ABSTRACT.
Tuberculosis (TB) is a major cause of illness and public health concern, especially in resource-limited countries. This study analyzed the characteristics related to anti-TB drug resistance. Moreover, we examined the evidence-based indications for the treatment of active TB in Angola. This study evaluated the medical records of 176 patients screened for TB from January to September 2016 in Luanda, the capital city of Angola. Approximately 66.5% of the patients were newly diagnosed with active TB. The residence area showed a significant relationship with TB (P = 0.025), whereas age group (P = 0.272), gender (P = 0.853), and HIV status (P = 0.284) did not showed any relationship with TB. Overall, 72.4% of TB patients had resistance to at least one of the anti-TB drugs. The risk of anti-TB drug resistance was higher in males (odds ratio [OR]: 1.22; 95% confidence interval [CI]: 0.42–3.58, P = 0.685] and in TB-HIV coinfected patients [OR: 1.39; (95% CI: 0.26–7.28), P = 0.700], whereas it was lower in patients aged 30 years or older (OR: 0.56; 95% CI: 0.18–1.69) P = 0.303) and in patients living in urbanized areas (OR: 0.74; 95% CI: 0.17–3.25; P = 0.685). Our findings showed that drug-resistant TB is emerging in Angola. Further studies on factors related to anti-TB drug resistance are urgently needed to ascertain the magnitude of the problem and to proffer strategies toward TB control in Angola.
INTRODUCTION
Tuberculosis (TB) is a communicable disease caused by the bacillus Mycobacterium tuberculosis (Mtb), which is a major cause of health problems and death by a single infectious agent.1 Despite the advances made in recent decades, TB remains a major public health concern in many regions of the world, mainly in low- and middle-income countries (LMICs).2 In 2019, approximately 10 million cases of TB were reported, and there were more than a million tuberculosis-related deaths worldwide.3 Today, TB remains one of the most deadly infectious diseases, with an estimated 1.8 million deaths occurring per year, mainly in the LMICs.3,4
The use of isoniazid, rifampicin, ethambutol, and streptomycin antibiotics has helped control Mtb infection and reduce the death rate.5 However, the presence of resistant strains of Mtb limits the number of compounds available for treatment and threatens the global goals to reduce the TB incidence by 90% by 2035.4–7 In 2017, it was estimated that 558,000 people developed rifampin-resistant TB, and of these, 82% had multidrug resistance (MDR) or resistance to isoniazid and rifampicin.3 Previous studies showed that the acquisition or emergence of MDR-TB can occur from low adherence to the anti-TB drug, previous treatment against TB, and coinfection with HIV.8
Angola is one of the 30 countries with a high incidence of tuberculosis.3,9 In 2019, a survey carried out in Angola estimated that the prevalence of MDR in Angola was higher than that estimated by the WHO.10 To date, controlling TB in Angola remains a major public health challenge. However, understanding the sociodemographic factors of patients infected with TB could help define control strategies and immediate intervention in the main risk groups and reduce the rate of spread of the disease.11 Due to the lack of published studies on sociodemographic factors that affect the spread of the disease, it is difficult to make a reliable estimate of the TB burden in different communities in Angola. In this study, we evaluated the epidemiological characteristics, TB-related risk factors, and TB drug resistance in Luanda, the capital city of Angola, to help the policymakers develop strategies to strengthen MDR-TB control measures.
MATERIALS AND METHODS
Study design and setting.
This was a retrospective cohort study carried out on the medical records of patients screened for TB in public or private health units of reference for monitoring and treatment of respiratory diseases in Luanda, from January to September 2016. The study was carried out at the Instituto Nacional de Investigação em Saúde (INIS), located in Luanda, Angola. TB patients’ medical records were collected from all patients, regardless of age or gender, using a specific questionnaire designed for this study. The data collected included the sociodemographic characteristics (age, gender, and area of residence) and risk factors for TB infection, such as the presence of HIV. Patients who did not have sociodemographic characteristics and/or clinical data in the medical records were excluded from the study. The study protocol was reviewed and approved by the National Ethics Committee of Angola (approval no. 11/2021). Individual informed consent was not required because the study was a retrospective study of medical records. However, the data were anonymized and used for analysis.
Sample collection and laboratory testing.
