Abstract
Hepatitis E virus (HEV) is increasingly recognized as a cause of acute viral hepatitis, with particular concern in immunocompromised populations, including people living with HIV (PLHIV). Despite global interest, data on HEV epidemiology among PLHIV in Thailand remain limited. This study aimed to estimate the seroprevalence of anti-HEV IgG antibodies among PLHIV in Chiang Mai, Thailand, and to identify demographic, behavioural, and clinical factors independently associated with HEV exposure. A cross-sectional study was conducted among 200 PLHIV attending four referral hospitals in Chiang Mai province. Plasma samples were tested for anti-HEV IgG using enzyme-linked immunosorbent assay (ELISA). Other hepatotropic viruses including hepatitis B and C were also assessed. Logistic regression was employed to assess predictors of HEV seropositivity. Anti-HEV IgG antibodies were detected in 13.0% (26/200) of participants. Seroprevalence did not significantly differ by sex (15.0% in males vs. 11.0% in females; p = 0.543). HBsAg and anti-HCV were observed in 6.5% and 8.0% of participants, respectively, with anti-HCV significantly more prevalent among males (15.0%) than females (1.0%; p < 0.001). Multivariate analysis identified three independent factors associated with HEV seropositivity: age ≥ 50 years (adjusted OR 3.40; 95% CI 1.47–7.86; p = 0.004), history of needle stick injury (adjusted OR 3.33; 95% CI 1.20–9.24; p = 0.021), and renal failure (adjusted OR 15.69; 95% CI 2.93–83.87; p = 0.001). HEV exposure among PLHIV in northern Thailand is not uncommon and is associated with older age, history of renal dysfunction, and potential parenteral exposures. These findings support targeted HEV screening and reinforce the need for broader HEV surveillance and preventive strategies within vulnerable HIV-positive populations in Southeast Asia.
Keywords: HEV, Seroprevalence, People living with HIV, Chiang Mai, Thailand
Subject terms: Diseases, Health care, Medical research, Microbiology, Risk factors
Introduction
Hepatitis E is an inflammation of the liver caused by the hepatitis E virus (HEV), representing a significant public health challenge, particularly in developing countries. It is one of the leading causes of acute viral hepatitis, contributing to substantial morbidity and mortality. The World Health Organization (WHO) estimates that in 2021, approximately 19.47 million people were infected with HEV, resulting in about 3,450 deaths globally1. HEV accounted for 5.4% of the global burden of disease (DALYs) due to acute hepatitis, according to WHO’s 2025 update1,2. HEV is widespread in low- and middle-income countries (LMICs) with poor access to clean water, sanitation, and healthcare. It spreads mainly through the fecal–oral route, typically via contaminated water. Other transmission routes include blood transfusions increasingly reported, especially in immunocompromised patients3,4. Vertical transmission from mother to fetus during pregnancy associated with high fetal mortality5–7.
Most HEV infections are acute and self-limiting. Chronic HEV infection is rare but poses a major risk for immunocompromised individuals, such as organ transplant recipients, chemotherapy patients, and those with HIV. In these groups, the virus can cause progressive liver damage and severe complications, threatening health and survival if not properly managed8. A study by Pineda et al. found a 26% prevalence of anti-HEV antibodies among HIV-infected patients, highlighting the importance of HEV in this population9,10. Low CD4 counts (< 350 cells/mm³) in HIV/HCV-coinfected patients were associated with a higher prevalence of anti-HEV IgG, indicating increased susceptibility due to immunosuppression9. The coexistence of both HEV and HIV creates a more complex clinical situation, increasing the risk of complications beyond the liver, including kidney, blood, pancreatic, and neurological problems.
Globally, studies on HIV-positive individuals have shown varying rates of HEV infection. HEV seroprevalence among HIV-positive individuals is highest in some African countries (exceeding 40%), ranges from 10% to 20% in the countries of Europe, and is not exceeding 10% in the Americas11,12. A study in the Central African Republic found 68% of individuals had IgG antibodies13.
In Thailand, several studies have reported the seroprevalence of HEV in the general population, blood donors, and other groups, with varying prevalence rates. For instance, a nationwide sero epidemiological survey conducted among young Thai men during 2007–2008 revealed an overall anti-HEV IgG seroprevalence of approximately 14%, with substantial inter-provincial variation ranging from3 to 26%. Additionally, another nationwide serosurvey carried out in 2019 among adult blood donors across multiple provinces of Thailand reported a considerably higher anti-HEV IgG seroprevalence of 29.7% 14,15. Consistently, a systematic review and meta-analysis by Mirzaev et al. (2024) reported a relatively high seroprevalence of hepatitis E virus in the Thai population, estimated a pooled anti-HEV IgG seroprevalence of 21.03%16. However, data on HEV infection among people living with HIV in Thailand are limited, and routine screening is not widely practiced as hepatitis B and C.
