Malaria attributable fractions with changing transmission intensity: Bayesian latent class vs logistic models
Kennedy Mwai1,2, Irene Nkumama3,4, Amos Thairu3
1Epidemiology and Biostatistics Division, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa. kmwai@kemri-wellcome.org.
Malaria Journal
|November 11, 2022
Summary
This study shows that using probabilities, not just thresholds, improves malaria research. Probabilities offer a better statistical approach for identifying immune protection, even as malaria transmission declines.
Area of Science:
- Malariology
- Biostatistics
- Epidemiology
Background:
- Asymptomatic malaria parasite carriage is common in high-transmission areas, complicating research definitions for clinical malaria.
- Accurate identification of immune correlates of protection against clinical malaria is crucial.
- Parasite density thresholds are commonly used to define malaria-related fevers, but varying transmission intensity can impact these thresholds.
Purpose of the Study:
- To investigate the impact of varying malaria transmission intensity on parasite density thresholds.
- To compare statistical approaches for estimating malaria-attributable fractions and fever probabilities.
- To evaluate the utility of probabilities versus binary data in identifying immune correlates of protection.
Main Methods:
- Longitudinal data from children in Kilifi, Kenya (2005-2017) were analyzed.
- Bayesian latent class and logistic power models were compared for estimating malaria-attributable fractions and fever probabilities.
- Zero-inflated beta regressions and multilevel binary regression were used to assess antibody correlates of protection.
Main Results:
- Malaria transmission intensity decreased significantly from 2006 to 2017.
- The Bayesian latent class approach yielded lower malaria-attributable fractions and fever probabilities compared to the logistic regression model.
- Both models showed similar trends with changing transmission intensity, and probability-based approaches improved statistical fit for identifying immune correlates.
Conclusions:
- Malaria transmission intensity influenced malaria-attributable fractions but not significantly the identification of immune correlates of protection.
- The study confirms the statistical advantage of using probabilities over binary data in malaria research.
- Probability-based statistical methods provide a more robust approach for analyzing malaria data and identifying protective immunity.
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