(Table 1).
Quality assessment
We assessed the quality of studies using the Joanna Briggs Institute
Critical Appraisal (JBI) tools designed for cross-sectional and
case-control studies (Moola et al., 2020). The critical appraisal
checklist for the cross-sectional and case-control studies contains
eight and ten questions respectively. Each question was scored out of
100% and finally, the sum of all questions was turned into 100%. The
quality score was graded as low if < 60%, medium if 60–80%
and high if > 80% (Porritt et al., 2014; Munn et al.,
2019). Two authors (AA, GD) independently assessed the quality of the
studies and the third author (ZWB) resolved the inconsistencies through
discussion (Additional file 3) .
Outcomes
The primary outcome of this systematic review and meta-analysis study
was drug-resistant TB. Whereas, the risk factors associated with DR-TB
were the secondary outcomes. The pooled odds ratio along with their 95%
CIs were estimated to assess the risk factors associated with DR-TB in
Ethiopia.
Data analysis
Data that were summarized in Microsoft Excel 16 spreadsheet were
exported to STATA version 15 for statistical analysis. The pooled OR
with 95%CI of each risk factor was estimated by assuming the true
effect size varies between studies. We presented the pooled results
using a forest plot. We used the forest plot and I 2heterogeneity test to assess the heterogeneity among the
studies. The I 2 values of 25%, 50%,
and 75% were interpreted as the presence of the low, medium, and high
heterogeneity, respectively (Sterne and Egger, 2001; Riley et
al., 2011). In this study, we used a random-effects model for all risk
factors to perform the analysis by considering substantial variability
among the studies (Riley et al., 2011). We explored the presence of
publication bias through visual inspection of the funnel plot and
statistical significance of Egger’s regression test.
RESULTS
Study characteristics
After the systematic article searching in the available databases and
other literature sources, we identified 2238 articles. After removing
244 duplicates, 1994 articles were screened by title and abstract. Then,
full-text screening was conducted for 43 articles. Finally, after the
full-text screening, 27 eligible articles were included in the study
(Abay et al., 2020; Abdella et al., 2015; Adane et al., 2015; Alene et
al., 2019; Arega et al., 2019; Assefa et al., 2017; Babure et al., 2019;
Bedewi et al., 2017; Biru et al., 2020; Deressa et al., 2014; Desissa et
al., 2018; Dessalegn et al., 2016; Fikre et al., 2019; Gobena et al.,
2018; Hamusse et al., 2016; Hirpaet al., 2013; Jaleta et al., 2017;
Mehari et al., 2019; Mekonnen et al., 2015; Mesfin etal., 2018; Mulisa
et al., 2015; Mulu et al., 2015; Seyoum et al., 2014; Tadesse et al.,
2015; Tesfay et al., 2016; Tsega et al., 2017; Welekidan et al., 2020).
We presented the detail using the PRISMA flow diagram (Figure
1). The studies were conducted in different administrative regions
across the country with the most frequent studies were from Addis Ababa
(7 studies) followed by the Amhara region (6 studies), the Oromia region
(5 studies), and the Tigray region (4 studies). However, the studies
were reported from the majority of the regional administrative states.
The studies were based on data collected from TB patients in health care
facilities. The study period for these studies ranges from 2008 (Tadesse
et al., 2015) to 2019 (Welekidan et al., 2020). Based on the study
designs, 15 studies were cross-sectional studies while the remaining 12
studies used a case-control study design. The majority of the studies
included all the age group categories while three studies and two
studies included TB patients above 15 and 18 years of age respectively.
The studies assessed the risk factors/determinates of either MDR-TB (18
studies), RR-TB (3 studies), or any type of drug resistance (6 studies).
The sample size of individual studies ranges from 65 in a study
conducted by Babure et al. (2019) to 1876 in a study done by Arega et
al. (2019) (Table 1).
