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

Mapping Intimacies ◽

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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Family environmental risk factors for developmental speech delay in children in Northern China

Scientific Reports ◽

10.1038/s41598-021-83554-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shengfu Fan ◽

Ying Zhang ◽

Jiangbo Qin ◽

Xuan Song ◽

Meiyun Wang ◽

...

Keyword(s):

Risk Factors ◽

Environmental Risk ◽

Medical Records ◽

Maternal Age ◽

Parental Education ◽

Northern China ◽

Environmental Risk Factors ◽

Family Type ◽

Speech Delay ◽

Study Results

AbstractMost reported risk factors for developmental speech delay (DSD) remain controversial, and studies on paternal influencing factors are rare. This study investigated family environmental risk factors for DSD in northern China. The medical records of 276 patients diagnosed with DSD at four centres between October 2018 and October 2019 were retrospectively analysed. A questionnaire was designed that contained items such as maternal age at the child’s birth, child sex, child age, birth order, family type and parental personality. Patients whose medical records lacked complete information for this investigation were contacted by e-mail or phone. Additionally, 339 families whose children received routine physical examinations at the four involved centres completed the survey. Data were collected, and potential risk factors were analysed using the t test or chi-square test; the obtained outcomes were subjected to multivariable logistic regression for further analysis. The multivariable regression showed that older maternal age at the child’s birth (OR = 1.312 (1.192–1.444), P < 0.001), introverted paternal personality (OR = 0.023 (0.011–0.048), P < 0.001), low average parental education level (OR = 2.771 (1.226–6.263), P = 0.014), low monthly family income (OR = 4.447 (1.934–10.222), P < 0.001), and rare parent–child communication (OR = 6.445 (3.441–12.072), P < 0.001) were independent risk factors for DSD in children in North China. The study results may provide useful data for broadening and deepening the understanding of family risk factors for DSD.

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Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10070448 ◽

2021 ◽

Vol 10 (7) ◽

pp. 448

Author(s):

Jingtao Sun ◽

Sensen Wu ◽

Zhen Yan ◽

Yadong Li ◽

Cheng Yan ◽

...

Keyword(s):

Risk Factors ◽

Socioeconomic Factors ◽

Potential Risk ◽

Geographically Weighted Regression ◽

Chinese Mainland ◽

Weighted Regression ◽

The Chinese Mainland ◽

Different Seasons ◽

Potential Risk Factors ◽

Gwr Model

Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, and per capita GDP) on the Chinese mainland, we established a geographically weighted regression (GWR) model to analyze their impacts in different seasons and provinces. The GWR model successfully describes the spatial changes of the influence of potential risks, and shows greatly improved estimation performance compared with the ordinary linear regression (OLR) method. Our findings help to understand the seasonally and spatially relevant effects of natural environmental and socioeconomic factors on the I-HFMD, and can provide information to be used to develop effective prevention strategies against HFMD at different locations and in different seasons.

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

Scientific Reports ◽

10.1038/s41598-019-53814-x ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Oluyemi A. Okunlola ◽

Oyetunde T. Oyeyemi

Keyword(s):

Risk Factors ◽

Malaria Transmission ◽

Environmental Risk ◽

Malaria Incidence ◽

Environmental Risk Factors ◽

Least Square ◽

Annual Rainfall ◽

Hotspot Analysis ◽

Spatial Error ◽

Ecological Predictors

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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