The incidence and mortality of COVID-19 related TB infection in Sub-Saharan Africa: A systematic review and meta-analysis
Jacques L Tamuzi1*; Gomer Lulendo2; Patrick Mbuesse2
1Division of Epidemiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
2Africa Centre for HIVAIDS management, Stellenbosch University, Cape Town, South Africa.
Received Date: 10/01/2022; Published Date: 18/02/2022.
*Corresponding author: Jacques L Tamuzi, Division of Epidemiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, SouthmAfrica
Background: Coronavirus disease 2019 (COVID-19) is also associated with other co-morbidities among with previous and current pulmonary tuberculosis (PTB). PTB is a risk factor for COVID-19, both in terms of severity and mortality, regardless of human immunodeficiency virus (HIV) status. However, there is less information available on COVID-19 associated with PTB in point of view incidence and mortality rates in sub-Saharan Africa (SSA) as a high burden TB region. This systematic review served to provide data synthesis of available evidence on COVID-19/PTB incidence and case fatality rates, and mortality rate found in clinical and post-mortem COVID-19/PTB diagnostics in SSA.
Methods: We conducted a systematic electronic search in the PubMed, Medline, Google Scholar, Medrxix and COVID-19 Global literature on coronavirus disease databases for studies including COVID-19 associated with PTB in sub-Saharan Africa. The main outcomes were the proportion of people with COVID-19 associated to current /or previous PTB and the case fatality associated to COVID-19/PTB. The combination method was based on methodological similarities in the included random effect model studies using Prometa 3 software. We further undertook sensitivity analysis and meta-regression.
Results: From the 548 references extracted by the literature search, 25 studies were selected and included in the meta-analysis with a total of 191, 250 COVID-19 infected patients and 11, 452 COVID-19 deaths. The pooled COVID-19/PTB incidence was 2% [1%-3%] and mortality of 10% [4%-20%]. The pooled estimates for case fatality rate among COVID-19/PTB were 6% [3%-11%] for clinical PTB diagnostic and 26% [14%-48%] for post-mortem PTB diagnostic. Meta-regression model including the effect sizes and cumulative COVID-19 cases (P= 0.032), HIV prevalence (P= 0.041) and TB incidence (P= 0.002) to explained high heterogeneity between studies.
Conclusion: As a summary, the incidence of TB associated with COVID-19 and case fatality rates are higher in SSA. However, COVID-19 associated to TB may be underreported in the studies conducted in SSA as the post-mortem TB diagnostic was higher. Large-scale cohort studies that adequately clear tool on previous and/or current TB diagnostic tools are required to confirmed COVID-19/TB incidence and case fatality in SSA.
Keywords: COVID-19; PTB; Incidence; Mortality; sub-Saharan Africa Review registration: PROSPERO (CRD42021233387).
Abbreviations: ACE2: Angiotensin-Converting Enzyme 2; ARDS: Acute Respiratory Distress Syndrome; COVID-19: Coronavirus Disease 2019; HIV: Human Immunodeficiency Virus; MCP-1: Monocyte Chemoattractant Protein-1; NOS: Newcastle-Ottawa scale; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PTB: Pulmonary Tuberculosis; SSA: Sub-Saharan Africa; SARS-CoV-2: Severe Acute Respiratory Syndrome Coronavirus 2; TB: Tuberculosis; TGF-β: Transforming Growth Factor; WHO: World Health Organization.
The coronavirus disease 2019 (COVID-19) pandemic has caused significant morbidity and mortality all over the world, with total confirmed cases of 296,496,809 globally and 5,462,631 deaths in January 2022 . In African region, 7,493,439 confirmed cases are already recorded, among them, 154, 837 deaths in January 2022 . Though it is reported that the African region showed a decreasing trend in the number deaths over the past several weeks compared to other WHO regions . The age groups at highest risk of severe COVID-19 disease and death (those >60 years old) [2-4] may be proportionately less in many SSA countries than in other parts of the world . In contradistinction to potential health vulnerabilities, sub-Saharan African countries could be “protected” from COVID-19 mortality by an age structure differing significantly from countries where morbidity and mortality has been particularly high such as Italy, Spain, the United States, and in Hubei Province in China [5-7].
However, COVID-19 is also associated with other co-morbidities among with previous and current pulmonary tuberculosis (PTB) . A recent review has shown that TB is a risk factor for COVID-19, both in terms of severity and mortality, regardless of HIV status . Geographically, most people who developed TB are in South-East Asia (44%), Africa (25%) and the Western Pacific (18%) . It is estimated that 94% of all TB infections and deaths occur appropriately in low-and middle-income countries, including sub-Saharan Africa (SSA) . An observational study from the epicenter of the pandemic in Wuhan, suggested that individuals with latent or active TB were more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection . This study found infection with Mycobacterium tuberculosis to be a more common co-morbidity for COVID-19 (36%) . They also found Mycobacterium tuberculosis co-infection to be linked to more severe COVID-19 and more rapid progression . Another study conducted in Zambia, revealed that 31.4% post-mortem PTB diagnostic in COVID-19 deaths . In countries where tuberculosis (TB) risk factors for mortality are highly prevalent among young individuals (poverty, overcrowding, diabetes mellitus, smoking, alcohol and substance abuse, HIV co-infection, among others), particularly in the presence of drug resistance and difficult access to diagnosis (delayed diagnosis) [14, 15], COVID-19 incidence and mortality associated to TB may be hypothesized as significant. However, COVID-19 associated to previous, current or both TB has been lower in high burden TB countries. Both active and previous history of TB may play a deleterious synergism SARS-CoV-2, increasing then the risk of COVID-19 associated mortality, and for patients with PTB may increase the severity of COVID-19 and of death due to chronic lung disease and immunosuppression . This may contribute to higher than expected mortality in high TB burden region such as SSA.