Risk factors of drug-resistant tuberculosis
In the current study, we extracted data to assess the risk factors of
drug-resistant TB in the Ethiopian setting. The risk factors include;
socio-demographic characteristics (age, sex, marital status, residence,
occupation, and family size), behavioral characteristics (smoking
status, alcohol consumption, and khat chewing), and clinical
characteristics (HIV serostatus, DM co-occurrence, contact history,
imprisonment, previous TB treatment, number of TB episodes, and site of
TB infection). We performed a pooled analysis for each variable using a
random effect model by considering substantial variability among the
individual studies. Based on the pooled analysis of the odds ratio,
unemployment (OR; 2.71, 95% CI; 1.64, 3.78, I2;
0.0%) (Figure 2) , having a history of previous TB (OR; 4.83,
95% CI; 3.02, 6.64, I2; 69.6%) (Figure 3) ,
having contact with a known TB patient (OR; 1.72, 95% CI; 1.05, 2.40,
I2; 73.8%) (Figure 4) , having contact with a
known MDR-TB patient (OR; 2.54, 95% CI; 1.46, 3.63,
I2; 0.0%) (Figure 5) , and having pulmonary
TB (OR; 1.80, 95% CI; 1.14, 2.45, I2; 71.4%)(Figure 6) were found to be the risk factors associated with
drug-resistant TB in Ethiopia. While, old age individuals (OR; 0.77,
95% CI; 0.60, 0.95, I2; 47.2%) (Figure 7)including above 45 years of age (OR; 0.76, 95% CI; 0.55, 0.97,
I2; 47.6%) (Figure 8) , and males (OR; 0.86,
95% CI; 0.76, 0.97, I2; 19.0%) (Figure 9)had lower risk of DR-TB compared to their counterparts (Table
2) .
However, statistically significant association was not found for the
following variables; urban residence (OR; 0.86, 95% CI; 0.58, 1.15,
I2; 75.2%), being single (OR; 1.12, 95% CI; 0.84,
1.40, I2; 46.0%), being a house wife (OR; 0.86, 95%
CI; 0.50, 1.21, I2; 0.0%), being a farmer (OR; 0.81,
95% CI; 0.42, 1.19, I2; 48.0%), being a daily
laborer (OR; 0.97, 95% CI; 0.16, 1.78, I2; 33.4%),
family size above three members (OR; 0.87, 95% CI; 0.61, 1.14,
I2; 0.0%), alcohol consumption (OR; 0.96, 95% CI;
0.53, 1.38, I2; 70.01%), khat chewing (OR; 1.01, 95%
CI; 0.63, 1.38, I2; 0.0%), smoking (OR; 0.75, 95%
CI; 0.34, 1.16, I2; 58.3%), imprisoned (OR; 1.00,
95% CI; 0.43, 1.56, I2; 0.0%), being HIVpositive (OR; 1.35, 95% CI; 0.95, 1.74, I2; 73.6%),
having DM (OR; 0.85, 95% CI; -0.85, 1.93, I2; 0.0%),
and having two or more number of TB episodes (OR; 1.03, 95% CI; -0.03,
2.99, I2; 79.2%) (Table 2) (Supplementary
figure 1) .
Accordingly, those individuals who were unemployed had 2.71 times the
odds to develop DR-TB compared to the employed ones. Likewise, those
individuals who had a history of previous TB had 4.83 times the odds to
develop DR-TB compared to new TB patients. Similarly, those who had a
contact history with a known TB patient had 1.72 times the odds to
develop DR-TB compared to their counterparts. Also, those who had a
contact history with a known MDR-TB patient had 2.54 times the odds to
develop DR-TB compared to individuals who didn’t have a contact history
with a known MDR-TB patient. Besides, individuals with pulmonary TB
had1.80 times the odds to develop DR-TB compared with patients with
extrapulmonary TB. While the risk of DR-TB decreased by 23% among older
age groups, i.e. individuals who were above 45 years had a 24%
decreased risk of DR-TB compared to individuals below 45 years of age.
Likewise, the risk of DR-TB decreased by 14% among males compared to
females (Table 2) .
Publication bias assessment
Based on the funnel plot and the Egger’s regression test publication
bias was detected for older age (P<0.001), above 45 years age
(P<0.001), male sex (P=0.0047), being single (P=0.0036), being
farmer (P<0.0299), urban residence (P<0.001), HIV
seropositive (P<0.001), smoking (P<0.001), alcohol
consumption (P<0.001), previous TB history
(P<0.001), having two or more TB episodes (P<0.001),
and contact with known TB patient (P=0.001). While publication bias was
not detected for age above 40 years (P=0.0608), family size above three
members (P=0.7071), being a daily laborer (P=0.7341), being a house wife
(P=0.3798), unemployed (P=0.8805), imprisoned (P=0.3692), having contact
with known MDR-TB patient (P=0.3425), khat consumption (P=0.2586),
having DM (P=0.3082), and having pulmonary TB (P=0.0622)
DISCUSSION
In the current study, we explored in detail the risk factors associated
with drug-resistant TB in Ethiopia using 27 eligible articles. After
performing a pooled analysis for 18 variables extracted from individual
studies, we found that five variables such that unemployment, having a
history of previous TB, having contact with a known TB patient, having
contact with a known MDR-TB patient, and having pulmonary TB were the
risk factors associated with drug-resistant TB in Ethiopia. However,
older age and male individuals had a lower risk compared to their
counterparts.
