A Longitudinal Cohort Study of Malaria Exposure and Changing Serostatus in a Malaria Endemic Area of Rural Tanzania
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BackgroundMeasurements of anti-malarial antibodies are increasingly used as a proxy of transmission intensity. Most serological surveys are based on the use of cross-sectional data that, when age-stratified, approximates historical patterns of transmission within a population. Comparatively few studies leverage longitudinal data to explicitly relate individual infection events with subsequent antibody responses.
MethodsThe occurrence of seroconversion and seroreversion events for two Plasmodium falciparum asexual stage antigens (MSP-1 and AMA-1) was examined using three annual measurements of 691 individuals from a cohort of individuals in a malaria-endemic area of rural east-central Tanzania. Mixed-effect logistic regression models were employed to determine factors associated with changes in serostatus over time.
ResultsWhile the expected population-level relationship between seroprevalence and disease incidence was observed, on an individual level the relationship between individual infections and the antibody response was complex. MSP-1 antibody responses were more dynamic in response to the occurrence and resolution of infection events than AMA-1, while the latter was more correlated with consecutive infections. The MSP-1 antibody response to an observed infection seemed to decay faster over time than the corresponding AMA-1 response. Surprisingly, there was no evidence of an age effect on the occurrence of a conversion or reversion event.
ConclusionsWhile the population-level results concur with previously published sero-epidemiological surveys, the individual-level results highlight the more complex relationship between detected infections and antibody dynamics than can be analysed using cross-sectional data. The longitudinal analysis of serological data may provide a powerful tool for teasing apart the complex relationship between infection events and the corresponding immune response, thereby improving the ability to rapidly assess the success or failure of malaria control programmes.
Published Version (Please cite this version)
Simmons, RA, L Mboera, A Stresman, E Turner, R Kramer, C Drakeley and WP O'Meara (2017). A Longitudinal Cohort Study of Malaria Exposure and Changing Serostatus in a Malaria Endemic Area of Rural Tanzania. Malaria Journal, 16(309). p. 309. 10.1186/s12936-017-1945-2 Retrieved from https://hdl.handle.net/10161/24522.
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Education: Masters Degree, Biostatistics. Duke University School of Medicine. 2015
Dr. Turner is Associate Professor of Biostatistics and Global Health and serves as Director of the Research Design and Analysis Core of the Duke Global Health Institute. Her primary methodological focus is on the design and analysis of randomized controlled trials, particularly those that involve clustering such as cluster randomized trials (CRTs), stepped wedge CRTs and individually-randomized group treatment trials. She is expert in the implementation of trials in low resource settings, with a substantive focus on malaria, mental health and cardiovascular disease.
Dr. Turner joined the Department of Biostatistics & Bioinformatics and Duke Global Health Institute in March 2012 following four years as Research Fellow in the Department of Medical Statistics at the London School of Hygiene and Tropical Medicine (LSHTM). Since then, she has continued to hold a joint position with Duke's Global Health Institute (DGHI) where she serves as faculty statistician and collaborates with faculty and affiliates. Dr. Turner earned her undergraduate honors degree in Mathematics from the University of Warwick, UK, during which she spent an intercalated year at the Universite of Pierre et Marie Curie, Paris, France. She then earned her MSc and PhD in Statistics from McGill University, Canada, with her doctoral studies funded by the prestigious Commonwealth Scholarship.
Thanks to her participation in multi-disciplinary projects, Dr. turner has a great appreciation for the importance of good study design and data collection and is well aware that no fancy statistical analyses can save researchers from the scourge of bad data. Through those experiences and her teaching in different settings, including the UK, Canada, France and Tanzania, she is aware that statisticians and their collaborators sometimes “speak a different language”. As a result, her approach is very much one of translation, pragmatism and collaboration.
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