Abstract
Setting
Tuberculosis (TB) is the most common HIV-related opportunistic infection and AIDS-related death. TB often affects those from low socio-economic background.
Objective
This matched case-control study was designed to assess socio-economic determinants of TB in HIV-infected patients in Asia.
Design
HIV-positive-TB-positive cases were matched to HIV-positive-TB-negative controls according to age, sex and CD4 cell count. A socio-economic questionnaire consisting of 23 questions including education level, employment, housing and substance use, was distributed. Socio-economic risk factors for TB was analysed using conditional logistic regression analysis.
Results
A total of 340 patients (170 matched pairs) were recruited, with 262 (77.1%) matched on all three criteria. Pulmonary TB was the predominant type (115, 67.6%). The main risk factor for TB was not having university level education (OR=4.45, 95%CI (1.50-13.17), p=0.007). Burning wood or coal regularly inside the house and living in the same place of origin were weakly associated with TB diagnosis.
Conclusions
Our data suggests that lower socio-economic status is associated with increased risk of TB in Asia. Integrating clinical and socio-economic factors into the treatment of HIV may help in the prevention of opportunistic infections and disease progression.
Keywords: socio-economic, questionnaire, matched, HIV, TB
INTRODUCTION
There were an estimated 10.4 million new cases of tuberculosis (TB) worldwide in 2015, of which people living with HIV accounted for 1.2 million (11%). Six countries, three of which are in the Asia-Pacific (India, Indonesia and China), accounted for 60% of new TB cases (1). TB is the most common HIV-related opportunistic infection and AIDS-related death (2). The management of HIV and TB co-infection is challenging and often associated with poorer treatment outcomes (3) due to their profound effects on the immune system.
It is recognised that low socio-economic background can contribute to the increased risk of TB and unfavourable TB treatment outcomes (4–6). In sub-Saharan Africa, being single, low education, unemployment, low income, poverty, smoking, and alcohol use were shown to be associated with TB (7, 8).There are currently few data on the effects of socio-economic risk factors for TB in HIV-infected patients in Asia. A Taiwanese study reported HIV-negative patients with lower income levels were at significant risk of having recurrence of TB (9). Furthermore, a Malaysian study found that one fifth of TB patients registered in the national registry in 2012 had unfavourable outcomes. Lower education levels and HIV infection were found to be significant predictors of poor treatment outcomes (10). A study investigating the risk of TB diagnosis after recruitment into the TREAT Asia HIV Observational Database (TAHOD) reported TB incidence of 1.98 per 100 person-years of follow-up with younger age, lower CD4 cell count, antiretroviral therapy (ART) duration, and living high TB burden countries being significantly associated with the diagnosis of TB (11). Additionally, it was found that delayed ART initiation after TB therapy did not affect mortality rates in TAHOD patients (12).
This matched case-control study consisted of two sub-studies: (i) to conduct a detailed survey and assessment of socio-economic risk factors for TB diagnosis in HIV-infected patients in Asia; and (ii) to determine TB treatment outcomes and the development of multidrug resistant TB. In this particular sub-study, we report results from sub-study (i) with the hypothesis that TB diagnoses would be more common in patients with a range of social and economic pressures that impact health, including education level, employment, housing and substance use.
METHODS
Case-control selection
TAHOD is a prospective adult HIV cohort that collects clinical information on patients attending HIV care at urban referral sites across 12 countries in Asia. Patients were recruited into the study between 2012–2014 if they were ART naïve HIV-infected and receiving care at one of the participating TAHOD (13) sites. Consecutive new TB cases were enrolled after a diagnosis (not recurrence) of pulmonary or extra-pulmonary localisation or both, confirmed by either a positive Acid-Fast Bacilli (AFB) smear microscopy of any clinical specimen other than stool, or positive culture and identification of mycobacteria of the tuberculosis-complex specimen, or positive polymerase chain reaction (PCR) -based detection and identification of mycobacteria of the tuberculosis-complex from any clinical specimen. As the World Health Organization (WHO) recommends systematic screening for TB in HIV-positive patients (1), bias due to access to TB testing and treatment would be limited.
TB-negative controls were selected based on medical record information showing a normal chest x-ray within the past six months; the absence of symptoms suggestive of TB, including, cough, fever, unexplained weight loss and night sweats; no prior TB diagnosis; and no prior TB prophylaxis.
Matching
The control patients were expected to be matched 1:1 to TB cases on sex, age (+/− five years) and CD4 count (by strata: 0–100, 101–200, >200 cells/μL). However, due to difficulty in acquiring appropriate TB-negative controls with the required matching categories, we allowed for mismatches in any of the three criteria.
Socio-economic questionnaire
All TB cases and control patients were required to complete the study questionnaire, consisting of 23 questions. To ensure that the forms were filled correctly, patients were asked to complete the questionnaire with the study staff.
Statistical analyses
Due to incomplete case-control matching, socio-economic risk factors for TB, obtained from the questionnaire, were assessed using matched and unmatched logistic regression. The matched (conditional) logistic regression was analysed by including only the pairs that were matched on all three categories (age, sex, and CD4 count). A sensitivity analysis was performed by fitting unmatched logistic regression on all patients, adjusting for age and CD4 count due to incomplete matching on these variables. As this sub-study focused specifically on socio-economic determinants, no other clinical characteristics were included in the analyses. Smoking as a risk factor was not fitted in the multivariate model due to concerns regarding causality relationship with TB. Comparison of proportions was conducted using chi-squared or Fisher’s exact test. Regression models were fitted using backward stepwise procedures. Factors significant in univariate analysis at p<0.10 were included in the multivariate analysis. Factors with p<0.05 in the final multivariate model were considered statistically significant.
