Early Detection of Disease Outbreaks Prospective Surveillance

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Early Detection of Disease OutbreaksProspective
Surveillance
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For a pre-specified geographical area, there are
existing purely temporal statistical methods for
the detection of a sudden disease outbreak.
Two Important Issues

1. Such methods can be used simultaneously for
   multiple geographical areas, but that leads to
   multiple testing, providing more false alarms
   than what is reflected in the nominal
   significance level.
2. Disease outbreaks may not conform to the
   pre-specified geographical areas.

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ExampleThyroid Cancer Incidence in New Mexico

• Data Source New Mexico Tumor Registry
• Time Period 1973-1992
• Gender Male
• Population 580,000
• Annual Incidence Rate 2.8/100,000
• Aggregation Level 32 Counties
• Adjustments for Age and Temporal Trends
• Monte Carlo Replications 999

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Example Thyroid Cancer

• Median age at diagnosis 44 years
• United States (SEER) incidence 4.5 / 100,000
• United States mortality 0.3 / 100,000
• Five year survival 95
• Known risk factors
• Radiation treatment for head and neck
  conditions.
• Radioactive downfall (Hiroshima/Nagasaki,
  Chernobyl, Marshall Islands)
• Work as radiologic technician (USA) or x-ray
  operator (Sweden).

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Detecting Emerging Clusters

• Instead of a circular window in two dimensions,
  we use a cylindrical window in three dimensions.
• The base of the cylinder represents space, while
  the height represents time.
• The cylinder is flexible in its circular base and
  starting date, but we only consider those
  cylinders that reach all the way to the end of
  the study period. Hence, we are only considering
  alive clusters.

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Hypothesis Test

• Find Likelihood for Each Choice of Cylinder
• Through Maximum Likelihood Estimation, Find the
  Most Likely Cluster
• Apply Likelihood Ratio Test
• Evaluate Significance Through Monte Carol
  Simulation

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Space-Time Scan Statistic Alive Clusters
Cluster Period
Cases
Expected
Years
Most Likely Cluster
RR
p
73-78 Bernadillo 7 counties West
75-78 48 36 1.4 0.60
73-79 LosAlamos, Rio Arriba
75-79 9 3.3 2.7 0.58
73-80 LosAlamos, Rio Arriba
75-80 10 3.8 2.6 0.54
73-81 North Central SanMiguel
75-81 72 53 1.4 0.19
73-82 North Central SanMiguel
75-82 85 62 1.4 0.08
73-83 Bernadillo, Valencia
73-83 84 62 1.4 0.13
73-84 North Central
73-84 113 90 1.3 0.14
73-85 Lincoln
85 3 0.2 13.8
0.23
73-86 North Central Colfax, Harding
73-86 129 108 1.2 0.49
73-87 North Central Colfax, Harding
73-87 142 117 1.2 0.21
73-88 North Central SanMiguel
73-88 143 115 1.2 0.08
73-89 North Central Colfax,Harding 73-89
165 134 1.2 0.06
North Central Counties Bernadillo, Los Alamos,
Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe
and Taos.
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Space-Time Scan Statistic Alive Clusters
Cluster Period
Cases
Expected
RR
p
Years
Most Likely Cluster
73-78 Bernadillo 7 counties West
75-78 48 36 1.4 0.60
73-79 LosAlamos, Rio Arriba
75-79 9 3.3 2.7 0.58
73-80 LosAlamos, Rio Arriba
75-80 10 3.8 2.6 0.54
73-81 North Central SanMiguel
75-81 72 53 1.4 0.19
73-82 North Central SanMiguel
75-82 85 62 1.4 0.08
73-83 Bernadillo, Valencia
73-83 84 62 1.4 0.13
73-84 North Central
73-84 113 90 1.3 0.14
73-85 Lincoln
85 3 0.2 13.8
0.23
73-86 North Central Colfax, Harding
73-86 129 108 1.2 0.49
73-87 North Central Colfax, Harding
73-87 142 117 1.2 0.21
73-88 North Central SanMiguel
73-88 143 115 1.2 0.08
73-89 North Central Colfax,Harding 73-89
165 134 1.2 0.06
73-90 LosAlamos, RioArriba,
79-90 41 22 1.8 0.06
SantaFe, Taos
73-91 LosAlamos
89-91 7 0.9 7.6 0.02
North Central Counties Bernadillo, Los Alamos,
Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe
and Taos.
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Los Alamos
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Space-Time Scan Statistic Alive Clusters
Cluster Period
Cases
Expected
RR
p
Years
Most Likely Cluster
73-78 Bernadillo 7 counties West
75-78 48 36 1.4 0.60
73-79 LosAlamos, Rio Arriba
75-79 9 3.3 2.7 0.58
73-80 LosAlamos, Rio Arriba
75-80 10 3.8 2.6 0.54
73-81 North Central SanMiguel
75-81 72 53 1.4 0.19
73-82 North Central SanMiguel
75-82 85 62 1.4 0.08
73-83 Bernadillo, Valencia
73-83 84 62 1.4 0.13
73-84 North Central
73-84 113 90 1.3 0.14
73-85 Lincoln
85 3 0.2 13.8
0.23
73-86 North Central Colfax, Harding
73-86 129 108 1.2 0.49
73-87 North Central Colfax, Harding
73-87 142 117 1.2 0.21
73-88 North Central SanMiguel
73-88 143 115 1.2 0.08
73-89 North Central Colfax,Harding 73-89
165 134 1.2 0.06
73-90 LosAlamos, RioArriba,
79-90 41 22 1.8 0.06
SantaFe, Taos
73-91 LosAlamos
89-91 7 0.9 7.6 0.02
73-92 LosAlamos
89-92 9 1.2 7.4 0.002
North Central Counties Bernadillo, Los Alamos,
Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe
and Taos.
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Adjusting for Yearly SurveillanceThe Los Alamos
Cluster

