Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria

AbstractMalaria still poses a significant threat in Nigeria despite the various efforts to abate its transmission. Certain environmental factors have been implicated to increase the risk of malaria in Nigeria and other affected countries. The study aimed to evaluate the spatial and temporal association between the incidence of malaria and some environmental risk factors in Nigeria. The study used malaria incidence and environmental risk factors data emanating from 2015 Nigeria Malaria Indicator Survey accessed from the Demographic and Health Survey database. A total of 333 and 326 clusters throughout the country were used for malaria incidence study and environmental variables respectively. The spatial autocorrelation of malaria incidence and hotspot analysis was determined by the Moran’s diagram and local Moran’s I index, respectively. The relationships between the malaria incidence and the ecological predictors of transmission were analysed in all the six geopolitical zones of Nigeria from 2000–2015 using ordinary least square (OLS), spatial lag model (SLM), and spatial error model (SEM). Annual rainfall, precipitation and proximity to water showed significant positive relationship with the incidence rate of malaria in the OLS model (P < 0.01), whereas aridity was negatively related to malaria incidence (P < 0.001) in the same model. The rate of incidence of malaria increased significantly with increase in temperature, aridity, rainfall and proximity to water in the SEM whereas only temperature and proximity to water have significant positive effect on malaria incidence in the SLM. The modelling of the ecological predictors of malaria transmission and spatial maps provided in this study could aid in developing framework to mitigate malaria and identify its hotspots for urgent intervention in the endemic regions.

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Environmental risk factors and hotspot analysis of dengue distribution in Pakistan

International Journal of Biometeorology ◽

10.1007/s00484-015-0982-1 ◽

2015 ◽

Vol 59 (11) ◽

pp. 1721-1746 ◽

Cited By ~ 10

Author(s):

Bushra Khalid ◽

Abdul Ghaffar

Keyword(s):

Risk Factors ◽

Environmental Risk ◽

Environmental Risk Factors ◽

Hotspot Analysis

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Socio-environmental conditions associated with geospatial clusters of urothelial carcinoma: A multi-institutional analysis.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.6_suppl.392 ◽

2021 ◽

Vol 39 (6_suppl) ◽

pp. 392-392

Author(s):

Daniel C. Edwards ◽

Gabrielle R Yankelevich ◽

Paulette C Dreher ◽

Gabriela Narowska ◽

Daniel Kim ◽

...

Keyword(s):

Risk Factors ◽

Urothelial Carcinoma ◽

Environmental Risk ◽

Environmental Risk Factors ◽

Traffic Density ◽

Census Block ◽

Hotspot Analysis ◽

White Race ◽

Block Level ◽

Univariate Analyses

392 Background: Industrial byproducts and environmental pollutants (IBP/EP) are associated with the development of urothelial carcinoma (UC). While tobacco exposure (TE) is the major risk factor for UC, the interaction between sources of IBP/EP and incidence of UC in surrounding communities has been infrequently explored. We seek to identify high-density microregions of UC prevalence and spatially-related industrial and environmental risk factors. Methods: We queried a multi-institutional database for patients diagnosed with UC between 2008-2018. Geocoded addresses and ArcGIS software were used to calculate the Getis-Ord-Gi* statistic and perform hotspot analysis on the census-block level to identify UC hotspots. Demographics, clinicopathologic disease characteristics, and proximity to sources of IBP/EP were compared using Pearson’s chi-square and Student’s T-test. Univariate analyses and multivariable multilevel logistic random-intercept regression models were fitted to test the association between patient and census block-level factors and living in a UC hot spot. Results: Of 5,080 patients meeting inclusion/exclusion criteria, 148 patients (2.9%) were associated with one of three UC hotspots. In univariate analyses, hotspot patients were less likely to be tobacco users (OR 0.24, p=0.004) or of white race (OR 0.10, p<0.001) and less likely to have higher income (OR 0.73, p=0.005). They were more likely to be associated with IBP/EP exposure (OR 8.24, p=0.001) (Table). Multivariable analysis confirmed increased likelihood of residing in a UC hotspot and proximity to high-traffic density (OR >999, p=<0.001) and sites of IBP/EP contamination (OR 106.90, p=0.009), with decreased likelihood of tobacco use (OR 0.11, p=0.045) and white race (OR 0.02, p=0.004). Conclusions: Patients residing in geospatial hotspots of UC prevalence are less likely to be white, higher income or tobacco users and more likely to reside in proximity to sources of IBP/EP. Further research is necessary to investigate the interplay between socioeconomic status, race and environmental risk factors in order to better identify at-risk populations and improve screening, referral, diagnosis and timely intervention. [Table: see text]

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Modeling Malaria Incidence Associated with Environmental Risk Factors in Ethiopia using the Geographically Weighted Regression Model, 2015-2016

10.21203/rs.3.rs-907839/v1 ◽

2021 ◽

Author(s):

Berhanu Berga Dadi

Keyword(s):

Risk Factors ◽

Relative Humidity ◽

Spatial Patterns ◽

Environmental Risk ◽

Geographically Weighted Regression ◽

Malaria Incidence ◽

Environmental Risk Factors ◽

Weighted Regression ◽

Study Results ◽

Gwr Model

Abstract Background: In Ethiopia, still, malaria is killing and affecting a lot of people of any age group somewhere in the country at any time. However, due to limited research, little is known about the spatial patterns and correlated risk factors on the wards scale. Methods: In this research, we explored spatial patterns and evaluated related potential environmental risk factors in the distribution of malaria incidence in Ethiopia in 2015 and 2016. Hot Spot Analysis (Getis-Ord Gi* statistic) was used to assess the clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semiparametric geographically weighted regression (s-GWR) models were compared to describe the spatial association of potential environmental risk factors with malaria incidence.Results: Our results revealed a heterogeneous and highly clustered distribution of malaria incidence in Ethiopia during the study period. The s-GWR model best explained the spatial correlation of potential risk factors with malaria incidence and was used to produce predictive maps. The GWR model revealed that the relationship between malaria incidence and elevation, temperature, precipitation, relative humidity, and normalized difference vegetation index (NDVI) varied significantly among the wards. During the study period, the s-GWR model provided a similar conclusion, except in the case of NDVI in 2015, and elevation and temperature in 2016, which were found to have a global relationship with malaria incidence. Hence, precipitation and relative humidity exhibited a varying relationship with malaria incidence among the wards in both years. Conclusions: This finding could be used in the formulation and execution of evidence-based malaria control and management program to allocate scare resources locally at the wards level. Moreover, these study results provide a scientific basis for malaria researchers in the country.

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