Wellcome open research · 2026
New title: Re-evaluating environmental associations with meningitis risk across Africa.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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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.
Cliff Molly, Jahn Sally, Bita Fouda Andre, Latt Anderson, Lingani Clement, Trotter Caroline
Аннотация
BACKGROUND: Previous analytical work, defining the distribution of meningitis epidemics in Africa is over 20 years old, with climate change representing an ongoing issue. We aim to update this analysis and determine if the meningitis belt geography and associated environmental risk factors have changed in the last two decades.
METHODS: Epidemic bacterial meningitis data from 2003-2022 were provided by WHO-AFRO. Districts across Africa were coded 1 if they experienced a meningitis outbreak and 0 if not. Monthly means of windspeed, rainfall, dust, and humidity were processed into climatic profiles using k-means clustering. We undertook logistic regression with meningitis epidemic history as the dependent variable and k-means clusters of rainfall, dust, humidity, and windspeed, alongside land-cover type as independent variables. A sensitivity analysis was conducted, excluding the Democratic Republic of Congo (DRC), due to limited laboratory confirmation of cases.
RESULTS: Rainfall, dust, windspeed and humidity demonstrated the strongest statistical association with outbreaks and were included in our final model. With a probability cut-off >0.4, our model had specificity and sensitivity of 82.07% and 82.22%, respectively, in identifying districts having experienced a meningitis epidemic. The Sahelian region had the highest risk of meningitis outbreaks (probability >0.8), consistent with previous findings. The inclusion/exclusion of the DRC had a significant impact on our model. In the full model the Republic of the Congo, Gabon, and Angola had a moderate risk of meningitis (probability >0.4), suggesting a possible expansion of the belt. However, when the DRC was excluded, no countries surrounding the meningitis belt were at risk for outbreaks, highlighting the importance of laboratory testing and case confirmation.
CONCLUSIONS: The apparent extension of risk beyond the belt possibly reflects surveillance limitations rather than alterations in disease ecology. Where possible, laboratory confirmation should be used to support surveillance of suspected meningitis outbreaks and cases.
METHODS: Epidemic bacterial meningitis data from 2003-2022 were provided by WHO-AFRO. Districts across Africa were coded 1 if they experienced a meningitis outbreak and 0 if not. Monthly means of windspeed, rainfall, dust, and humidity were processed into climatic profiles using k-means clustering. We undertook logistic regression with meningitis epidemic history as the dependent variable and k-means clusters of rainfall, dust, humidity, and windspeed, alongside land-cover type as independent variables. A sensitivity analysis was conducted, excluding the Democratic Republic of Congo (DRC), due to limited laboratory confirmation of cases.
RESULTS: Rainfall, dust, windspeed and humidity demonstrated the strongest statistical association with outbreaks and were included in our final model. With a probability cut-off >0.4, our model had specificity and sensitivity of 82.07% and 82.22%, respectively, in identifying districts having experienced a meningitis epidemic. The Sahelian region had the highest risk of meningitis outbreaks (probability >0.8), consistent with previous findings. The inclusion/exclusion of the DRC had a significant impact on our model. In the full model the Republic of the Congo, Gabon, and Angola had a moderate risk of meningitis (probability >0.4), suggesting a possible expansion of the belt. However, when the DRC was excluded, no countries surrounding the meningitis belt were at risk for outbreaks, highlighting the importance of laboratory testing and case confirmation.
CONCLUSIONS: The apparent extension of risk beyond the belt possibly reflects surveillance limitations rather than alterations in disease ecology. Where possible, laboratory confirmation should be used to support surveillance of suspected meningitis outbreaks and cases.