Ethics approvals were obtained from UNSW Sydney Ethics Committee, and respective local ethics committees of all participating sites, the data management and biostatistical centre (UNSW Sydney Ethics Committee), and the coordinating centre (TREAT Asia/amfAR). A written informed consent was obtained from the participants. All data management and statistical analyses were performed using SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA) and Stata software version 14.2 (Stata Corp., College Station, TX, USA).
RESULTS
A total of 340 HIV-infected patients (170 matched pairs) were recruited from China, Hong Kong SAR, Indonesia, Malaysia, the Philippines, Singapore, Taiwan, Thailand and Vietnam. Of 340 patients, 262 (131 matched pairs, 77.7%) were matched on all three criteria (sex, age and CD4 count), 16 (8 matched pairs, 4.7%) were matched on sex and age only, 50 (25 matched pairs, 14.7%) were matched on sex and CD4 only, 2 (1 matched pair, 0.6%) were matched on age and CD4 only, and 10 (5 matched pairs, 2.9%) were matched on sex only. Table 1 shows the demographics of the included patients according to the matching characteristics. Overall, the median age at enrolment was 33 years (interquartile range (IQR): 29–38). There were 284 (83.5%) males, and more than half (58.2%) had a CD4 cell count of ≤50 cells/μL at enrolment. The median CD4 cell count was 43 cells/μL (IQR: 14–109). Of the TB cases, 115 (67.6%) were diagnosed with pulmonary TB, 25 (14.7%) had extra- pulmonary TB, and 30 (17.6%) had both.
Table 1.
Patient demographics.
| Total patients (n=340) (%) | Cases (n=170) | Controls (n=170) | |
|---|---|---|---|
| Age at enrolment (years) | |||
| ≤30 | 117 (34.4) | 57 | 60 |
| 31-40 | 157 (46.2) | 79 | 78 |
| 41-50 | 52 (15.3) | 26 | 26 |
| >50 | 14 (4.1) | 8 | 6 |
|
| |||
| Sex | |||
| Male* | 284 (83.5) | 142 | 142 |
| Female | 56 (16.5) | 28 | 28 |
|
| |||
| CD4 at enrolment (cells/μL) | |||
| ≤50 | 197 (57.9) | 106 | 91 |
| 51-100 | 56 (16.5) | 25 | 31 |
| 101-200 | 37 (10.8) | 18 | 19 |
| >200 | 50 (14.7) | 21 | 29 |
|
| |||
| TB Diagnosis Information | N/A | N/A | |
| Pulmonary | 115 | ||
| Extra Pulmonary | |||
| lymph node | 10 | ||
| pleura | 4 | ||
| menigitis | 3 | ||
| other | 8 | ||
| Both | 30 | ||
|
| |||
| Country | |||
| China | 18 (5.3) | 9 | 9 |
| Hong Kong | 8 (2.4) | 4 | 4 |
| Indonesia | 96 (28.2) | 48 | 48 |
| Malaysia | 26 (7.6) | 13 | 13 |
| Philippines | 40 (11.8) | 20 | 20 |
| Singapore | 6 (1.8) | 3 | 3 |
| Taiwan | 2 (0.6) | 1 | 1 |
| Thailand | 22 (6.5) | 11 | 11 |
| Vietnam | 122 (35.9) | 61 | 61 |
|
| |||
| Matching Status | |||
| Matched on all criteria (sex, age, CD4) | 262 (77.1) | 131 | 131 |
| Matched on sex, age only | 16 (4.7) | 8 | 8 |
| Matched on sex, CD4 only | 50 (14.7) | 25 | 25 |
| Matched on age, CD4 only* | 2 (0.6) | 1 | 1 |
| Matched on sex only | 10 (2.9) | 5 | 5 |
includes 1 transgender.
The questionnaire each participant was asked to complete is presented in Table 2. Two patients did not complete the questionnaire. Overall, almost 50% of patients were married. More than half lived in urban areas in a 2–4 persons household, had high-school education level and were in full time employment at the time of enrolment. Most patients reported having electricity, refrigerator, television and telephone available in their homes. Approximately 70% have ever smoked, 8% drank alcohol every day and 25% have ever injected drugs. Sixty-seven percent have never been vaccinated against TB or did not know their vaccination status.
Table 2.