Therefore, there is less information available on COVID-19 associated to TB in point of view incidence and fatality rates in SSA. This systematic review served to provide data synthesis of available evidence on COVID-19/TB incidence and case fatality rates, and case fatality rates found in clinical and post-mortem COVID-19/TB diagnostics in SSA.
The review followed a predesigned protocol registered in PROSPERO (CRD42021233387). The systematic review met the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines .
Search strategy and eligibly criteria
A search of the literature was systematically conducted using Pubmed, Medline, Google Scholar, Medrxix and COVID-19 Global literature on coronavirus disease. All searches were limited to articles written in English given that such language restriction does not alter the outcome of systematic reviews and meta-analyses. The search was restricted to studies related to the incidence and fatality rates of COVID-19 related to TB in SSA since December 2019 to November 2021 including the key words and term as follows: “Covid-19 or 2019-nCoV or coronavirus disease 2019 or Novel coronavirus or SARS-CoV-2 ” and “tuberculosis or PTB or TB or Mycobacterium tuberculosis infection” and “mortality rate or death rate or case fatality rate” and “incidence or Incidence proportions or Incidence rate or incidence rate or attack rate” and “Angola or Benin or Botswana or Burkina Faso or Burundi or Cameroon or Cape Verde or "Central African republic" or Chad or Congo or "Democratic Republic of Congo" or DRC or Djibouti or Equatorial guinea or Eritrea or Ethiopia or Gabon or Gambia or Ghana or Guinea or Bissau or Ivory coast or "“Cote d’ ivoire" or Kenya or Lesotho or Liberia or Madagascar or Malawi or Mali or Mayotte or Mozambique or Namibia or Niger or Nigeria or Principe or Reunion or Rhodesia or Rwanda or "Sao Tome" or Senegal or "Sierra Leone" or Somalia or "South Africa" or Swaziland or Tanzania or Togo or Uganda or Zambia or Zimbabwe or "Central Africa" or "Sub-Saharan Africa" or "East Africa" or "Southern Africa" or “South Africa”, without language restrictions to identify citations from prior to January 2020. The review included observational studies conducted in Sub-Saharan Africa, including the incidence and mortality related to COVID-19 and TB. We included confirmed COVID-19 participants with TB diagnosed previously, currently or in post-mortem.
Study quality and risk of bias assessment
The methodological quality of the included studies was independently assessed by two of the authors (JLT and GL). Any inconsistencies were resolved by consensus, and if no agreement was reached yet again, the case was resolved by seeking the views of a third author (PB). The Newcastle-Ottawa scale (NOS)  was used by two reviewers (JTL and GL) to independently assess study quality. The NOS evaluated the case series, cross-sectional, case-control study's selection, comparability, and exposure, as well as the cohort study's selection, comparability, and outcome. The sample with more than 6 stars was considered to be of reasonably high quality, and the sample with nine stars reflects the highest ranking. Any discrepancies in the content of the included studies were resolved with the help of another reviewer (PB).
Three levels of screening were performed. The first and second rounds of screening were based on titles and abstracts only while the third round consisted of a review of full text articles. The first screening was performed by JTL and excluded references that did not contain information on the pathogens of interest or those that were not the study designs of interest (included observational studies only). The second screening was performed independently by JLT and GL with differences solved by consensus. The third level of screening identified those publications related COVID-19 incidence and mortality associated to PTB in SSA and data extraction was performed on those that met the criteria. Screening and data extraction were performed by JLT and GL independently reviewing each full text article. Conflicts were resolved via discussion to achieve consensus, with any remaining disagreements resolved by a third reviewer. Included studies were observational studies that included COVID-19 incidence and/or mortality related to PTB in SSA. Data were extracted based on the study year of publication, first author's name, country of study, design, setting, target population, sampling method, sample size, total study period, items related to the quality assessment of the study (the score of each item and the overall study quality score), incidence data and mortality of COVID-19 associated with PTB (including attack rates, death rate, clinical and post-mortem COVID-19/TB diagnostic mortality, cumulative incidence and incidence rate, based on the measured 95% CI and p-value).
The main outcomes were the incidence of COVID-19 associated to current or/and previous PTB and the case fatality rates associated to this proportion. This was calculated as the number of persons developing COVID-19 associated to PTB divided by the total number of COVID-19 cases. Standard errors and confidence intervals for a single proportion were derived. P as the proportion of COVID-19/TB infections (previous and/or current TB) or clinical and post-mortem diagnostics of COVID-19/TB deaths and N was the total number of cases of COVID-19 for the incidence rate and total COVID-19 deaths for the case fatality rate.
