Integrated screening reveals rare co-infections of gambiense human African trypanosomiasis, malaria and loiasis in Banga, northern Angola

A retrospective analysis of over 6,000 individuals in northern Angola reveals that while malaria remains highly prevalent and co-infections with rare gambiense sleeping sickness and loiasis are uncommon, integrated screening platforms effectively support the surveillance and elimination of sleeping sickness in co-endemic settings when robust confirmatory algorithms are maintained.

The Gist

Imagine a bustling village in northern Angola called Banga. In this village, three invisible "guests" are constantly trying to move in: Malaria, Sleeping Sickness (a type of African Trypanosomiasis), and Loiasis (a worm infection).

This paper is like a report card from a massive health check-up that happened in 2021. The doctors didn't just look for one guest; they set up a "triple-threat" security system to catch all three at once. Here is what they found, explained simply:

1. The Three "Guests"

• Malaria: Think of this as the loud, rowdy party guest. It shows up constantly, especially in the summer, and loves to crash the house. It's very common in this village.
• Sleeping Sickness (gHAT): This is the rare, quiet intruder. It used to be a big problem, but thanks to hard work, it's now very rare. However, because it's so dangerous, the doctors still need to keep a very sharp eye out for it.
• Loiasis: This is the sneaky guest. It's a worm that lives under the skin. You might not even know it's there until a doctor looks closely. It's rare, but it's tricky because treating it can sometimes cause big problems if you aren't careful.

2. The Big Check-Up (The Study)

The health team went door-to-door and stopped at community gathering spots to check 6,117 people. They used a smart strategy: instead of sending people to three different hospitals for three different tests, they did one integrated screening.

• The Malaria Result: The "rowdy party guest" was everywhere. About 63% of the people had malaria. It was most common in children (ages 5–14) and men. It's like a heavy fog that covers the village, but the fog is thicker in some neighborhoods than others.
• The Sleeping Sickness Result: The "quiet intruder" was almost gone. They only found about 0.5% of people with it. This is great news! It means the village is very close to kicking this disease out completely.
• The Loiasis Result: The "sneaky guest" was found in only 8 people out of the thousands. It was so rare that the doctors mostly found it by accident while looking for the other two.

3. The "Double Trouble" (Co-infections)

The most interesting part of the story is when people had more than one guest at the same time.

• Most people had either no guests (36%) or just one (63%).
• Only 0.4% of people had a "double booking" (two diseases) or a "triple booking" (three diseases).

Why does this matter?
Imagine you are a doctor. If a patient has a fever, it could be Malaria. But if they also have Sleeping Sickness, the treatment changes. If they have Loiasis too, you have to be extra careful with the medicine so you don't hurt them.
The study found that while these "double bookings" are very rare, they are critical to catch. If you miss them, the treatment could go wrong.

4. The Big Lesson

The main takeaway is that doing things together is smarter than doing them separately.

In the past, you might have needed one team to check for Malaria, another for Sleeping Sickness, and a third for worms. That's expensive and slow.
This study shows that in places like Banga, you can send one team with one toolkit to check for all three.

• The Good News: It works! They found the rare Sleeping Sickness cases without missing the common Malaria cases.
• The Catch: Because the rare diseases are so rare, the doctors have to be very precise. They can't just guess; they need to do extra tests to make sure they aren't making a mistake.

Summary Analogy

Think of the village as a house.

• Malaria is like a mosquito buzzing around the kitchen. It's annoying and common, so you keep a fly swatter ready.
• Sleeping Sickness is like a burglar who hasn't been seen in years. You still check the locks every night, just in case, because if they get in, it's a disaster.
• Loiasis is like a hidden leak in the pipes. You only find it when you are already looking for the burglar.

The study says: "Don't hire three different security guards. Hire one smart guard who knows how to spot the mosquito, check for the burglar, and find the leak all at the same time."

This approach saves money, saves time, and keeps the village safe from all three threats.

Technical Summary

1. Problem Statement

In Central Africa, Gambiense human African trypanosomiasis (gHAT), malaria, and loiasis overlap geographically and share non-specific clinical features (e.g., fever, headache). As gHAT incidence declines toward the WHO 2030 elimination target, the operational challenge shifts to detecting residual transmission in co-endemic settings.

• Diagnostic Complexity: High malaria prevalence can obscure early gHAT symptoms, and cross-reactivity or co-infections can complicate clinical management.
• Programmatic Risk: Loiasis (Loa loa) poses a specific risk in co-endemic areas because high microfilarial loads can trigger severe adverse events (SAEs) during mass drug administration or treatment of other conditions.
• Knowledge Gap: There is a need to evaluate the efficiency of integrated screening platforms that simultaneously target these three diseases to balance sensitivity, specificity, and resource allocation in settings approaching gHAT elimination.