Given the high prevalence of both HIV and HEV in Southeast Asia, this study was conducted to address the knowledge gap by assessing the seroprevalence of HEV among PLHIV in Chiang Mai, Thailand. The study also aimed to identify associated demographic, behavioural, and clinical factors, as well as to evaluate co-infection rates with other hepatotropic viruses, including HBV and HCV in this vulnerable population.
Materials and methods
Study population and data collection
This study is part of a cross-sectional study conducted from 2023 to 2024 to evaluate self-stigma, health behaviours, and health conditions among people living with HIV in Chiang Mai, Thailand. Participants eligible for inclusion in the study were individuals assigned male or female who are living with HIV and aged 18 years or older. They were either Thai nationals or migrants who work or reside in Chiang Mai Province, and must currently be receiving HIV care at an HIV clinic within one of the designated hospitals in Chiang Mai Province. Participants were excluded from the study if, they are not in adequate health to participate in the interview, if they are unable to speak and understand Thai sufficiently for the interview process, or if they decline or do not consent to be interviewed for any reason. Participants were recruited from four referral hospitals in Chiang Mai Province: Sansai Hospital, Saraphi Hospital, Hangdong Hospital, and Chiangdao Hospital. All associated data on socio-demographic characteristics, medical history have already been collected via structured interviewer-administered questionnaires using the REDCap® platform. Plasma was kept and stored at − 70 °C until testing.
Ethical approval
This study was approved by the Human Experimentation Committee of the Research Institute for Health Sciences, Chiang Mai University (Certificate of Ethical Approval No. 22/2025). The committee approved the use of stored plasma samples and waived the requirement for informed consent. All study methods were carried out in accordance with relevant guidelines and regulations.
Serological test of anti-HEV IgG, HBsAg, and anti-HCV
The presence of anti-HEV immunoglobulin G (IgG) antibodies was assessed using a commercial ELISA kit (EUROIMMUN Medizinische Labordiagnostika AG, Lübeck, Germany) in accordance with the manufacturer’s instructions. The Euroimmun HEV IgG ELISA demonstrates a specificity of 100% and a sensitivity ranging from 95.5% to 100%. A positive result for anti-HEV IgG antibodies signifies past exposure to the virus or immunity, as IgG antibodies develop later in the infection and can persist for years, providing long-term protection against future infections. All plasma samples were also analysed using rapid immunochromatographic diagnostic tests to detect HBsAg (STANDARD™Q HBsAg test, SD BIOSENSOR, 100% sensitivity and 100% specificity), and HCV antibodies (SD BIOLINE HCV, 99.3–100% sensitivity and 98.1–100% specificity).
Data analysis
Categorical variables were expressed as numbers and percentages, and continuous variables were presented as medians with interquartile ranges (IQRs). Baseline characteristics between male and female participants were compared using the Chi-square test for categorical variables and the Mann–Whitney U test for continuous variables.
Univariable and multivariable logistic regression analyses were used to examine the association between HEV infection and a range of independent variables, including sociodemographic characteristics, health behaviours, medical history, and sexual behaviours. Variables with a p-value less than 0.250 in the univariable analysis were included in the multivariable model using a forward stepwise selection. Results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). All statistical analyses were conducted using Stata version 16.0 (StataCorp, College Station, TX, USA), and a p-value < 0.05 was considered statistically significant.
Results
Characteristics of the study population
Among 200 PLHIV (100 men, 100 women), the median age was 48 years (IQR: 38–58), slightly higher in women (49.5 vs. 45 years). Most participants were Thai nationals (85.5%). Notable gender differences were observed. Men more often had higher education (16.1 vs. 2.3%, p = 0.035), were single (42.0% vs. 10.0%. p = < 0.001), smoked (37 vs. 7%, p < 0.001), drank alcohol (71% vs. 34%, p < 0.001), used injection drugs (7 vs. 0%, p = 0.007), and had tattoos (46% vs. 28%, p = 0.008). Women were more frequently unemployed (27 vs. 13%, p = 0.039), had lower income (≤ 10,000 THB: 67 vs. 49%, p = 0.010), were separated/divorced/widowed (34.0% vs. 6.0%, p < 0.001), had children (82 vs. 38%, p < 0.001), experienced needle-stick injuries (26% vs. 10%, p = 0.003), had piercings (79 vs. 32%, p < 0.001), and reported never using condoms (24.5 vs. 8%, p = 0.036).
Clinically, liver disease (17 vs. 4%, p = 0.003) and jaundice (3 vs. 1%, p = 0.004) were more common in men, while women had longer HIV infection duration and ART use. Detailed characteristics are presented in (Table 1).
Table 1.
