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I have counts of people with a condition, by age (5-year groups), sex, and area (100-200 areas).

For each area, I have:

  • estimated population by age (5-year groups) and sex,
  • estimated population percent living in city, small town or rural (sum to 100%),
  • estimated population in high, middle and low socioeconomic status (sum to 100%), and
  • estimated population proportion treated (via food or water supply fortification).

I know nothing about individual-level correlation between rurality and socioeconomic status (or even if it's the same across areas). I expect proportion treated is near 100% in cities, lower in small towns, much lower in rural areas.

Researcher wants adjusted rate (or odds) ratios for having the condition (and p-values) for rural vs cities, low vs high SES, untreated vs treated, young vs older.

Can I produce valid estimates?

If so, how should I model it?

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Multilevel analysis based on aggregate data leads to ecological fallacy. Here is an interesting paper to read: http://www.stanford.edu/class/ed260/freedman549.pdf

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    $\begingroup$ Could you perhaps summarize the main points of that paper so that this reply remains self-contained. $\endgroup$ – chl Jul 17 '12 at 9:56
  • $\begingroup$ @Omar So are you saying no I cannot produce valid estimates? Or are you saying I cannot produce valid estimates from multilevel analysis? Is there any way i can produce valid estimates? $\endgroup$ – timbp May 25 '18 at 13:10

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