Tuberculosis Clinical Trial
Official title:
Spatial Epidemiology of Tuberculosis Infection
Data will be extracted from the NTUH medical records database, geocoded according to the street address so that the case number, incidence, etc could be mapped. The spatial data would be used to detect aggregation of TB cases, existence of "hot spots" and then determine if they may merit further investigation or may have occurred by chance. The results from these GIS-based analyses would address local variations in TB incidence and transmission.
This is a Geographic Information System (GIS)-based retrospective study. Data will be
extracted from the NTUH medical records database, including the date of notification, sex,
birth date, residence address, etc of each tuberculosis case. Each case will be geocoded to
generate spatial coordinates for mapping purpose based on the street address and postal
code. To protect the privacy of these patients, the personal identification data of TB
patients, including name and ID numbers, will not be encoded. The encoded data file will
also be strictly kept confidential.
Disease mapping is the usual means of presenting descriptive geographic data on disease
occurrence and creating accurate maps of disease morbidity and mortality. Maps convey
instant visual information on the spatial distribution of disease and can identify subtle
patterns which may be missed in tabular presentations. The purpose is to display variations
in ill-health (for example, related to the underlying sociodemography), formulate etiologic
hypotheses, aid surveillance to detect areas of high disease incidence, and help place
specific disease.
In detecting the spatial patterns of the points geocoded from the addresses, point pattern
analytical methods are used. Quadrat Analysis is one of them. This method evaluates a case
distribution by examining how its density changes over space. The density measured by
Quadrat Analysis is then compared with the density of a theoretically constructed random
pattern to see if the case distribution in question is more clustered or more dispersed than
the random pattern. Another one is Nearest Neighbor Statistic, which is derived from the
average distance between cases and each of their nearest neighbors and captures information
on cases between quadrats. Using ordered neighbor statistic can evaluate the pattern at
different spatial scales. A case pattern is said to be more clustered if its observed
average distance between nearest neighbors is found to be less than that of a random
pattern. Ripley's K statistic is an extension of the ordered neighbor statistics, which can
be used to depict the randomness of a case distribution over different spatial scales and
capture the characteristic of local variations.
To measure and test how dispersed/clustered the case locations are with respect to their
attribute values, e.g. socioeconomic status, spatial autocorrelation can be performed. If
significant positive spatial autocorrelation exists in a case distribution, cases with
similar characteristics tend to be near each other.
For polygon data in interval or ratio form, such as the tuberculosis incidence of different
districts, Moran's I index, Geary's Ratio and the G-statistic can be used. Moran's I uses
the mean of the attribute's data values as the benchmark for comparison when neighboring
values are evaluated; Geary's Ratio is based on a direct comparison of neighboring values;
G-statistic is based on the concept of spatial association or cross-product statistics and
is capable of detecting the presence of hot spots or cold spots. In case of spatial
heterogeneity, i.e. the magnitude of spatial autocorrelation varies over space, modified
versions of the previous three statistics can be used to evaluate spatial association at the
local scale.
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Observational Model: Ecologic or Community, Time Perspective: Retrospective
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