Characteristics of people living with HIV in Chiangmai, Thailand (N = 200).
| Characteristics | Total (%) | Male (%) | Female (%) | p-value* | |
|---|---|---|---|---|---|
| Sociodemographic characteristics | |||||
| Sex at birth | 200 | 100 (50.0) | 100 (50.0) | ||
| Age (years) | Median | 48 (IQR: 38–58) | 45 (IQR: 34.25-56) | 49.5 (IQR: 42.25-59) | |
| Age group (years) | < 50 | 115(57.5) | 64 (64.0) | 51(51.0) | 0.063 |
| ≥ 50 | 85 (42.5) | 36 (36.0) | 49 (49.0) | ||
| Ethnicity | Thai | 171 (85.5) | 84 (84.0) | 87 (87.0) | 0.547 |
| Non-Thai | 29 (14.5) | 16 (16.0) | 13 (13.0) | ||
| Attend school | 172 (86.0) | 87 (87.0) | 85 (85.0) | 0.684 | |
| Education level (N = 172) | Primary | 80 (46.5) | 35 (40.2) | 45 (52.9) | 0.035 |
| Secondary | 33 (19.2) | 16 (18.4) | 17 (20.0) | ||
| High school | 30 (17.4) | 15 (17.2) | 15 (17.7) | ||
| College/university | 16 (9.3) | 14 (16.1) | 2 (2.3) | ||
| Other | 13 (7.6) | 7 (8.1) | 6 (7.1) | ||
| Occupation | Unemployed (Retired, housewife) | 40 (20.0) | 13 (13.0) | 27 (27.0) | 0.039 |
| Employed | 147 (73.5) | 81 (81.0) | 66 (66.0) | ||
| Other | 13 (6.5) | 6 (6.0) | 7 (7.0) | ||
| Marital Status | Single | 52 (26.0) | 42 (42.0) | 10 (10.0) | < 0.001 |
| Have a partner | 108 (54.0) | 52 (52.0) | 56 (56.0) | ||
| Separated/ Divorced/ Widowed | 40 (20.0) | 6 (6.0) | 34 (34.0) | ||
| Kid | 120 (60.0) | 38 (38.0) | 82 (82.0) | < 0.001 | |
| Monthly income | ≤ 10,000 | 116 (58.0) | 49 (49.0) | 67 (67.0) | 0.010 |
| > 10,000 | 84 (42.0) | 51 (51.0) | 33 (33.0) | ||
| Behavioral characteristics | |||||
| Smoking (past 3 months) | 44 (22.0) | 37 (37.0) | 7 (7.0) | < 0.001 | |
| Drinking Alcohol (past 12 months) | 105 (52.5) | 71 (71.0) | 34 (34.0) | < 0.001 | |
| Used drugs (N = 199) | 13 (6.5) | 8 (8.1) | 5 (5.0) | 0.379 | |
| Ever used drug injection | 7 (3.5) | 7 (7.0) | 0 (0.0) | 0.007 | |
| Ever had surgery | 90 (45.0) | 36 (36.0) | 54 (54.0) | 0.011 | |
| Ever had blood transfusion (N = 199) | 47 (23.6) | 21 (21.0) | 26 (26.3) | 0.063 | |
| Ever had needle stick injuries | 36 (18.0) | 10 (10.0) | 26 (26.0) | 0.003 | |
| Ever had sharing sharp objects with others | 84 (42.0) | 40 (40.0) | 44 (44.0) | 0.567 | |
| Piercing | 111 (55.5) | 32 (32.0) | 79 (79.0) | < 0.001 | |
| Tattoo | 74 (37.0) | 46 (46.0) | 28 (28.0) | 0.008 | |
| Ever visited Barber (only male) | 51 (51.0) | ||||
| Circumcision (only male) | 4 (4.0) | ||||
| Sexual behaviors | |||||
| Having sex as usual after HIV Positive | 153 (76.5) | 84 (84.0) | 69 (69.0) | 0.012 | |
| Having sexual activity (past 12 month) | 114 (57.0) | 65 (65.0) | 49 (49.0) | 0.022 | |
| Having sex with spouse or lover (past 12-month, N = 114) | 99 (86.8) | 50 (80.0) | 49 (100.0) | < 0.001 | |
| Condom use with spouse or lover (N = 99) | Never | 16 (16.2) | 4 (8.0) | 12 (24.5) | 0.036 |
| Occasionally | 24 (24.2) | 16 (32.0) | 8 (16.3) | ||
| All the time | 59 (59.6) | 30 (60.0) | 29 (59.2) | ||
| Medical history | |||||
| HBV vaccination | 26 (13.0) | 14 (14.0) | 12 (12.0) | 0.834 | |
| Comorbidity | Hypertension | 45 (22.5) | 18 (18.0) | 27 (27.0) | 0.128 |
| Renal failure | 8 (4.0) | 4 (4.0) | 4 (4.0) | 1.000 | |
