| Summary: | Background: Tuberculosis (TB) cases have increased drastically over the last
two decades and remains as one of the deadliest infectious diseases in
Malaysia. Preventing and controlling the disease is not only depend on molecular
epidemiology but there is also a need to explicitly understand spatial
epidemiology, which assesses the distribution of disease in different locations.
However, there is a lack of studies clarifying the spatial evaluation of both
sociodemographic and environmental factors with the TB cases in the country.
Objective: This study utilized the geospatial technologies i) to investigate the
trend and spatial pattern of TB cases; ii) to investigate the spatial distribution of
TB cases and its association with the sociodemographic and environmental
factors; iii) to develop the prediction model of TB cases; and iv) to develop a
web-GIS application for plotting TB cases. Methodology: The sociodemographic
data of 3325 cases of TB such as age, gender, race, nationality, country of origin,
educational level, employment status, health care worker status, income status,
residency, and smoking status from January 2013 to December 2017 in Gombak
were collected from the MyTB web and Tuberculosis Information System (TBIS)
file. Environmental data consisting of air pollution data such as air quality index
(AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and
particulate matter 10 (PM10) were obtained from the Department of Environment
Malaysia from July 2012 to December 2017, whereas weather data such as
rainfall were obtained from the Department of Irrigation and Drainage Malaysia
and relative humidity, temperature, wind speed, and atmospheric pressure were
obtained from the Malaysian Meteorological Department in the same period.
Global Moran’s I, kernel density estimation, and Getis-Ord Gi* statistics were
applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS)
and geographically weighted regression (GWR) models were used to determine
the spatial association of sociodemographic and environmental factors with the
TB cases. Multiple linear regression (MLR) and artificial neural network (ANN)
were applied to develop the prediction model of TB cases. A web-GIS application
was set up in the Python Shapefile (PHP) CodeIgniter framework with the aid of
ArcGIS JavaScript Application Programming Interface (API) 3.7 and HyperText
Markup Language (HTML), Cascading Style Sheet (CSS), JavaScript, and PHP
as programming languages. The ESRI map was used as the base map and
combined with the web GIS technology via ArcGIS API. Results: Spatial
autocorrelation analysis indicated that the cases were clustered (p<0.05) over
five-year period and years 2016 and 2017. Kernel density estimation identified
the high-density regions while Getis-Ord Gi* statistics observed the hotspot
locations, whereby its were consistently located in the southwestern part of the
district. This could be attributed to the overcrowding of inmates in the Sungai
Buloh prison located there. The GWR model based on the environmental factor
(GWR2) was the best model to determine the spatial distribution of TB cases
based on the highest values of R2 i.e. 0.98 and local R2 > 0.70, which consisted
of 2006 cases of TB. The ANN was found to be superior to MLR with higher
adjusted R2 values in predicting TB cases, in which the ranges were from 0.35
to 0.47 compared to 0.07 to 0.14. The sensitivity analysis of the relative important
of each input variable illustrated that using both the sociodemographic and
environmental data through ANN3, with highest adjusted R2 value of 0.47, errors
below 6, and accuracies above 96%, revealed the best performance in predicting
TB cases than using the sociodemographic and environmental data individually
for each ANN model. The web-GIS application displays the location of TB cases
and its sociodemographic factors on an interactive map. Conclusion: This study
identified the spatial variability in the association between risk factors and TB
cases, and visualized the high risk areas using a user-friendly web mapping
application, which helps in improving case detection and targeted surveillance.
The prediction of TB cases were possible with the utilization of geospatial data.
|
|---|