Data included in the study were generated in a laboratory at biosafety level 3 at the INIS. The patients were submitted to the Mtb identification test and antibiotic sensitivity test (AST). The specimens were stained for Ziehl-Neelsen,12 and susceptibility to first-line drugs containing streptomycin, isoniazid, rifampicin, and ethambutol was carried out using the automatic BACTEC MGIT 960 system (Becton Dickinson, Franklin Lakes, NJ). The susceptibility test was performed in liquid culture with a contamination rate of ∼7.6%. However, the contamination problem was corrected by repeating the process. On the other hand, if contamination prevailed, the research team requested a new sample collection. Individuals were classified as nonresistant when they were susceptible to all drugs. Resistant patients were classified as monoresistant (when they were resistant to at least one of the drugs), polyresistant (when they were resistant to more than one drug), and MDR-TB (when they were at least resistant to isoniazid and rifampicin).
Statistical analysis.
The data obtained in this study were analyzed using SPSS v25 (IBM SPSS Statistics, Armonk, NY). Absolute and relative frequencies were determined. The χ2 and logistic regression tests were used to assess the relationship between categorical variables. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess the strength and direction of the relationship. All reported P value were two‐tailed and deemed significant at P < 0.05.
RESULTS
Sociodemographic characteristics and tuberculosis prevalence.
The epidemiological characteristics and risk factors related to Mtb infection were presented in Table 1. This study included a total of 176 medical records from patients screening for MTB. All the samples were obtained from new TB patients. Patients’ age ranged from 8 to 68 years. The average age was 34.4 ± 12.0 years. Patients between 30 and 39 years old (34.7%, 61/176), male (65.3%, 115/176), living in nonurbanized areas (92.6%, 163/176), and patients without HIV coinfection (89.8%, 158/176) were the most frequent in this studied population. Approximately 66.5% (117/176) tested positive for Mtb in the sputum smear samples. TB prevalence was most predominant in the 20- to 29-year age group (72.9%), followed by the 30- to 39-years age group (72.1%). Moreover, male patients (67%), patients from nonurbanized areas (68.7%) and patients coinfected with HIV (77.8%) also had high TB prevalence. A significant relationship was observed between TB prevalence and residence area (P = 0.025), whereas age group (P = 0.272), gender (P = 0.853), and HIV status (P = 0.284) did not show any relationship with TB prevalence. Although no statistical significance was found in univariate analysis, high odds related to TB were observed in the following age groups: 20–29 years (OR: 2.09; 95% CI: 0.65–6.78; P = 0.218), 30–39 years (OR: 2.01; 95% CI: 0.65–6.27; P = 0.227), and 40–49 (OR: 1.24; 95% CI: 0.35–4.41; P = 0.735), whereas low odds were observed in patients aged 50 years or older (OR: 0.84; 95% CI: 0.24–2.98; P = 0.790), compared with patients younger than 20 years. In addition, a slight nonsignificant increase related to TB was also observed in males (OR: 1.06; 95% CI: 0.55–2.05; P = 0.853) and patients coinfected with HIV (OR: 1.87; 95% CI: 0.59–5.95; P = 0.290). On the other hand, odds were significantly lower in urbanized areas (OR: 0.29; 95% CI: 0.09–0.91; P = 0.035) compared with nonurbanized areas. The multivariate analysis also showed high odds in patients between ages 20 and 49 years in males and patients coinfected with HIV infection, whereas low odds were observed in patients from urbanized areas.
Table 1.
