Spatial Modelling Methods

Spatial information and spatial technologies can bring significant value to health agencies through improved decision support, resource management and allocation, and clinical outcomes. Disease mapping is used to explain and predict patterns of diseases outcomes across geographical areas, identify areas of increased risk, and assist in understanding the causes of diseases. As such, its use in informing policy recommendations is growing, and diseases of national importance, such as cancer, are being increasingly mapped across small regions. Yet there are many potential methodological approaches for examining disease data over small areas, and understanding the benefits and disadvantages of any single approach when applied to a given situation is critical. The aims of this report are threefold. First, to provide an accessible overview of the methods used in analysing spatial public health data, ranging from raw (unsmoothed) estimates through to complex Bayesian hierarchical models. Secondly, to outline the practical computational implementation of these methods. Finally, by comparing the advantages and disadvantages of these methods, to provide general guidelines and recommendations for their use. Examples of the methods used in existing cancer atlases and other small-area analyses are also provided, as well as Bayesian approaches to incorporating multiple nested regions; considering the combined influence of related variables, such as remoteness and area-level socioeconomic disadvantage; small-area estimation from survey data; and extending the spatial analyses to also consider differences over time (spatio-temporal models). Key issues to consider when using spatial data include data quality, including the reliability of location measures, and the degree of similarity between nearby areas (spatial correlation). Although unsmoothed estimates such as crude or age-standardised rates may be useful for exploratory analyses, they are rarely appropriate for small-area analyses due to the small numbers involved, and should not be used when: 1. The addition of one event (disease case/death), or one more person at risk, results in a large difference (such as 25% or more) in at least one area’s rates. 2. The number of events (rate numerator) is less than three for at least one area. 3. The population at risk per area is small (typically less than 500 people), and these numbers vary by an order of magnitude across the areas. Smoothing methods may be either direct (e.g. locally-weighted, kernel smoothing) or model based (e.g. Poisson kriging, Empirical Bayes or fully Bayesian). In general, direct smoothing methods are also more appropriate for exploratory analyses, but less useful when investigating contributing factors as they have more limited capacity for adjusting for covariates. Model-based smoothing approaches have several advantages over the direct smoothing methods, and their use is recommended when assessing the impact of covariates is important, or the underlying pattern of risk needs to be understood. There is no one model that represents the ultimate approach for disease mapping. The aims of the analysis, data quality, and expected results (such as disparate risks between nearby areas) can all influence the selection of the final model. Nonetheless, Bayesian hierarchical models are increasingly used in disease mapping, have been shown to perform well overall, and with the more recent application of approximation methods are able to generate results quickly. For a cancer atlas, we generally recommend the use of Bayesian hierarchical models. The fully Bayesian approach enables the development of more complex, realistic models with reliable disease rates in low population areas, clearer summaries of spatial and temporal correlation, more precise and interpretable confidence intervals, and greater ability to account for and quantify measured sources of uncertainty than other possible approaches. The Bayesian approach also has excellent flexibility in handling changing inferential goals, such as obtaining smoothed risk maps as well as identifying motivating predictors of disease such as ethnicity or socioeconomic status.