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Evaluation of the Tuberculosis Local Organization Network 3 Programme for Early Detection of Tuberculosis Cases in Sagamu Local Government Area, Ogun State, Nigeria, 2015-2024

While the TB Local Organization Network 3 program in Sagamu, Nigeria, significantly increased tuberculosis case detection and bacteriological confirmation between 2015 and 2024, it failed to improve treatment success rates, which actually declined post-implementation, highlighting an urgent need to strengthen patient support and follow-up systems.

Original authors: AYO ADEBOWALE, Samuel Odunjo

Published 2026-06-29

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: AYO ADEBOWALE, Samuel Odunjo

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: Catching the "Invisible Thief"

Imagine Tuberculosis (TB) as a sneaky thief that steals people's health. In Nigeria, this thief has been hard to catch because many people don't know they have it, or they are afraid to go to the doctor.

This study looks at a specific town called Sagamu in Nigeria. The researchers wanted to see if a new program called TB LON 3 (think of it as a "Super Detective Squad") launched in 2020 actually helped find more of these thieves and keep people healthy. They looked at data from 2015 to 2024 to compare life before the squad arrived and life after.

The Detective Squad (The TB LON 3 Program)

Before 2020, the town mostly waited for people to walk into the hospital and say, "I think I'm sick." This is called passive detection. It's like waiting for a lost dog to wander back to your porch.

The TB LON 3 program changed the strategy. They sent trained staff out into the community to actively look for sick people, link them to tests, and make sure they started treatment. This is like sending a search party into the woods to find the lost dog instead of just waiting for it to show up.

What They Found: The Good News

1. They found way more "thieves" (Cases):
Before the program, most sick people were found by hospitals. After the program, the "search party" (community referrals) found almost half of all the cases.

The Analogy: Imagine a fishing net. Before 2020, they were using a tiny net in the harbor. After 2020, they dragged a massive net through the whole ocean. They caught way more fish (TB cases).
The Result: The number of people diagnosed with TB skyrocketed. In 2024, they found nearly four times as many cases as they did in 2015.

2. They got better at proving it (Diagnosis):
The program helped more people get the "gold standard" test (a machine that looks for the bacteria directly) rather than just guessing based on symptoms.

The Analogy: Before, they were guessing someone had a cold because they were sneezing. After, they gave everyone a high-tech breathalyzer to prove it was actually the flu.
The Result: The percentage of confirmed cases went up significantly.

What They Found: The Bad News

1. The "Finish Line" problem (Treatment Success):
Here is the tricky part. While the program was amazing at finding people, it didn't do as well at keeping them healthy until they were fully cured.

The Analogy: The Super Detective Squad is great at finding the lost dog and bringing it home. But once the dog is home, it keeps running away before it learns to stay.
The Result: Before the program, about 80% of people finished their treatment successfully. After the program, that number dropped to about 71%. More people started treatment but then stopped (lost to follow-up) or died.

2. The "Mid-Game Slump" (Follow-up):
The study noticed that people were good at showing up for check-ups in the first month, but many dropped off around the 5th month.

The Analogy: It's like a marathon. Everyone starts strong and runs the first mile fast. But by mile 20, many people are too tired, busy, or discouraged to finish the race.

Who is Most at Risk?

The researchers looked at who succeeded and who struggled:

Women vs. Men: Women were slightly more likely to finish treatment successfully than men.
HIV Status: People who also had HIV were less likely to finish their TB treatment. It's like trying to fix a flat tire while the car is also on fire; it's much harder to succeed.
Weight: People who started treatment with a healthy weight were more likely to succeed. Being underweight made it harder to beat the disease.

The Verdict

The TB LON 3 program was a huge success at finding the problem. It turned the lights on in a dark room and revealed just how many people were sick.

However, the program hit a wall when it came to finishing the job. Finding the patient is only step one; keeping them in the system until they are cured is step two, and that is where the system is currently struggling.

The Bottom Line:
The town of Sagamu now has a much better "search party," but they need to build a better "support team" to make sure the people they find actually stay in treatment until they are fully healed. The study concludes that to win the war against TB, they need to keep the community search going but also fix the "drop-off" point where patients lose interest or get lost in the system.

Technical Summary: Evaluation of the Tuberculosis Local Organization Network 3 Programme in Sagamu LGA, Nigeria (2015–2024)

Problem Statement
Tuberculosis (TB) remains a critical public health challenge in Nigeria, characterized by a significant gap between estimated incident cases and those notified to health authorities. Despite national improvements, suboptimal access to diagnosis, passive case detection strategies, and weak surveillance structures persist at the Local Government Area (LGA) level. In Sagamu LGA, Ogun State—a high-burden area with rapid urbanization and a notable HIV co-infection rate—there was a need to evaluate the impact of the USAID-funded TB Local Organization Network 3 (TB LON 3) project. Implemented in 2020, this intervention aimed to scale up early detection through active case finding, community referrals, and diagnostic strengthening. The study sought to determine whether these interventions successfully improved case detection, bacteriological confirmation, and treatment outcomes compared to the pre-intervention period.