Epidemiological characteristics and risk factors related to Mycobacterium tuberculosis infection in Luanda, Angola
| Tuberculosis prevalence | Univariate analysis | Multivariate analysis# | ||||||
|---|---|---|---|---|---|---|---|---|
| Patient characteristics | n (%) | Neg (%) | Pos (%) | P value* | OR (95% CI) | P value | aOR (95% CI) | P value |
| Overall | 176 (100) | 59 (33.5) | 117 (66.5) | |||||
| Age group, years | ||||||||
| < 20 | 16 (9.1) | 7 (43.8) | 9 (56.3) | 0.272 | 1.00 | – | 1.00 | – |
| 20–29 | 48 (27.3) | 13 (27.1) | 35 (72.9) | 2.09 (0.65–6.78) | 0.218 | 2.45 (0.73–8.19) | 0.147 | |
| 30–39 | 61 (34.7) | 17 (17.9) | 44 (72.1) | 2.01 (0.65–6.27) | 0.227 | 1.90 (0.60–6.07) | 0.279 | |
| 40–49 | 26 (14.8) | 10 (38.5) | 16 (61.5) | 1.24 (0.35–4.41) | 0.735 | 1.18 (0.33–4.24) | 0.801 | |
| ≥ 50 | 25 (14.2) | 12 (48.0) | 13 (52.0) | 0.84 (0.24–2.98) | 0.790 | 0.95 (0.26–3.46) | 0.932 | |
| Gender | ||||||||
| Female | 61 (34.7) | 21 (34.4) | 40 (65.6) | 0.853 | 1.00 | – | 1.00 | – |
| Male | 115 (65.3) | 38 (33.0) | 77 (67.0) | 1.06 (0.55–2.05) | 0.853 | 1.09 (0.55–2.17) | 0.811 | |
| Residence area | ||||||||
| Rural | 163 (92.6) | 51 (31.3) | 112 (68.7) | 0.026 | 1.00 | – | 1.00 | – |
| Urban | 13 (7.4) | 8 (61.5) | 5 (38.5) | 0.29 (0.09–0.91) | 0.035 | 0.20 (0.06–0.74) | 0.015 | |
| HIV infection | ||||||||
| No | 158 (89.8) | 55 (34.8) | 103 (65.2) | 0.284 | 1.00 | – | 1.00 | – |
| Yes | 18 (10.2) | 4 (22.2) | 14 (77.8) | 1.87 (0.59–5.95) | 0.290 | 2.66 (0.70–10.2) | 0.153 | |
aOR = adjusted odds ratio; CI = confidence interval; OR = odds ratio; Neg = negative; Pos = positive. Bold numbers indicate statistical significance (P < 0.05).
Chi-square test.
# Adjusted for all the independent variables listed
Characteristics related to tuberculosis resistance.
The putative characteristics related to TB drug resistance are summarized in Table 2. Drug susceptibility test (DST) results were available on isolates from 76 of 176 TB patients enrolled in the study. From these, ∼72.4% (55/76) presented resistance to the anti-TB drug. No relationship was observed between age group, gender, residence area, and HIV status with TB-resistant prevalence (P > 0.05). The TB resistance among patients younger than 30 years was 82.4%, whereas among patients aged 30 years or older was 64.3%. The TB resistance was high in males (73.6%) compared with females (69.6%). Patients from nonurbanized areas presented 73.1% of TB resistance, whereas patients from urbanized areas presented 66.7%. We found high odds related to TB resistance in male patients (OR: 1.22; 95% CI: 0.42–3.58; P = 0.719) and patients with HIV (OR: 1.39; 95% CI: 0.26–7.28; P = 0.700). Patients aged 30 years or older (OR: 0.56; 95% CI: 0.18–1.69; P = 0.303) and patients from urbanized areas (OR: 0.74; 95% CI: 0.17–3.25; P = 0.685) had low odds related to TB resistance.
Table 2.
Epidemiological characteristics related to drug-resistance among tuberculosis patients in Luanda, Angola
| Patient characteristics | n (%) | Tuberculosis resistance prevalence | Univariate analysis | |||
|---|---|---|---|---|---|---|
| No (%) | Yes (%) | P value* | OR (95% CI) | P value | ||
| Overall | 76 (100) | 21 (27.6) | 55 (72.4) | |||
| Age group, years | ||||||
| < 30 | 34 (44.7) | 6 (17.6) | 28 (82.4) | 0.080 | 1.00 | – |
| ≥ 30 | 42 (55.3) | 15 (35.7) | 27 (64.3) | 0.56 (0.18–1.69) | 0.303 | |
| Gender | ||||||
| Female | 23 (30.3) | 7 (30.4) | 16 (69.6) | 0.719 | 1.00 | – |
| Male | 53 (69.7) | 14 (26.4) | 39 (73.6) | 1.22 (0.42–3.58) | 0.719 | |
| Residence area | ||||||
| Rural | 67 (88.2) | 18 (26.9) | 49 (73.1) | 0.684 | 1.00 | – |
| Urban | 9 (11.8) | 3 (33.3) | 6 (66.7) | 0.74 (0.17–3.25) | 0.685 | |
| HIV infection | ||||||
| No | 67 (88.2) | 1 (1.5) | 66 (98.5) | 0.712 | 1.00 | – |
| Yes | 9 (11.8) | 0 (0.0) | 9 (100) | 1.39 (0.26–7.28) | 0.700 | |
CI = confidence interval; OR = odds ratio.
Chi-square test.
Characteristics related to drug susceptibility.
