Impact of a Mobile Application for Public-Private Tuberculosis Screening on Case Detection and Notification in Nigeria

The implementation of the Mobile Application for Tuberculosis Screening (MATS) in Nigeria significantly enhanced case detection and surveillance, particularly within the private sector and northern regions, by identifying over 4,700 new cases, though its full impact is currently constrained by logistical and economic barriers to diagnostic follow-up.

The Gist

Imagine Nigeria is a massive, bustling city trying to find a few specific people who are hiding in plain sight. These "hiding people" are individuals suffering from Tuberculosis (TB), a serious lung disease. The problem is that for a long time, the city's health system was like a net with huge holes in it. Many sick people were walking around, getting sicker, and never getting caught because they visited private shops or small clinics that didn't talk to the big government hospitals.

This paper is the story of how Nigeria tried to fix those holes using a digital fishing rod called MATS (Mobile Application for Tuberculosis Screening).

Here is the story of what happened, broken down simply:

1. The Problem: The "Lost and Found" Gap

For years, Nigeria had a lot of TB cases that were never officially counted. It was like having a library where books were being borrowed but never checked out.

• The Public Sector: Government hospitals were good at finding sick people, but they were often too far away or too crowded for many Nigerians.
• The Private Sector: Most people went to private clinics or small "patent medicine vendors" (local drug shops) first. These places were great at seeing patients, but they were like isolated islands. They didn't report the sick people to the main government system, so the "sick count" was always too low.

2. The Solution: The Digital Fishing Rod (MATS)

To fix this, they introduced a smartphone app called MATS. Think of this app as a smart metal detector for health workers.

• How it worked: Instead of just asking "Do you have a cough?", health workers used the app to ask a specific set of questions (like "Do you have night sweats?" or "Have you lost weight?").
• The Magic: The app acted like a traffic cop. If a patient's answers matched the pattern of TB, the app immediately flagged them as "High Risk" and gave the health worker a digital ticket to send them to a lab for a real test. If they didn't match, the app said, "Okay, this is probably something else," saving time.

3. The Results: Catching More Fish with a Better Net

The researchers looked at data from 2021 to 2023 and compared it to the years before the app existed. Here is what they found:

• The Big Net: They screened over 1 million people using the app.
• The Catch: They found nearly 284,000 people who were suspected of having TB.
• The Private Sector Win: The biggest surprise? The app worked best in the private sector. Over 60% of the suspected cases were found in private clinics and drug shops. It's like realizing that the best place to find lost keys wasn't the front door, but the back porch where everyone actually hangs out.
• Better Accuracy: Before the app, the health system cast a wide net and caught a lot of "false alarms" (people who looked sick but weren't). The yield was only 6.5%. After the app, the yield jumped to 16.9%.
  • Analogy: Imagine you are looking for a needle in a haystack. Before, you grabbed a huge chunk of hay and found a needle once every 15 grabs. With the app, you used a magnet that only picked up the metal parts, and now you find a needle once every 6 grabs. You are catching the sick people much more efficiently.

4. The Bumpy Road: Why Some Fish Got Away

Even though the app was great at finding the sick people, getting them to the finish line (getting tested and treated) was still hard.

• The "Last Mile" Problem: The app successfully flagged the sick person, but then they had to travel to a lab.
• The Barriers: Many people couldn't make it because:
  • Money: They couldn't afford the bus fare to the lab.
  • Time: They had to take a day off work.
  • Fear: They were scared of being judged or stigmatized if people found out they had TB.
  • Tech Issues: Sometimes the internet was too slow to send the data, like trying to send a text message in a dead zone.

5. What the People Said

The researchers talked to doctors, clinic owners, and patients.

• The Good: Doctors loved the app because it made their paperwork easier and helped them keep track of patients. Patients liked that it felt fast and organized.
• The Bad: Everyone agreed that the internet connection was often terrible, and the app sometimes froze.
• The Wish List: They asked for:
  • Better internet and free data for the app.
  • Money for patients to pay for bus fare to get tested.
  • More public awareness so people aren't scared to come forward.

The Bottom Line

The paper concludes that MATS was a huge success. It proved that giving health workers a simple digital tool can help them find sick people in the private sector much better than before. It turned a scattered, messy system into a more organized one.

However, the app is just the first step. It's like having a great map that shows you where the treasure is, but you still need a boat (money, transport, and support) to actually go get it. If Nigeria fixes the "boat" issues (transport costs, stigma, and internet), this digital tool could help save thousands of lives.

In short: The app was the flashlight that finally showed where the sick people were hiding. Now, the country just needs to build the bridge to get them the help they need.

Technical Summary

1. Problem Statement

Tuberculosis (TB) remains a critical public health crisis in Nigeria, which ranks sixth globally and first in Africa for TB incidence. Despite recent gains in case notifications, a significant diagnostic and treatment gap persists, with an estimated 38% of TB cases remaining undiagnosed or unreported annually.

