  Introduction: Methods now exist to detect residual confounding. One requires
an "indicator" with two key properties: conditional independence of the outcome
(given exposure and measured covariates) absent confounding and other model
miss-specification; and an association with unmeasured confounders (like the
exposure). We now present a new method for correcting for residual confounding
in time-series and other epidemiological studies. We argue that estimators from
models that include an indicator with these key properties should have less
bias than those from models without the indicator.
  Methods: Using causal reasoning and basic regression theory we present
theoretical arguments to support our claims. In simulations, we empirically
evaluate our approach using a time-series study of ozone effects on emergency
department visits for asthma (AV). We base simulations on observed data for
ozone, meteorological factors and asthma.
  Results: In simulations, results from models that included ozone
concentrations one day after the AV yielded effect estimators with slightly or
modestly less residual confounding.
  Conclusion: Theory and simulations show that including the indicator based on
future air pollution levels can reduce residual confounding. Our method differs
from available methods because it uses a regression approach involving an
exposure-based indicator rather than a negative outcome control.