Responses to socioeconomic risk factor questionnaire
| Questions | Total cases n=170 (%) | Total controls n=170 (%) | *p-value |
|---|---|---|---|
| What is your current marital status? | 0.657 | ||
| Married/cohabitating | 78 (47) | 87 (53) | |
| Never married/cohabitating | 69 (52) | 63 (48) | |
| Divorced/separated | 9 (53) | 8 (47) | |
| Widowed and not cohabitating | 12 (60) | 8 (40) | |
| No response | 2 (50) | 2 (50) | |
| Missing | 0 (0) | 2 (100) | |
| Do you live in a rural or urban area? | 0.065 | ||
| Rural | 69 (57) | 52 (43) | |
| Urban | 101 (47) | 116 (53) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| What is your place of origin? | 0.078 | ||
| Where I currently live | 118 (53) | 103 (47) | |
| Another province | 44 (42) | 61 (58) | |
| Another country | 8 (67) | 4 (33) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| What is your highest education level? | 0.008 | ||
| No formal education | 7 (54) | 6 (46) | |
| Primary school | 21 (57) | 16 (43) | |
| High school | 111 (56) | 87 (44) | |
| University level | 31 (35) | 58 (65) | |
| No response | 0 (0) | 1 (100) | |
| Missing | 0 (0) | 2 (100) | |
| Are you employed? | 0.053 | ||
| Yes, full-time | 100 (48) | 110 (52) | |
| Yes, part-time, or occasionally | 17 (40) | 25 (60) | |
| No | 51 (61) | 33 (39) | |
| No response | 2 (100) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| What is your main occupation? | 0.070 | ||
| Farmer | 14 (64) | 8 (36) | |
| Construction/industry worker | 17 (47) | 19 (53) | |
| Market/street seller | 13 (46) | 15 (54) | |
| Civil servant | 6 (25) | 18 (75) | |
| Private company employee | 31 (41) | 44 (59) | |
| Entertainment worker | 2 (50) | 2 (50) | |
| Other | 33 (59) | 23 (41) | |
| No response | 3 (33) | 6 (67) | |
| Not employed/missing | 51 (59) | 35 (41) | |
| How many persons live in your household? | 0.724 | ||
| 1, myself | 12 (41) | 17 (59) | |
| 2-4 persons | 95 (51) | 91 (49) | |
| 5-7 persons | 50 (51) | 49 (49) | |
| >7 persons | 13 (57) | 10 (43) | |
| No response | 0 (0) | 1 (100) | |
| Missing | 0 (0) | 2 (100) | |
| How many rooms (kitchen, living rooms, bedrooms) are there in your house/apartment? | 0.865 | ||
| One | 15 (48) | 16 (52) | |
| Two | 20 (48) | 22 (52) | |
| Three | 43 (56) | 34 (44) | |
| Four | 35 (48) | 38 (52) | |
| Over four | 57 (50) | 57 (50) | |
| No response | 0 (0) | 1 (100) | |
| Missing | 0 (0) | 2 (100) | |
| Do you have a house with a tiled or iron sheet roof? | 0.470 | ||
| Tiled | 94 (48) | 103 (52) | |
| Iron sheet | 23 (51) | 22 (49) | |
| Other | 51 (55) | 41 (45) | |
| No response | 2 (50) | 2 (50) | |
| Missing | 0 (0) | 2 (100) | |
| What type of toilets do you use? | 0.732 | ||
| Flush toilet | 107 (49) | 111 (51) | |
| Latrine | 60 (51) | 57 (49) | |
| Other | 1 (100) | 0 (0) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 2 (50) | 2 (50) | |
| Are the following facilities available at your home? (Choose all that applies) | ~ | ||
| Electricity (power line) | 164 (50) | 167 (50) | |
| Generator | 13 (35) | 24 (65) | |
| Refigerator | 133 (48) | 145 (52) | |
| Radio | 118 (52) | 110 (48) | |
| Television | 166 (50) | 165 (50) | |
| Bicycle | 93 (52) | 87 (48) | |
| Motorbike | 116 (50) | 117 (50) | |
| Car | 35 (42) | 48 (58) | |
| Telephone (fixed or mobile) | 159 (49) | 165 (51) | |
| Do you burn wood or coal inside your house? | 0.012 | ||
| Yes, regularly | 23 (74) | 8 (26) | |
| Yes, occasionally | 26 (48) | 28 (52) | |
| Never | 118 (47) | 132 (53) | |
| Used in the past, but stopped | 2 (100) | 0 (0) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 1 (33) | 2 (67) | |
| What is the main source of drinking water you use at home? | 0.330 | ||
| Piped water to your house/plot | 75 (50) | 74 (50) | |
| Well/bore water | 46 (58) | 34 (43) | |
| Mineral water | 41 (43) | 54 (57) | |
| Pond/river | 2 (50) | 2 (50) | |
| Other | 6 (67) | 3 (33) | |
| No response | 0 (0) | 1 (100) | |
| Missing | 0 (0) | 2 (100) | |
| Do you eat proteins (meat, seafood, eggs, milk products, soya beans) | 0.439 | ||
| Every day | 119 (48) | 128 (52) | |
| 2-3 times per week | 41 (56) | 32 (44) | |
| 1 time per week or less | 10 (56) | 8 (44) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| Smoking | 0.047 | ||
| Currently smoke | 45 (42) | 62 (58) | |
| Ever smoked (previously smoked) | 76 (58) | 55 (42) | |
| Never smoked | 49 (49) | 51 (51) | |
| No response | 0 | 0 | |
| Missing | 0 (0) | 2 (100) | |
| Do you drink alcohol? | 0.585 | ||
| Every day | 17 (63) | 10 (37) | |
| Occasionally | 73 (49) | 76 (51) | |
| Never | 57 (50) | 56 (50) | |
| Used to in the past, but stopped | 23 (48) | 25 (52) | |
| No response | 0 (0) | 1 (100) | |
| Missing | 0 (0) | 2 (100) | |
| Do you use inject drugs such as heroin, opium, others? | 0.002 | ||
| Every day | 4 (80) | 1 (20) | |
| >1/week but not every day | 11 (100) | 0 (0) | |
