Early Detection of Disease Outbreaks Prospective Surveillance

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

Example: Thyroid Cancer Incidence in New Mexico Data Source: New Mexico Tumor Registry Time Period: 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

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).

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.

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

Space-Time Scan Statistic Alive Clusters YearsMost Likely Cluster Cases Cluster Period Expected RRp=p= LosAlamos, Rio Arriba Bernadillo + 7 counties West LosAlamos, Rio Arriba North Central – SanMiguel North Central – SanMiguel Bernadillo, Valencia North Central Counties = Bernadillo, Los Alamos, Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe and Taos North Central Lincoln North Central + Colfax, Harding North Central + Colfax, Harding North Central – SanMiguel North Central + Colfax,Harding

Space-Time Scan Statistic Alive Clusters YearsMost Likely Cluster CasesCluster Period Expected RRp=p= LosAlamos, Rio Arriba Bernadillo + 7 counties West LosAlamos, Rio Arriba North Central – SanMiguel North Central – SanMiguel Bernadillo, Valencia North Central Counties = Bernadillo, Los Alamos, Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe and Taos North Central Lincoln North Central + Colfax, Harding North Central + Colfax, Harding North Central – SanMiguel North Central + Colfax,Harding LosAlamos, RioArriba, SantaFe, Taos LosAlamos

Los Alamos

Space-Time Scan Statistic Alive Clusters YearsMost Likely Cluster CasesCluster Period Expected RRp=p= LosAlamos, Rio Arriba Bernadillo + 7 counties West LosAlamos, Rio Arriba North Central – SanMiguel North Central – SanMiguel Bernadillo, Valencia North Central Counties = Bernadillo, Los Alamos, Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe and Taos North Central Lincoln North Central + Colfax, Harding North Central + Colfax, Harding North Central – SanMiguel North Central + Colfax,Harding LosAlamos, RioArriba, SantaFe, Taos LosAlamos LosAlamos

Adjusting for Yearly Surveillance The Los Alamos Cluster 1991 Analysis: p=0.13 (unadjusted p=0.02) 1992 Analysis: p=0.016 (unadjusted p=0.002)

Los Alamos cases

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 All were confirmed cases.

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.

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.

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.

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.

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 All were confirmed cases.

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.

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.

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)

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.

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

References Kulldorff M. Prospective time-periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society, A164:61-72, Software: Kulldorff M et al. SaTScan v