The pooled COVID-19 incidence proportion and case fatality rate TB-related were calculated in this meta-analysis. The combination method was based on methodological similarities in the included random effect model studies using Prometa 3 software . Forest plots were plotted for all studies to show the separate and pooled incidence and fatality rates and the corresponding 95% CIs. Both COVID-19/TB incidence proportion and fatality rate were measured in term of the ratios of proportions (RR-Ps). Heterogeneity assessment with the Q-Statistic Test and I2-Statistics and their corresponding 95% CIs were used to assess the statistical heterogeneity of incidence and mortality in the included studies. The following references were used as the basis for determining the degree of heterogeneity: (1) Heterogeneity values of 0% – 40% will be considered 'maybe not important;' (2) Heterogeneity values of 30% – 60% as 'moderate heterogeneity;' (3) Heterogeneity values of 50% – 90% as 'substantial heterogeneity;' and (4) Heterogeneity values of 75% – 100% as 'significant heterogeneity . The statistical significance level was set at p<0.05 for the Q-test . The subgroup, meta-regression and sensitivity analysis were used to explore potential sources of heterogeneity if the I2 value was higher than 75%. We also undertook meta-regression to find out the source of heterogeneity. Lastly, we explored the publication bias with the Egger’s and Begg and Mazumdar’s rank correlation tests.
From the 548 references extracted by the literature search (Figure 1), 64 articles were analysed, among which 33 were excluded with reasons, and 25 studies were selected and included in the meta-analysis and six studies were excluded as they were study protocols. We included a total of 191, 250 COVID-19 infected patients and 11, 452 COVID-19 deaths. The selected articles reported data from South Africa (n = 9), Nigeria (n = 4), Democratic Republic of Congo (n = 2), Angola (n=1), Uganda (n = 1), Zambia (n = 5), Ethiopia (n=2) and Kenya (n = 1) (Figure 2).
Figure 1: Flow chart of TB incidence and mortality proportions associated to COVID-19 in Sub-Saharan Africa.
Figure 2: Distribution of included studies in Sub-Saharan African countries.
Twenty-five studies were included for quantitative analysis. All the studies analysed clinical characteristics and co-morbidities of COVID-19 patients in SSA. Among them, twenty studies included the incidence proportion of COVID-19 associated to TB [20-39], and nine studies included the case fatality rate of COVID-19 associated to TB [13, 20, 30, 33, 38, 40-43]. The minimum age for the study population was 13 years and the maximum was 72 years. Incidence and mortality of TB associated to COVID-19, stratified by sex were not obvious as all the studies included common co-morbidities associated to COVID-19 without sex stratification. The median study duration was 15 months (range, 4 months to 45 years) and the study period ranged from 2020 to 2021. Eleven studies used retrospective case identification [13, 30-37, 40, 43], six prospective case identification [20-24, 39], and three were Case series [25, 41, 42], three Cross-sectional studies [26-28], one cross-sectional and retrospective cohort study  and one case control study  (Supplementary material Table 1).
Supplementary material Table 1: Characteristics of included studies in the incidence and mortality of COVID-19 related TB infection in Sub-Saharan Africa.
The NOS  was used to determine the methodological validity of included research for determining the consistency of cohort, case-control, and descriptive studies in meta-analyses. The three essential components of this strategy are range, comparability, and exposure. For case–control and cohort studies, the NOS employs a star system with ratings ranging from 0 to 9. We considered a study with a higher score than the six of each type of study to be a high-quality study because the criteria for determining whether a study is high or poor quality are unknown. Two studies received an eight, five received a seven, eight received a six, five received a five, and one received a four. Supplementary material Table 2 shows the NOS scores for the studies that were included.
Supplementary material Table 2: Results of Assessment of Study Quality and Risk of Bias.
Newcastle-Ottawa Scale was obtained to assess the selection, comparability and exposure of the case-control study, while the selection, comparability and outcome for the cohort study. -: no point; *: one point; **: two points; ***: three points; ****: four points.
In this review, we defined the incidence proportion as the number of new cases of COVID-19 associated to TB over the total number of people in the population at risk for having COVID-19 during a specified period. The case fatality rate was defined as total number of new deaths due to COVID-19 associated to TB divided by the total number of COVID-19 associated to TB. The incidence and case fatality rates were summarized as specific period cases per 100.
Meta-analysis and meta-regression
Incidence proportion of COVID-19 associated to TB
In total, 20 studies were identified for incidence proportion of COVID-19 associated to TB in SSA. The pooled RR-P [95% CI] was 2% [1%-3%]. The test for heterogeneity was statistically significant with (I2 = 93.53 P<0.0001) (Figure 3).
Figure 3: pooled incidence proportion of COVID-19 associated to TB in sub-Saharan Africa
Case fatality rate of COVID-19 associated to TB
We identified nine studies [13, 20, 30, 33, 38, 40-43] meeting the inclusion criteria relating mortality proportion of COVID-19 associated to TB in SSA. The pooled RR-P [95%CI] estimates for mortality proportion among patients with COVID-19 associated to TB were 6% [3%-11%] for clinical TB diagnostic and 26% [14%-48%] for post-mortem TB diagnostic. The overall pooled RR-P was 10% [4%-20%]. Heterogeneity between studies was high (I2 = 98.82, P <0.001) however, the test for subgroup analysis did not show any difference between the groups (Figure 4).