| Sometimes, <1/week | 12 (63) | 7 (37) | |
| Never | 116 (47) | 130 (53) | |
| Used in the past, but stopped | 23 (46) | 27 (54) | |
| No response | 4 (67) | 2 (33) | |
| Missing | 0 (0) | 3 (100) | |
| Have you ever been vaccinated against tuberculosis? | 0.280 | ||
| Yes | 54 (50) | 55 (50) | |
| No | 72 (55) | 59 (45) | |
| Do not know | 43 (44) | 54 (56) | |
| No response | 1 (100) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| Have you ever been diagnosed with diabetes? | 0.641 | ||
| Yes | 1 (33) | 2 (67) | |
| No | 144 (50) | 146 (50) | |
| Do not know | 25 (56) | 20 (44) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| Have you ever been diagnosed with increased lipids levels? | |||
| Yes | 5 (45) | 6 (55) | 0.933 |
| No | 132 (51) | 129 (49) | |
| Do not know | 32 (49) | 33 (51) | |
| No response | 1 (100) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| In the last 2 years, has anyone in your immediate family or circle of friends been diagnosed with TB? (Choose all that applies) | ~ | ||
| A household member | 13 (48) | 14 (52) | |
| A relative, living elsewhere | 16 (50) | 16 (50) | |
| A friend | 8 (42) | 11 (58) | |
| A colleague | 5 (71) | 2 (29) | |
| No one I know | 39 (49) | 40 (51) | |
| Do not know | 29 (45) | 35 (55) | |
| Do you do physical exercise? | 0.101 | ||
| Often | 37 (43) | 49 (57) | |
| Rarely | 87 (50) | 88 (50) | |
| Never | 46 (60) | 31 (40) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 0 (0) | 2 (100) | |
| Do you use public transport? | 0.335 | ||
| Everyday | 43 (55) | 35 (45) | |
| Occasionally | 75 (46) | 87 (54) | |
| Never | 52 (54) | 45 (46) | |
| No response | 0 (0) | 0 (0) | |
| Missing | 0 (0) | 3 (100) |
p-value excludes no response/missing values
Non-independent observations
Table 3 shows socioeconomic risk factors associated with being diagnosed with TB in the patient group who were matched on all three criteria. In univariate analysis, living in rural areas (p=0.064), place of origin (p=0.021), highest education level (p=0.002), occupation (p=0.021), wood/coal burning (p=0.024) were associated with having TB. Here it can be seen that when unadjusted, those who previously smoked were more likely to have TB compared to those who were current smokers (Odds ratio (OR) = 2.07, 95% confidence interval (CI) 1.02–4.21, p=0.045). We believe that these counter-intuitive results may indicate cause-and-effect relationship between smoking and TB. It is plausible that early symptoms of TB prior to diagnosis would lead to smoking cessation. To avoid misinterpretation, we excluded smoking from the multivariate model in both the main and sensitivity analyses.
Table 3.
Factors associated with the diagnosis of TB – matched analysis, n=262 (131 matched pairs)
| Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p-value | OR | 95% CI | p-value | |
| What is your current marital status? | 0.375 | |||||
| Married/cohabitating | 1 | |||||
| Never married/cohabitating | 1.58 | (0.82, 3.05) | 0.174 | |||
| Divorced/separated | 1.64 | (0.50, 5.31) | 0.412 | |||
| Widowed and not cohabitating | 2.23 | (0.55, 9.04) | 0.263 | |||
| No response/missing | 0.53 | (0.05, 5.56) | 0.594 | |||
|
| ||||||
| Do you live in a rural or urban area? | ||||||
| Rural | 1 | |||||
| Urban | 0.60 | (0.35, 1.03) | 0.064 | |||
| No response/missing | N/A | |||||
|
| ||||||
| What is your place of origin? | 0.021 | 0.043 | ||||
| Where I currently live | 1 | 1 | ||||
| Another province | 0.48 | (0.26, 0.89) | 0.019 | 0.53 | (0.27, 1.02) | 0.058 |
| Another country | 2.83 | (0.58, 13.92) | 0.200 | 3.27 | (0.65, 16.48) | 0.151 |
| No response/missing | N/A | N/A | ||||
|
| ||||||
| What is your highest education level? | ||||||
| Below university level | 5.50 | (1.90, 15.97) | 0.002 | 4.45 | (1.5, 13.17) | 0.007 |
| University level | 1 | 1 | ||||
| No response/missing | N/A | N/A | ||||
|
| ||||||
| Are you employed? | ||||||
| No | 1.59 | (0.91, 2.75) | 0.102 | |||
| Yes | 1 | |||||
| No response/missing | 1.26 | (0.17, 9.23) | 0.821 | |||
|
| ||||||
| What is your main occupation? | ||||||
| Civil servant or private employee | 1 | |||||
| Other occupation | 2.35 | (1.13, 4.85) | 0.021 | |||
| No response/missing | 2.42 | (1.18, 4.95) | 0.016 | |||
|
| ||||||
| How many persons live in your household? | 0.993 | |||||
| 1, myself | 1 | |||||
| 2-4 persons | 0.99 | (0.39, 2.50) | 0.978 | |||
| 5-7 persons | 1.04 | (0.36, 2.99) | 0.943 | |||
| >7 persons | 1.12 | (0.31, 4.08) | 0.862 | |||
| No response/missing | ||||||
|
| ||||||
| How many rooms (kitchen, living rooms, bedrooms) are there in your house/apartment? | 0.771 | |||||
| One | 1 | |||||
| Two | 0.93 | (0.31, 2.75) | 0.889 | |||
| Three | 1.43 | (0.50, 4.06) | 0.503 | |||
| Four | 1.01 | (0.35, 2.91) | 0.988 | |||
| Over four | 1.39 | (0.55, 3.52) | 0.489 | |||
| No response/missing | ||||||
|
| ||||||