| Diabetes | 21 (10.5) | 10 (10.0) | 11 (11.0) | 0.818 | |
| Hyper cholesterol | 67 (33.5) | 27 (27.0) | 40 (40.0) | 0.051 | |
| Jaundice | 4 (2.0) | 3 (3.0) | 1 (1.0) | 0.004 | |
| Liver Disease | 21 (10.5) | 17 (17.0) | 4 (4.0) | 0.003 | |
| Family history of | Liver disease | 22 (11.0) | 11 (11.0) | 11 (11.0) | 0.784 |
| Hepatitis | 6 (3.0) | 3 (3.0) | 3 (3.0) | 1.000 | |
| Cirrhosis | 9 (4.5) | 4 (4.0) | 5 (5.0) | 0.733 | |
| Liver cancer | 8 (4.0) | 4 (4.0) | 4 (4.0) | 1.000 | |
| HIV infection (years) | Median | 15 (IQR: 5.5–20.5) | 10 (IQR: 4.3–20) | 17 (IQR: 8–24) | |
| HIV infection (years) | ≤ 5 | 50 (25.0) | 31 (31.0) | 19 (19.0) | 0.050 |
| > 5 | 150 (75.0) | 69 (69.0) | 81 (81.0) | ||
| Taking ART (years) | Median | 12 (IQR: 6–20) | 10 (IQR: 5-19.8) | 16 (IQR: 8.5–21) | |
| Taking ART | No | 1 (0.5) | 1 (1.0) | 0 | 0.605 |
| < 1 year | 14 (7.0) | 7 (7.0) | 7 (7.0) | ||
| > 1 year or over | 185 (92.5) | 92 (92.0) | 93 (93.0) | ||
| ART adherence (N = 199) | Excellent (95–100%) | 151 (75.9) | 78 (78.8) | 73 (73.0) | 0.399 |
HEV, HCV, and HBV seropositivity
Among 200 participants, 26 (13.0%) were seropositive for HEV IgG (Table 2). Seropositivity did not differ significantly between males (15%) and females (11%, p = 0.543). Borderline results were observed in 15 participants (7.5%), including 6 males (6.0%) and 9 females (9.0%). The majority of participants (159, 79.5%) tested negative, with similar proportions in males (79.0%) and females (80.0%).
Table 2.
Hepatitis viruses Seroprevalence in people living with HIV in Chiang Mai, Thailand (N = 200).
| Hepatitis viruses seroprevalence | Total (N = 200) | Male (N = 100) | Female (N = 100) | p-value* | ||||
|---|---|---|---|---|---|---|---|---|
| n (%) | 95% CI | n (%) | 95% CI | n (%) | 95% CI | |||
| HEV Ab | Positive | 26 (13.0) | 8.7–18.5 | 15 (15.0) | 8.6–23.5 | 11 (11.0) | 5.6–18.8 | 0.543 |
| Borderline | 15 (7.5) | 4.3–12.1 | 6 (6.0) | 2.2–12.6 | 9 (9.0) | 4.2–16.4 | ||
| Negative | 159 (79.5) | 73.2–84.9 | 79 (79.0) | 69.7–86.5 | 80 (80.0) | 70.8–87.3 | ||
| HCV Ab | Positive | 16 (8.0) | 4.6–12.7 | 15 (15.0) | 8.6–23.5 | 1 (1.0) | 0.0-5.4 | < 0.001 |
| Negative | 184 (92.0) | 87.3–95.4 | 85 (85.0) | 76.5–91.4 | 99 (99.0) | 94.6–100 | ||
| HBsAg | Positive | 13 (6.5) | 3.5–10.9 | 9 (9.0) | 4.2–16.4 | 4 (4.0) | 1.1–10.0 | 0.152 |
| Negative | 187 (93.5) | 89.1–96.5 | 91(91.0) | 83.6–95.8 | 96 (96.0) | 90.1–98.9 | ||
| HEV/HCV | Positive | 2 (1.0) | 0.1–3.6 | 2 (2.0) | 0.2-7.0 | 0 (0.0) | 0.0-3.6 | 0.155 |
| Negative | 198 (99.0) | 96.4–99.9 | 98 (98.0) | 92.9–99.8 | 100 (100.0) | 96.4–100.0 | ||
| HEV/HBV | Positive | 2 (1.0) | 0.1–3.6 | 1 (1.0) | 0.0-5.4 | 1 (1.0) | 0.0-5.4 | 1.000 |
| Negative | 198 (99.0) | 96.4–99.9 | 99 (99.0) | 94.6–100.0 | 99 (99.0) | 94.6–100.0 | ||
| HBV/HCV | Positive | 3 (1.5) | 0.3–4.3 | 3 (3.0) | 0.6–8.5 | 0 (0.0) | 0.0-3.6 | 0.081 |
| Negative | 197 (98.5) | 95.7–99.7 | 97 (97.0) | 91.5–99.4 | 100 (100.0) | 96.4–100.0 | ||
HCV antibodies were detected in 16 participants (8.0%), with a markedly higher prevalence in males than females (15% vs. 1%, p < 0.001).