Figure 4: pooled case fatality rate of COVID-19 associated to TB
We built multivariate meta-regression model including cumulative COVID-19 cases, HIV prevalence and TB incidence to explore heterogeneity between studies. People living with HIV (all ages), TB incidence rate and cumulative COVID-19 cases were (7, 800, 000; 360 in thousands; 3, 533, 106) for South Africa, (1, 700, 000; 440 in thousands; 231, 413) for Nigeria, (1 700 000; 59 in thousands, 221, 880) for Zambia, (1, 500, 000; 157 in thousands; 382, 371) for Ethiopia, (510, 000; 278 in thousands; 70, 059) for Democratic republic of Congo, (1, 400, 000; 140 in thousands; 270, 899) for Kenya, (340, 000; 112 in thousands; 65, 938) for Angola and ( 1, 400, 000 ; 253 in thousands; 130, 178 ) for Uganda [1, 44, 45]. This model showed that the variability across studies was explained by COVID-19 cumulative cases by countries (P= 0.032), HIV prevalence (P= 0.041) and TB incidence (P= 0.002).
Figure 5: Funnel plot of incidence proportion of COVID-19 associated to TB in sub-Saharan Africa
Egger’s and Mazumdar’s rank correlation test and Begg’s funnel plot were used to evaluate publication bias quantitatively and qualitatively respectively. Asymmetry was found in the plot including COVID-19/TB incidence proportion (Figure 5). Both Egger’s and Mazumdar’s rank correlation tests did not exhibit obvious publication bias in different studies included in the review because the P-values of both tests for COVID-19/TB incidence rate were (-1.10, P = 0.285) and (-0.26, P = 0.795), respectively. Furthermore, P-values of both tests for COVID-19/TB mortality rate were [Egger’s test (t = 0.49, p = 0.642)] 0.173 and [Begg and Mazumdar’s rank correlation test (z = -0.83, p = 0.404)], respectively.
For all the studies including in COVID-19/TB incidence and mortality rates in SSA, sensitivity analysis was performed by sequential omission of every study respectively Prometa 3. For every incidence and mortality rates, the RR-P was not significantly influenced by omitting any single study.
The objective of this review was to help us to estimate the burden of COVID-19 associated with TB in SSA; the meta‐analysis including twenty studies and 191, 250 COVID-19 infected cases demonstrated that the overall pooled incidence proportion was 2% [1%-3%]. Our COVID-19/TB incidence was higher than the incidence found in a recent meta-analysis forty-three studies which showed the pooled estimate for proportion of active pulmonary tuberculosis was 1.07% [0.81%-1.36%] . This systematic review included more studies conducted in low and moderate TB prevalence compared to our review which included studies in high burden TB countries in SSA. Additionally, this review only included active TB associated with COVID-19, compared to our study which included COVID-19 cases with previous and/or active TB.
The case fatality rate of COVID-19/TB including nine studies and 11,452 COVID-19 deaths was 10% [4%-20%]. However, the meta-analysis of subgroup analysis (clinical vs post-mortem TB diagnostics) showed post-mortem TB diagnostic counted higher case fatality rate than clinical TB diagnostic with 26% [14%-48%] for post-mortem TB diagnostic compared to 6% [3%-11%] for TB clinical diagnostic. Our case fatality rate was higher than a meta-analysis including seventeen studies with 42,321 COVID-19 patients, of whom 632 (1.5%) had tuberculosis, reported on deaths due to COVID-19 . Our high case fatality rate may be justified by same reasons referred to COVID-19/TB incidence. Interestingly, our review has shown that TB was among the commonest co-morbidity in COVID-19 patients in sub-Saharan Africa. This is consistent with findings in other studies including high TB post-mortem diagnostic in sub-Saharan Africa [47, 48]. Referring to a meta-analysis showing that TB exposure was high-risk COVID-19 group (OR 1.67, 95% CI 1.06–2.65, P = 0.03) . COVID-19/TB clinical diagnostic may be underestimated in SSA as post-mortem TB diagnostic has shown high mortality rate. Tamuzi et al. have suggested an algorithm for suspected COVID-19/TB diagnostic in high burden HIV/TB countries. This algorithm may reflect the true COVID-19/TB incidence in high burden TB countries and reduce COVID-19/TB severity rate OR 4.50 (95% CI 1.12–18.10, P = 0.03) and mortality (OR 2.23, 95% CI 1.83–2.74, P < 0.001) compared to non-TB group .
