Sample size determination for estimating antibody seroconversion rate under stable malaria transmission intensity

Area of Science:

• Epidemiology
• Biostatistics
• Infectious Disease Modeling

Background:

• Seroprevalence (SP) and seroconversion rate (SCR) are key malaria burden indicators in low-transmission areas.
• Traditional studies often focus on parasite rates, not these serological measures.
• Sample size determination for SCR is complex due to age distribution impacts on precision and power.

Purpose of the Study:

• To propose two novel sample size calculators for estimating SCR.
• To address situations with known and unknown seroreversion rates (SRR).
• To optimize study design for malaria sero-epidemiological surveys.

Main Methods:

• Developed a calculator transforming SP confidence intervals to SCR confidence intervals (known SRR).
• Created a second calculator for unknown SRR using data simulation and linear regression.
• Evaluated calculator performance using coverage analysis and realistic SCR/age distribution data.

Main Results:

• The first calculator showed adequate coverage for sample sizes ≥50 in most settings, but issues arose for very low/high transmission with ≤250 samples.
• African studies often require larger sample sizes than non-African ones for SCR < 0.058.
• The second calculator, for unknown SRR, requires larger sample sizes (≥500) to avoid underestimation in low transmission settings.

Conclusions:

• The proposed calculators are valuable tools for designing cross-sectional malaria surveys.
• Sample size decisions must balance statistical precision with ethical and resource considerations.
• The methodology can inform studies comparing different transmission intensities and guide targeted sampling strategies.