Epidemiologists commonly study the geographical variation of disease rates to generate and refine testable hypotheses regarding etiology. Poisson regression is often used to estimate the parameters that characterize the effects of risk factors on disease. Hierarchical models have been proposed to utilize spatial locations and neighbors as surrogates for unknown or unmeasured risk factors in the analysis of disease rates. Although hierarchical models are useful in modeling spatial disease rates in a scientifically meaningful way, the analytic tools for them are generally computationally intensive. We overcome this issue by applying a conditional spatial modeling technique using unbiased estimating functions. The resulting estimator of regression coefficients is consistent and asymptotically normally distributed under mild conditions. A simulation study compares the performance of the proposed scheme with the approximate inference methods for hierarchical models. We apply the proposed scheme to Scottish lip cancer incidence data to illustrate its use in practice.