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	Ecological Fallacy Diagnostics	Disease Mapping
Ämnesområde	Social Epidemiology	Spatial Epidemiology
Familj	Process / pipeline	Process / pipeline
Ursprungsår	1950	1987
Upphovsperson	William S. Robinson (ecological correlation); Sander Greenland & Hal Morgenstern (ecological bias theory)	David Clayton & Jack Kaldor (empirical Bayes); Andrew Lawson (Bayesian hierarchical synthesis)
Typ	Diagnostic and design pipeline for detecting and avoiding cross-level inferential bias	Pipeline for estimating and smoothing small-area disease relative risk from counts
Ursprungskälla	Robinson, W. S. (1950). Ecological Correlations and the Behavior of Individuals. American Sociological Review, 15(3), 351-357.	Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43(3), 671-681.
Alias	Cross-Level Bias Diagnostics, Ecological Bias Assessment, Aggregation Bias Diagnostics, Ecological Inference Bias Checks	Small-Area Risk Mapping, Relative-Risk Smoothing, Empirical Bayes Disease Mapping, Spatial Risk Estimation
Närliggande	4	4
Sammanfattning	Ecological fallacy diagnostics are the design and analysis tools used to detect, quantify, and avoid the bias that arises when associations measured on groups are mistakenly taken to hold for individuals. The problem was crystallized by W. S. Robinson (1950), who showed that the correlation between, say, immigrant share and illiteracy across U.S. states bore no resemblance to the correlation between being an immigrant and being illiterate among individuals, sometimes even reversing sign. Greenland and Morgenstern (1989) gave the modern account, decomposing ecological bias into within-group confounding, effect modification, and model misspecification, and clarifying that the ecological fallacy is not a single artifact but a family of cross-level biases. As a pipeline, the diagnostics contrast ecological and individual associations, attribute any discrepancy to its sources, model the within-group covariate distribution that aggregate analyses ignore, place bounds on the individual-level quantity, and where possible move to hybrid or multilevel designs that recover individual effects.	Disease mapping is the set of model-based methods for estimating and displaying the geographic distribution of disease risk across small areas. Its central problem is that raw area-level rates, especially standardized mortality or incidence ratios, are statistically unstable where populations are small: a handful of cases can produce wildly high or low rates that reflect chance rather than true risk. Clayton and Kaldor's 1987 empirical-Bayes paper showed how to stabilize these estimates by shrinking each area's rate toward an overall mean using a Poisson-gamma (or log-normal) hierarchical model, and the approach was developed into the fully Bayesian, spatially smoothed hierarchical framework synthesized in Lawson's textbook. As a pipeline, disease mapping computes expected counts, places the counts in a hierarchical risk model, borrows strength globally and across neighbors to smooth the estimates, and produces a risk map with quantified uncertainty, including probabilities that risk exceeds a threshold.