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Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors.

ABSTRACT: BACKGROUND:Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. METHODS:Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. RESULTS:Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. CONCLUSION:The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

SUBMITTER: Chirombo J

PROVIDER: S-EPMC6945411 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors.

Chirombo James J Ceccato Pietro P Lowe Rachel R Terlouw Dianne J DJ Thomson Madeleine C MC Gumbo Austin A Diggle Peter J PJ Read Jonathan M JM

Malaria journal 20200106 1

<h4>Background</h4>Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progres ...[more]

PMID: 31906963

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Project description:BackgroundHealth indicators, such as mortality rates or life expectancy, need to be presented at the local level to improve the health of local residents and to reduce health inequality across geographic areas. The aim of this study was to estimate life expectancy at the district level in Korea through a spatio-temporal analysis.MethodsSpatio-temporal models were applied to the National Health Information Database of the National Health Insurance Service to estimate the mortality rates for 19 age groups in 250 districts from 2004 to 2017 by gender in Korea. Annual district-level life tables by gender were constructed using the estimated mortality rates, and then annual district-level life expectancy by gender was estimated using the life table method and the Kannisto-Thatcher method. The annual district-level life expectancies based on the spatio-temporal models were compared to the life expectancies calculated under the assumption that the mortality rates in these 250 districts are independent from one another.ResultsIn 2017, district-level life expectancy at birth ranged from 75.5 years (95% credible interval [CI], 74.0-77.0 years) to 84.2 years (95% CI, 83.4-85.0 years) for men and from 83.9 years (95% CI, 83.2-84.6 years) to 88.2 years (95% CI, 87.3-89.1 years) for women. Between 2004 and 2017, district-level life expectancy at birth increased by 4.57 years (95% CI, 4.49-4.65 years) for men and by 4.06 years (95% CI, 3.99-4.12 years) for women. To obtain stable annual life expectancy estimates at the district level, it is recommended to use the life expectancy based on spatio-temporal models instead of calculating life expectancy using observed mortality.ConclusionIn this study, we estimated the annual district-level life expectancy from 2004 to 2017 in Korea by gender using a spatio-temporal model. Local governments could use annual district-level life expectancy estimates as a performance indicator of health policies to improve the health of local residents. The approach to district-level analysis with spatio-temporal modeling employed in this study could be used in future analyses to produce district-level health-related indicators in Korea.

Project description:BACKGROUND:Plasmodium falciparum malaria is a threat to public health, but Plasmodium vivax malaria is most prevalent in Latin America, where the incidence rate has been increasing since 2016, particularly in Venezuela and Brazil. The Brazilian Amazon reported 193,000 cases in 2017, which were mostly confirmed as P. vivax (~?90%). Herein, the relationships among malaria incidence rates and the proportion of accumulated deforestation were contrasted using data from the states of Acre and Rondônia in the south-western Brazilian Amazon. The main purpose is to test the hypothesis that the observed difference in incidence rates is associated with the proportion of accumulated deforestation. METHODS:An ecological study using spatial and temporal models for mapping and modelling malaria risk was performed. The municipalities of Acre and Rondônia were the spatial units of analysis, whereas month and year were the temporal units. The number of reported malaria cases from 2009 until 2015 were used to calculate the incidence rate per 1000 people at risk. Accumulated deforestation was calculated using publicly available satellite images. Geographically weighted regression was applied to provide a local model of the spatial heterogeneity of incidence rates. Time-series dynamic regression was applied to test the correlation of incidence rates and accumulated deforestation, adjusted by climate and socioeconomic factors. RESULTS:The malaria incidence rate declined in Rondônia but remained stable in Acre. There was a high and positive correlation between the decline in malaria and higher proportions of accumulated deforestation in Rondônia. Geographically weighted regression showed a complex relationship. As deforestation increased, malaria incidence also increased in Acre, while as deforestation increased, malaria incidence decreased in Rondônia. Time-series dynamic regression showed a positive association between malaria incidence and precipitation and accumulated deforestation, whereas the association was negative with the human development index in the westernmost areas of Acre. CONCLUSION:Landscape modification caused by accumulated deforestation is an important driver of malaria incidence in the Brazilian Amazon. However, this relationship is not linearly correlated because it depends on the overall proportion of the land covered by forest. For regions that are partially degraded, forest cover becomes a less representative component in the landscape, causing the abovementioned non-linear relationship. In such a scenario, accumulated deforestation can lead to a decline in malaria incidence.

Project description:IntroductionDespite the implementation of control strategies at the national scale, the malaria burden remains high in Mali, with more than 2.8 million cases reported in 2019. In this context, a new approach is needed, which accounts for the spatio-temporal variability of malaria transmission at the local scale. This study aimed to describe the spatio-temporal variability of malaria incidence and the associated meteorological and environmental factors in the health district of Kati, Mali.MethodsDaily malaria cases were collected from the consultation records of the 35 health areas of Kati's health district, for the period 2015-2019. Data on rainfall, relative humidity, temperature, wind speed, the normalized difference vegetation index, air pressure, and land use-land cover were extracted from open-access remote sensing sources, while data on the Niger River's height and flow were obtained from the National Department of Hydraulics. To reduce the dimension and account for collinearity, strongly correlated meteorological and environmental variables were combined into synthetic indicators (SI), using a principal component analysis. A generalized additive model was built to determine the lag and the relationship between the main SIs and malaria incidence. The transmission periods were determined using a change-point analysis. High-risk clusters (hotspots) were detected using the SatScan method and were ranked according to risk level, using a classification and regression tree analysis.ResultsThe peak of the malaria incidence generally occurred in October. Peak incidence decreased from 60 cases per 1000 person-weeks in 2015, to 27 cases per 1000 person-weeks in 2019. The relationship between the first SI (river flow and height, relative humidity, and rainfall) and malaria incidence was positive and almost linear. A non-linear relationship was found between the second SI (air pressure and temperature) and malaria incidence. Two transmission periods were determined per year: a low transmission period from January to July-corresponding to a persisting transmission during the dry season-and a high transmission period from July to December. The spatial distribution of malaria hotspots varied according to the transmission period.DiscussionOur study confirmed the important variability of malaria incidence and found malaria transmission to be associated with several meteorological and environmental factors in the Kati district. The persistence of malaria during the dry season and the spatio-temporal variability of malaria hotspots reinforce the need for innovative and targeted strategies.

Dataset Information

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors.

Publications

Childhood malaria case incidence in Malawi between 2004 and 2017: spatio-temporal modelling of climate and non-climate factors.

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