• To ascertain the prevalence of co-infections (including tuberculosis, hepatitis B, hepatitis C, etc.) among HIV/AIDS patients in Faisalabad, Punjab, Pakistan.

• To pinpoint the precise risk factors linked to co-infections in individuals with HIV/AIDS.

The study utilized a cross-sectional design to assess the prevalence of co-infections and their associated risk factors among HIV/AIDS patients in Faisalabad, Punjab, Pakistan. Interviews, medical records, and lab tests were used to gather the data. Demographic data, HIV treatment history, medical history, and behavioral risk factors were evaluated. A statistical method was used to determine the frequency of concurrent infections and to identify any significant associations between them and risk factors. During the research process, strict adherence to ethical principles, including informed consent and confidentiality, was maintained.

The research’s inclusion criteria encompassed individuals aged eighteen years or older with a confirmed diagnosis of HIV or AIDS. Participants could write a statement indicating their agreement to participate in the study, and they were treated by licensed healthcare professionals in Faisalabad. Only individuals who could read and understand the survey questions in Punjabi, Urdu, or another approved language were eligible to participate. Additionally, to obtain a comprehensive understanding of the prevalence of co-infections, individuals with and without these conditions (such as syphilis, hepatitis B, hepatitis C, or tuberculosis) were included.

However, the criteria for exclusion did not include people who lived in Faisalabad, were younger than 18, or could not give informed permission because they had serious mental illnesses or disorders. Patients lacking comprehensive health records regarding their HIV assessment and treatment, or those not undergoing treatment at proximate medical facilities, were also excluded.

The study’s target demographic consisted of all individuals living with HIV/AIDS (PLHIV) in Faisalabad, Punjab. This group includes individuals who are currently receiving medical care, regardless of whether they have other infections. The study population comprises 250 participants who fulfill the eligibility criteria, chosen from the target group. To determine the prevalence of concurrent infections and their associated risk factors, the study sample comprised a heterogeneous cohort of individuals, including both those with co-infection diagnoses and those without.

Participants were recruited during their routine medical appointments at healthcare centers to ensure they met the participation criteria and had given their consent to participate. This method is ideal for this study because the target population is readily accessible in a medical setting. In certain instances, purposive sampling is used to ensure the inclusion of specific subgroups, such as patients with co-infections like tuberculosis or hepatitis. The total sample size of 250 patients was chosen based on their availability and willingness to participate.

The first step was to sort the variables into two groups: categorical and continuous. Categorical variables, including gender, co-infection status, marital status, and education level, denoted distinct groups or categories. In contrast, constant variables, such as age, CD4 count, viral load, and income level (when recorded numerically), were quantitative and could assume any value within a defined range. This classification was essential for identifying the suitable statistical techniques for analysis. We conducted a univariate analysis to examine the distribution and characteristics of each variable independently. We made frequency tables for categorical variables to show the number and percentage of each category. We also used bar charts to illustrate how the data was distributed. A bivariate analysis was conducted to investigate the correlation between two variables. The chi-square test was utilized to ascertain the presence of a significant association between categorical variables. A chi-square test was used to examine the relationship between gender and co-infection status, for example. The SPSS software tool was used to do the analysis. In Excel, charts were used to visualize data, and pivot tables were utilized to create frequency tables and cross-tabulations. In SPSS, the “Descriptives” and “Frequencies” options were used for univariate analysis, “Crosstabs” for bivariate analysis of categorical variables, and “Compare Means” for categorical vs. continuous variables. The results of the analyses were interpreted in the context of the research questions. The association between gender and co-infection status was evaluated, and the relationship between age and viral load was examined. This comprehensive approach ensured a thorough understanding of the factors associated with co-infections in HIV/AIDS patients.

The study utilized the chi-square test to analyze the relationship between various risk factors and co-infection status among HIV/AIDS patients, focusing on co-infections like Hepatitis B, Hepatitis C, Syphilis, and Tuberculosis. Key demographic, behavioral, clinical, and healthcare access factors were examined. Significant findings included higher diagnosed co-infections among individuals aged 26 - 35 and 46 and above, with females showing a greater prevalence than males. Marital status revealed that widowed individuals had the highest diagnosed co-infections, while education level indicated that lower education correlates with higher co-infection prevalence. Monthly income also showed significance, with lower-income groups facing more diagnosed co-infections, whereas occupation was not significantly associated with co-infection status (Table 2).

Table 2.Association between demographic factors and co-infection status among HIV/AIDS patients: Chi-square test analysis.