Methodology
This was a retrospective secondary data analysis covering a decade (2015–2024) of TB case records from all Directly Observed Treatment, Short-course (DOTS) centers within Sagamu LGA. The study utilized a total of 5,718 notified TB cases.

Data Sources: Historical data from TB Central Registers, including demographic details, clinical characteristics, referral sources, diagnostic methods, and treatment outcomes.
Analytical Approach:
- Descriptive Statistics: Used to characterize the population (age, sex, HIV status, facility type).
- Comparative Tests: The z-test and Cochran–Armitage Chi-square test were employed to compare proportions of diagnostic methods and treatment success rates between the pre-intervention period (2015–2019) and the post-intervention period (2020–2024).
- Trend Analysis: Interrupted Time Series (ITS) analysis was conducted to evaluate the immediate and long-term effects of the TB LON 3 implementation on case notification trends, distinguishing between the pre- and post-intervention slopes.
- Predictive Modeling: Multivariable binary logistic regression was used to identify predictors of treatment success (defined as cured or treatment completed), adjusting for variables such as weight, gender, HIV status, and referral source.
Significance Level: Statistical significance was set at $\alpha = 0.05$ .

Key Results

Demographics and Clinical Profile: The mean age of patients was 37.1 years, with 56.1% being female. The 30–39 age group constituted the highest proportion (20.9%). Pulmonary TB accounted for 98.8% of cases, and 12.1% of patients were HIV-positive. The majority (64.9%) accessed care at Primary Health Care centers.
Case Detection and Referral Trends:
- Referral Sources: A major shift occurred post-2020. Community referrals surged from 1.2% (pre-intervention) to 49.5% (post-intervention), while health facility referrals decreased correspondingly.
- Notification Rates: The case notification rate per 1,000 population increased significantly after 2020. ITS analysis revealed a non-significant pre-intervention slope ( $\beta = 40.9$ , $p=0.166$ ) but a significant positive post-intervention slope ( $\beta = 226.5$ cases/year, $p=0.001$ ). Although there was a short-term dip in 2020 (attributed to the pandemic and intervention onset), the program was associated with a sustained acceleration in case detection.
Diagnostic Confirmation: Bacteriological confirmation rates increased significantly from 68.1% pre-intervention to 79.1% post-intervention ( $p < 0.001$ ), indicating improved access to diagnostic tools like GeneXpert.
Treatment Outcomes:
- Success Rates: Contrary to detection gains, treatment success rates declined significantly from 79.5% (pre-intervention) to 71.3% (post-intervention) ( $p < 0.001$ ).
- Attrition: Loss to follow-up increased from 8.6% pre-intervention to 15.1% post-intervention.
- Follow-up Monitoring: Monitoring was highest at Month 2 but declined markedly by Month 5, particularly among HIV-positive patients and older adults.
Predictors of Treatment Success: Logistic regression identified several significant predictors:
- Positive Associations: Female gender (AOR = 1.199), higher baseline weight, and clinical diagnosis (vs. bacteriological) were associated with higher success.
- Negative Associations: HIV-positive status (AOR = 0.726), community referral sources (AOR = 0.602), and treatment at tertiary facilities (AOR = 0.604) were associated with lower odds of success.

Key Contributions and Significance
The study provides empirical evidence that the TB LON 3 program successfully achieved its primary goal of expanding case detection and improving diagnostic quality in Sagamu LGA. By shifting from passive facility-based detection to active community-based referral, the program nearly doubled the proportion of cases identified through community channels and significantly increased the rate of bacteriological confirmation.

However, the paper highlights a critical divergence: while the "front end" of the TB cascade (detection and diagnosis) improved, the "back end" (treatment retention and success) deteriorated. The authors claim that the rapid scale-up of case finding placed additional demands on treatment and follow-up systems that were not adequately strengthened. The study underscores that increased detection alone is insufficient without parallel improvements in patient tracking, adherence counseling, and support for high-risk groups (specifically HIV-positive individuals and those referred from the community).

Conclusion
The evaluation concludes that while the TB LON 3 program was effective in scaling up early detection and bacteriological confirmation, it did not improve treatment outcomes in Sagamu LGA. The authors assert that future interventions must prioritize strengthening patient follow-up systems, addressing mid-treatment attrition (specifically at Month 5), and integrating nutritional and HIV support to ensure that the increased number of detected cases translates into cured patients. The findings serve as a policy-relevant call to balance active case finding with robust treatment support mechanisms to achieve Sustainable Development Goal 3 targets for TB elimination.