Assignment Week 2

Assignment Week 2

by Ekland Abdiwahab -

Freedman, V. A., Grafova, I. B., & Rogowski, J. (2011). Neighborhoods and chronic disease onset in later life. American journal of public health, 101(1), 79-86.

 

Unit of clustering: neighborhood

Hypothesized effects: neighborhood conditions would affect chronic disease (coronary heart disease, angina, congestive heart failure or other heart problems, high blood pressure, stroke, diabetes, cancer, and arthritis or rheumatism) onset in later life.

Level of exposure measure: Exposure is observed as a characteristic of the cluster, in this case a characteristic of the Neighborhood. Neighborhood conditions were characterized by using 8 previously validated scales reflecting the economic, social, and built environments.  

Statistical model: Two-level random-intercept logistic model

If the authors were interested in estimating the average population effects as opposed to individual effects, then GEE would be a better approach. For example, comparing low SES neighborhoods vs. High SES neighborhoods with regard to disease incidence. 

Re: Assignment Week 2

by Maria Glymour -

Nice example Ekland.  And they report the ICCs (pseudo-ICCs since they analyzed binary outcomes): 

"Pseudo-ICCs (not shown) for models with no predictors were less than 5% for 8 of the 12 models. For men, ICCs reached 8% for heart problems and cancer and 16% for stroke. For women, ICCs were above 5% only for heart problems (ICC=6%). Controlling for individual-level factors reduced the ICCs to less than 0.01% for heart problems for men and to less than 5% for heart problems for women, increased the ICCs for stroke for women from 4% to 8%, but in other cases had negligible effects. "

It's helpful to keep this in mind when you are contemplating neighborhood clustering for power calculations for example.