TB drug susceptibility among patients enrolled in this study is presented in Table 3. Only 21 patients of 76 who underwent the DST showed sensitivity for all anti-TB drugs used to treat TB as described by the WHO. Approximately 75% of patients (57/76) presented monoresistance, 52.6% (40/76) presented polyresistance, and 50% (38/76) were MDR-TB. Overall, monoresistance to any of the two major first-line drugs such as isoniazid and rifampicin was identified in 65.8% and 55.3%, respectively. A statistically significant relationship was observed between the age group and MDR-TB (P = 0.021), whereas monoresistance (P = 0.062) and polyresistance (P = 0.151) did not show significant relationships with the age group. Age group was also statistically related to the monoresistance to the rifampicin (55.3%, P = 0.016), polyresistance to the rifampicin + ethambutol (31.6%, P = 0.034), and the MDR-TB to the rifampicin + isoniazid (50.0%, P = 0.021). On the other hand, no significant relationship was observed between drug susceptibility and gender, residence area, or HIV infection (P > 0.05).
Table 3.
Drug resistance patterns of Mycobacterium tuberculosis isolates from individuals infected with untreated tuberculosis in Luanda, Angola (N = 76)
| Drug susceptibility | n (%) | Age group (years) | Gender | Residence area | HIV infection | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| < 30 | ≥ 30 | P value* | Female | Male | P value* | Rural | Urban | P value* | No | Yes | P value* | ||
| Susceptible | 21 (27.6) | 6 (28.6) | 15 (71.4) | 0.080 | 7 (33.3) | 14 (66.7) | 0.719 | 18 (85.7) | 3 (14.3) | 0.684 | 19 (90.5) | 2 (9.5) | 0.699 |
| Monoresistance | 57 (75.0) | 22 (38.6) | 35 (61.4) | 0.062 | 16 (28.1) | 41 (71.9) | 0.471 | 49 (86.0) | 8 (14.0) | 0.305 | 49 (86.0) | 8 (14.0) | 0.305 |
| INH | 50 (65.8) | 26 (52.0) | 24 (48.0) | 0.077 | 14 (28.0) | 36 (72.0) | 0.551 | 46 (92.0) | 4 (8.0) | 0.151 | 43 (86.0) | 7 (14.0) | 0.419 |
| RMP | 42 (55.3) | 24 (57.1) | 18 (42.9) | 0.016 | 13 (31.0) | 29 (69.0) | 0.884 | 37 (88.1) | 5 (11.9) | 0.985 | 37 (88.1) | 5 (11.9) | 0.985 |
| EMB | 32 (42.1) | 17 (53.1) | 15 (46.9) | 0.210 | 11 (34.4) | 21 (65.6) | 0.506 | 30 (93.8) | 2 (6.3) | 0.198 | 29 (90.6) | 3 (9.4) | 0.570 |
| SM | 29 (38.2) | 16 (55.2) | 13 (44.8) | 0.151 | 11 (37.9) | 18 (62.1) | 0.253 | 25 (86.2) | 4 (13.8) | 0.679 | 26 (89.7) | 3 (10.3) | 0.751 |
| Polyresistance | 40 (52.6) | 21 (52.5) | 19 (47.5) | 0.151 | 14 (35.0) | 26 (65.0) | 0.343 | 36 (90.0) | 4 (10.0) | 0.600 | 35 (87.5) | 5 (12.5) | 0.852 |
| EMB+SM | 20 (26.3) | 12 (60.0) | 8 (40.0) | 0.110 | 8 (40.0) | 12 (60.0) | 0.269 | 19 (95.0) | 1 (5.0) | 0.270 | 19 (95.0) | 1 (5.0) | 0.270 |
| INH+SM | 29 (38.2) | 16 (55.2) | 13 (44.8) | 0.151 | 11 (37.9) | 18 (62.1) | 0.253 | 25 (86.2) | 4 (13.8) | 0.679 | 26 (89.7) | 3 (10.3) | 0.751 |
| INH+EMB | 30 (39.5) | 16 (53.3) | 14 (46.7) | 0.224 | 11 (36.7) | 19 (63.3) | 0.326 | 29 (96.7) | 1 (3.3) | 0.064 | 27 (90.0) | 3 (10.0) | 0.688 |
| INH+EMB+SM | 20 (26.3) | 12 (60.0) | 8 (40.0) | 0.110 | 8 (40.0) | 12 (60.0) | 0.269 | 19 (95.0) | 1 (5.0) | 0.270 | 19 (95.0) | 1 (5.0) | 0.270 |
