Does living in a deprived neighborhood increase colorectal cancer mortality among men?
Population of interest: men, 50 years and older, living in Northern California Bay Area
Study design: cohort study of men 50-75, living in Northern California Bay Area diagnosed with colorectal cancer over a 4-year-period.
Sampling strategy: systematic random sampling; men will be identified from the Northern California Cancer registry and will be chosen at a pre-determined interval (every nth diagnosed case of colorectal cancer).
Advantage: the registry receives a detailed record of patient diagnosis and includes demographic information including address at the time of diagnosis. Patient address can be geocoded to construct a neighborhood deprivation score for each case.
Disadvantage: there may be extra security measures and hoops to jump through to obtain information on patient residence and other identifiable information. Will need to determine a strategy to link patients in the study to mortality records. One approach is to obtain either the death certificate or the cause of death from the National Death Index. This process may be burdensome.
1) Incorporating this sampling strategy will minimize selection bias into the study although Kandola et al point out that the sampling intervals themselves can coincide with systematic variation in the sampling frame. Unclear what they mean by this, clarification would be helpful. Minimizing selection bias will help reduce bias associated with estimating mortality rate ratio. Without a random sampling strategy there is a chance that I would end up selecting for the sickest patients/patients with more aggressive form of colorectal cancer or the healthiest patients.
2) Incorporating this strategy should ideally help reduce bias in estimation of causal effects but there would still be issues with confounding both measured and unmeasured. One challenge in estimating a causal relationship between neighborhood deprivation and cancer mortality is teasing apart neighborhood factors and hospital services. Patients in poor neighborhood tend to have access to hospitals that provide worse services (e.g. less skilled providers, non-optimal treatment options, etc.). Patients in poorer neighborhoods also tend to report at later stages. So, I would need to control for these factors in order to estimate a causal effect that is minimally biased.