This deleterious synergism of SARS-CoV-2 and Mycobacterium tuberculosis increases the risk of COVID-19-associated morbidity and mortality , and patients with PTB may increase the severity of COVID-19 and death due to chronic lung disease and immunosuppression. In fact, advanced PTB is characterized by significant collagen deposition and fibrosis [49-49], although tissue remodelling during fibrosis is a healing process, extensive fibrosis with scar formation impairs lung function . A study reported a series of 454 cases of massive fibrosis with evidence of tuberculosis in 40% . Furthermore, ACE2 has been reported to play a protective role in lung fibrosis . In lung biopsy specimens of patients with lung fibrosis, ACE2 mRNA and enzyme activity decreased significantly [53, 54]. Interestingly, SARS-CoV-2 spike protein decrease the amount of ACE2 expression during viral infection . Decreased ACE2 expression results in increased ANG-II levels and contributes to lung fibrosis and pulmonary failure . In three different acute lung injury models, loss of ACE2 expression precipitated serious acute lung failure, while RhACE2 attenuated ARDS and further decreased Angiotensin II levels in the lungs [56, 57]. In addition to TGF- β and ACE2, other pathways can contribute to SARS-CoV-2 mediated lung fibrosis. MCP-1 is a chemokine that causes lung fibrosis. In addition, there are permanent changes in lung architecture after TB due, in part, to aberrant wound healing processes . Regulation of the TGF-β signalling pathway was also associated with elevated levels of collagen in lung lesions prior to and during TB [58, 59]. The TGF-β activation pathways in both SARS-CoV and PTB contribute to the production of fibrin, collagen and secreted proteases (Matrix metalloproteinases) associated with human cavities involved in the formation of fibrosis and tissue remodelling . As a summary the presence of cavitary lesions, fibrosis and extensive lung pathology was then identified as a major risk factor for poor COVID-19/TB outcomes, which could be explained by reduced drug penetration due to minimal blood supply in fibrotic lungs sites . Lastly, the transient immunosuppression characterized both conditions, a reason for poorer IgG antibody response and a delayed viral clearance in co-infected SARS-CoV-2 patients and the use of corticoid therapy in SARS added even more on immunosuppression .
There are several implications to our findings in screening TB concomitantly to COVID-19 in SSA. Our findings indicate that the risks of COVID-19 associated to previous and/or current TB may be underestimated in SSA, as this co-infection is poorly reported. Although there is a paucity of accurate epidemiological data about COVID-19 associated to previous and/or current TB, the fatality rate is estimated high. Therefore, COVID-19 associated to TB should be taken in a context of proper history taking, accurate diagnostic tools and clear management . Then, clinicians dealing with a possible SARS-CoV-2 patient from high burden TB region, one should never forget TB as a coexisting pathology.
High heterogeneity was observed between studies exploring the incidence and mortality rates. This heterogeneity may be explained by a model including the cumulative COVID-19 cases, HIV prevalence and TB incidence which varied considerably across the countries. Meta-regression has shown statistically significant p-values between the effect size and our model. Besides, sensitivity analysis performed by sequential omission of every study for every incidence and mortality rates, the RR-P was not significantly influenced by omitting any single study. Egger’s and Mazumdar’s rank correlation test and Begg’s funnel plot were used to evaluate publication bias quantitatively and qualitatively respectively. Asymmetry was found in the plot including COVID-19/TB incidence proportion (Fig 5). Both Egger’s and Mazumdar’s rank correlation tests did not exhibit obvious publication bias in different studies included in the review because the P-values of both tests for COVID-19/TB incidence rate were (-1.10, P = 0.285) and (-0.26, P = 0.795), respectively. Furthermore, P-values of both tests for COVID-19/TB mortality rate were [Egger’s test (t = 0.49, p = 0.642)] 0.173 and [Begg and Mazumdar’s rank correlation test (z = -0.83, p = 0.404)], respectively.
Our systematic review is limited by the quality of the included studies: first, the majority involved retrospective data analyses, which increase the risk of bias associated with the recording of baseline data, the need for imputation, and potential selection bias. Retrospective studies did not report how missing data were handled or if imputation was used. High risk of selection bias was noted in retrospective and cross-sectional studies. Finally, the results of the case fatality meta‐analysis should be interpreted with caution because data pooling the post-mortem PTB diagnostic were all extracted in studies conducted in Zambia [13, 41-43]. This could limit the external validity of the review. Future studies should be properly designed with high‐quality and systematic methods of TB diagnostic associated with COVID-19. This will play a substantial role in reflecting the true incidence and mortality rates of COVID-19/TB in SSA.
This systematic review of the incidence proportion and case fatality rate of COVID-19 associated to TB in SSA. This analysis showed that the incidence of TB associated with COVID-19 and case fatality rates are higher in SSA. However, COVID-19 associated to TB may be underreported in the studies conducted in SSA due to no specific COVID-19/TB diagnostic tools. This is strengthened by high case fatality rate of COVID-19/TB in post-mortem diagnostic. Large-scale cohort studies that adequately clear tool on previous and/or current TB diagnostic tools are required to confirmed COVID-19/TB incidence and case fatality.
Contributions: JLT conceived the study and developed the protocol. JLT did the literature search and selected the studies. JLT and GL reviewed the methodological quality of the study and extracted the relevant information. JLT synthesized the data. JLT wrote the first draft of the paper. GL and PB revised successive drafts of the paper. All the authors approved its final version. JLT is the guarantor of the study.
Ethics approval and consent to participate: Not required.
Consent for publication: Not applicable.
Competing interests: None of the authors in this study have any conflict of interest regarding the publication of the paper.
