Predictors of Tuberculosis in HIV Patients in the Clinic Voluntary Counseling and Testing in Medan City

Background: The incidence of tuberculosis in HIV patients (TB-HIV) w as estimated at 126 per 100,000 in the world. The number of HIV infected patients in Indonesia around 190,000 to 400,00 0 and the prevalence of TB-HIV is 5%. Data TB-HIV still increase s and they are un aware of that until a late stage. Understanding the risk factors of people with TB-HIV co-infection is important to know. This study aims to know the predictors of TB-HIV patients in the clinic voluntary counseling and testing (VCT) in Medan city. Methods: This is a case-control study. The case is TB-HIV patients (aged > 20 years) seen at clinic VCT Medan in 2016. Control is HIV patients without TB (aged > 20 years) seen in the same clinic VCT. The number of cases was 120 and the number of control 120. Data were collected from the medical record. Data were analyzed using the chi-square test in SPSS software. Results: The t otal was 240 patients. The majority were in the age group 31-40 years old (52.9%), male (75.8%), married (71.7%), had tertiary education (85.4%), had employment (89.2%) . Significant factors were CD4 < 500 cells/ml (OR 3.92; 95% CI 2.13-7.22), BMI < 18.5 kg/m2 (OR 5.79; 95% CI 3.25-10.21), had history of TB family (OR 7.9; 95% CI 3.67-18.18), adherence ARV (OR 1.35; 95% CI 1.02-1.79). Conclusions: The predictors of incidence TB-HIV co-infection was low CD4, low BMI, and had a family history of TB. Pay the attention for the nutritional status of TB-HIV patients and provide appropriate nutritional intake needs and adherence ARV.

about 49% of them are unaware of their co-infection and they did not under treatment 2 .
In developing countries incidence of TB occurrence has been associated with factors like socioeconomic 3 , lifestyle habits 4 , clinical and laboratory 5 , also other co-morbidities for example diabetes 6 . Many patients either have a history of TB in family, or they develop TB while receiving HIV treatment in the developing world 7 . It has not been well delineated what factors influence the development of TB in HIV infected patients on antiretroviral therapy (ART) 8 . In Indonesia, the number of TB cases in 2017 reaches the second rank in the world, and it is endemic in the general population and has a high incidence of TB-HIV 9 . Incidence of Tuberculosis per 100000 population per year is 254 in 2017, tuberculosis in adults receiving ART is higher than in HIV negative adults 10  Tuberculosis is a major cause of death in people with HIV, which is most likely due to delays in diagnosis and TB therapy. People with HIV will be at risk 26 times more likely to get Tuberculosis compared to people without HIV or HIV negative 11 . Uyainah (2009) states that TB co-infection is a problem because one of the factors is the high incidence of TB itself in Indonesia, which increases the risk of contact with HIV patients. The higher the prevalence of HIV in an area, the higher the

Methods
This is a case-control study was conducted in three Clinic VCT in Medan city consist of Clinic VCT in Dr. Pirngadi Hospital, in Haji hospital and Adam Malik hospital. Data collection from June to September 2018. The inclusion criteria of cases were defined as adult HIV infected patients aged 20 years old and above who develop TB after ART initiation. Controls were adult HIV infected patients aged 20 years old and above who did not develop TB after ART initiation. The number of cases was 120 and the number of control was 120. TB Diagnosis in HIV infected patients was made by a clinician based on signs and symptoms of clinical. Acid Fast Bacilli (AFB) direct microscopy laboratory test done three times that is sputum at random, sputum in the morning after wake up and sputum at random. At least one AFB positive can be diagnosed with TB. The exclusion criteria are patients who are severely ill and who not on antiretroviral therapy (ART).
Data collection from medical records of three clinic VCT in Medan city. Cases and controls were identified by the principal investigator through the help of the ART and TB registries. The ratio of case and control was 1:1. The sample size was calculated using the following parameters: 5% significance level, power of 80%, and by using the two proportion formula. The calculated sample size was 120 for cases and 120 for controls; the resulting minimum sample size was 240. The sample size was calculated for exposure status in different variables of the most significant predictors of TB-HIV. The data were collected from medical record and the variables were age, gender, ethnicity, marital status, education level, occupational type, CD4 count, Hb, BMI, had a family history of TB.
Data were entered, cleaned and using SPSS software for analysis. Frequencies and proportions were used to describe the study population in relation to relevant variables. Bivariate analysis was performed to examine the effect of each variable of interest on the risk of TB-HIV. Odds ratios (OR) and their 95% confidence intervals (CIs) were estimated using the Chi-square test, with TB-HIV as an outcome.

