Space-Time Clustering Analysis of Acute Diarrhoeal Disease ...

Space-Time Clustering Analysis of
Acute Diarrhoeal Disease (ADD) -

An Indian Case Study

Dr. Valli Manickam

Administrative Staff College of India, Hyderabad

Dr. I V Murali Krishna

RCI, DRDO, Hyderabad

Introduction

• Acute gastrointestinal illnesses are amongst the most common
diseases worldwide: ranging from mild annoyances to
devastating, dehydrating illnesses that can kill within hours.

• Although acute gastroenteritis is a common illness in all
seasons, it definitely shows change in the number of cases at
different times of the year.

• The World Health Organization (WHO) estimated that, nearly
1.8 million people die every year from diarrheal diseases in
developing countries.

• Around 88% of diarrheal disease is attributed to unsafe water
supply, inadequate sanitation and hygiene. It is estimated by
World Health Organization (WHO) and United Nations
International Children's Emergency Fund (UNICEF) that 1.1
billion people lack access to improved water supplies and 2.6
billion people lack adequate sanitation

ADD has both bacterial and viral causes, and spreads
through the faecal-oral route due to water contamination.

Setting the Context

• In India, approximately 72% of the population
does not use any method of water disinfection and
74% have no sanitary toilets.

• At the national level, about 16% of households
have no access of safe drinking water and Andhra
Pradesh also follows the same as per 2011 census
in urban areas.

• The contamination may be at main drinking water
source or due to contamination of water supply
system and result in outbreaks of diarrheal disease
.

• Analysis of the major types of diseases in Andhra
Pradesh has shown that over the years ADD has
been the highest in occurrence in the state in last
couple of years

Motivation for the Study

• Overall burden not well studied in Andhra
Pradesh

• Andhra Pradesh recorded 20.9 lakh Acute
Diarrhoea Disease (ADD) cases in 2013,
nearly four times higher compared to its
neighbours Karnataka, Kerala and ten times
higher than that of Tamil Nadu.

• Most infections occur in children aged
between 1 and 5 years. Majority of deaths
occur in children aged below 2 years.

Scope of the
Study includes

- 23 districts in the
State of AP

- Specific mandal /
block level analysis

The Spatial Scan Statistic

• The spatial scan statistic is by far the most
popular cluster detection technique,
largely due to the availability of SaTScan
software by Martin Kulldorff.

• Scan data with a moving ‘window’, calculating local autocorrelation for spatial
units that fall within the window.

• Select the window(s) with the highest calculated autocorrelation value as
possible cluster(s).

Methodology

• Statistical analysis was carried out for a period 2009-2013 using the
ADD data.

• SaTScan v9.2 was used for statistical and spatio-temporal study and
detection of space-time clusters. The parameters for analysis include
rainfall, population, location and disease data including ground
truthing.

• Retrospective analysis was carried out using space-time permutation
and discrete Poisson models to identify disease clusters and the risk to
population.

• While the space-time permutation model requires only case data, with
information about the spatial location and time for each case, the
discrete Poisson model requires case data with both spatial location
and time of occurrence along with the background population.

• Retrospective study was performed as a series of analyses with the
maximum temporal size of the window set to 50% of the study period
and the spatial window size was set to vary from 10% to 50% of
population at risk to identify the most appropriate measurement of the
representation with ground truth data.

Major Findings

• Retrospective analysis of the disease data was carried
out for period 2009-2013 using temporal window size
of 50% study period and spatial window size of 50%
population at risk for identifying the clusters of high
rates of disease incidence in space and time using
permutation model

• The cluster analysis shows that 6 clusters were
identified, out of which 4 clusters had individual
districts (East-Godavari, Kadapa, Krishna and
Rangareddy) and

• Two clusters had multiple districts of 4 districts
(Adilabad, Karimnagar, Medak, Nizamabad) and 3
districts (Ananthapur, Kurnool, Mahabubnagar).

Retrospective
Analysis for the
period 2009-2013
using Space time

permutation
model

Major Findings

• Cluster analysis at spatial window size of 50%
population at risk gave 4 clusters of which 3
had individual districts (Krishna, Nizamabad
and Rangareddy) and

• One Cluster of six districts (Ananthapur,
Chittoor, Kadapa, Kurnool, Nellore and
Prakasam).

Retrospective Analysis
using Discrete Poisson

Model

Comparison of the two models for the
study area

10% window – Population at risk

Determination of Spatial Window size
for space time permutation model

• At the spatial window size of 10% population at risk, 18
districts were detected with disease incidence (16
clusters, 15 with individual districts and one cluster
with 3 districts).

• Scans using window size of 20% and 30% risk showed
17 districts (10 clusters: 7 individual and 3 with
multiple districts) and 10 districts (6 clusters: 4
individual and 2 with multiple districts) with high
disease incidence respectively.

• Overall analysis showed that spatial window sizes of
10% and 20% population at risk were appropriate for
space-time permutation model.

Retrospective analysis using space-time permutation model at
20% population at risk for the years 2009 – 2013

Retrospective analysis using discrete Poisson model 20%
population at risk for the years 2009 to 2013.

Major Findings

• Both the models (2009-2013) aggregate data
and individual years, showed that analysis
with spatial window size of 20% population at
risk is coinciding with the ground truth data.

• Both models have shown that Krishna district
is having the highest relative risk for the
occurrence of ADD followed by Nizamabad

Calculation of Relative Risk

• Spatial window of 20% and 30% population at risk
identified the same districts for both the models, however
it is seen that 30% scan gave less number of clusters with
individual districts when compared to the scan at 20%.

• The advantage of using discrete Poisson model over the
space time model is that the model uses population data
and also allows for the calculation of relative risk.

Relative risk at scan
window of 20%
population at risk
using discrete

Poisson model for
2009-2013

Conclusions

• Visualization of spatial distribution of the disease over a defined
area helps to analyze clusters easily and also detect unusual
patterns of disease outbreaks if any.

• In the present study SaTScan v9.2 was used to detect space-time
disease clusters and understand the spatio-temporal distribution of
Acute Diarroheal Disease (ADD) in Andhra Pradesh.

• Studies have found that both the models at 10% population risk
gave more clusters with individual districts than represented by
ground truth data

• Windows of 20 – 30% are most suited in both the models studies for
the representation of ADD in Andhra Pradesh.

• Krishna district is having the highest relative risk for the occurrence
of ADD followed by Nizamabad

• Prospective analysis can be used for the detection of ADD in the
state and preparation of an Information System for ADD Disease
Control and Prevention is in progress

Thank You