HBsAg positivity, indicating HBV infection, was found in 13 participants (6.5%), more frequently in males (9%) than females (4%), though the difference was not statistically significant (p = 0.152).
Dual infections were observed in 7 participants: two with HEV and HCV, two with HEV and HBV, and three with HBV and HCV. No triple infections were identified. Overall, while co-infection was uncommon, a subset of PLHIV showed evidence of multiple hepatotropic virus exposures, predominantly among men.
Factors associated with HEV seroprevalence
Associations between HEV IgG seropositivity and potential risk factors were assessed using two logistic regression models: Model A (borderline results classified as positive) and Model B (borderline results classified as negative). Univariable and multivariable logistic regression analyses are shown in (Table 3). Variables with p < 0.250 in univariable analysis were included in the multivariable models.
Table 3.
Factor associated with HEV seropositivity among people living with HIV in Chiang Mai, Thailand.
| Characteristics | HEV IgG Positivity (Borderline considered as Positive) | HEV IgG Positivity (Borderline considered as Negative) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| n/N (%) | Univariable | Multivariable | n/N (%) | Univariable | Multivariable | ||||||
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | ||||
| Gender | Female | 20/100 (20.0) | 1.00 | 11/100 (11.0) | 1.00 | ||||||
| Male | 21/100 (21.0) | 1.06 (0.54–2.11) | 0.861 | 15/100 (15.0) | 1.43 (0.62–3.28) | 0.402 | |||||
| Age group (years) | < 50 | 17/115 (14.8) | 1.00 | 13/115 (11.3) | 1.00 | ||||||
| ≥ 50 | 24/85 (28.2) | 2.27 (1.13–4.56) | 0.022 | 3.40 (1.47–7.86) | 0.004 | 13/85 (15.3) | 1.42 (0.62–3.24) | 0.408 | |||
| Ethnicity | Non-Thai | 5/29 (17.2) | 1.00 | 4/29 (13.8) | 1.00 | ||||||
| Thai | 36/171 (21.1) | 1.28 (0.46–3.59) | 0.639 | 22/171 (12.9) | 0.92 (0.29–2.90) | 0.891 | |||||
| Marital Status | Single | 7/52 (13.5) | 1.00 | 6/52 (11.5) | 1.00 | ||||||
| Have a partner | 24/108 (22.2) | 1.84 (0.73–4.59) | 0.194 | N.S. | 15/108 (13.9) | 1.24 (0.45–3.40) | 0.680 | ||||
| Separated/ Divorced/Widowed | 10/40 (25.0) | 2.14 (0.73–6.25) | 0.163 | N.S. | 5/40 (12.5) | 1.10 (0.31–3.88) | 0.888 | ||||
| Smoking | No | 30/156 (19.2) | 1.00 | 18/156 (11.5) | 1.00 | ||||||
| Yes | 11/44 (25.0) | 1.40 (0.64–3.08) | 0.404 | 8/44 (18.2) | 1.70 (0.69–4.23) | 0.251 | |||||
| Alcohol consumption | No | 20/95 (21.1) | 1.00 | 10/95 (10.5) | 1.00 | ||||||
| Yes | 21/105 (20.0) | 0.94 (0.47–1.86) | 0.854 | 16/105 (15.2) | 1.53 (0.66–3.55) | 0.325 | |||||
| Ever had surgery | No | 25/110 (22.7) | 1.00 | 16/110 (14.6) | 1.00 | ||||||
| Yes | 16/90 (17.8) | 0.74 (0.36–1.48) | 0.389 | 10/90 (11.1) | 0.73 (0.32–1.71) | 0.474 | |||||
| Ever had blood Transfusion | No | 29/147 (19.7) | 1.00 | 19/147 (12.9) | 1.00 | ||||||
| Yes | 10/47 (21.3) | 1.10 (0.49–2.47) | 0.818 | 6/47 (12.8) | 0.99 (0.37–2.63) | 0.977 | |||||
| Don’t know | 2/5 (40.0) | 2.71 (0.43–16.99) | 0.286 | 1/5 (20.0) | 1.68 (0.18–15.88) | 0.649 | |||||
| Ever had needle stick injuries | No | 31/164 (18.9) | 1.00 | 18/164 (11.0) | 1.00 | ||||||
| Yes | 10/36 (27.8) | 1.65 (0.72–3.77) | 0.235 | 1.36 (0.50–3.68) | 0.548 | 8/36 (22.2) | 2.32 (0.92–5.85) | 0.075 | 3.33 (1.20–9.24) | 0.021 | |
| Ever had sharing Sharp objects | No | 22/116 (19.0) | 1.00 | 14/116 (12.07) | 1.00 | ||||||
