BibTex format
@article{Hicks:2026:10.1016/j.lanmic.2026.101415,
author = {Hicks, J and Munsey, A and Mousa, A and Cairns, M and Hill, A and Gowelo, S and Knock, E and Baguelin, M and Matambisso, G and Pujol, A and Mayor, A and Madanitsa, M and Khairallah, C and van, Eijk A and Chandramohan, D and Chacky, F and Greenwood, B and O, ter Kuile F and Fitzjohn, R and Gutman, JR and Walker, P and on, behalf of the Antenatal Care-Based Malaria Prevalence Study Group},
doi = {10.1016/j.lanmic.2026.101415},
journal = {The Lancet Microbe},
title = {Disentangling patterns of community malaria transmission and burden using malaria prevalence among pregnant women attending antenatal care: a modelling study},
url = {http://dx.doi.org/10.1016/j.lanmic.2026.101415},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - BackgroundMalaria prevalence measured among pregnant women at the first antenatal care (ANC1) visit provides longitudinal estimates of malaria burden in pregnancy and correlates well with cross-sectional community prevalence, but additional analysis is required to estimate community incidence. We aimed to test whether ANC1-based malaria prevalence can, via an open-source, mechanistic, model-based framework, recover seasonal patterns of clinical incidence suitable for subnational programmatic decision making.MethodsWe conducted a modelling study using monthly ANC1 malaria prevalence data from six previously published studies of malaria in pregnancy in six sub-Saharan African countries between May, 2010, and August, 2014. An extended, validated, age-structured malaria transmission model was fitted to monthly ANC1 malaria prevalence using particle Markov chain Monte Carlo (pMCMC) to infer monthly clinical incidence and seasonality metrics. Agreement between model-derived incidence and independently observed time series was assessed using the Markham Seasonality Index (MSI) and peak timing with concordance correlation coefficients (CCCs) and 95% CIs.FindingsAcross the six intermittent screening and treatment in pregnancy (ISTp) datasets, total ANC1 sample sizes and positivity were Ghana, 622 (47·9%) of 1298; Burkina Faso, 592 (41·9%) of 1413; Mali, 284 (21·7%) of 1308; The Gambia, 105 (8·8%) of 1194; Kenya, 323 (21·1%) of 1528; and Malawi, 291 (15·9%) of 1825. Strong agreement was observed between model-derived incidence and independent cohort data for MSI (CCC 0·82 [95% CI 0·31–0·97]) and for peak timing (CCC 0·98 [95% CI 0·87–1·00]).InterpretationA mechanistic pMCMC framework applied to routine ANC1 data can recover clinically relevant seasonality in incidence for the broader community, enabling subnational timing of seasonal interventions (such as seasonal malaria chemopre
AU - Hicks,J
AU - Munsey,A
AU - Mousa,A
AU - Cairns,M
AU - Hill,A
AU - Gowelo,S
AU - Knock,E
AU - Baguelin,M
AU - Matambisso,G
AU - Pujol,A
AU - Mayor,A
AU - Madanitsa,M
AU - Khairallah,C
AU - van,Eijk A
AU - Chandramohan,D
AU - Chacky,F
AU - Greenwood,B
AU - O,ter Kuile F
AU - Fitzjohn,R
AU - Gutman,JR
AU - Walker,P
AU - on,behalf of the Antenatal Care-Based Malaria Prevalence Study Group
DO - 10.1016/j.lanmic.2026.101415
PY - 2026///
SN - 2666-5247
TI - Disentangling patterns of community malaria transmission and burden using malaria prevalence among pregnant women attending antenatal care: a modelling study
T2 - The Lancet Microbe
UR - http://dx.doi.org/10.1016/j.lanmic.2026.101415
ER -