Results
A total of 240 patients included in this study. More proportions of case and control patients were in the age group of 30-39 (53.3% and 52.5%) respectively. High proportions of men, Bataknese and married status were reported in both groups, 73.3%, 57.5% and 65% in cases and 78.3%, 56.7% and 78.3% in controls respectively. More than half the patients have completed secondary school, 59.2% in cases and 45% in controls. The majority of subjects, 65% in cases and 60% in controls were nonmanual workers of occupational type ( Table 1). Majority of cases 84.2% had CD4+ cell count less than 500 cells/dl. But 57.5% of patients in controls had CD4+ cell count less than 500 cells/ml. Among cases, 75.8% of them had Hg level less than 11 mg/dl and 66.6% of controls had Hg level less than 11 mg/dl ( Table 2). Of the total 120 patients in cases, 59.22% of patients had BMI less than 18.5. On the contrary, 80.0% of controls had BMI of more than 18.5. Regarding TB family history, the majority of cases and control did not have a history of TB, in cases, only 39.1% had TB family history (Table 1) The bivariate analysis showed that a higher proportion of male patients (OR = 1.73; 95% CI: 1.23, 2.46) develop TB compared to female patients. The divorced/widowed (OR = 0.560; 95% CI: 0.36, 0.87) patients were less likely to develop TB compared with unmarried (single) individuals. But educational status and occupation were not associated with the occurrence of TB (Table 2).
In multivariate analysis, the risk for TB-HIV was higher among persons with low education, low CD4 counts (< 500 cells/dl), BMI < 18.5, had a family history of TB. We did not find any interactions among the significant predictor variables in the model, multivariate analysis showed in Table 3:

Discussion
It was found that from nine variables, four variables were the strongest predictors of TB-HIV patients in this study. The majority of patients were in the age group of fewer than 40 years (77.9%) and it was found that age variables did not significantly contribute to TB-HIV (p = 0.876). These results are consistent with research conducted by Melkamu and Mama in Ethiopia which states that age is not a predictor of cases of TB-HIV patients 13,14 . From these results, it can be interpreted that any age group still has the same risk of developing TB. This finding similar to a study from Addis Ababa which showed a high frequency of infection in patients with age less than 50 years 15 .
Regarding the gender, the majority of patients were men (75.8%) both in the case and control groups.
After the results of the multivariate test showed that gender was not a predictive factor for TB-HIV patients (p = 0.366). Similar to the research conducted in India which states that male is more than female of TB-HIV patients caused by their different lifestyles 16 . The behavior of males who are vulnerable to unhealthy behaviors such as cigarettes and alcohol are the strongest boosters and drivers for TB 17 . Whereas in general, it can be seen that TB the attacks any class of male or female. This is consistent with this study. When viewed from the poverty factor, it is the strongest predictor of TB-HIV in any gender, both male and female. Research conducted in US (2013) states that HIV and AIDS is not only hitting adults in their most economically productive years 18 .
A person's marital status is not a predictor of TB-HIV patients. In this study, it can be seen that the majority of patients are married (71.7%). The results of the bivariate test showed that p = 0.022. This study are different from the research conducted in Africa (2016) which states that marital status is a predictor of TB-HIV that attacks more unmarried people 19 This study states that unmarried patients will increase the risk of contact with many people because it is associated with socioeconomic activities. Whereas in Indonesia it can be seen that whether married or unmarried, there is no difference in the risk of contact with other people who can contribute to TB-HIV. A study in Iran (2014) showed that seroprevalence of TB-HIV co-infection was higher among unmarried males and those living singly than male tuberculosis patients living with their spouses 20 Based on the results of the bivariate test, it was found that education level was a predictor of TB-HIV patients (p = 0.006). The majority of patients have a low level of education at the primary and secondary level (66.7%). Even based on the results of the multivariate test showed that patients with low education will be at risk 2,319 times for TB-HIV than patients with higher education. It appears that as low as education, the higher the risk. Research conducted in Tehran stated that the level of education can influence behavior. A person with low education is closely related to low knowledge about maintaining health and the risk of exposure to diseases including TB co-infection 20 . A person with higher education tends to pay attention to his health with his knowledge of health for not being exposed and in contact with many people. The educational level can affect the behavior of HIV patients and understanding of education can help target interventions in order to prevent infection 21 .
There was a significant association between educational level and drinking or smoking. Most of the TB-HIV patients had less than nine years of schooling and also had low incomes. This apparent association might be a consequence of other health hazards, including a lack of health education and having access to health care 22 .
The results of the analysis state that employment is not a predictor of TB-HIV patients (p = 0.196). In this study, the majority of patients were working (89.1%). The results of this study are different from the statement of The National AIDS Fund which states that HIV sufferers are at risk of being exposed to TB co-infection at their workplace. In this study, work is related to one's socioeconomic status.
Patients with low socioeconomic conditions are closely related to unhealthy living behavior due to lack of access to adequate health facilities. Patients with high socioeconomic will pay more attention to lifestyle with their work. Work is also closely related to low education and knowledge. The majority of patients in this study work as labor with low education in the factory and close rooms so that the spread of TB was more widespread. A similar explanation was delivered by UNAIDS (2012) which stated that patients working in the transport and construction as well as business sectors were a highrisk group for TB-HIV 23 .
Based on the results of the bivariate test in this study a group of patients with TB-HIV and had CD4 levels less than 500 cells / mm3 were at risk of 3.929 times compared with patients non-TB-HIV patients had CD4 levels more than or same as 500 cells / mm3. The results of the analysis showed that CD4 levels were predictors and contributed to TB-HIV patients (p <0.001), wherein this study the majority of patients had CD4 levels less than 500 cells / mm3 (70.0%). This study is in line with a systematic review by Ellis (2017) which states that the decreasing level of CD4 levels increases the risk of TB. High viral load increased disease progression. The results of multivariate analysis showed that patients with CD4 levels less than 500 cells / mm 3 were at risk of being infected with 3,141 times compared to patients with CD4 levels more than 500 24 .
The majority of patients were classified as anemia (71.3%). The results of the bivariate analysis showed that Hb levels were not predictors of TB-HIV (p = 0.117). This result is different from the research conducted by Karima, et al (2017) which states that there is an increased risk of TB in groups with anemia. In this case, anemia is associated with malnutrition and conditions of immune deficiency which increases risk. But in this study different results were obtained which stated that the anemia condition of patients did not contribute to TB-HIV 11 .
The majority of patients with a BMI of more than 18.5. It was found that BMI was a predictor of TB-HIV (p <0.001). The results of the multivariate test also stated that patients with low BMI levels would be at risk of 8,632 times than patients with normal BMI. This study is in line with research conducted in Africa (2013) which states that the risk of TB is greater in patients with low BMI 13 . The same thing was stated by Choun, et al (2013) in Cambodia which states that risk factors for early TB included low BMI. Nutritional factors of patients who are not good can reduce a person's immunity so that the range is more susceptible to disease, one of them is TB. So it is better for patients with HIV positive to pay more attention to nutritious food intake to avoid disease 25 The results of the multivariate analysis also state that a family history of TB is the most predictive contributing factor to TB-HIV. Patients with a family history of TB will be at risk of 10,584 times than negative ones. Similar to study in Uganda (2015) showed that the prevalence of a family history of TB