| Yes | 19/84 (22.6) | 1.25 (0.63–2.49) | 0.528 | 12/84 (14.3) | 1.21 (0.53–2.78) | 0.646 | |||||
| Piercing | No | 22/89 (24.7) | 1.00 | 15/89 (16.9) | 1.00 | ||||||
| Yes | 19/111 (17.1) | 0.63 (0.32–1.25) | 0.188 | N.S. | 11/111 (9.9) | 0.54 (0.24–1.25) | 0.151 | 0.46 (0.18–1.14) | 0.092 | ||
| Tattoo | No | 27/126 (21.4) | 1.00 | 16/126 (12.7) | 1.00 | ||||||
| Yes | 14/74 (18.9) | 0.86 (0.42–1.76) | 0.671 | 10/74 (13.5) | 1.07 (0.46–2.51) | 0.869 | |||||
| Sex since having HIV | No | 10/47 (21.3) | 1.00 | 5/47 (10.6) | 1.00 | ||||||
| Yes | 31/153 (20.3) | 0.94 (0.42–2.10) | 0.880 | 21/153 (13.7) | 1.34 (0.47–3.76) | 0.583 | |||||
| Condom use with partner | Never | 10/40 (25.0) | 1.00 | 5/40 (12.5) | 1.00 | ||||||
| All the time or occasionally | 14/59 (23.7) | 0.93 (0.37–2.37) | 0.885 | 12/59 (20.3) | 1.79 (0.58–5.54) | 0.314 | |||||
| Hypertension | No | 30/155 (19.4) | 1.00 | 19/155 (12.3) | 1.00 | ||||||
| Yes | 11/45 (24.4) | 1.35 (0.61–2.96) | 0.458 | 7/45 (15.6) | 1.32 (0.52–3.37) | 0.563 | |||||
| Renal failure | No | 37/192 (19.3) | 1.00 | 22/192 (11.5) | 1.00 | ||||||
| Yes | 4/8 (50.0) | 4.19 (1.00-17.53) | 0.050 | 3.76 (0.77–18.30) | 0.101 | 4/8 (50.0) | 7.73 (1.80-33.12) | 0.006 | 15.69 (2.93–83.87) | 0.001 | |
| Diabetes | No | 36/179 (20.1) | 1.00 | 22/179 (12.3) | 1.00 | ||||||
| Yes | 5/21 (23.8) | 1.24 (0.43–3.61) | 0.692 | 4/21 (19.0) | 1.68 (0.52–5.45) | 0.388 | |||||
| Hypercholesterolemia | No | 31/133 (23.3) | 1.00 | 20/133 (15.0) | 1.00 | ||||||
| Yes | 10/67 (14.9) | 0.58 (0.26–1.26) | 0.169 | 0.49 (0.20–1.20) | 0.118 | 6/67 (9.0) | 0.56 (0.21–1.46) | 0.232 | 0.38 (0.12–1.20) | 0.098 | |
| Liver Disease | No | 31/156 (19.9) | 1.00 | 20/156 (12.8) | 1.00 | ||||||
| Yes | 4/21 (19.0) | 0.95 (0.30–3.02) | 0.929 | 3/21 (14.3) | 1.13 (0.31–4.20) | 0.851 | |||||
| Family history of Liver Disease | No | 33/157 (21.0) | 1.00 | 20/157 (12.7) | 1.00 | ||||||
| yes | 2/22 (9.1) | 0.38 (0.08–1.69) | 0.202 | 0.39 (0.08–1.83) | 0.235 | 1/22 (4.6) | 0.33 (0.04–2.56) | 0.287 | |||
| Family history of Liver Cancer | No | 39/192 (20.3) | 1.00 | 25/192 (13.0) | 1.00 | ||||||
| Yes | 2/8 (25.0) | 1.31 (0.25–6.73) | 0.748 | 1/8(12.5) | 0.95 (0.11–8.09) | 0.966 | |||||
| HIV infection (Years) | ≤ 5 | 7/50 (14.0) | 1.00 | 6/50 (12.0) | 1.00 | ||||||
| > 5 | 34/150 (22.7) | 1.80 (0.74–4.37) | 0.193 | N.S. | 20/150 (13.3) | 1.13 (0.43–2.99) | 0.808 | ||||
| Taking ART (Years) | ≤ 10 | 13/84 (15.5) | 1.00 | 10/84 (11.9) | 1.00 | ||||||
| > 10 | 26/101 (25.7) | 1.89 (0.90–3.97) | 0.091 | N.S. | 14/101 (13.9) | 1.19 (0.50–2.84) | 0.694 | ||||
| HBsAg | Negative | 39/187 (20.9) | 1.00 | 24/187 (12.8) | 1.00 | ||||||
| Positive | 2/13 (15.4) | 0.69 (0.15–3.24) | 0.638 | 2/13 (15.4) | 1.23 (0.26–5.91) | 0.792 | |||||
| Anti HCV | Negative | 39/184 (21.2) | 1.00 | 24/184 (13.0) | 1.00 | ||||||
| Positive | 2/16 (12.5) | 0.53 (0.12–2.44) | 0.416 | 2/16 (12.5) | 0.95 (0.20–4.45) | 0.951 | |||||
In Model A, older age (≥ 50 years) was independently associated with HEV seropositivity (OR = 3.40, 95% CI: 1.47–7.86, p = 0.004), highlighting age as a strong predictor of HEV exposure. In Model B, significant predictors were a history of needle-stick injuries (OR = 3.33, 95% CI: 1.20–9.24, p = 0.021) and renal failure (OR = 15.69, 95% CI: 2.93–83.87, p = 0.001), suggesting that medical exposures and underlying comorbidities increase susceptibility to HEV.