• Key Challenges:
  • Fragmented Healthcare System: A significant portion of healthcare seeking occurs in the private sector (up to 70% of services), which often operates outside national surveillance systems.
  • Weak Case Finding: Traditional screening methods struggle with incomplete reporting, weak linkage to diagnostic services, and poor real-time data capture.
  • Access Barriers: Rural and peri-urban populations face logistical and economic barriers to accessing diagnostic services.
• The Intervention: To address these gaps, the Mobile Application for Tuberculosis Screening (MATS) was introduced. It is a digital tool designed to screen for presumptive TB cases, utilize a clinical decision algorithm for triage, and facilitate referral to diagnostic testing within Public-Private Mix (PPM) healthcare settings.

2. Methodology

The study employed a mixed-methods design combining retrospective quantitative analysis with qualitative thematic analysis to evaluate the impact of MATS.

• Study Setting: Conducted across 24 states in five geopolitical zones (North-West, North-Central, South-West, South-East, South-South). The North-East was excluded due to security instability.
• Study Period:
  • Pre-Implementation (Control): 2017–2019 (Data from routine facility registers and NTBLCP surveillance).
  • Post-Implementation (Intervention): 2021–2023 (Data from the MATS database).
• Data Sources:
  • Quantitative: MATS application database, routine facility records, and national TB surveillance data.
  • Qualitative: 60 Key Informant Interviews (KIIs) with stakeholders (program managers, health workers, patients) and 6 Focus Group Discussions (FGDs) across geopolitical zones.
• Analysis:
  • Quantitative: Comparative analysis of TB indicators (presumptive identification, testing rates, diagnostic yield) using STATA 17.
  • Qualitative: Thematic analysis using NVivo 14 based on Braun and Clarke's framework to explore user experiences, barriers, and integration dynamics.

3. Key Contributions

• Evidence on Digital Health in PPM: Provides robust empirical data on the effectiveness of mobile health (mHealth) tools in bridging the gap between private sector providers and national TB control programs in a high-burden, low-resource setting.
• Refinement of Screening Algorithms: Demonstrates how algorithmic screening (MATS) can shift case finding from a "volume-based" approach to a "quality-focused" approach, improving the precision of presumptive case identification.
• Private Sector Engagement: Quantifies the critical role of non-state actors (Private-for-profit facilities and Patent Medicine Vendors) in TB case detection, showing they contributed over 60% of presumptive identifications.
• Operational Insights: Offers a granular view of the "TB Cascade," identifying specific drop-off points between screening and diagnosis, and highlighting systemic barriers (logistics, cost, stigma) that digital tools alone cannot solve.

4. Key Results

Quantitative Findings

• Screening Volume: Between 2021 and 2023, 1,048,575 individuals were screened using MATS across 24 states.
• Presumptive Identification: 283,807 individuals were identified as presumptive TB cases.
  • Note: 127,938 records were excluded for having zero TB-compatible symptoms, leaving 155,869 symptom-positive presumptive cases for cascade analysis.
• Diagnostic Yield:
  • Pre-MATS: Diagnostic yield was 6.5% (15,249 cases diagnosed from 233,646 tested).
  • During MATS: Diagnostic yield increased significantly to 16.9% (4,725 cases diagnosed from 27,901 tested).
  • Interpretation: While the absolute number of people tested decreased (due to stricter algorithmic exclusion of low-risk individuals), the efficiency of case detection more than doubled.
• Regional Impact: The most significant improvements in diagnostic yield were observed in the North-West (rising from 7.0% to 44.5%) and North-Central (rising from 5.9% to 31.9%).
• Facility Type: Private-for-profit facilities (34.1%) and Patent Medicine Vendors (28.4%) accounted for 62.5% of all presumptive identifications, underscoring the necessity of private sector engagement.

Qualitative Findings

• User Experience: Health workers and patients generally viewed MATS positively, citing improved data documentation, ease of use, and streamlined workflows.
• Barriers to Linkage: Despite successful screening, significant attrition occurred before diagnosis due to:
  • Economic Constraints: Transport costs and loss of daily wages.
  • Logistical Issues: Poor internet connectivity, device limitations, and long waiting times at facilities.
  • Social Factors: Stigma, fear of discrimination, and cultural beliefs.
• Integration Challenges: While MATS improved data flow, full integration with national surveillance systems was hampered by infrastructural issues (network instability) and the need for offline capabilities.

5. Significance and Implications

• Strategic Shift: The study validates the shift from broad, volume-based screening to targeted, algorithm-driven screening. MATS successfully filtered out low-risk individuals, allowing resources to be focused on high-probability cases, thereby increasing the diagnostic yield.
• Policy Recommendations:
  • Infrastructure: Investment in reliable internet and dedicated devices for health workers is critical.
  • Patient Support: Financial incentives (e.g., transport support) and community sensitization are required to overcome the "last mile" barrier of getting presumptive cases to diagnostic centers.
  • Integration: MATS should be integrated with other health programs, particularly HIV services, to create a holistic digital health ecosystem.
• Scalability: The findings suggest that digital tools like MATS are highly scalable and effective for strengthening TB surveillance in pluralistic health systems, provided that complementary investments are made in health system strengthening and patient support mechanisms.

Conclusion: MATS significantly enhanced the efficiency and quality of TB case detection in Nigeria's private sector. While operational and systemic barriers to diagnostic follow-through remain, the tool demonstrates strong potential for scale-up and integration into broader national disease management frameworks.