- WHO. WHO Coronavirus (COVID-19) Dashboard. Situation by Region, Country, Territory & Area, 2022. https://covid19.who.int/table
- Candido DDS, Watts A, Abade L, Kraemer MUG, Pybus OG, Croda J, et al. Routes for COVID-19 importation in Brazil. J Trav Med 2020; 27:taaa042.
- World Health Organization, Electronic State Parties Self-Assessment Annual Reporting Tool (e-SPAR), 2019. Available at: https://extranet.who.int/e-spar.
- Skrip LA, Selvaraj P, Hagedorn B, Ouédraogo AL, Noori N, Orcutt A, et al. Seeding COVID-19 across Sub-Saharan Africa: An Analysis of Reported Importation Events across 49 Countries. Am J Trop Med Hyg. 2021; 104(5):1694-702.
- Chintalapudi N, Battineni G, Amenta F. COVID-19 virus outbreak forecasting of registered and recovered cases after sixty-day lockdown in Italy: a data driven model approach. J Microbiol Immunol Infect. 2020; 53: 396-403.
- Chen Y, Li Z, Zhang YY, Zhao YH, Yu ZY. Maternal health care management during the outbreak of coronavirus disease 2019. J Med Virol. 2020; 92:731-739.
- Bell D, Hansen KS, Kiragga AN, Kambugu A, Kissa J, Mbonye AK. Predicting the Impact of COVID-19 and the Potential Impact of the Public Health Response on Disease Burden in Uganda. Am J Trop Med Hyg. 2020; 103(3):1191-1197.
- Tamuzi JL, Ayele BT, Shumba CS, Adetokunboh OO, Uwimana-Nicol J, Haile ZT, et al. Implications of COVID-19 in high burden countries for HIV/TB: A systematic review of evidence. BMC Infect Dis. 2020; 20(1):744.
- WHO. Global Tuberculosis report, 2020. https://www.who.int/tb/publications/global_report/TB20_Exec_Sum_20201014.pdf
- Kuupiel D, Vezi P, Bawontuo V, Osei E, Mashamba-Thompson TP. Tuberculosis active case-finding interventions and approaches for prisoners in sub-Saharan Africa: a systematic scoping review. BMC Infect Dis. 2020; 20(1):570.
- Chen Y, Wang Y, Fleming J, Yu Y, Gu Y, Liu C, et al. Active or latent tuberculosis increases susceptibility to COVID-19 and disease severity. MedRxiv. 2020 Jan 1. https://www.medrxiv.org/content/10.1101/2020.03.10.20033795v1.full.pdf
- Udwadia ZF, Vora A, Tripathi AR, Malu KN, Lange C, Sara Raju R. COVID-19 -Tuberculosis interactions: When dark forces collide. Indian J Tuberc. 2020; 67(4S):S155-S162.
- Mwananyanda L, Gill CJ, MacLeod W, Kwenda G, Pieciak R, Mupila Z, et al. Covid-19 deaths in Africa: prospective systematic postmortem surveillance study. BMJ. 2021; 372:n334.
- Migliori GB, Thong PM, Akkerman O, Alffenaar JW, Álvarez-Navascués F, et al. Worldwide Effects of Coronavirus Disease Pandemic on Tuberculosis Services, January-April 2020. Emerg Infect Dis. 2020; 26(11):2709-2712.
- Mousquer GT, Peres A, Fiegenbaum M. Pathology of TB/COVID-19 Co-Infection: The phantom menace. Tuberculosis (Edinb). 2021; 126:102020.
- Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6(7):e1000097.
- Wells GA, B Shea, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp.
- ProMeta 3 [Computer program] https://idostatistics.com/prometa-3-available/
- Tarsilla M. Cochrane handbook for systematic reviews of interventions. Journal of Multidisciplinary Evaluation. 2010; 6(14):142-8.
- Western Cape Department of Health in collaboration with the National Institute for Communicable Diseases, South Africa. Risk Factors for Coronavirus Disease 2019 (COVID-19) Death in a Population Cohort Study from the Western Cape Province, South Africa. Clin Infect Dis. 2021; 73(7):e2005-e2015.
- Nachega JB, Ishoso DK, Otokoye JO, Hermans MP, Machekano RN, Sam-Agudu NA, et al. Clinical Characteristics and Outcomes of Patients Hospitalized for COVID-19 in Africa: Early Insights from the Democratic Republic of the Congo. Am J Trop Med Hyg. 2020; 103(6):2419-2428.
- van der Zalm MM, Lishman J, Verhagen LM, Redfern A, Smit L, Barday M, et al. Clinical Experience With Severe Acute Respiratory Syndrome Coronavirus 2-Related Illness in Children: Hospital Experience in Cape Town, South Africa. Clin Infect Dis. 2021; 72(12):e938-e944.
- Kirenga B, Muttamba W, Kayongo A, Nsereko C, Siddharthan T, Lusiba J, et al. Characteristics and outcomes of admitted patients infected with SARS-CoV-2 in Uganda. BMJ Open Respir Res. 2020;7(1):e000646.