| Do you have a house with a tiled or iron sheet roof? | 0.823 | |||||
| Tiled | 1 | |||||
| Iron sheet | 1.09 | (0.47, 2.56) | 0.836 | |||
| Other | 1.22 | (0.66, 2.25) | 0.532 | |||
| No response/missing | N/A | |||||
|
| ||||||
| What type of toilets do you use? | ||||||
| Flush toilet | 1 | |||||
| Latrine | 0.93 | (0.49, 1.78) | 0.826 | |||
| Other | N/A | |||||
| No response/missing | 0.49 | (0.04, 5.44) | 0.560 | |||
|
| ||||||
| Do you regularly burn wood or coal inside your house? | ||||||
| No | 0.37 | (0.15, 0.88) | 0.024 | 0.43 | (0.18, 1.07) | 0.070 |
| Yes | 1 | 1 | ||||
| No response/missing | ||||||
|
| ||||||
| What is the main source of drinking water you use at home? | 0.246 | |||||
| Piped water to your house/plot | 1 | |||||
| Well/bore water | 1.30 | (0.70, 2.42) | 0.405 | |||
| Mineral water | 0.65 | (0.31, 1.38) | 0.264 | |||
| Pond/river | 1.00 | (0.06, 15.99) | >0.999 | |||
| Other | 6.18 | (0.73, 52.34) | 0.095 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Do you eat proteins (meat, seafood, eggs, milk products, soya beans) | 0.437 | |||||
| Every day | 1 | |||||
| 2-3 times per week | 1.29 | (0.66, 2.51) | 0.455 | |||
| 1 time per week or less | 1.98 | (0.64, 6.07) | 0.235 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Smoking | 0.117 | |||||
| Currently smoke | 1 | |||||
| Previously smoked | 2.07 | (1.02, 4.21) | 0.045 | |||
| Never smoked | 1.38 | (0.59, 3.23) | 0.459 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Do you drink alcohol? | 0.285 | |||||
| Every day | 1 | |||||
| Occasionally | 0.37 | (0.11, 1.22) | 0.103 | |||
| Never | 0.44 | (0.13, 1.51) | 0.191 | |||
| Used to in the past, but stopped | 0.29 | (0.08, 1.04) | 0.057 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Do you use inject drugs such as heroin, opium, others? | 0.842 | |||||
| Every day | N/A | |||||
| >1/week but not every day | N/A | |||||
| Sometimes, <1/week | 1.42 | (0.44, 4.57) | 0.558 | |||
| Never | 1 | |||||
| Used in the past, but stopped | 1.05 | (0.43, 2.58) | 0.909 | |||
| No response/missing | 0.67 | (0.11, 3.99) | 0.657 | |||
|
| ||||||
| Have you ever been vaccinated against tuberculosis? | 0.287 | |||||
| Yes | 1 | |||||
| No | 1.19 | (0.62, 2.26) | 0.601 | |||
| Do not know | 0.71 | (0.34, 1.49) | 0.364 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Have you ever been diagnosed with diabetes? | ||||||
| Yes | N/A | |||||
| No | 1 | |||||
| Do not know | 1.17 | (0.54, 2.52) | 0.695 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Have you ever been diagnosed with increased lipids levels? | 0.788 | |||||
| Yes | 1 | |||||
| No | 1.08 | (0.21, 5.47) | 0.923 | |||
| Do not know | 0.85 | (0.16, 4.51) | 0.923 | |||
| No response/missing | 0.54 | (0.03, 9.8) | 0.923 | |||
|
| ||||||
| Do you regularly do physical exercise? | ||||||
| No | 1.50 | (0.83, 2.72) | 0.183 | |||
| Yes | 1 | |||||
| No response/missing | N/A | |||||
|
| ||||||
| Do you use public transport? | 0.456 | |||||
| Everyday | 1 | |||||
| Occasionally | 0.70 | (0.33, 1.47) | 0.346 | |||
| Never | 0.94 | (0.41, 2.14) | 0.881 | |||
| No response/missing | N/A | |||||
Global p-values are tests for heterogeneity excluding missing cases.
Smoking was not adjusted in the multivariate analysis.
The final multivariate model in Table 3 were adjusted for place of origin, education level, and wood burning. We have included place of origin and wood burning in the regression model due to its weak association with TB. Our matched analysis shows that patients who did not reach university level education were more likely to have TB (OR=4.45, 95% CI (1.50–13.17), p=0.007), compared to those who had attended university. Patients who moved from another province showed almost 50% odds reduction (OR=0.53, 95% CI (0.27–1.02), p=0.058) compared to patients who were originally from the same area. Those who did not burn wood or coal regularly inside the home were also less likely to be diagnosed with TB (OR=0.43, 95% CI (0.18–1.07), p=0.070), compared to those who did. No other factors were statistically significant.
We performed a sensitivity analysis (Table 4) by including all 340 patients using unmatched logistic regression. We have also included age and CD4 cell count in the regression analysis, as patients were not fully matched on these two criteria. Similar to the matched analysis, living in rural areas (p=0.065), place of origin (p=0.082), highest education level (p=0.001), employment (p=0.024), occupation (p=0.010), wood/coal burning (p=0.007), and smoking status were associated with having TB in the univariate analysis. In the multivariate regression, controlling for age and CD4, not having university level education was associated with TB (OR=2.11, 95% CI (1.24–3.61), p=0.006), and not being exposed to regular wood/coal burning at home significantly reduced the chance of being diagnosed with TB (OR=0.34, 95% CI (0.14–0.79), p=0.013).