No significant associations were observed with gender, ethnicity, marital status, smoking, alcohol consumption, tattoos, piercings, surgery, blood transfusion, diabetes, ART duration, or HBV/HCV co-infection.
Discussion
This is the first study to investigate HEV seroprevalence among people living with HIV in Chiang Mai, Thailand. We found a 13% anti-HEV IgG seropositivity rate, suggesting moderate prior exposure in this immunocompromised population. Independent associated factors included older age (≥ 50 years), history of needle stick injuries, and renal failure, pointing to cumulative exposure and medical contact are key determinants.
Our seroprevalence estimate aligns with previous studies in Thailand, although differences are evident based on geography, population demographics, and exposure risk. A 2019 national survey among blood donors reported a 29.7% seroprevalence, with considerable regional variation from 35.8% in central to 14.2% in southern15. Lop Buri province in central Thailand, characterized by high densities of swine farming, reported a prevalence of 37.3%, suggesting zoonotic and occupational exposure as major contributors. In contrast, Narathiwat province in the far south, with predominantly Muslim dietary practices that limit pork consumption, showed much lower rates at 8.9%, emphasizing the role of cultural dietary habits in shaping HEV risk17. Hospital-based cohorts, such as one in Bangkok, have reported even higher anti-HEV IgG rates (40.8%)18. In contrast, lower rates in children (< 6%) reflect age-related cumulative exposure19. These findings underscore the geographic and demographic heterogeneity of HEV exposure in Thailand and highlight the importance of targeted surveillance and risk stratification, particularly among immunocompromised groups such as PLHIV.
Globally, HEV seroprevalence among HIV-infected individuals shows marked regional variation, shaped by differences in endemicity, socioeconomic factors, sanitation infrastructure, and dietary habits13,20–24. In our study, the 13% anti-HEV IgG seroprevalence among PLHIV in Chiang Mai is higher than reported rates in several low-prevalence countries. For instance, a study in Turkey (2017–2018) involving 126 HIV-positive individuals reported a prevalence of only 4.0%22, while in Cameroon, a study of 270 HIV-infected patients (2009–2015) found an 8.5% prevalence23. Another investigation by Feldt et al. (2013) showed a seroprevalence of 14.2% among HIV-infected adults in Cameroon—closely comparable to our findings—yet notably higher at 45.3% in Ghana, suggesting localized endemic transmission likely linked to sanitation and water quality25.
This pattern holds across other regions as well. Reported HEV IgG seroprevalence among PLHIV is 4.1% in Brazil, 7.3% in Greece, 10.9% in Bulgaria, and 11.7% in the Netherlands. In contrast, higher rates are documented in Iran (16.4%), southern France (38.7%), and Poland (50.8%)21,26–31. These global discrepancies reinforce the influence of environmental exposure routes, including consumption of undercooked meat, contact with animals (particularly swine), and access to safe water. The similarity of our findings to those from moderate-endemicity regions supports the idea that HEV exposure in PLHIV is not uniformly elevated but is instead driven by local environmental and sociocultural determinants.
In our study, individuals aged ≥ 50 years had significantly higher odds of HEV seropositivity (OR = 3.40, 95% CI: 1.47–7.86, p = 0.004), suggesting cumulative exposure as a key risk factor. This association between age and HEV seropositivity is consistently reported across diverse settings. Studies in Nepal, Turkey, Bulgaria, Poland, and Greece have all demonstrated increasing HEV seroprevalence with age among PLHIV21,27,31–34. For example, in Poland, rates increased from 36.2% in those aged 18–30 to 63.3% in those aged 41–5531. Similarly, a population-based study in Ghana and a meta-analysis from the Americas confirmed age as an independent predictor of HEV seropositivity35,36. These findings underscore age-related cumulative exposure as a robust and generalizable determinant of HEV infection risk in HIV-infected populations.