- Gebrecherkos T, Gessesse Z, Kebede Y, Gebreegzabher A, Tassew G, Abdulkader M, et al. Effect of co-infection with parasites on severity of COVID-19. medRxiv. 2021 Jan 1. https://www.medrxiv.org/content/10.1101/2021.02.02.21250995v1
- Zamparini J, Venturas J, Shaddock E, Edgar J, Naidoo V, Mahomed A, et al. Clinical characteristics of the first 100 COVID-19 patients admitted to a tertiary hospital in Johannesburg, South Africa. Wits Journal of Clinical Medicine. 2020;2(2):105-14.
- Otuonye NM, Olumade TJ, Ojetunde MM, Holdbrooke SA, Ayoola JB, Nyam IY, Iwalokun B, Onwuamah C, Uwandu M, Abayomi A, Osibogun A, Bowale A, Osikomaiya B, Thomas B, Mutiu B, Odunukwe NN. Clinical and Demographic Characteristics of COVID-19 patients in Lagos, Nigeria: A Descriptive Study. J Natl Med Assoc. 2021;113(3):301-306.
- Parker A, Koegelenberg CFN, Moolla MS, Louw EH, Mowlana A, Nortjé A, Ahmed R, Brittain N, Lalla U, Allwood BW, Prozesky H, Schrueder N, Taljaard JJ. High HIV prevalence in an early cohort of hospital admissions with COVID-19 in Cape Town, South Africa. S Afr Med J. 2020;110(10):982-987.
- Sebastião CS, Neto Z, Martinez P, Jandondo D, Antonio J, Galangue M, de Carvalho M, David K, Miranda J, Afonso P, Inglês L, Carrelero RR, de Vasconcelos JN, Morais J. Sociodemographic characteristics and risk factors related to SARS-CoV-2 infection in Luanda, Angola. PLoS One. 2021;16(3):e0249249.
- du Bruyn E, Stek C, Daroowala R, Said-Hartley Q, Hsiao M, Goliath RT, Abrahams F, Jackson A, Wasserman S, Allwood B, Davis AG. Communicable and non-communicable co-morbidities and the presentation of COVID-19 in an African setting of high HIV-1 and tuberculosis prevalence. medRxiv. 2021 Jan 1. https://www.medrxiv.org/content/medrxiv/early/2021/05/11/2021.05.11.21256479.full.pdf
- Jassat W, Cohen C, Tempia S, Masha M, Goldstein S, Kufa T, Murangandi P, Savulescu D, Walaza S, Bam JL, Davies MA. COVID-19 in-hospital mortality in South Africa: The intersection of communicable and non-communicable chronic diseases in a high HIV prevalence setting, 2020. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3783089
- Hassan Z, Hashim MJ, Khan G. Population risk factors for COVID-19 deaths in Nigeria at sub-national level. Pan Afr Med J. 2020; 35(2):131.
- Hesse R, van der Westhuizen DJ, George JA. COVID-19-Related Laboratory Analyte Changes and the Relationship Between SARS-CoV-2 and HIV, TB, and HbA1c in South Africa. Adv Exp Med Biol. 2021; 1321:183-197.
- Ombajo LA, Mutono N, Sudi P, Mutua M, Sood M, Loo AM, et al. Epidemiological and clinical characteristics of COVID-19 patients in Kenya. medRxiv. 2020 Jan 1. https://www.medrxiv.org/content/10.1101/2020.11.09.20228106v1
- Abayomi A, Osibogun A, Kanma-Okafor O, Idris J, Bowale A, Wright O, et al. Morbidity and mortality outcomes of COVID-19 patients with and without hypertension in Lagos, Nigeria: a retrospective cohort study. Glob Health Res Policy. 2021; 6(1):26
- Abraha HE, Gessesse Z, Gebrecherkos T, Kebede Y, Weldegiargis AW, Tequare MH, et al. Clinical features and risk factors associated with morbidity and mortality among patients with COVID-19 in northern Ethiopia. Int J Infect Dis. 2021; 105:776-783.
- Bepouka BI, Mandina M, Makulo JR, Longokolo M, Odio O, Mayasi N, et al. Predictors of mortality in COVID-19 patients at Kinshasa University Hospital, Democratic Republic of the Congo, from March to June 2020. Pan Afr Med J. 2020; 37:105.
- Ibrahim OR, Suleiman BM, Abdullahi SB, Oloyede T, Sanda A, Gbadamosi MS, et al. Epidemiology of COVID-19 and Predictors of Outcome in Nigeria: A Single-Center Study. Am J Trop Med Hyg. 2020; 103(6):2376-2381.
- Mash RJ, Presence-Vollenhoven M, Adeniji A, Christoffels R, Doubell K, Eksteen L, et al. Evaluation of patient characteristics, management and outcomes for COVID-19 at district hospitals in the Western Cape, South Africa: descriptive observational study. BMJ Open. 2021; 11(1):e047016.
- Chanda D, Minchella PA, Kampamba D, Itoh M, Hines JZ, Fwoloshi S, et al. COVID-19 Severity and COVID-19-Associated Deaths Among Hospitalized Patients with HIV Infection - Zambia, March-December 2020. MMWR Morb Mortal Wkly Rep. 2021; 70(22):807-810.