The current study revealed that those individuals who were unemployed
had 2.71 times the odds to develop DR-TB compared to the employed ones.
It was also supported in a global pooled estimate performed by Pradipa
et al. (2018). The poor living condition of unemployed individuals that
leads them to pathological changes might be the possible cause
(Przybylski et al., 2014). The present study also revealed that previous
history of TB treatment is a major risk factor associated with DR-TB in
Ethiopia, such that those individuals who had a history of previous TB
treatment had 4.83 times the odds to develop DR-TB compared to their
counterparts. In line with this study, pooled estimates conducted at
different countries across the globe reported a statistically
significant association of previous TB treatment with DR-TB (Pradipta et
al., 2018; Lukoye et al., 2015; Jimma et al., 2017; Faustini et al.,
2006; Zhao et al., 2012). Studies reported that poor treatment adherence
during anti-TB treatment results in the subsequent emergence of drug
resistance (Zhao et al., 2012).
The other risk factor identified based on the pooled estimates in this
study was having contact with a known TB patient whether with an MDR-TB
patient specifically or with a TB patient as a general. Our study
revealed that those individuals who had a contact history with a known
TB patient had 1.72 times the odds to develop DR-TB compared to their
counterparts. The risk becomes higher among individuals who had contact
with an MDR-TB patient. Such that those who had a contact history with a
known MDR-TB patient had 2.54 times the odds to develop DR-TB compared
to individuals who did not have a contact history with a known MDR-TB
patient. Studies from Burkina Faso and Bangladesh also supported it
(Flora et al., 2013; Diande´ et al., 2009). Screening contacts could
help to early detect DR-TB cases before disseminated across the
population. Besides, the present study revealed that the site of
infection was associated with DR-TB. Based on the pooled estimate in
this study, individuals with pulmonary TB had 1.80 times the odds to
develop DR-TB compared with individuals with extrapulmonary TB. A higher
risk of DR-TB among PTB cases was also reported from India in a study
based on a 13 years retrospective hospital-based analysis (Raveendran et
al., 2015). Another study also supported this (Peto et al., 2009).
Difficulties in detecting EPTB cases with lower bacterial loads might be
the reason. The mycobacterial strains circulating in the country might
be also a reason. For example, a study from China revealed that the
DR-TB is higher among extrapulmonary TB cases compared to pulmonary TB
cases due to the high prevalence of the Beijing strain (Pang et al.,
2019).
The present study also revealed that older individuals had a lower risk
to develop DR-TB. The pooled estimate revealed that individuals who were
above 45 years had a 24% decreased risk of DR-TB compared to
individuals below 45 years of age. Such that those productive age groups
are at high risk of DR-TB in Ethiopia. Likewise, a pooled estimate in
Jimma et al’s. (2017) study based on a systematic review and
meta-analysis conducted in Iran and its neighboring countries revealed
that individuals below 45 years of age had 1.57 times the odds to
develop MDR-TB compared with those individuals above 45 years old.
Higher pooled odds of MDR-TB among individuals below the age of 65 was
also reported from a systematic review performed in Europe (Faustini et
al., 2006). Besides, a study conducted at Northeastern China reported
that those individuals between 28-54 years of age had double odds of
MDR-TB when compared with those 65 years or older (Liu et al., 2013).
The treatment adherence in the productive age group and the working
style of these groups who have a higher contact chance to DR-TB patients
might also contribute. The findings of the present study revealed that
males had a lower risk of DR-TB compared to females. The risk of DR-TB
decreases by 14% in males compared to females. Likewise, individual
studies also reported a higher risk of DR-TB among females (Lomtadze et
al., 2009; Shivekar et al., 2020). However, the reason behind it should
be explored in detail in future works. In the end, this study was based
on studies published in the English language that might affect the true
estimates. Besides, publication bias was confirmed by the Egger’s
regression test for about half of the study variables that might bias
the true estimates.
In conclusion, a previous history of TB treatment is a major risk factor
for acquiring DR-TB in Ethiopia that might be due to poor adherence
during the first-line anti TB treatment. Besides, having contact with a
known TB patient, having contact with a known MDR-TB patient, having
pulmonary TB, and being unemployed were the risk factors of DR-TB in
Ethiopia. Thus, active screening of TB contacts for DR-TB might help to
detect DR-TB cases as early as possible and could help to decrease its
transmission across the population.