Needle stick injuries were significantly associated with HEV seropositivity in our study (OR = 3.33, 95% CI: 1.20–9.24, p = 0.021), indicating that bloodborne exposure may be a relevant transmission route, particularly in immunocompromised individuals. Although direct evidence in PLHIV is limited, a study in Hong Kong reported higher HEV IgG seroprevalence among people who inject drugs, supporting the plausibility of parenteral transmission37. These findings highlight the need for heightened attention to occupational and injection-related exposures as potential HEV transmission risks in HIV care settings.
Renal failure was significantly associated with HEV seropositivity in our study (OR = 15.69, 95% CI: 2.93–83.87, p = 0.001), suggesting increased vulnerability to HEV exposure among HIV-infected individuals with kidney impairment. This may reflect both immunosuppression and frequent medical interventions. While renal failure has not been consistently identified as a direct risk factor for HEV, studies in immunocompromised populations support this link. For instance, HIV-infected transplant candidates in the U.S. showed a 20% HEV IgG prevalence regardless of organ type, although acute infection was more common in liver recipients38. Other reports found lower seroprevalence in kidney transplant recipients, yet consistently higher HEV rates among PLHIV compared to the general population39. The findings underscore the importance of monitoring HEV in immunocompromised individuals, particularly those with renal impairment, as they may carry a higher burden of past HEV exposure, even if acute infection is less frequent.
In this cohort, behavioural factors such as alcohol use, smoking, tattoos, piercings, and sexual practices were not significantly associated with HEV seropositivity. This contrasts with findings from other settings. For instance, a 2014 study reported higher HEV IgG seropositivity among alcohol users and highlighted links to open-field defecation and animal contact as behavioural risk factors40. Our findings suggest that, among PLHIV in Chiang Mai, environmental and healthcare-related exposures may play a more prominent role in HEV transmission than individual lifestyle behaviours.
In our cohort, co-infections with HBV or HCV were infrequent, and no cases of triple infection (HEV, HBV, and HCV) were detected. Similar findings from Vietnam and India indicate that HBV and HCV co-infections do not appear to influence HEV seropositivity among PLHIV41,42. These results underscore the importance of routine screening for hepatotropic viruses in immunocompromised populations, even when triple infection is rare.
This study has some limitations. First, the data were drawn from a pre-existing cross-sectional dataset focused on self-stigma, health behaviours, and health conditions among people living with HIV in Chiang Mai, Thailand. As such, critical clinical variables including CD4 count, HIV viral load, liver function tests, and detailed infection history were not available. Second, the cross-sectional design limits causal interpretation, preventing assessment of temporal relationships or identification of ongoing HEV transmission. Despite its limitations, this study provides valuable data on HEV seroprevalence among people living with HIV in Chiang Mai, Thailand.
Conclusion
In conclusion, this study demonstrates that HEV exposure among people living with HIV in Chiang Mai, northern Thailand, is not uncommon, with a 13% rate of prior infection. The findings indicate that HEV may be an underrecognized health concern in this population, particularly among older individuals, those with a history of renal dysfunction, and those with potential parenteral exposures. Incorporating routine HEV screening alongside HBV and HCV testing in HIV care could help identify at-risk individuals and prevent related complications. Moreover, these results underscore the importance of enhanced HEV surveillance and preventive strategies for vulnerable HIV-positive populations across Southeast Asia. Further longitudinal studies are essential to better understand HEV transmission dynamics and clinical outcomes, ultimately guiding targeted interventions to reduce its burden in immunocompromised groups.
Acknowledgements
The authors gratefully acknowledge the School of Health Sciences Research, Research Institute for Health Sciences, Chiang Mai University, for supporting this study through the student research fund. We also extend our sincere thanks to the Research Center for Molecular and Cell Biology, Research Institute for Health Sciences, along with their dedicated team members, for their valuable assistance in facilitating the research work.
Author contributions
S.H. and W.K. conceptualized the study. R.C., K.J., L.K., and S.H. designed the methodology. S.H. supervised the work and administered the project. R.C., S.H., and W.K. curated the data, while R.C., M.U., and S.H. performed the formal analysis. K.S. and S.H. provided resources. Validation was carried out by R.C., K.J., M.U., N.K., W.K., and S.H. R.C. prepared the visualizations. R.C. and S.H. wrote the original draft, and R.C., K.J., M.U., L.K., N.K., K.S., W.K., and S.H. reviewed and edited the manuscript. All authors reviewed and approved the final version of the manuscript.
Funding
This research was funded by the Student Research Grant, Research Institute for Health Sciences, Chiang Mai University (Grant No. 039/2568), and by the PhD program in Health Sciences Research, School of Health Sciences Research, Research Institute for Health Sciences, Chiang Mai University, Thailand under the CMU Presidential Scholarship (Ref No. 8393 (25)/3450).
Data availability
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.