-: Welcome back.
This lecture is a bonus presentation on disease clusters.
I've done some research in the past on disease clusters,
and I thought I could share some of that work with you.
However, if you are not
particularly interested in this topic,
or find yourself very busy with other work,
then you could definitely skip this lecture
and just focus on the previous two lectures
related to epidemiological study types
and Hill's causal criteria.
This slide shows an image from another cluster study
that I was not involved in
examining spatiotemporal clusters of cholera cases in Haiti,
and I wanted to include this as another example,
should you be interested.
So let's quickly run through some basics.
What are disease clusters?
They're defined as a group of disease cases
in time or in space, or both,
in excess of the number that would normally be expected.
So you could have temporal clusters,
spatial clusters, or space-time clusters.
So why do clusters occur?
They can occur for a number of different reasons.
They could occur due to the infectious spread of disease,
the occurrence of disease vectors
like mosquitoes or ticks in a specific location,
the clustering of one or more risk factors,
or the existence of health hazards
like localized sources of pollution.
Why is cluster analysis useful?
Cluster detection methods can be used
to identify high risk areas and time periods.
They can also be used to link disease patterns
to sources of contamination.
They can highlight retrospective outbreaks,
so we can look back in time
and identify outbreaks of disease,
or we can look forward and prospectively predict outbreaks
using routine surveillance data.
And generally cluster analysis can help to determine
whether the clustering is statistically significant
and worthy of further investigation,
or whether it's likely to be a chance occurrence,
or just reflects the distribution of the population at risk.
Because in some cases, if you, for example,
are looking at disease in children,
maybe more children just happen to live in one area
and that's why it looks like a cluster of disease.
Now I wanna walk through a case study
that's from my own research
on the detection of space-time clusters
of cryptosporidiosis in New Zealand.
So let's start with a little bit of background.
Cryptosporidium species are parasites
that can cause gastrointestinal symptoms,
and in very rare cases sometimes pulmonary
or other symptoms in humans.
Cryptosporidiosis cases are often linked
to contaminated water
or contact with infected animals or their feces.
Animals that can carry cryptosporidium
range from cattle, sheep,
to other mammals as well.
New Zealand generally has high rates of enteric diseases,
and enteric disease just means infections
occurring in the gastrointestinal system.
But they have high rates of these diseases in general,
and that includes cryptosporidiosis,
especially compared to other high income nations.
Cryptosporidiosis cases are, or rates are,
almost 2.5 times higher in rural areas of New Zealand
than in urban areas,
and a dose response relationship has been detected
between cases and rurality.
So for each step
towards more rural that you go,
you see an increase in the number of cases.
The highest incidence rates have been reported
in infants and in children under the age of five,
so they're a vulnerable population.
So cluster detection may help to identify risk factors
and inform disease prevention and control efforts.
So here are some of the methods
that were used in my research.
I'll walk through them really quickly,
but you don't really need to remember these details.
So we use SaTScan software
using a Kulldorff method
of retrospective space-time permutation.
So essentially we looked back in time from 1997 to 2015
to detect clusters of cryptosporidiosis,
both spatially and temporarily, in New Zealand.
So this method scans the study area for each location,
and in this case we looked at small areas
called census area units within the country,
and then it scans the specified time period
and highlights potential clusters of disease cases.
Here are a few of the key results.
65 clusters were detected during the study period,
and 38 of those were statistically significant
with a P value of less than 0.05.
However, only about 4.1% of the notified cases,
of all notified cases from 1997 to 2015,
so across the entire study period,
were part of significant clusters.
So that means that the majority of cases
of this disease in New Zealand are actually sporadic,
so they pop up here and there
but they're exhibiting clusters.
This map shows the locations
of the 38 statistically significant clusters
that were detected.
You can see that there are clusters
located throughout the country,
but what this map doesn't show you
is the timing of these clusters.
What I can tell you from those results
are that clusters in rural areas
often occurred in the spring,
while clusters in urban areas
often occurred during autumn or winter months.
So we asked ourselves,
why might we be seeing clusters of cryptosporidiosis
in rural areas of New Zealand in the spring months?
What's happening in the spring in rural New Zealand
that might explain the timing and location
of these clusters of disease?
Well, it just so happens
that spring is calving and lambing season in New Zealand,
and calves and lambs can carry
high levels of Cryptosporidium parvum.
It's a species of cryptosporidium
that can spread from animal feces to humans
through direct contact or through indirect contact.
You could have direct contact with feces
if you were out in a field,
but it could also wash into waterways
and contaminate water that you drink
or water that you recreate in.
So what we might be seeing is the zoonotic transmission
of this particular species of cryptosporidium
in rural areas.
Some of our takeaways were that clusters in urban areas
in autumn and winter months
might be due to person to person transmission,
and may be due to the species Cryptosporidium hominis,
whereas clusters in rural areas in spring months
might be subject to zoonotic, environmental,
or waterborne transmission
and the spread of Cryptosporidium parvum.
This is in line with some of the previous research
that's come out of the country,
suggesting that there are seasonal,
strain-specific transmission cycles.
Going forward,
the typing of all notified cases of cryptosporidium,
and the typing of all outbreaks,
would improve our understanding of the pathways
and enable development of targeted disease control measures
to help prevent this disease.
Finally, we asked ourselves what the next steps would be
in such an investigation.
One of the things that we've actually done
is to go back and compare the detected clusters of disease
to known outbreaks that have been reported
by the National Disease Surveillance System in New Zealand.
That way we get a better sense
of whether some outbreaks are going undetected.
We also get a sense of potential transmission pathways
from those previous outbreak investigations.
The other thing that we did was to compare
the location and timing of the clusters
to potential environmental risk factors.
For example, we can compare the clusters
to spatial patterns of livestock density in the country.
We can also compare them to spatiotemporal patterns
of severe weather events,
because for example, outbreaks,
especially those that are waterborne,
might be more common after heavy rains
wash animal feces off of the land and into waterways,
especially in rural areas
where many people rely on drinking water sources
that are not reticulated,
so that is to say they aren't going through
a drinking water treatment plant.
So that was a very brief introduction
to the type of research
that can be done with cluster detection analysis.
The key takeaways are that cluster analysis
can help to do a number of different things,
including identify those high risk areas and time periods.
They can help you to link disease patterns
to sources of contamination,
and it can highlight both retrospective outbreaks
and prospectively predict outbreaks
using routine surveillance data.
So that's something to bear in mind as you go forward.
And if you're interested in more
on cluster detection and analysis,
I'm happy to provide you with additional resources
that you might find interesting.