The Lancet. Microbe · 2026
Disentangling patterns of community malaria transmission and burden using malaria prevalence among pregnant women attending antenatal care: a modelling study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hicks Joseph T, Munsey Anna, Mousa Andria, Cairns Matthew E, Hill Alexandra, Gowelo Steven, Matambisso Glória, Pujol Arnau, Mayor Alfredo, Madanitsa Mwayiwawo, Khairallah Carole, van Eijk Anna M, Chandramohan Daniel, Chacky Frank, Greenwood Brian, Ter Kuile Feiko O, FitzJohn Richard G, Gutman Julie R, Walker Patrick Gt, Antenatal Care-Based Malaria Prevalence Study Group
Аннотация
BACKGROUND: Malaria 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.
METHODS: We 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.
FINDINGS: Across 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]).
INTERPRETATION: A 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 chemoprevention). These capabilities are especially valuable when high-quality case surveillance is scarce and household surveys are underfunded. Our work operationalises WHO guidance, highlighting ANC1 as an opportunity to strengthen malaria surveillance.
FUNDING: Bill & Melinda Gates Foundation and MRC Centre for Global Infectious Disease Analysis.
METHODS: We 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.
FINDINGS: Across 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]).
INTERPRETATION: A 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 chemoprevention). These capabilities are especially valuable when high-quality case surveillance is scarce and household surveys are underfunded. Our work operationalises WHO guidance, highlighting ANC1 as an opportunity to strengthen malaria surveillance.
FUNDING: Bill & Melinda Gates Foundation and MRC Centre for Global Infectious Disease Analysis.