1)
Initial Viral load count <.0001
<=400 1644 (14.63) 1629 (99.09) 15 (0.91)
401–10,000 2454 (21.84) 2426 (98.86) 28 (1.14)
10,001–100,000 3943 (35.09) 3857 (97.82) 86 (2.18)
>100,000 3197 (28.45) 3076 (96.22) 121 (3.78)
Initial CD4 count <.0001
<=200 3372 (30.01) 3231 (95.82) 141 (4.18)
201–350 2296 (20.43) 2243 (97.69) 53 (2.31)
>350 5570 (49.56) 5514 (98.99) 56 (1.01)
Recent Viral Load count <.0001
<=400 8715 (77.55) 8623 (98.94) 92 (1.06)
401–10,000 892 (7.94) 858 (96.19) 34 (3.81)
10,001–100,000 877 (7.8) 824 (93.96) 53 (6.04)
>100,000 754 (6.71) 683 (90.58) 71 (9.42)
Recent CD4 count <.0001
<=200 1591 (14.16) 1441 (90.57) 150 (9.43)
201–350 1421 (12.64) 1379 (97.04) 42 (2.96)
>350 8226 (73.2) 8168 (99.29) 58 (0.71)
Median days from HIV diagnosis to ADC NA NA 712.5
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Table 2.

Time dependent proportional hazards model of developing ADC and NADC

ADC NADC
Hazards Ratio (95% C.I.) p-value Hazards Ratio (95% C.I.) p-value
Age group 18–29 Ref. Ref.
30–39 1.035 (0.719, 1.490) 0.8540 1.669 (1.145, 2.433) 0.0078
40–49 1.386 (0.981, 1.959) 0.0639 3.884 (2.794, 5.400) <.0001
50+ 1.010 (0.660, 1.546) 0.9622 6.726 (4.749, 9.527) <.0001
Sex Female Ref. Ref.
Male 1.480 (1.008, 2.174) 0.0455 1.397 (1.085, 1.798) 0.0095
Race White Ref. Ref.
Black 0.496 (0.368, 0.669) <.0001 0.882 (0.699, 1.113) 0.2892
Hispanic 0.452 (0.231, 0.884) 0.0204 0.710 (0.381, 1.320) 0.2788
Other/unknown 0.680 (0.277, 1.669) 0.3996 0.851 (0.377, 1.921) 0.6983
Transmission risk Heterosexual Ref. Ref.
IDU/MSM 0.720 (0.403, 1.286) 0.2672 0.572 (0.359, 0.911) 0.0188
MSM 0.893 (0.596, 1.336) 0.5813 0.851 (0.641, 1.131) 0.2670
Other/unknown 0.790 (0.542, 1.152) 0.2215 1.007 (0.785, 1.292) 0.9542
Residency Rural Ref. Ref.
Urban 0.782 (0.572, 1.068) 0.1218 0.815 (0.647, 1.025) 0.0805
Alcohol Use Yes 0.746 (0.502, 1.109) 0.1473 0.956 (0.731, 1.250) 0.7408
Tobacco Use Yes 1.541 (1.157, 2.052) 0.0031 2.304 (1.842, 2.881) <.0001
Illicit Drug Abuse Yes 0.880 (0.615, 1.260) 0.4853 1.094 (0.840, 1.424) 0.5060
Hepatitis B or C Yes 1.947 (1.255, 3.021) 0.0030 2.021 (1.531, 2.670) <.0001
Hypothyroidism Yes 3.054 (2.239, 4.166) <.0001 1.593 (1.259, 2.017) 0.0001
Renal disease Yes 2.237 (1.594, 3.139) <.0001 1.629 (1.270, 2.089) 0.0001
Diabetes Yes 0.737 (0.480, 1.133) 0.1647 1.034 (0.788, 1.357) 0.8091
Obesity Yes 1.191 (0.774, 1.832) 0.4279 1.091 (0.796, 1.497) 0.5875
Dyslipidemia Yes 1.138 (0.715, 1.810) 0.5862 1.173 (0.882, 1.558) 0.2723
HIV diagnosis year (continuous) 1.008 (0.971, 1.047) 0.6831 0.959 (0.931, 0.988) 0.0056
Viral Load count <=400 Ref. Ref.
401–10,000 1.438 (0.949, 2.181) 0.0868 0.910 (0.662, 1.253) 0.5644
10,001–100,000 1.433 (0.977, 2.102) 0.0656 0.948 (0.706, 1.274) 0.7237
>100,000 1.703 (1.173, 2.474) 0.0051 0.769 (0.534, 1.108) 0.1592
CD4 count <=200 Ref. Ref.
201–350 0.373 (0.257, 0.540) <.0001 0.773 (0.585, 1.020) 0.0690
>350 0.151 (0.105, 0.217) <.0001 0.516 (0.400, 0.666) <.0001
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Similar to ADC, few participants (454; 4.4%) of the PLWH included in the analyses developed NADC. The median time from HIV diagnosis to developing NADC diagnosis was 3.77 years. We also observed a significant difference in the baseline demographics and clinical characteristics of individuals who developed NADC when compared with those who did not develop NADC. The difference existed by age, race, transmission risk, residency, substance use (alcohol, tobacco, illicit drugs), hepatitis infection, some comorbidities, VL count and CD4 count.