Table 4.
Factors associated with the diagnosis of TB – unmatched analysis, n=340
| Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p-value | OR | 95% CI | p-value | |
| Age at enrolment | 0.947 | 0.910 | ||||
| ≤30 | 1 | 1 | ||||
| 31-40 | 1.07 | (0.66, 1.72) | 0.793 | 1.08 | (0.65, 1.81) | 0.759 |
| 41-50 | 1.05 | (0.55, 2.02) | 0.878 | 0.83 | (0.42, 1.65) | 0.597 |
| >50 | 1.40 | (0.46, 4.30) | 0.553 | 1.11 | (0.35, 3.52) | 0.858 |
|
| ||||||
| CD4 cell count at enrolment (cells/μL) | 0.380 | 0.624 | ||||
| ≤50 | 1 | 1 | ||||
| 51-100 | 0.69 | (0.38, 1.26) | 0.227 | 0.73 | (0.39, 1.37) | 0.333 |
| 101-200 | 0.81 | (0.40, 1.64) | 0.564 | 1.10 | (0.53, 2.32) | 0.793 |
| >200 | 0.62 | (0.33, 1.16) | 0.138 | 0.70 | (0.36, 1.38) | 0.307 |
|
| ||||||
| What is your current marital status? | 0.659 | |||||
| Married/cohabitating | 1 | |||||
| Never married/cohabitating | 1.22 | (0.77, 1.93) | 0.392 | |||
| Divorced/separated | 1.25 | (0.46, 3.41) | 0.656 | |||
| Widowed and not cohabitating | 1.67 | (0.65, 4.31) | 0.286 | |||
| No response/missing | 0.56 | (0.10, 3.13) | 0.507 | |||
|
| ||||||
| Do you live in a rural or urban area? | ||||||
| Rural | 1 | |||||
| Urban | 0.66 | (0.42, 1.03) | 0.065 | |||
| No response/missing | N/A | |||||
|
| ||||||
| What is your place of origin? | 0.082 | |||||
| Where I currently live | 1 | |||||
| Another province | 0.63 | (0.39, 1.01) | 0.053 | |||
| Another country | 1.75 | (0.51, 5.97) | 0.374 | |||
| No response/missing | N/A | |||||
|
| ||||||
| What is your highest education level? | ||||||
| Below university level | 2.39 | (1.44, 3.95) | 0.001 | 2.11 | (1.24, 3.61) | 0.006 |
| University level | 1 | 1 | ||||
| No response/missing | N/A | N/A | ||||
|
| ||||||
| Are you employed? | ||||||
| No | 1.78 | (1.08, 2.95) | 0.024 | |||
| Yes | 1 | |||||
| No response/missing | 1.15 | (0.16, 8.32) | 0.887 | |||
|
| ||||||
| What is your main occupation? | ||||||
| Civil servant or private employee | 1 | |||||
| Other occupation | 1.98 | (1.17, 3.33) | 0.010 | |||
| No response/missing | 2.21 | (1.24, 3.92) | 0.007 | |||
|
| ||||||
| How many persons live in your household? | 0.727 | |||||
| 1, myself | 1 | |||||
| 2-4 persons | 1.48 | (0.67, 3.27) | 0.333 | |||
| 5-7 persons | 1.45 | (0.63, 3.34) | 0.388 | |||
| >7 persons | 1.84 | (0.61, 5.57) | 0.28 | |||
| No response/missing | N/A | |||||
|
| ||||||
| How many rooms (kitchen, living rooms, bedrooms) are there in your house/apartment? | 0.866 | |||||
| One | 1 | |||||
| Two | 0.97 | (0.38, 2.46) | 0.948 | |||
| Three | 1.35 | (0.58, 3.11) | 0.483 | |||
| Four | 0.98 | (0.42, 2.28) | 0.967 | |||
| Over four | 1.07 | (0.48, 2.36) | 0.873 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Do you have a house with a tiled or iron sheet roof? | 0.471 | |||||
| Tiled | 1 | |||||
| Iron sheet | 1.15 | (0.60, 2.19) | 0.681 | |||
| Other | 1.36 | (0.83, 2.24) | 0.222 | |||
| No response/missing | 0.55 | (0.10, 3.06) | 0.493 | |||
|
| ||||||
| What type of toilets do you use? | ||||||
| Flush toilet | 1 | |||||
| Latrine | 1.09 | (0.70, 1.71) | 0.701 | |||
| Other | N/A | |||||
| No response/missing | 1.04 | (0.14, 7.50) | 0.971 | |||
|
| ||||||
| Do you regularly burn wood or coal inside your house? | ||||||
| No | 0.32 | (0.14, 0.73) | 0.007 | 0.34 | (0.14, 0.79) | 0.013 |
| Yes | 1 | 1 | ||||
| No response/missing | 0.17 | (0.01, 2.19) | 0.176 | N/A | ||
|
| ||||||
| What is the main source of drinking water you use at home? | 0.343 | |||||
| Piped water to your house/plot | 1 | |||||
| Well/bore water | 1.33 | (0.77, 2.31) | 0.301 | |||
| Mineral water | 0.75 | (0.45, 1.26) | 0.274 | |||
| Pond/river | 0.99 | (0.14, 7.19) | 0.989 | |||
| Other | 1.97 | (0.48, 8.19) | 0.349 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Do you eat proteins (meat, seafood, eggs, milk products, soya beans) | 0.440 | |||||
| Every day | 1 | |||||
| 2-3 times per week | 1.38 | (0.81, 2.33) | 0.231 | |||
| 1 time per week or less | 1.34 | (0.51, 3.52) | 0.547 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Smoking | 0.049 | |||||
| Currently smoke | 1 | |||||
| Previously smoked | 1.90 | (1.13, 3.19) | 0.015 | |||
| Never smoked | 1.32 | (0.76, 2.29) | 0.316 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Do you drink alcohol? | 0.593 | |||||
| Every day | 1 | |||||
| Occasionally | 0.57 | (0.24, 1.31) | 0.185 | |||
| Never | 0.60 | (0.25, 1.42) | 0.244 | |||
| Used to in the past, but stopped | 0.54 | (0.21, 1.42) | 0.212 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Do you use inject drugs such as heroin, opium, others? | 0.314 | |||||
| Every day | 1 | |||||
| >1/week but not every day | N/A | |||||
| Sometimes, <1/week | 0.43 | (0.04, 4.64) | 0.486 | |||
