period behavior. For deaths and mortality, cohort mortality patterns raise interesting findings looking as far back as the 1930s and the first studies of cohort mortality patterns. In particular, when sex differentials in mortality are arrayed on a cohort rather than a period basis, the sex differential peaks for the cohort born around 1905—exactly the cohort for which sex differences in smoking behaviors also reached a peak.

Goss concurred that this suggests that vital statistics—and cohort analyses of them—provide great opportunities for investigation of patterns in mortality and fertility. Goss said that significant work had been done internationally on this, particularly in the United Kingdom. Goss said that OCA had also compared its data with counterparts in Canada; Canada has not seen the same broad improvement in its national rate of mortality as in the United States, but further analysis of trends could be useful.


A workshop session moderated by Kenneth Prewitt (Columbia University) considered important applications of vital statistics beyond the health care planning domain. Michael Stoto (Georgetown University) spoke of his recent work in health surveillance for national security, also known as syndromic surveillance or biosurveillance. Although originally focused on the detection of terrorist attacks using biological agents, Stoto argued that biosurveillance has come to be interpreted more broadly, as a means for situational awareness for public health emergencies. In either event, Stoto noted that the data systems he was discussing have a much more exacting standard for timeliness than the current vital statistics collections—timeliness measured in weeks and days, and sometimes hours, rather than years. Indeed, the basic point of near-real-time acquisition and use of prediagnostic health data is that waiting until people are diagnosed with diseases or, in the case of vital statistics, die from them would be too late to inform possible interventions. Still, he said, there are important linkages between biosurveillance and the current vital statistics.

The central statistical challenges in biosurveillance are, first, obtaining and integrating accurate data from a variety of sources in a timely way and, second, determining whether something is “unusual.” The latter task is complicated by high variability in the background and a possible unstable process generating the data; it involves making critical trade-offs among sensitivity (i.e., false negatives), specificity (i.e., false positives), and timeliness.

Current work in biosurveillance has sought to build on existing data systems in the health care world—such as emergency department reports, sales of over-the-counter medication, and absenteeism from work and school.

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