Mobile Messaging Could be a Viable New Surveillance Tool

In the September 2010 issue of Emerging Infectious Diseases, CDC's monthly peer-reviewed journal, Martin Lajous, of the department of epidemiology at Harvard School of Public Health, describes how mobile messaging was used as a surveillance tool during the H1N1 influenza pandemic.

Pandemic (H1N1) 2009 highlighted challenges faced by disease surveillance systems, writes Lajous. New approaches to complement traditional surveillance are needed, and new technologies provide new opportunities.

Lajous and colleagues evaluated cell phone technology for surveillance of influenza outbreaks during the outbreak of pandemic (H1N1) 2009 in Mexico. As Lajous, et al. (2010) explains further, On May 12, 2009, at 2:20 p.m., a random sample of 982,708 telephones from an 18 million nationwide network of mostly prepaid cell phones received a text message invitation to a Ministry of Health survey. Influenza-like illness (ILI) in April, date of fever onset, severity, number of household members with ILI, age, influenza vaccination, household size, and number of children in each household were assessed ILI was defined as fever and cough or sore throat, and severe ILI was defined as inability to work, study, or maintain family care. Unstructured supplementary service data, an interactive platform available on most cell phones, was used. We obtained daily counts of suspected and confirmed cases of pandemic (H1N1) 2009 from the nationwide clinic-based surveillance system Sistema Nacional de Vigilancia Epidemiológica (SINAVE)

Of 70,856 responses received, the researchers report that 56,551 (78.1 percent) were unique mobile numbers. Within three hours, 53 percent of responses were received and by 24 hours, 89 percent were received. Mean (SD) age of respondents was 25.2 (10.4) years. A total of 9,333 persons reported ILI and 49.3 percent had severe symptoms. The mean number of other persons with ILI in the household was 1.6 among respondents reporting severe disease and 0.3 among those with non-severe disease. The proportion of severe cases increased throughout the month beginning on April 1 (36.4 percent) and peaking on April 26 (57.9 percent).

The researchers point out that the pattern of change in the proportion of severe ILI may be consistent with a decrease in transmission after control measures were implemented. The low response rate (5.8 percent) made it likely that respondents were not representative of the total population. Therefore, we did not make estimates of disease incidence. We were unable to determine whether a pathogen for which susceptibility was higher was responsible for the difference in number of ILI cases within the household of those reporting severe disease or whether respondents in households with several affected persons were more likely to report severe disease. We observed unexpected peaks and a clustering of date of fever onset. However, the peak on April 1 may reflect disease at the end of March, and the decrease in daily proportion of severe cases may indicate lower incidence of ILI after school closures. Comparison of these data with epidemic curves for pandemic (H1N1) 2009 showed less variability than expected; no geographic variation was detected.

The researchers conclude, Efficient estimation of extent of disease caused by a novel infectious agent may be costly and logistically difficult. When carefully deployed, unstructured supplementary service data surveys may be a practical, low-cost, and timely complement to traditional surveillance. Further refinements of this tool are required to improve its validity. To limit recall errors and increase response rate, repeated surveys at short intervals and specific strategies to improve response rate should be considered.

Reference: Lajous M, et al. Mobile Messaging as Surveillance Tool during Pandemic (H1N1) 2009, Mexico. Emerg Infect Dis. Vol. 16, No. 9. September 2010.