The chi-square test for association revealed significant relationships between certain behavioral risk factors and diagnosed co-infections. Intravenous drug use showed a strong association (χ² = 17.246, p < 0.001), while needle sharing had a borderline significance (χ² = 3.818, p = 0.051). Past STIs, tobacco, and alcohol consumption were not significantly associated with co-infections. Unprotected sex exhibited a strong link (χ² = 29.971, p < 0.001). In terms of clinical factors, significant associations were found between the year of HIV diagnosis (χ² = 46.853, p = 0.000), current ART status (χ² = 7.549, p = 0.006), and duration on ART (χ² = 16.248, p = 0.001) with diagnosed co-infections. Adherence to ART was critical, showing a strong association (χ² = 39.212, p = 0.000). Additionally, frequency of healthcare visits (χ² = 22.335, p < 0.001) and access to healthcare services (χ² = 12.288, p = 0.000) were significantly related to co-infections. Lack of access to clean drinking water (χ² = 31.901, p = 0.000) and poor knowledge about HIV/AIDS (χ² = 11.922, p = 0.008) also indicated strong associations. However, counseling or education did not show a significant relationship (χ² = 3.181, p = 0.074), nor did interest in educational sessions (χ² = 1.418, p = 0.234) (Table 3).

Table 3.Association between behavioral, clinical, healthcare, and knowledge-related risk factors and co-infection status: Chi-Square test analysis.

The research examined the correlation between demographic factors and the likelihood of obtaining a co-infection diagnosis. The regression analysis indicated that age, marital status, occupation, education level, living situation, and monthly household income were significant predictors of co-infection diagnosis. People between the ages of 26 and 35 were more likely to be diagnosed with co-infections. There were no significant differences in people between the ages of 36 and 45 or older. Sex was not a significant predictor of co-infection diagnosis. The diagnosis of co-infection was also influenced by factors such as marital status, occupation, level of education, place of residence, and monthly income. The multivariate regression analysis indicated that the comprehensive regression model accounted for roughly 20.64% of the variance in diagnosed co-infections. Intravenous drug use showed the strongest link to diagnosed co-infections. Sharing needles also had a strong link to diagnosed co-infections, but with a negative coefficient, which could mean that it protects against them or that it was a confounding factor in the dataset. Some findings from the adjusted model were counterintuitive and were therefore interpreted cautiously. Although needle sharing showed a negative adjusted association with co-infection status, this should not be interpreted as a protective effect. The estimate may have been influenced by the small number of participants reporting needle sharing, sparse-data bias, overlap with intravenous drug use, or residual confounding. Similarly, the higher adjusted odds among participants who reported never engaging in unprotected sexual intercourse may reflect reporting bias, prior behaviour modification after diagnosis, reverse causality, or confounding by other clinical and healthcare-seeking factors. The unexpected negative association observed for needle sharing and the positive association among those reporting no unprotected sexual intercourse should be interpreted with caution, as these findings may reflect sparse data, reporting bias, reverse causality, residual confounding, or model instability rather than true protective or causal effects.

The study investigated the correlation between sexually transmitted infections (STIs) and identified co-infections. The findings indicated a significant correlation between adherence to antiretroviral therapy (ART) and diagnosed co-infections, with individuals who infrequently or never adhered to ART exhibiting elevated probabilities of diagnosis. Nonetheless, individuals who adhered occasionally did not exhibit a statistically significant effect. The present ART status exhibited a tenuous correlation, indicating that being on ART may influence diagnosed co-infections. Another aspect examined was the frequency of doctor visits.

The study also examined how diagnosed co-infections impacted access to healthcare and clean drinking water. Having access to healthcare services was associated with a higher likelihood of co-infections, while access to clean drinking water was linked to a lower likelihood of co-infections. Understanding HIV/AIDS was a major predictor, and people who didn’t know much about it were the most likely to have co-infections. Moreover, obtaining counseling or education and exhibiting an interest in educational sessions were significantly correlated with diagnosed co-infections, accounting for 11.47% of the variance in diagnosed co-infections. The study indicates that knowledge and educational factors influence the diagnosis of co-infections (Table 4 and Table 5).

Table 4.Multivariate analysis of demographic, behavioral, clinical, and healthcare factors associated with HIV/AIDS co-infections.

Table 5.Multivariable binary logistic regression model for factors associated with co-infection status among HIV/AIDS patients.

The study shows that HIV is most common in young adults and middle-aged people [11], with a large number of men and a lot of single people. This suggests that we need different approaches to prevent issues affecting men and women. Education is essential for HIV control, given that many individuals lack formal education. Health outcomes are also affected by people’s economic situation and job status. Many people have low incomes and can’t get clean water. People may not stick to ART because of side effects, stigma, lack of education, or trouble getting to medical facilities.