| RMP+SM | 28 (36.8) | 16 (57.1) | 12 (42.9) | 0.097 | 10 (35.7) | 18 (64.3) | 0.429 | 24 (85.7) | 4 (14.3) | 0.615 | 25 (89.3) | 3 (10.7) | 0.816 |
| RMP+EMB | 24 (31.6) | 15 (62.5) | 9 (37.5) | 0.034 | 8 (33.3) | 16 (66.7) | 0.692 | 23 (95.8) | 1 (4.2) | 0.159 | 22 (91.7) | 2 (8.3) | 0.520 |
| RMP+EMB+SM | 19 (25.0) | 12 (63.2) | 7 (36.8) | 0.062 | 7 (36.8) | 12 (63.2) | 0.471 | 18 (94.7) | 1 (5.3) | 0.305 | 18 (94.7) | 1 (5.3) | 0.305 |
| MDR-TB | 38 (50.0) | 22 (57.9) | 16 (42.1) | 0.021 | 11 (28.9) | 27 (71.1) | 0.803 | 34 (89.5) | 4 (10.5) | 0.723 | 33 (86.8) | 5 (13.2) | 0.723 |
| RMP+INH | 38 (50.0) | 22 (57.9) | 16 (42.1) | 0.021 | 11 (28.9) | 27 (71.1) | 0.803 | 34 (89.5) | 4 (10.5) | 0.723 | 33 (86.8) | 5 (13.2) | 0.723 |
| RMP+INH+EMB | 23 (30.3) | 14 (60.9) | 9 (39.1) | 0.062 | 8 (34.8) | 15 (65.2) | 0.572 | 22 (95.7) | 1 (4.3) | 0.183 | 21 (91.3) | 2 (8.7) | 0.576 |
| RMP+INH+SM | 28 (36.8) | 16 (57.1) | 12 (42.9) | 0.097 | 10 (35.7) | 18 (64.3) | 0.429 | 24 (85.7) | 4 (14.3) | 0.615 | 25 (89.3) | 3 (10.7) | 0.816 |
| RMP+INH+EMB+SM | 19 (25.0) | 12 (63.2) | 7 (36.8) | 0.062 | 7 (36.8) | 12 (63.2) | 0.471 | 18 (94.7) | 1 (5.3) | 0.305 | 18 (94.7) | 1 (5.3) | 0.305 |
INH = isoniazid; RMP = rifampicin; EMB = ethambutol; SM = streptomycin. Bold numbers indicate statistical significance (P < 0.05).
Chi-square test.
DISCUSSION
TB remains an important global public health problem, especially in LMICs.3 The spread and worsening of the disease have been boosted in recent decades mainly due to sociodemographic conditions such as poverty and the rapid spread of HIV infection. According to the national statistical report of 2014, Luanda is the epicenter of TB in Angola.13 Data on sociodemographic characteristics related to the emergence and spread of Mtb, as well as MDR-TB, are unreported in Luanda. However, continuous monitoring of this disease is fundamental to the definition of future strategies to reinforce ongoing measures on TB control. In this study, we detected ∼66.5% of cases for Mtb, which is a high TB prevalence compared with the results described in a study carried out in Nigeria where only 7.1% of the studied population tested positive for Mtb.14 The TB prevalence in this study was higher (> 70%) in the 20- to 40-year age group (Table 1), which is similar to the results reported in patients from Nigeria, where the majority (67.9%) of the patients were in the 20- to 49-year age group.14 In contrast, studies in developed countries where individuals aged 60 years or older were involved reported that this group of TB patients is usually the most affected by Mtb.15 TB has a great impact on the working-age population, which corresponds to the largest workforce; however, it is worth mentioning that this factor constitutes a huge burden for the loss of economic productivity, as well as for the worsening of the socioeconomic conditions in resource-limited countries such as Angola. Thus, to control TB, the National TB programs (NTPs) in Angola, as well as in neighboring countries, should make efforts to reduce the impact of sociodemographic factors related to the emergence of new cases of infection to decrease the number of cases and costs related to treatments in Africa.
