Prevalence and factors associated with multidrug resistant Mycobacterium tuberculosis infection in Cameroon: a systematic review and meta-analysis

The Rising Tide of Resistance in Cameroon

Tuberculosis (TB) remains one of the most persistent global killers. This is especially true in low- and middle-income countries. Here, healthcare infrastructure often struggles to keep pace with bacterial evolution. While standard TB is treatable with a predictable antibiotic regimen, a more dangerous variant exists: multidrug-resistant tuberculosis (MDR-TB). This form of the disease is resistant to at least isoniazid and rifampicin. These are two of the most powerful frontline weapons in the medical arsenal. Because MDR-TB requires longer, more expensive, and more toxic treatments, it frequently results in higher mortality rates.

In Cameroon, the battle against TB has been characterized by fragmented data. Various studies have attempted to map the landscape of drug resistance. However, they have often relied on different diagnostic methods. They have studied different populations or focused on specific regions. This leaves a massive gap in our understanding of the national burden. Researchers have lacked a unified picture of how prevalent these resistant strains truly are. Crucially, they have lacked clarity on which specific histories drive their emergence. This systematic review and meta-analysis aims to bridge that gap. It synthesizes nearly three decades of research to provide a look at the state of drug resistance in the country.

The Gap in National Surveillance

To manage a public health crisis, one must first be able to measure it accurately. Currently, TB control programs in Cameroon face a fundamental visibility problem. Diagnostic capabilities are not uniform across the country. Urban centers might utilize advanced molecular tools (tests that detect bacterial DNA). Meanwhile, rural areas often rely on older, less sensitive methods like sputum microscopy (examining mucus under a microscope). This creates a "blind spot" in surveillance. Resistant strains can circulate undetected in these areas.

Furthermore, the existing body of literature is highly heterogeneous (showing significant variation). Some studies focus on "initial resistance." These are strains resistant from the very first time they are detected. Other studies look at "acquired resistance." This occurs when a patient develops resistance during the course of treatment. Without a way to pool these disparate findings, policymakers cannot easily discern the nature of the threat. They cannot tell if the rise comes from new infections or from failures in current treatment protocols. As noted in the study's introduction, this lack of consolidated evidence has historically limited informed decision-making. This affects both resource allocation and the scaling of rapid molecular diagnostics.

Synthesizing Decades of Evidence

The authors addressed this fragmentation through a rigorous meta-analysis. This method transforms hundreds of individual data points into a coherent national profile. Their methodology followed a structured hierarchy to ensure the integrity of the pooled estimates:

1. Comprehensive Extraction: The researchers searched multiple major databases. These included PubMed, Embase, and African Journals Online. They identified 28 studies spanning from 1995 to 2022.
2. Quality Appraisal: Using the Joanna Briggs Institute (JBI) critical appraisal tools, the team evaluated the risk of bias. This ensured the mathematical weight given to each finding matched its methodological rigor.
3. Statistical Modeling: To handle the inherent "noise" in the data, the authors employed a random-effects meta-analysis. They used the DerSimonian–Laird method to account for differences between studies.
4. Binary Transformation: Since drug resistance is a binary outcome, they used generalized linear mixed models (GLMM). They paired this with a probit-logit transformation (PLOGIT) to accurately calculate the odds of resistance.

By applying these layers, the study moved beyond simple averaging. This allowed the researchers to tease apart the influences of geography, time, and specific diagnostic tests.

Mapping the Burden of Resistance

The results reveal a significant landscape of resistance. The authors report a pooled prevalence of MDR-TB in Cameroon of 5.2% (95% CI: 2.7-9.6) among 7,515 patients .

However, a sharp distinction exists in how that resistance is categorized. The prevalence of acquired resistance was 11.6%. This is significantly higher than the 2.0% observed for initial resistance. This statistical difference characterizes the composition of the MDR-TB burden in Cameroon.

The breakdown of resistance patterns provides further granularity: * Any Resistance: The prevalence of resistance to at least one anti-TB drug was 16.0% .

This means roughly one in six patients carries some form of resistance. * Rifampicin Resistance: Resistance to rifampicin—a key marker for MDR-TB—stood at 4.6% .

• Monoresistance: The highest rates of resistance to a single drug were seen in streptomycin (6.4%) and isoniazid (4.7%).

The study also identifies biological and social predictors of this phenomenon. Through a synthetic analysis, the authors found that a previous history of TB infection was the strongest indicator. It increased the odds of resistance by a factor of 3.9 (OR = 3.9). This was followed by alcohol consumption (OR = 1.8) and a history of incarceration (OR = 1.7). These findings highlight a link between certain socio-behavioral factors and the presence of drug resistance.

Limits of the Synthesis

While the study provides a comprehensive view, it is not a perfect mirror of reality. First, the authors acknowledge substantial heterogeneity across their analyses. This means the "5.2%" figure is a mathematical approximation. Resistance levels fluctuate wildly depending on the region or the diagnostic method used.

Second, the geographic distribution of the underlying studies is uneven. Much of the data is concentrated in the Centre and Littoral regions. Consequently, the national estimate may not perfectly reflect the reality in under-studied provinces. Finally, the researchers detected potential publication bias in the MDR-TB prevalence analysis. This was evidenced by an asymmetric funnel plot and a significant Egger's test (p = 0.038). This suggests that studies with extreme results might be more likely to be published. This could potentially skew the pooled results.

The Verdict: A Call for Targeted Intervention

The evidence presented by the authors indicates that drug-resistant tuberculosis is a major challenge in Cameroon. The fact that acquired resistance (11.6%) is much higher than initial resistance (2.0%) is a significant observation. This pattern is often associated with issues in treatment adherence and monitoring.

The findings suggest a need for systemic reform. To curb this trend, Cameroon may need to move away from reliance on manual, slow-moving diagnostic methods. Instead, the country could benefit from the widespread deployment of rapid molecular diagnostics, such as GeneXpert MTB/RIF. Furthermore, since previous infection and lifestyle factors like alcohol use are identified predictors, TB control must be multifaceted. Integrating social support and counseling for high-risk groups may be essential. Addressing these structural gaps is vital to preventing the further spread of resistant strains.

Figures from the paper