When Privacy Doesn’t Matter

Last Thursday MSR hosted Professor Sam Clark from the University of Washington for a talk entitled "Relational Databases in the Social and Health Sciences: The View from Demography." For someone interested in using data for driving decision-making, it was interesting to hear about someone using empirical data to model the impact of different policies on a societal problem.My main take-aways from the talk were as follows:

• Social scientists today rarely use relational database (RDBMS) technology, or when they do, they use antique software. Apparently much analysis is done in statistical packages (I'm guessing SAS, and the like), which apparently lack much of the data management technology that is indispensable when working with larger datasets. For social scientists in general, the potential of current database technologies is only just becoming apparent.
  • As I am not familiar with many of the alternatives, had I been physically at the talk on campus rather than watching on-line, I would have liked to clarify what has changed to make RDBMS more attractive than it was before. I can only surmise from the talk that large data sets have only recently become available to social scientists, and that previous data sets were too small to warrant the RDBMS.
  • Even now, Clark said his colleagues would categorize a "large" dataset to be around 500 Megabytes.

• Many early attempts at moving demographic data to relational data structures failed because the impact of the schema design on the demographical data uses was underestimated by those developing such systems.

• Breadth of data has significant value to the longitudinal studies social scientists are conducting. Yet, lack of agreement on how to collect and store data is hampering their ability to interrelate data sets. Therefore, developing and agreeing on a standard is very desirable. (Incidentally, this problem is not exclusive to demographic datasets.)

• Clark has done several iterations on a standard schema, particularly for capturing "Event-Influence-State" type datasets, commonly used in demography.
  • The Structured Population Event History Register (SPEHR).
  • One example he shared with us assessed "the impact of male circumcision as an HIV prevention strategy". By using a longitudinal study (2 years, 3,000 people) to feed his simulation, he was able to demonstrate likely outcomes of the policy intervention in different phases of the epidemic; data to feed a real-world policy decision. :)

He also shared lots of anecdotal statistics about the AIDS epidemic in Africa, massive infection rates and death rates, which I continue to find mind-boggling: What would day-to-day living look like in the U.S. if 20% of Americans were infected with HIV? Or if we suddenly had millions of "dual-orphans" (normally a rare phenomenon) to raise? How will Africa recover?All in all a very interesting talk with food for thought on many fronts, but one issue was conspicuously missing: Privacy.Up Next: How to evaluate a privacy statement when you're dying of AIDS.