Trend and Prevalence of individual cancers among SC PLWH, 2005–2016

Figure 1.a shows the cancer rates over time and 1.b show the prevalence of the individual cancers. There is a sharper decrease for both cancer types in 2009 with continuing decrease for ADC. For NADC, there is a sharp rise in 2010 which continues to fluctuate thereafter. The top five most common cancers were non-Hodgkin lymphoma, cancer of the bronchus or lung, Kaposi sarcoma, Hodgkin lymphoma and prostate cancer.

Figure 1.a:

Figure 1.a:

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Trend of cancers incidence over time among SC PLWH, 2005–2020.

Figure 1.b:

Figure 1.b:

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The prevalence of the individual cancers

Kaplan-Meier Analysis of patients with cancer

As presented in figure 2a, the time to diagnosis was significantly different between the two cancer types. The probability of not being diagnosed with NADC is greater than ADC. The NADC curves dropped much lower than ADC after 1 follow-up year and more dramatically over time. The risk of being diagnosed with NADC decreased with increasing calendar year of diagnosis. Figure 2b shows the association between demographics and survival probability. Older age, rural, heterosexual, and female are more likely to develop NADC while rural, IDU/MSM, male, and white have higher probability of ADC diagnosis.

Figure 2a:

Figure 2a:

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K-M plot of time to first ADC and NADC diagnosis.

Figure 2b: K-M plot of time to first ADC and NADC diagnosis by demographics (age, sex, race, risk, residence).

Figure 2b:

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Solid line: NADC; dash line: ADC

Factors associated with developing cancer

Table 2 shows the time dependent proportional hazards model analysis for predictors of developing cancer. The analysis included time updated VL and CD4 count. In the adjusted model the following factors were most predictive of developing ADC: men [HR=1.48 (1.01–2.17), p=0.045], tobacco use [HR=1.54 (1.16–2.05), p=0.0031], hepatitis infection [HR=1.94 (1.25–3.02), p=0.0030], hypothyroidism [HR=3.05 (2.24–4.16), p=<.0001], renal disease [HR=2.24 (1.59–3.14), p=<.0001], and VL greater than 100,000copies/ml [HR=1.703 (1.17–2.47), p=0.0051] were more likely to develop an ADC. However, black race [HR=0.49 (0.37–0.67), p=<.0001], Hispanic [HR=0.45 (0.37–0.88), p=0.0204], and individuals with CD4 greater than 350 cells/mm3 [HR=0.516 (0.40–0.67, p=< 0.001] were less likely to develop an ADC.