| Never | 0.22 | (0.02, 2.02) | 0.182 | |||
| Used in the past, but stopped | 0.21 | (0.02, 2.04) | 0.180 | |||
| No response/missing | 0.20 | (0.02, 2.58) | 0.217 | |||
|
| ||||||
| Have you ever been vaccinated against tuberculosis? | 0.282 | |||||
| Yes | 1 | |||||
| No | 1.24 | (0.75, 2.07) | 0.403 | |||
| Do not know | 0.81 | (0.47, 1.40) | 0.455 | |||
| No response/missing | 0.51 | (0.04, 5.78) | 0.586 | |||
|
| ||||||
| Have you ever been diagnosed with diabetes? | 0.646 | |||||
| Yes | 1 | |||||
| No | 1.97 | (0.18, 22.00) | 0.581 | |||
| Do not know | 2.50 | (0.21, 29.60) | 0.467 | |||
| No response/missing | N/A | |||||
|
| ||||||
| Have you ever been diagnosed with increased lipids levels? | 0.934 | |||||
| Yes | 1 | |||||
| No | 1.23 | (0.37, 4.12) | 0.740 | |||
| Do not know | 1.16 | (0.32, 4.20) | 0.817 | |||
| No response/missing | 0.60 | (0.04, 8.73) | 0.708 | |||
|
| ||||||
| Do you regularly do physical exercise? | ||||||
| No | 1.48 | (0.90, 2.42) | 0.119 | |||
| Yes | 1 | |||||
| No response/missing | N/A | |||||
|
| ||||||
| Do you use public transport? | 0.336 | |||||
| Everyday | 1 | |||||
| Occasionally | 0.70 | (0.41, 1.21) | 0.201 | |||
| Never | 0.94 | (0.52, 1.71) | 0.841 | |||
| No response/missing | N/A | |||||
Global p-values are tests for heterogeneity excluding missing cases.
Smoking was not adjusted in the multivariate analysis.
DISCUSSION
In summary, the study recruited a total of 340 patients with 170 matched case-control pairs. Of these, 77% were successfully matched on all criteria. Among the TB cases, the majority had pulmonary TB. A set of questionnaire was distributed to each participant, with 99% completion rate. The majority were married, living in urban areas and had full-time employment. In the matched pair analysis, having education below university level significantly increased the chance of having TB, while place of origin and wood/coal burning showed weak associations. In the un-matched analysis, education level and wood/coal burning were significant risk factors for TB.
Socio-economic factors have continued to contribute to TB incidences in both resource-rich and resource-poor settings. In resource-rich settings, social-economic characteristics of TB can differ between those who are migrants and those who are native to the country (14). In Asia, social-determinants of TB often include unemployment, co-habitation with TB infection, poverty, and other risk factors known to be associated with TB (15–18). The results from our main and sensitivity analyses indicate that education level was an important socio-economic risk factor for TB. The majority of our patients were high school educated but did not complete university. Approximately 10% were primary school leavers. The association of low education status and TB has been documented in many settings (19, 20). It was estimated that one third of the population in South-east Asia lived below the poverty line. Families living in poverty usually prefer their children to work for an income rather than attending school (21). Education is not often seen as an urgent priority for these families. In India, it has been reported that the main reason for dropping out of school for boys and girls was due to financial reasons (22). The lack of proper education, in turn, can lead to further poverty. For HIV-infected patients, the prolonged duration of the disease and the associated stigma can lead to job losses and the need to sell family assets (23). Without good education background, HIV-infected patients in resource-limited settings may become stuck in the vicious poverty cycle, which could subsequently increase the risk of contracting TB.
Using biomass fuels (wood, charcoal, etc.) in open fires or stoves exposes the household occupants to extremely high levels of indoor air pollution which could lead to a range of illnesses including stroke, chronic obstructive pulmonary disease and lung cancer (24). There is evidence from various studies suggesting that indoor air pollution, caused by usage of biomass fuels, can increase the risk of TB (25, 26). This increased risk was also evident in our study. Furthermore, our study also shows that there was a weak association between patients who were currently living in their place of origin and TB, compared to patients who have moved from another province. It is known that migrants from high TB-burden countries are at increased risk of TB (27). However, we suspect that the HIV-positive patients in our study group who internally migrated from another province, may have been in better physical health compared to those who have stayed or returned home (28), and therefore were less susceptible to contracting TB.