Previous studies have reported that the vulnerability to TB is influenced mainly by biological factors such as malnutrition, HIV infection, age, or social factors such as unhealthy housing, high population density, inappropriate working conditions, and lack of access to health services.16,17 Here, we also showed that sociodemographic conditions such as age, gender, place of residence, and HIV status are important factors that could promote the dissemination of Mtb in the Angolan population (Table 1). Furthermore, our results show that age 20 years and older, infection with HIV, and living in nonurbanized areas could be independent factors for contracting Mtb infection, which is in agreement with previous studies.17 Also, a previous study showed that Luanda province is characterized by a large part of the population living in regions with high population density, low health coverage, and poor access to health services, which could be indicators associated with a higher risk of TB infection and resistance to anti-TB.13 A significant association between place of residence with Mtb infection (P = 0.026) was observed in our study. Also, we observed that individuals from urbanized areas (OR: 0.29; P = 0.035) have a low chance of contracting TB infection and spreading of infection, which could indicate that measures to contain Mtb infection should be implemented mainly in the nonurbanized areas (Table 1). However, it is worth emphasizing that although our study demonstrates a strong association between TB prevalence with rural areas, these data must be analyzed carefully because Luanda does not have a useful epidemiological surveillance system, so there is a likelihood of underreporting cases of the disease in rural or urbanized areas.
Our results show that 72.4% of newly diagnosed patients with Mtb were resistant to at least one anti-TB drug (Table 2). The fact that we observed high resistance in naive patients indicates that more vigilance and training of the medical staff responsible for TB control is needed in Luanda. The anti-TB drug resistance rate observed in our study was higher than that observed in other African countries, such as Cameroon (18%),18 Mozambique (15.9%),19 Benin (12%),20 and Ethiopia (15.58%).21 Previous studies have reported that high rates of resistance to anti-TB drugs are seen in TB patients who have had preexposure to treatment;22 however, our study showed a high rate of resistance to anti-TB drugs, although patients were newly diagnosed with the disease. It is noteworthy that only 76 of 117 newly diagnosed patients with Mtb underwent a DST, indicating that guidelines for TB treatment in Angola should be revised to consider the possibility of including DST in all TB patients before undergoing treatment. Patients’ age, gender, place of residence, and HIV status were not significantly associated with the occurrence of resistance to anti-TB (P > 0.05). Despite this, we observed a high likelihood of presenting resistance in males (OR: 1.22; 95% CI: 0.42–3.58; P = 0.719) and in patients with HIV infection (OR: 1.39; 95% CI: 0.26–7.28; P = 0.700), whereas age 30 years or older (OR: 0.56; 95% CI: 0.18–1.69; P = 0.303) and residence in urbanized areas (OR: 0.74; 95% CI: 0.17–3.25; P = 0.685) were possible protective factors and presented low likelihood of resistance to anti-TB drugs (Table 2). Unlike our results, a study conducted in Serbia showed that the risk of presenting resistance to anti-TB is higher in patients up to 30 years old (OR: 3.76; 95% CI: 2.01–7.04) and lower in males (OR: 0.61; 95% CI: 0.34–1.10), compared with the group older than 30 years or female patients, respectively.23 Furthermore, another study conducted in Georgia showed a high probability for the emergence of resistance to anti-TB drugs in female patients (OR: 1.60), compared with male patients.24
The TB resistance rate in Luanda is worrisome not only because it is remarkably high (72.4%) (Table 2) but also because it is likely to be underestimated. However, due to the limited number of samples analyzed, our results might not represent the real situation of anti-TB resistance in Luanda or other regions of Angola. Even so, the presence of high resistance in naive patients is a sensitive indicator to assess the performance of the NTPs because the increase in primary resistance to drugs could indicate a high risk of spreading resistant strains in the community. In our study, the rate of resistance to any anti-TB drug ranged between 38.2% and 65.8%, whereas polyresistance and MDR-TB ranged between 25% and 39.5% and 25% and 50%, respectively. Our results are high compared with those obtained in a study carried out in Brazil, where the resistance rate to any anti-TB drug was 10.6% and for MDR-TB was 5%.25 Moreover, our results of anti-TB drug resistance are also high compared with the results obtained in a study carried out in Nigeria, where monoresistance ranged between 3.3% and 16.7%, whereas polyresistance or MDR-TB had a frequency of 3%.26
There was a predominance of isolated resistance to isoniazid (65.8%) followed by rifampicin (55.3%), ethambutol (42.1%), and streptomycin (38.2%) (Table 3). Our results are similar to those obtained by Fregona et al., who observed a higher frequency of isolated resistance to isoniazid and streptomycin;25 however, our results differ from those obtained by Otokunefor et al., where isoniazid and rifampicin were the drugs with the least resistance.26 Generally, the lowest rates of anti-TB resistance have been reported against rifampicin,21,26,27 but high rates of resistance were observed to rifampicin (55.3%) in the population analyzed in this study, which could indicate the need to carry out studies for the monitoring of rifampicin resistance in Angola. On the other hand, the low resistance to streptomycin compared with other drugs in our study could be related to the limited use of this drug in first-line regimens.26 The highest frequency of MDR-TB was observed for isoniazid + rifampicin (Table 3), which is similar to those observed in other studies.25,28 We believe that the free availability of these drugs and the fact that they often are not monitored by public health services are among the reasons for the high rate of resistance observed for these anti-TB drugs in treatment-naive patients. Variation in anti-TB drug resistance rates has been reported worldwide.29 Some regions such as Japan have shown low rates of resistance to anti-TB drugs (∼0.2%),30 whereas Pakistan has shown high rates of resistance to anti-TB (∼69%).31 In addition to the variations observed between countries, these resistance rates might also vary even within countries; therefore, further studies should be carried out in the most varied regions of Angola to obtain the rate of change of anti-TB drug resistance in the different regions of the country.