The adjusted model showed that increasing age at HIV diagnosis, men [HR=1.40 (1.08–1.79), p=0.0095], tobacco use [HR=2.30 (1.84–2.88), p=< 0.001], hepatitis infection [HR=2.02 (1.53–2.67), p=< 0.001], hypothyroidism [HR=1.59 (1.26–2.02), p=0.0001] and renal disease [HR=1.62 (1.27–2.09), p=0.0001] were predictive of developing NADC. Conversely, IDU/MSM [HR=0.57 (0.36–0.91), p=0.0188], and individuals with CD4 count greater than 350 cells/mm3 [HR=0.51 (0.40–0.66), p < 0.001] had reduced likelihood of NADC. No other predictors were statistically significant.

Discussions

Like many chronic illnesses, PLWH also experience other comorbidities such as cancer. In our study, more than half of the study population had an HIV diagnosis before 40years of age, while, the majority of those who developed cancer were diagnosed with HIV after 40years. Similar characteristic were observed in other large population studies where most PLWH with cancer were older than 40years at HIV diagnosis (Albini, et al., 2013). Our finding showed that tobacco use was more common in PLWH who developed cancer, similar to findings made in previous study (Park, et al., 2016; Albini, et al., 2013; Reddy, et al., 2017). Substance use is a known risk factor for a variety of cancers; and substance use are commonly reported among PLWH globally (Park, et al., 2016), and in the United States (Reddy, et al., 2017). Irrespective of smoking status, studies suggest PLWH are at increased risk for many types of cancer (Reddy, et al., 2017). Exposure and subsequent infection with cancer-causing viruses, may in turn increase the risk for developing cancer (Shiels,, et al., 2011; Park, e tal., 2016; Kesselring, et al., 2011). Not surprisingly, this was in line with our finding in which individuals who developed cancer has a higher proportion for hepatitis B or C infection and all comorbidities than those who did not develop cancer.

We observed low prevalence of cancer, which 4.4% were NADC and 2.2% were ADC. This is similar to the rate reported in some study (Long, et al., 2008;). In contrast to our results, others have found higher rates of ADC in their population (Calabresi, et al., 2013; Yanik, et al., 2013). A study in North Carolina reported one third of the observed cancer were ADCs (Yanik, et al., 2013), and about half were ADCs in an Italian study (Calabresi, et al., 2013). The differences may be explained by differences in the HIV treatment adherence. NHL and KS have been found to be the most common ADC in many studies, consistent with our finding (Yanik, et al., 2013; Meijide, et al., 2017; Hernández-Ramírez, et al., 2017). A retrospective study of 2318 PLWH followed for 22years observed NHL as the most common ADC (26.5%), followed by KS (15.1%) (Meijide, et al., 2017). Yanik and colleagues (2013) reported in absolute numbers, NHL and KS were the most common ADC in the HIV-infected population. The commonality of NHL and KS may be related to poor suppression of HIV replication and immune recovery, which were also observed in this study. The most prevalent NADCs observed in this study is consistent with the most prevalence NADCs reported in prior studies (Park, et al., 2017). Given the association of lung cancer with tobacco use, and the high rates of tobacco use in PLWH, this finding is not unexpected. We also found a difference in trends with ADC sslowly decreasing and NADC increasing fluctuating in recent years. Previous studies assessing ADC reported a declining trend for ADCs (Hernández-Ramírez, et al., 2020; Robbins, et al., 2014; Hernández-Ramírez, et al., 2017; Park, et al., 2016), which is contrary to our findings. A continuing decline in the trend for ADC with both the NHL and KS declining by more than one-half were observed among 44,787 PLWH from 1997–2012 (Park, et al., 2016). The declines in prior studies were attributed to improved HIV care (Yanik, et al., 2013; Vishnu, & Aboulafia, 2012).

