medical-sociology-epidemic-diseases-2018-posters

Page 69

Journal of Community Medicine & Health Education | ISSN: 2161-0711 | Volume 8

Medical Sociology & Public Health

World Congress on

Public health and Epidemic diseases

International Conference on

September 21-22, 2018 | Dallas, USA

Spatial distribution and predictors of vitamin A deficiency among children 6-23 months in Bungoma and

Busia counties, Kenya

Mary Anyango Oyunga

Kenya Agriculture & Livestock Research Organization, Kenya

he study analyses in details the existing spatial patterns using spatial indices and geographical visualizations of the presence and

absence significant high and low values of vitamin A deficiency) (VAD) in Busia and Bungoma counties. ArcGIS and GeoDa

1.6 have been used for spatial analysis. A null hypothesis of spatial randomness was tested at α=0.005 against the thought of Spatial

Autocorrelation (SA) and rejected giving a strong evidence of significant spatial patterns in VAD distribution. Local Indicators of

Spatial Association (LISA) was used to assess clustering. Regression analysis was conducted to model the most significant prediction

equation for a set of 12 covariates both spatial and demographics. Exploratory Spatial Data Analysis (ESDA) was conducted followed

by Ordinary Least Squares Regression (OLSR) on predictor variables. Corrected VAD was the dependent variable while spatial and

demographic variables were independent. The results of the OLSR were scrutinized by a set of test diagnostics for the existence of

spatial dependence (Lagrange Multiplier diagnostics). Analysis of Moran’s Index in Bungoma and Busia revealed a heavy clustering of

High-High (MI≥0.9) values on upper parts of Bungoma and lower parts of Bungoma and Busia showed heavy clustering of Low-Low

values of VAD (MI≥0.9). Spatial error model yielded varying levels of coefficients with diverse spatial and non-spatial independent

variables at α≤0.005 with a sensitivity of 999 permutations and model variables suffered from extreme cases of multicollinearity

and heteroskedasticity. OLSR identified the length of the crop growing period, distance to health facilities and towns as the most

significant spatial predictors of VAD.

J Community Med Health Educ 2018, Volume 8

DOI: 10.4172/2161-0711-C4-042