3.2.1. Association between Demographic Factors and Co-Infection Status

The study found a significant association between age, gender, marital status, and diagnosed co-infections. Individuals aged 26 - 35 and above are more prone to co-infections [12], possibly due to increased exposure, lifestyle factors, or underlying health conditions. Younger individuals have a lower prevalence, while females have a higher number due to healthcare-seeking behavior, hormonal variations, or societal roles. Marital status also plays a role, with widowed and single individuals having a higher risk. Education level also plays a role in co-infection prevalence.

3.2.2. Association between Behavioral Factors and Co-Infection Status

The study reveals a significant association between intravenous drug use and co-infections, highlighting the vulnerability of drug users to infectious diseases due to contaminated needles and compromised immune function. The study also suggests needle-sharing behaviors may increase the risk of transmitting infectious diseases [13]. Past STI exposure did not show a significant relationship with co-infections, suggesting other factors like healthcare access and treatment adherence may influence this outcome [14]. Unprotected sexual intercourse and tobacco smoking or alcohol consumption did not show significant associations with co-infections, suggesting indirect effects [15]. The study emphasizes the need for targeted intervention programs, prevention strategies, and increased healthcare accessibility for at-risk populations.

3.2.3. Association between Clinical Factors and Co-Infection Status

The study found significant associations between clinical factors and co-infections in HIV patients. The year of diagnosis was linked to increased risk factors or treatment differences. Lower co-infection rates in 2024 were attributed to improved treatment protocols and healthcare management. ART’s protective role against co-infections was evident [16]. However, prolonged ART use increased co-infections, possibly due to drug resistance.

3.2.4. Association between Access to Healthcare and Co-Infection Status

Access to essential healthcare services and clean drinking water is crucial for preventing co-infections, especially among immunocompromised individuals [17]. Lack of healthcare services increases the risk of co-infections, leading to delayed diagnoses and inadequate preventive measures. Clean drinking water quality is also linked to co-infection status, as contaminated sources harbor bacteria, viruses, and other pathogens, further compromising the immune system and exacerbating existing health conditions.

3.2.5. Association between Knowledge and Education and Co-Infection Status

The study found that knowledge about HIV/AIDS significantly impacts the likelihood of being diagnosed with co-infections [18]. People with poor knowledge had higher rates of co-infections. This suggests that inadequate understanding can lead to risky behaviors, poor treatment adherence, and delayed medical interventions. Educating individuals on HIV/AIDS could reduce co-infection rates. However, the effectiveness of counseling or education on HIV/AIDS may depend on factors like quality, duration, and frequency.

3.3.1. Multivariate Analysis of Demographic Factors

The study reveals that demographic factors such as age, marital status, education level, living situation, and household income significantly influence the diagnosis of co-infection [19]. Individuals aged 26 - 35 are at higher risk due to lifestyle and environmental factors. Marital status and education level also play a role in susceptibility to co-infection. Living in stable housing is a protective factor, potentially due to better sanitation and access to healthcare. Financial stability may facilitate better healthcare access and preventive measures, while higher incomes may result in increased diagnoses.

3.3.2. Multivariate Analysis of Behavioral Factors

The study found a strong association between intravenous drug use and co-infections, highlighting the need for harm reduction strategies like needle exchange programs [20]. Sharing needles had a negative relationship, possibly due to unmeasured confounders. The history of STIs did not significantly predict co-infections, suggesting underreporting or misclassification. Smoking and alcohol consumption did not show significant associations, suggesting indirect effects. The model’s adjusted R-squared value (20.64%) suggests unmeasured factors may also play a role. Further research should consider additional variables to improve predictive accuracy.

3.3.3. Multivariate Analysis of Clinical Factors

The study highlights the importance of ART adherence in preventing co-infections among patients [21]. Non-adherence leads to higher odds of co-infections, indicating the need for enhanced adherence interventions. Consistency in ART intake is more crucial than duration, suggesting that longer ART use doesn’t necessarily reduce co-infection risk. Regular healthcare visits, especially monthly ones, are associated with higher likelihood of diagnosed co-infections. Routine medical checkups are more effective than symptom-driven care. The model provides valuable insights into the relationship between clinical characteristics and infection risk.

3.3.4. Multivariate Analysis of Health Care

The study found that healthcare accessibility and knowledge-related factors significantly impact the diagnosis of co-infections. Improved healthcare access reduces infectious disease burden by facilitating early diagnosis and treatment, while clean drinking water prevents additional infections.