- Pillay-van Wyk V, Bradshaw D, Groenewald P, Seocharan I, Manda S, Roomaney RA, et al. COVID deaths in South Africa: 99 days since South Africa's first death. S Afr Med J. 2020; 110(11):1093-1099.
- Mudenda V, Mumba C, Pieciak RC, Mwananyanda L, Chimoga C, Ngoma B, et al. Histopathological Evaluation of Deceased Persons in Lusaka, Zambia With or Without Coronavirus Disease 2019 (COVID-19) Infection: Results Obtained From Minimally Invasive Tissue Sampling. Clin Infect Dis. 2021; 73(5):S465-S471.
- Mucheleng'anga LA, Telendiy V, Hamukale A, Shibemba AL, Zumla A, Himwaze CM. COVID-19 and Sudden Unexpected Community Deaths in Lusaka, Zambia, Africa - A Medico-Legal Whole-Body Autopsy Case Series. Int J Infect Dis. 2021; 109:160-167.
- Himwaze CM, Telendiy V, Maate F, Mupeta S, Chitalu C, Chanda D, et al. Post-mortem examination of Hospital Inpatient COVID-19 Deaths in Lusaka, Zambia - A Descriptive Whole-body Autopsy Series. Int J Infect Dis. 2021; 108:363-369.
- UNAIDS. UNAIDS data 2021. https://www.unaids.org/en/resources/documents/2021/2021_unaids_data
- WHO. Global Tuberculosis Report 2021. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2021
- Aggarwal AN, Agarwal R, Dhooria S, Prasad KT, Sehgal IS, Muthu V. Active pulmonary tuberculosis and coronavirus disease 2019: A systematic review and meta-analysis. PLoS One. 2021; 16(10):e0259006.
- Bates M, Mudenda V, Shibemba A, Kaluwaji J, Tembo J, Kabwe M, Chimoga C, Chilukutu L, Chilufya M, Kapata N, Hoelscher M, Maeurer M, Mwaba P, Zumla A. Burden of tuberculosis at post mortem in inpatients at a tertiary referral centre in sub-Saharan Africa: a prospective descriptive autopsy study. Lancet Infect Dis. 2015; 15(5):544-51.
- Gupta RK, Lucas SB, Fielding KL, Lawn SD. Prevalence of tuberculosis in post-mortem studies of HIV-infected adults and children in resource-limited settings: a systematic review and meta-analysis. AIDS. 2015; 29(15):1987-2002.
- Hunter RL. Pathology of post primary tuberculosis of the lung: an illustrated critical review. Tuberculosis (Edinburgh, Scotland) 2011; 91(6):497-509.
- Subbian S, Tsenova L, Kim MJ, Wainwright HC, Visser A, Bandyopadhyay N, et al. Lesion-Specific Immune Response in Granulomas of Patients with Pulmonary Tuberculosis: A Pilot Study. PLoS One. 2015; 10(7):e0132249.
- Tsenova L, Singhal A. Effects of host-directed therapies on the pathology of tuberculosis. The Journal of pathology 2020; https://doi.org/10.1002/path.5407.
- Rivers D, James WRL, Davies DG, Thomson S. The prevalence of tuberculosis at necropsy in progressive massive fibrosis of coalworkers. British journal of industrial medicine 1957; 14(1):39.
- Zuo W, Zhao X, Chen Y-G. SARS Coronavirus and Lung Fibrosis. In: Molecular Biology of the SARS-Coronavirus, Springer. 2010: 247-58.
- Li X, Molina-Molina M, Abdul-Hafez A, Uhal V, Xaubet A, Uhal BD. Angiotensin converting enzyme-2 is protective but downregulated in human and experimental lung fibrosis. American Journal of Physiology-Lung Cellular and Molecular Physiology 2008; 295(1): L178-85.
- Kuba K, Imai Y, Rao S, Gao H, Guo, F., Guan B, Huan Y, et al. 2005. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus–induced lung injury. Nature medicine 2005; 11(8): 875-879.
- Imai Yumiko, Kuba Keiji, Rao Shuan, Huan Yi, Guo Feng, Guan Bin, et al. Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature 2005; 436(7047):112-6.
- Sarzani R, Giulietti F, Di PC, Giordano P, Spannella F. The Pathophysiology of COVID-19 and SARS-CoV-2 Infection: Disequilibrium between the classic renin-angiotensin system and its opposing arm in SARS-CoV-2-related lung injury. American Journal of Physiology-Lung Cellular and Molecular Physiology. 2020; 319(2): L325.
- Ravimohan S, Kornfeld H, Weissman D, Bisson GP. Tuberculosis and lung damage: from epidemiology to pathophysiology. European respiratory review: an official journal of the European Respiratory Society 2018; 27:147.
- DiFazio RM, Mattila JT, Klein EC, Cirrincione LR, Howard M, Wong EA, et al. Active transforming growth factor-beta is associated with phenotypic changes in granulomas after drug treatment in pulmonary tuberculosis. Fibrogenesis & tissue repair 2016; 9:6.
- Strydom N, Gupta SV, Fox WS, Via LE, Bang H, Lee M, et al. Tuberculosis drugs' distribution and emergence of resistance in patient's lung lesions: A mechanistic model and tool for regimen and dose optimization. PLoS Med. 2019; 16(4):e1002773.