Mayowa Lasode
Texas State University, Geography/GIS, Graduate Student
- Mayowa kayode Lasode is currently a doctoral candidate in geographic information science and works as an instructiona... moreMayowa kayode Lasode is currently a doctoral candidate in geographic information science and works as an instructional assistant at the Department of Geography, Texas State University. Mayowa does research in GIS and Human Geography.edit
Several studies have examined the impacts of water, sanitation, hygiene, and general housing conditions on childhood health in developing countries. However, up-to-date knowledge and change pattern in common childhood illness prevalence... more
Several studies have examined the impacts of water, sanitation, hygiene, and general housing conditions on childhood health in developing countries. However, up-to-date knowledge and change pattern in common childhood illness prevalence over time are scarce in Nigeria. To contribute toward meeting the Sustainable Development Goals three and six, we used pooled data (N = 94,053) from the Nigeria Demographic Health Surveys from 2008 to 2018 to examine the trend and determinants of four childhood illnesses: diarrhea, fever, cough, and respiratory infection. Multivariate logistic regression was used to estimate the determinants of the four health outcomes. Our results indicate that between 2008 and 2018, the prevalence of childhood diarrhea, cough, and fever slowly declined. However, there was a drastic decline in childhood-related respiratory illness. Housing conditions, sanitation facilities, and water sources were significantly associated with childhood illness based on the logistic ...
Research Interests:
This study retrospectively examined the health and social determinants of the COVID-19outbreak in 175 countries from a spatial epidemiological approach.Methods: We used spatial analysis to examine the cross-national determinants of... more
This study retrospectively examined the health and social determinants of the COVID-19outbreak in 175 countries from a spatial epidemiological approach.Methods: We used spatial analysis to examine the cross-national determinants of confirmed cases ofCOVID-19 based on the World Health Organization official COVID-19 data and the World Bank Indicatorsof Interest to the COVID-19 outbreak. All models controlled for COVID-19 government measures.Results: The percentage of the population age between 15-64 years (Age15-64), percentage smokers(SmokTot.), and out-of-pocket expenditure (OOPExp) significantly explained global variation in the cur-rent COVID-19 outbreak in 175 countries. The percentage population age group 15-64 and out of pocketexpenditure were positively associated with COVID-19. Conversely, the percentage of the total populationwho smoke was inversely associated with COVID-19 at the global level.Conclusions: This study is timely and could serve as a potential geospatial guide to developing publichealth and epidemiological surveillance programs for the outbreak in multiple countries. Removal ofcatastrophic medical expenditure, smoking cessation, and observing public health guidelines will notonly reduce illness related to COVID-19 but also prevent unecessary deaths.
Research Interests:
Among existing research on social vulnerability, virtually no studies have considered homelessness as a variable in their vulnerability assessments. This study identified the relevance of homelessness as a key index in social... more
Among existing research on social vulnerability, virtually no studies have considered homelessness as a variable in their vulnerability assessments. This study identified the relevance of homelessness as a key index in social vulnerability assessment to inform the public, policymakers and the broader body of literature of its impacts on shaping vulnerability patterns in cities. In this study, the 2018 Homeless data for Austin was disaggregated from the council district level to block group level using dasymetric mapping in Geographic Information System (GIS). Principal Component Analysis (PCA) was used to group highly correlated demographic and socioeconomic variables into factors, which were normalized and summed to model social vulnerability with homeless index (SOVI_H) and without homeless index (SOVI) for each Austin Blockgroup. The result revealed significant differences in geographic patterns between SOVI_H and SOVI. SOVI_H showed hotspots of vulnerabilities in Downtown and East-Austin neighborhoods, depicting a slight shift of social vulnerability westwards of the city. This finding differs from past results of social vulnerabilities in Austin where it used to be predominant in the East. This study showed that incorporating homelessness in identifying social vulnerability can help researchers and other associated organizations identify the most vulnerable groups when conducting social vulnerability assessments.