Smoking is another risk factor widely known to be associated with TB and can ultimately worsen the cure rates compared to non-smokers (29, 30). A case-control study investigating the effects of smoking on TB in HIV-infected patients in South Africa reported increased risks of acquiring TB in current and former smokers compared to those who had never smoked (31). Our unadjusted analysis, however, found that previous smokers who had ceased smoking were more likely to have TB compared to current smokers. We believe this could be explained by the perceived health-risks and the motivation to quit. Patients who experience a smoking-related illness may have a greater risk perception that increases the awareness of the harmful nature and detrimental effects of smoking on health (32). A intervention study on smoking cessation in rural China reported that more than half of current smokers with TB reported abstinence in cigarette smoking during the study(33). Therefore, the effects of smoking in our study should be interpreted with this in mind.
Over 50% of our patients were enrolled with a pre-ART CD4 cell count of ≤50 cells/μL. This indicates that HIV-infected patients in Asia continue to be diagnosed and/or present at advanced stages of HIV disease, despite WHO recommendations for earlier ART initiation. We expect the proportion of patients initiating ART with low CD4 cell count to decrease over time as countries adopt HIV “test and treat” strategies(34). However, more resources should be allocated towards improved HIV case finding and prompt initiation of HIV treatment.
The limitations of the study include the non-strict nature of the matching of the cases and controls. There was unexpected difficulty in obtaining HIV-positive-TB-negative controls with low CD4 cell counts across participating sites. We therefore broadened our search criteria by allowing matching in any of the age, sex and CD4 count category. Another limitation was that we were not able to adjust for other clinical characteristics of the patients, other than age and CD4 cell count in the unmatched analysis. The aim of the protocol was to specifically assess the socio-economic determinants of TB, and therefore other HIV-related measurements were not collected. Lastly, the heterogeneity of our study sites may lead to unobserved confounding. As our patients were matched within the same site, we believe this confounding would be minimised.
CONCLUSIONS
Our results are broadly consistent with the increased risk of TB in lower socio-economic background. Patients in this group should be closely monitored for early diagnosis and treatment. The interconnected relationship between HIV infection, TB co-infection and socio-economic factors suggests that the integration of socio-economic parameters into the management of HIV infection is crucial in optimising treatment outcomes and prolonging survival.
Acknowledgments
This study is an initiative of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the U.S. National Institutes of Health’s National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, and the National Cancer Institute, as part of the International Epidemiology Databases to Evaluate AIDS (IeDEA; U01AI069907). The Kirby Institute is funded by the Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, UNSW Sydney. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the governments or institutions mentioned above.
Footnotes
CONFLICTS OF INTEREST
There are no conflicts of interest.
AUTHORS CONTRIBUTIONS
AJ performed data collection, data management, statistical analysis, and drafting of the manuscript. ML initiated the study, conceived analysis ideas, and reviewed the manuscript. MPL, KVN, TPM, DDC, RD, EY, SP, FZ, SK, AV, OTN, BLHS, and WW provided data and reviewed the manuscript. JR provided project management and reviewed the manuscript. All authors have reviewed and approved the final version of the manuscript.
TB socio-economic determinants study members:
FJ Zhang, HX Zhao and N Han, Beijing Ditan Hospital, Capital Medical University, Beijing, China;
MP Lee, PCK Li, W Lam and YT Chan, Queen Elizabeth Hospital, Hong Kong, China;
TP Merati, DN Wirawan, F Yuliana and Y Gayatri, Faculty of Medicine Udayana University & Sanglah Hospital, Bali, Indonesia;
E Yunihastuti, D Imran, A Widhani, Wulunggono, and D Prahajna, Faculty of Medicine Universitas Indonesia – Dr. Cipto Mangunkusumo General Hospital, Jakarta, Indonesia;
A Kamarulzaman, SF Syed Omar, S Ponnampalavanar, I Azwa, A Kajindran, N Huda Ab Rahman, and A Raja Izam, Department of Medicine, University of Malaya, Kuala Lumpur, Malaysia;
BLH Sim, YM Gani, R David, A P Radhakrishnan, and H Chang, Hospital Sungai Buloh, Sungai Buloh, Malaysia;
R Ditangco, E Uy and R Bantique, Research Institute for Tropical Medicine, Muntinlupa City, Philippines;
OT Ng, PL Lim, LS Lee, PS Ohnmar, MT Tan and NA Zainuldin, Tan Tock Seng Hospital, Singapore;
WW Wong, WW Ku and PC Wu, Taipei Veterans General Hospital, Taipei, Taiwan;
S Kiertiburanakul, S Sungkanuparph, L Chumla and N Sanmeema, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand;
A Avihingsanon, S Gatechompol, P Phanuphak, C Phadungphon, P Thongpaeng, and Jaravee Jamthong, HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand;
KV Nguyen, HV Bui, DTH Nguyen, DT Nguyen, MA Ha, and PT Dang, National Hospital for Tropical Diseases, Hanoi, Vietnam;
DD Cuong, NV An, NT Luan, TT Pham, and GH Nguyen, Bach Mai Hospital, Hanoi, Vietnam;
AH Sohn, JL Ross, N Durier and B Petersen, TREAT Asia, amfAR – The Foundation for AIDS Research, Bangkok, Thailand;
DA Cooper, MG Law, A Jiamsakul, DC Boettiger, and S Wright, The Kirby Institute, UNSW Sydney, Australia.
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