The prevalence of TB-HIV coinfection (77.8%) in our study was higher than that reported in a study conducted in Brazil (50.8%).28 The fact that the province of Luanda has a high population density and the highest rates of HIV infection32 may explain the high rate of TB-HIV coinfection in Luanda. The risk of Mtb infection (OR: 1.87; 95% CI: 0.59–5.95; P = 0.290) (Table 1) as well as anti-TB resistance (OR: 1.39; 95% CI: 0.26–7.28; P = 0.700; Table 2), was higher in HIV patients. Further, the rate of susceptibility to all drugs in HIV patients was only 9.5%, which was lower compared with the rate of HIV patients with monoresistance (14%), polyresistance (12.5%), or MDR-TB (13.2%). Although previous studies have shown a statistically significant association between TB-HIV coinfection and anti-TB resistance, no evidence of an association was found in our study (Table 3). Despite this, the control of resistance to anti-TB should be strengthened mainly in regions with high HIV rates in Angola.
Our study has some potential limitations that need to be recognized when interpreting the results. First, the limited number of patients undergoing the sensitivity test limits our results and do not represent all TB patients in Luanda or patients from other regions of Angola. Second, there was no recent interview or contact with patients to investigate the history of TB treatment capable of increasing the chances of resistance to the anti-TB drugs. Finally, the outcomes of drug-resistant patients have not been described, which would be important for the evaluation of the impact of anti-TB drug resistance in disease mortality in Angola. Despite these limitations, our study provides important data on the sociodemographic profile of TB patients in Luanda and could allow us to estimate the profile of drug susceptibility among TB patients from Luanda. Future studies including a larger sample size should be carried out to assess the prevalence of TB in different groups, the resistance to anti-TB drugs, TB-HIV coinfection, and clinical outcome of TB patients with monoresistance, polyresistance, or MDR-TB in Luanda and other regions of Angola. Moreover, studies using molecular tools might be useful in this regard. The results of these studies could help the NTPs, particularly in developing countries 1) to develop strategies to improve TB surveillance and resistance to anti-TB drugs, 2) to investigate whether TB-HIV coinfection is associated with the presence of resistance in the main anti-TB drugs, and 3) to increase awareness campaigns for the treatment adherence to reduce the emergence and dissemination of resistant variants in Africa.
In conclusion, our study additionally contributes to the scarce data on epidemiological characteristics of TB patients in a resource-limited country, such as Angola. We identified a high prevalence of Mtb (66.5%) and anti-TB drug resistance (72.4%) among naive TB patients. The high level of anti-TB drug resistance observed in our study could compromise TB patient management and reduce treatment options in Angola. Further studies on factors related to the emergence and dissemination of strains resistant to anti-TB drugs should be carried out urgently to help assess the magnitude of the problem of anti-TB drug resistance and to reinforce ongoing measures to control TB in Angola.
ACKNOWLEDGMENTS
We thank the Instituto Nacional de Investigação em Saúde clinical staff at the tuberculosis surveillance laboratory for ensuring access to the database and generously making patients’ medical records available. The American Society of Tropical Medicine and Hygiene (ASTMH) assisted with publication expenses.
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