Investigating Non-infectious
Disease Clusters: The
California Experience
Richard Kreutzer, M.D.
Thanks to Dan Smith and Ray Neutra
California Department of Health Services
Environmental Health Investigations Branch
Montecito, CA Leukemia Cluster
1989
• Six cases of leukemia between 1981-1988
in children <19; four attend same school
• 5 times more cases than expected
• High SES community
• Recent articles by Paul Brodeur in the New
Yorker about EMF’s and cancer
• Transformer station on school grounds
Randomness
0
0
1
X
X
9
6
7
8
9
X
X
X
X
X
X
X
X
X
X
X
X
X
X
XX
XX
6
8
5
X
4
7
4
X
2
5
3
X
1
3
2
X
X
X
X
X
X
X
X
X
X
X
XX
X
X
Taylor & Wilde, “Drawing the line with Leukemia”
X
X
Some Well-Known
Cancer Clusters
Place
Time
Cancer
Obs Exp O/E
Niles, ILL
1956-60
Child Leukemia
8
1.7
4.6
Sellafield, UK
1968-84
Child Leukemia
5
1.5
3.3
Woburn, MA
1969-79
Child Leukemia
12
5.3
2.3
Child Cancer
-several types
10
3.0
3.3
McFarland, CA 1975-85
McFarland California
f
f
f
f
Population – 6200 (1980 census)
Hispanics – 75% of population
Per capita income – approximately $4,300
1984 observation of parent-too many child
cancers
f Previous loss of children in multiple car
accident
The investigation begins with…
• Observed number of cases and calculating
expected number of cases
• Can calculate:
– Excess cases = Observed - Expected
– SIR = Observed/Expected
– PAR = (Observed – Expected) / Observed
McFarland Calculations
• Difference = 10 – 3 = 7 excess cases
• SIR = 10 / 3 = 3.3 the smallest relative risk
if the entire population is exposed
• PAR = (10 – 3) / 10 = 0.7 the proportion of
cases in the community that is in excess
We can learn
• The risk ratio of the exposure – RR
• The proportion of population exposed – P(E)
• The proportion of cases exposed – P(E l D)
RR = (Obs / Exp) – 1 + 1
P(E)
P(E) = (Obs / Exp) – 1
RR – 1
Timing of Exposure
Distribution of Onset Times:
• Exposure at one point
in time
• Log normal
distribution of date of
onset
• Exposure at one stage
of life
• Log normal
distribution of age at
onset
Proportionate Incidence-McFarland
• There were only 2 out of 10 cases of
childhood leukemia. There were a
disproportionate number of solid tumors
with connective tissue histology.
• Raised question of a genetic association
perhaps due to a “founder” population
effect.
The History of McFarland:
Government Reports
f 1985-86: Phase I – Epidemiologic Study of Cancer in
Children in McFarland, CA [Kern Co. Health Dept.]
f 1987: McFarland Municipal Drinking Water Supply
and Its Relationship to adverse Health Effects in the
Community [CDHS]
f 1988: Aerial Photographic Analysis of the McFarland
Study Area [USEPA]
f 1989: Radiofrequency Radiation Survey in the
McFarland, California Area [USEPA]
The History of McFarland:
Government Reports (Cont.)
f 1990: X-Ray Fluorescence Site Screening Survey, Soil Sampling,
and Chemical Analyses McFarland, CA [USEPA]
f 1990: Interim Report on The Four County Study of Childhood
Caner Incidence #1 [CDHS]
f 1991: McFarland Child Health Screening Project 1989 [CDHS]
f 1991: Ground Water and Buried Drums Assessment [Kern Co.
Public Works]
f 1991: The Four county Study of Childhood Cancer IncidenceInterim Report #2 [CDHS]
f 1991: Summary of Environmental Data: McFarland Childhood
Cancer Cluster Investigation-Phase III Report [CDHS]
f 1993: Investigation of the Earlimart Childhood Cancer Cluster
[CDHS]
Current Status-child Cancer
in McFarland
f 7 New cases < 14; 10 new cases < 19 since 1990
f USEPA petitioned to investigate
f Town in midst of rebound from stigma of earlier
investigations
Comparison of Clusters
E.coli
Cancer Cluster
Rare
Common
Agent
Can be cultured from a
case
Can’t be determined
medically
Other
Causes
Few
Many
Latency
2-5 days
Years
Disease
Other Cluster Types
•
•
•
•
Communicable disease- E coli 0157
Cancer
Terrorism – syndromic surveillance
Toxicity – Toxic Taquitos (1988)
– Contaminated Watermelons (1985)
• Mass psychogenic events –
– Chico mystery disease
Type of Exposure
Type of Health Outcome
Common Outcome
Common, widespread
exposure
Rare, unique exposure
Rare, Unusual Outcome
(A)
(B)
Dietary fat and colon
cancer
DES and vaginal cancer
(C)
(D)
Chernobyl release and
thyroid cancer
Vinyl chloride and
hemangiosarcoma
How would different approaches to looking at clusters perform for these different situations?
Approaches:
1) Respond to inquires – Could pick up (B) and (D)
This approach could (and did) confirm the clusters of vaginal cancer and
hemangiosarcoma
2) Actively search for clusters – Could pick up (B) and (D)
A cluster hunting team scanning registry data could probably have found these
rare and unusual clusters, although perhaps later than Approach 1 because of the
lag time for registration.
3) Study unusual exposures – Could pick up (C)
EHIB has mainly concentrated on being vigilant for new, unusual exposures and
their possible consequences (e.g., aerial application of malathion).
Conclusions:
™ The combination of Approaches 1 and 3
could pick up (B), (C), and (D).
™ None of these approaches would be a
good way to detect (A).
™ Is there a compelling reason for
Approach 2?
Future of Cluster Investigations
• Molecular biology of cancer- specific markers
may allow for more precise associations with
exposures
• GIS- may allow opportunities to generalize cluster
concerns to larger populations and areas
• Statistical process tools- may allow for more
automated reviews of data that trigger
investigations when threshold conditions are met