The predictive factors for ADC observed in our study were consistent with prior studies (Rubinstein, et al., 2014; Cahoon, et al., 2016; Koshiol, et al., 2011; Wang, et al., 2017). Hepatitis coinfection has also been associated with an increased risk of developing many other cancers. These findings may be due to oncogenic nature of Hepatitis B and C viruses, which is currently and consistently attributed to excess cancer cases among PLWH (Calabresi et al., 2013; Meijide, et al., 2017; de Martel, et al., 2015). NHL associated with HIV was found approximately twice higher in whites than African Americans in a study of more than 4million US veterans (Koshiol, et al., 2011). This is similar to a nationwide study that demonstrated KS risk was significantly higher among whites than blacks (Cahoon, et al., 2016). Cahoon and colleagues (2016) added that one possible explanation of their findings is that human herpesvirus 8 prevalence is higher in whites with HIV because they are about twice as likely to be MSM. Other observed predictive risk factors such as recent CD4 counts and time spent with VL <200copies/ml were supported by other studies on risk for ADC (Borges, et al., 2016; Park, et al., 2018; Dubrow, et al., 2017). A retrospective cohort study of ADC among 42,441 veterans found that the risk of having ADC was lowest among persons with long-term suppression (Park, et al., 2018). The poor HIV-induced immune suppression may hinder the control of oncogenic viruses, and less able to detect and destroy cancer cells (Bouvard, et al., 2009).

Increasing age at HIV diagnosis was a significant risk for developing an NADC. There are several explanations for this. Those diagnosed with HIV at an older age may have spent more years with undiagnosed and hence uncontrolled HIV, putting them at greater risk for NADCs. In addition, age by itself is a known risk for cancer development. In previous studies, advanced age has been identified as a strong risk factor for developing an NADC (Shiels, & Engels, 2017; Albini, et al., 2013; Meijide et al., 2017). Though, it has also been widely reported that, greater time with VL <200copies/ml are associated with reduced risk for NADC, we did not found lower viral load to be associated with lower hazard for NADC (Borges, et al., 2016; Park, et al., 2018; Castilho et al., 2020). A possible explanation of our finding may be recent diagnosis of NADC and fewer follow up thereafter. Men also had a higher likelihood to develop NADC, which may be due to lower preventive care utilization and lack of help-seeking behavior seen in men (Vaidya et al., 2012). Further, sex differences have been observed in multiple facets of cancer biology and epidemiology (Li et al., 2020). Contratry to our finding, the report from a 15years cohort study of PLWH in Ontario, showed that men had lower hazards for NADC (Mondal et al., 2018). This increased risk of NADC in men may be due the racial differences in health care utilzation, comorbidities and health outcomes in US as majority of our population are black men (Gilbert et al., 2016). In addition to the above limitations, men are is more likely to use tobacco which is a correlate of cancer in this study (Piñeiro, et al., 2016).

Our study is not without limitations. First, our data included only PLWH in SC and only individuals linked to care. Consistent with the population of PLWH in SC, we have a small proportion of females, Hispanics, and rural residents, limiting generalizability. Second, since the dataset for this study was accumulated from different sources, we have no way to trace all PLWH who started receiving ART from the beginning to adjust for some cancer risk factors such as family history of cancers and cancer screening practices; hence we were not able to explore their influence on cancer prevalence in our analysis. Finally, we were unable to predict the risk factors associated with each individual cancer type because of limited low number of diagnoses of some cancer types. Lastly, we did not have individual-level data on ART regimen.

In summary, we found low overall rates of cancer. Our findings suggest that long-term viral suppression may contributes to marked risk reduction for cancer. Although the prevalence of cancer was low in our study, the lack of decreasing trend in the occurrence of ADC and NADC warrants further attention. Our findings highlight the importance of early HIV diagnoses for cancer prevention, retention in HIV medical care and viral suppression. It also shows that rural residents living with HIV may require even more targeted services and resources as they age. With increasing number of PLWH living over 50years, it is important to monitor the combined roles of social determinants of health, individual-level measure of ART use, with changes in the prevalence of oncogenic viral infections and other cancer risk factors and cancer-screening.

Table 1b.

Demographic and clinical characteristics of the study population, with subjects with NADC.