• 1991 Analysis p0.13
• (unadjusted p0.02)
• 1992 Analysis p0.016
• (unadjusted p0.002)

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Los Alamos
cases
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Thyroid Cancer in Los Alamos

• The New Mexico Department of Health have
  investigated the individual nature of all 17 male
  thyroid cancer cases reported in Los Alamos
  1970-1995. All were confirmed cases.

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Thyroid Cancer in Los Alamos

• 3/17 had a history of therapeutic ionizing
  radiation treatment to the head and neck.
• 8/17 had been regularly monitored for exposure to
  ionizing radiation due to their particular work
  at the Los Alamos National Laboratory.
• 2/17 had had significant workplace-related
  exposure to ionizing radiation from atmospheric
  weapons testing fieldwork.

A know risk factor, ionizing radiation, is hence
a likely explanation for the observed cluster.
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Practical Considerations

• Chronic or infectious diseases.
• Known or unknown etiology.
• Daily, weekly, monthly, or yearly data, depending
  on the type of disease.
• It is not possible to detect clusters much
  smaller than the level of data aggregation.
• Data quality control.
• Help prioritize areas for deeper investigation.
• P-values should be used as a general guideline,
  rather than in a strict sense.

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Limitations

• Space-time clusters may occur for other reasons
  than disease outbreaks
• Automated detection systems does not replace the
  observant eyes of physicians and other health
  workers.
• Epidemiological investigations by public health
  department are needed to confirm or dismiss the
  signals.

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Conclusions

• The space-time scan statistic can serve as an
  important tool in prospective systematic
  time-periodic geographical surveillance for the
  early detection of disease outbreaks.
• It is possible to detect emerging clusters, and
  we can adjust for the multiple tests performed
  over the years.
• The method can be used for different diseases.

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Thyroid Cancer in Los Alamos

• The New Mexico Department of Health have
  investigated the individual nature of all 17 male
  thyroid cancer cases reported in Los Alamos
  1970-1995. All were confirmed cases.

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Thyroid Cancer in Los Alamos

• 3/17 had a history of therapeutic ionizing
  radiation treatment to the head and neck.
• 8/17 had been regularly monitored for exposure to
  ionizing radiation due to their particular work
  at the Los Alamos National Laboratory.
• 2/17 had had significant workplace-related
  exposure to ionizing radiation from atmospheric
  weapons testing fieldwork.

A know risk factor, ionizing radiation, is hence
a likely explanation for the observed cluster.
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Practical Considerations

• Chronic or infectious diseases.
• Known or unknown etiology.
• Daily, weekly, monthly, or yearly data, depending
  on the type of disease.
• It is not possible to detect clusters much
  smaller than the level of data aggregation.
• Data quality control.
• Help prioritize areas for deeper investigation.
• P-values should be used as a general guideline,
  rather than in a strict sense.

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Practical Considerations (cont.)

• Possible to specify 0.05 probability of a false
  alarm
• - since start
• - during last 20 years
• - during last 5 years ( one false alarm per
  100 years)
• - during last year ( one false alarm per 20
  years)
• - during last 18 days ( one false alarm per
  year)

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Conclusions

• The space-time scan statistic can serve as an
  important tool in systematic time-periodic
  geographical disease surveillance.
• It is possible to detect emerging clusters, and
  we can adjust for the multiple tests performed
  over the years.
• The method can be used for different diseases.

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Computing Time
Each analysis took between 5 and 75 seconds to
run on a 400 MHz Pentium Pro.
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References

• Kulldorff M. Prospective time-periodic
  geographical disease surveillance using a scan
  statistic. Journal of the Royal Statistical
  Society, A16461-72, 2001.
• Software Kulldorff M et al. SaTScan v.3.1.
  http//www.satscan.org/