2. Methodology

Study Design & Setting:

• Type: Retrospective cross-sectional analysis of secondary data.
• Location: Banga Municipality, Cuanza Norte Province, Angola (a hyperendemic malaria area with active gHAT foci and moderate loiasis risk).
• Period: June to September 2021 (two phases covering post-rainy season and improved accessibility).
• Population: 6,117 residents screened via active community mobilization.

Diagnostic Workflow:

• Malaria: Diagnosed using HRP-2 based Rapid Diagnostic Tests (RDTs). Uncomplicated cases treated on-site; severe/co-infected cases referred to the municipal hospital.
• gHAT:
  • Screening: Serological tests (CATT 1.3, CATT Titration, and RDT-HAT 2.0).
  • Confirmation: Parasitological confirmation via Capillary Tube Centrifugation (CTC) and/or Mini Anion Exchange Centrifugation (mAECT) for seropositive individuals.
  • Staging/Treatment: CSF examination for staging; treated with Pentamidine (Stage 1) or NECT (Stage 2).
• Loiasis: Detected incidentally during the parasitological examinations (CTC) performed for gHAT confirmation. No systematic community-wide loiasis screening was conducted.

Statistical Analysis:

• Prevalence stratified by sex, age, and commune.
• Associations assessed via Chi-square or Fisher's exact tests.
• Multivariable logistic regression used to identify factors associated with malaria infection (adjusting for age, sex, and location).

3. Key Contributions

• Operational Validation: Demonstrates that integrated screening for gHAT and malaria is feasible and efficient in co-endemic settings, even as gHAT prevalence drops to very low levels.
• Co-infection Epidemiology: Provides rare empirical data on the frequency of triple co-infections (gHAT + Malaria + Loiasis) in a real-world screening campaign, showing they are statistically uncommon (<1%).
• Diagnostic Algorithm Refinement: Highlights the critical importance of maintaining robust confirmatory parasitological algorithms (CTC/mAECT) to manage false positives and detect incidental loiasis, which is vital for patient safety.
• Demographic Insights: Identifies specific demographic and geographic risk factors for malaria in a setting where gHAT is becoming rare.

4. Key Results

Demographics:

• Total screened: 6,117 individuals (53.8% female).
• 60.6% were children/adolescents (0–19 years).

Infection Prevalence:

• Malaria: Highly prevalent at 62.9% (3,862 cases).
  • Highest in children aged 5–14 years (>70%).
  • Significantly higher in males (68.5%) than females (59.1%).
  • Significant variation by commune (highest in Aldeia Nova and Cariamba).
• gHAT: Low prevalence at 0.56% (34 confirmed cases).
  • Slightly higher in females (0.7%) than males (0.4%), though not statistically significant.
  • Cases were distributed across age groups but slightly more common in adults (15–49 years).
• Loiasis: Very rare at 0.13% (8 cases), detected incidentally.

Co-infections:

• Mono-infection: 63.0% of participants.
• No Infection: 36.6% of participants.
• Co-infection: Occurred in only 0.43% (26/6,117) of individuals.
  • 25 individuals had dual infections.
  • 1 individual had a triple infection (Malaria + gHAT + Loiasis).

Multivariable Analysis (Malaria):

• Age: Each additional year of age reduced the odds of malaria by 2.8% (OR 0.97).
• Sex: Males had 39% higher odds of infection compared to females (OR 1.39).
• Location: Residents of Banga-Sede had significantly lower odds of malaria compared to Aldeia Nova (OR 0.37).

5. Significance and Implications

• Sustained Surveillance: Even as gHAT approaches elimination, integrated screening remains essential to detect residual cases without compromising malaria control, which remains the dominant health burden.
• Safety in Co-endemicity: The incidental detection of loiasis underscores the necessity of parasitological confirmation steps. Identifying loiasis is critical to prevent severe adverse events if patients require treatment for other conditions or if mass drug administration is planned.
• Resource Optimization: The study supports the "integrated approach" for mobile health teams, allowing for the simultaneous management of multiple NTDs. However, it emphasizes that diagnostic specificity must be maintained to avoid over-referral of false-positive gHAT cases in high-malaria settings.
• Policy Recommendation: Future campaigns in similar settings should consider pre-planned loiasis risk assessments (e.g., RAPLOA) alongside standard gHAT/malaria screening to ensure safe clinical management and accurate epidemiological mapping.

Conclusion:
In Banga, Angola, malaria is the dominant infection, while gHAT and loiasis are rare but persistent. Integrated screening successfully identified these overlapping infections, confirming that such platforms are operationally viable for sustaining gHAT elimination efforts while managing the high burden of malaria and the risks associated with loiasis.