Characteristics Overall (N=11233) No NADC (N=10779) NADC (N=454) p-value*
Age group <.0001
18–29 4585 (40.82) 4532 (98.84) 53 (1.16)
30–39 2501 (22.26) 2441 (97.6) 60 (2.4)
40–49 2265 (20.16) 2114 (93.33) 151 (6.67)
50+ 1882 (16.75) 1692 (89.9) 190 (10.1)
Sex 0.072
Female 2696 (24) 2571 (95.36) 125 (4.64)
Male 8537 (76) 8208 (96.15) 329 (3.85)
Race 0.0061
White 2511 (22.35) 2403 (95.7) 108 (4.3)
Black 7739 (68.9) 7412 (95.77) 327 (4.23)
Hispanic 648 (5.77) 636 (98.15) 12 (1.85)
Other/unknown 335 (2.98) 328 (97.91) 7 (2.09)
Transmission risk <.0001
Heterosexual 2088 (18.59) 1958 (93.77) 130 (6.23)
MSM 5698 (50.73) 5547 (97.35) 151 (2.65)
IDU/MSM 601 (5.35) 576 (95.84) 25 (4.16)
Other/unknown 2846 (25.34) 2698 (94.8) 148 (5.2)
Residency 0.0232
Rural 1910 (17) 1815 (95.03) 95 (4.97)
Urban 9323 (83) 8964 (96.15) 359 (3.85)
Alcohol Use <.0001
No 9571 (85.2) 9225 (96.38) 346 (3.62)
Yes 1662 (14.8) 1554 (93.5) 108 (6.5)
Tobacco Use <.0001
No 5458 (48.59) 5316 (97.4) 142 (2.6)
Yes 5775 (51.41) 5463 (94.6) 312 (5.4)
Illicit_Drug_use 0.0071
No 9106 (81.06) 8760 (96.2) 346 (3.8)
Yes 2127 (18.94) 2019 (94.92) 108 (5.08)
Hepatitis B or C <.0001
No 10525 (93.7) 10143 (96.37) 382 (3.63)
Yes 708 (6.3) 636 (89.83) 72 (10.17)
Renal disease <.0001
No 9257 (82.41) 8944 (96.62) 313 (3.38)
Yes 1976 (17.59) 1835 (92.86) 141 (7.14)
Diabetes <.0001
No 9893 (88.07) 9526 (96.29) 367 (3.71)
Yes 1340 (11.93) 1253 (93.51) 87 (6.49)
Obesity 0.3318
No 9863 (87.8) 9471 (96.03) 392 (3.97)
Yes 1370 (12.2) 1308 (95.47) 62 (4.53)
Dyslipidemia <.0001
No 9968 (88.74) 9604 (96.35) 364 (3.65)
Yes 1265 (11.26) 1175 (92.89) 90 (7.11)
Initial Viral load count 0.0032
<=400 1643 (14.63) 1593 (96.96) 50 (3.04)
401–10,000 2455 (21.86) 2373 (96.66) 82 (3.34)
10,001–100,000 3940 (35.08) 3775 (95.81) 165 (4.19)
>100,000 3195 (28.44) 3038 (95.09) 157 (4.91)
Initial CD4 count <.0001
<=200 3374 (30.04) 3167 (93.86) 207 (6.14)
201–350 2299 (20.47) 2200 (95.69) 99 (4.31)
>350 5560 (49.5) 5412 (97.34) 148 (2.66)
Recent Viral Load count <.0001
<=400 8740 (77.81) 8437 (96.53) 303 (3.47)
401–10,000 891 (7.93) 845 (94.84) 46 (5.16)
10,001–100,000 870 (7.75) 807 (92.76) 63 (7.24)
>100,000 732 (6.52) 690 (94.26) 42 (5.74)
Recent CD4 count <.0001
<=200 1550 (13.8) 1426 (92) 124 (8)
201–350 1422 (12.66) 1327 (93.32) 95 (6.68)
>350 8261 (73.54) 8026 (97.16) 235 (2.84)
Median days from HIV diagnosis to NADC NA NA 1375.5
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Acknowledgments

We would like to acknowledge the different organizations (SC Revenue and Fiscal Affairs Office, SC Department of Health and Environmental Control, Ryan White Service, and Health Sciences South Carolina) that provided their patients data that was used in this study.

Conflicts of Interest and Source of Funding:

The authors declared that they have no conflict of interest. Research reported in this publication was supported by the National Institute of Allergy and Infectious Disease of the National Institute of Health under award number RO1AI127203

Footnotes

Declaration of interest statement

We have nothing to declare.

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