3.3.5. Multivariate Analysis of Knowledge and Education

The study reveals that individuals with poor knowledge about HIV/AIDS are at a higher risk of being diagnosed with co-infections, suggesting misinformation or lack of awareness about prevention. Counseling and education are crucial for managing the condition. However, low R-squared values suggest additional factors like socioeconomic status, nutritional factors, and immune system status may also contribute to co-infection diagnosis. The findings emphasize the need for systemic healthcare improvements and targeted educational interventions

The difference between the non-significant chi-square result and the significant adjusted association for counseling/education may be explained by confounding or suppression effects, as the multivariable model adjusted for related factors such as education level, HIV/AIDS knowledge, healthcare access, and ART adherence.

This study was limited to patients in Faisalabad, Punjab. It does not represent the whole population of Pakistan and other regions, which limits the generalizability of demographic and socioeconomic events. Behavioral factors like art adherence, IV drug use, and sexual practice are reported by the patient themselves, which might be biased and can impact the overall results. There are certain con infections discussed in this study, and other infections, such as malignancies and fungal infections, are not addressed. This study only addresses the healthcare service access, but not the quality of the healthcare service, which could be a significant factor. This study only focuses on the income level of the individuals, but does not explore other factors such as financial burden or income loss, etc.

The study concludes that low income of the families is a significant factor for co-infection occurrence, which indicates that infection occurrence and low income can lead the patients to poverty, and limited healthcare systems can be assessed. The study showed that low-salaried persons and the unemployed who are affected by HIV/AIDS are led to unstable economic conditions. The healthcare system access suggested that there is a need to improve the resources of the healthcare system. A significant number of individuals lack education, which suggests that education is important to get employment and ultimately bring them out of poverty and poor health.

This research highlights the necessity of focused public health campaigns, such as prevention for addicts, more secure sexual behavior, and enhanced access to medical care, to lower co-infections amongst patients living with HIV/AIDS. Various demographic categories should be the focus of educational efforts that increase knowledge about co-infections and avoidance. Counselling and support groups are essential for increasing ART adherence. To strengthen healthcare systems, especially in areas with limited resources, it is crucial to increase the availability of diagnostic tools, medicines, and trained staff. Controlling socioeconomic factors, such as transportation, education, and poverty, can help reduce the number of co-infections. The report also requests additional funding and programs to combat stigma, demonstrating the impact of policy. Lastly, future studies should also examine social stigma, financial difficulties, and long-term health consequences.

This study assessed the prevalence of co-infections and factors associated with co-infection status among HIV/AIDS patients in Faisalabad, Punjab, Pakistan. Co-infections, particularly syphilis, tuberculosis, and hepatitis B, were commonly reported among the study participants. Several demographic, behavioural, clinical, and socioeconomic factors were associated with co-infection status, including intravenous drug use, unprotected sexual practices, lower educational attainment, inadequate ART adherence, unstable living conditions, lack of access to clean drinking water, and limited healthcare access. These findings should be interpreted as associations rather than causal effects because of the cross-sectional nature of the study. The results may help identify groups with a higher burden of co-infections and may inform integrated HIV and co-infection screening, ART adherence support, safer-sex education, harm-reduction services, and healthcare access strategies for vulnerable populations. Further longitudinal studies are needed to clarify temporal relationships and assess whether these factors contribute to subsequent co-infection risk.

Longitudinal studies should be employed in future research to track the progression of co-infections over time, identify persistent patterns, and evaluate treatment effectiveness. Additionally, including factors such as socioeconomic status, nutritional condition, and comorbidities can enhance intervention strategies and predictive modeling. Personalized healthcare strategies, such as ART adherence programs tailored to different age groups, may help improve the effectiveness of treatments. Investigating how factors such as pollution and climate can contribute to co-infections could help us gain a deeper understanding of how to effectively treat the disease. Finally, to improve patient outcomes, it is essential to focus on developing and utilizing rapid diagnostic tests to identify co-infections early on.

To improve public health programs, it is important to increase prevention efforts for people who are addicted to injecting drugs through needle exchange programs and rehabilitation services. It’s also very important to improve efforts to help people understand HIV and safe sex, especially for groups that are more likely to get it. Support groups, counseling services, and patient education in the community can all help people stay on ART. The primary goal of healthcare system reforms should be to increase the availability of equipment, enabling skilled workers, medications, and advanced technologies to be accessed promptly, particularly in areas with limited resources. Telemedicine, mobile clinics, and cost-cutting measures can all help lower barriers to healthcare services and lower the risk of infection. Everyone should also have access to clean drinking water and sanitary facilities.