Mapping disease at an approximated individual level using aggregate data: a case study of mapping New Hampshire birth defects

Understanding the relationship between health and place: A systematic review of methods to disaggregate data to small areas

BACKGROUND

A systematic review was conducted to examine the extent, range and nature of published research evidence using methods to attribute data reported at aggregate levels to small areas, taken from both the health sciences and geography literatures.

METHODS

Four electronic bibliographic health databases were searched (MEDLINE, Embase, PsychINFO and CINAHL) and one human geography database (GEOBASE). We reviewed titles, abstracts and then full-text articles for their relevance to this review, based on pre-determined exclusion and inclusion criteria. All attribution methods were identified, reviewed in tables and assessed against a set of criteria to robustly compare their applicability to health data.

RESULTS

Of 634 titles identified, 84 articles met the inclusion criteria. From these studies, we identified four broad categories of attribution methods: spatial interpolation, dasymetric mapping, regression methods and spatial microsimulation. Spatial interpolation and regression methods were the most utilised in the health science literature. Both groups of methods allow adjustments for the underlying demographic of the populations that are being disaggregated. In comparison, dasymetic mapping is the most utilised spatial attribution method in the geography literature. These methods did not adjust for the underlying demographic of the populations.

CONCLUSION

The type of spatial attribution method that should be applied to health data will depend on the health measures used. The prevalence of certain health conditions are much more dependent on the sociodemographic of a population than others. For the former, adjusting population distributions for underlying demographic factors is important. Where the prevalence of health conditions is more equally distributed across a population, we may wish to prioritise other criteria when selecting an attribution method.

Assessing the influence of the modifiable areal unit problem on Bayesian disease mapping in Queensland, Australia

BACKGROUND

Spatial data are often aggregated by area to protect the confidentiality of individuals and aid the calculation of pertinent risks and rates. However, the analysis of spatially aggregated data is susceptible to the modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While the impact of the MAUP has been examined previously, typically these studies have focused on well-populated areas. Understanding how the MAUP behaves when data are sparse is particularly important for countries with less populated areas, such as Australia. This study aims to assess different geographical regions' vulnerability to the MAUP when data are relatively sparse to inform researchers' choice of aggregation level for fitting spatial models.

METHODS

To understand the impact of the MAUP in Queensland, Australia, the present study investigates inference from simulated lung cancer incidence data using the five levels of spatial aggregation defined by the Australian Statistical Geography Standard. To this end, Bayesian spatial BYM models with and without covariates were fitted.

RESULTS AND CONCLUSION

The MAUP impacted inference in the analysis of cancer counts for data aggregated to coarsest areal structures. However, area structures with moderate resolution were not greatly impacted by the MAUP, and offer advantages in terms of data sparsity, computational intensity and availability of data sets.

A multi-constraint Monte Carlo Simulation approach to downscaling cancer data

This study employs an innovative multi-constraint Monte Carlo simulation method to estimate suppressed county-level cancer counts for population subgroups and extend the downscaling from county to ZIP Code Tabulation Areas (ZCTA) in the U.S. Given the known cancer counts at a higher geographic level and larger demographic groups at the same geographic level as constraints, this method uses the population structure as probability in the Monte Carlo simulation process to estimate suppressed data entries. It not only ensures consistency across various data levels but also accounts for demographic structure that drives varying cancer risks. The 2016-2020 cancer incidence data from the Utah Cancer Registry is used to validate our approach. The method yields results with high precision and consistency across the full urban-rural continuum, and significantly outperforms several machine-learning models such as Random Forest and Extreme Gradient Boosting.

Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease

Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the "big n" problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January-July and a decrease in the period August-December.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10109-021-00368-0.

Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas.

Spatial interaction between breast cancer and environmental pollution in the Monterrey Metropolitan Area

This research examines the spatial structure of a sample of breast cancer (BC) cases and their spatial interaction with contaminated areas in the Monterrey Metropolitan Area (MMA). By applying spatial statistical techniques that treat the space as a continuum, degrees of spatial concentration were determined for the different study groups, highlighting their concentration pattern. The results indicate that 65 percent of the BC sample had exposure to more than 56 points of PM10. Likewise, spatial clusters of BC cases of up to 39 cases were identified within a radius of 3.5 km, interacting spatially with environmental contamination sources, particularly with refineries, food processing plants, cement, and metals. This study can serve as a platform for other clinical research by identifying geographic clusters that can help focus health policy efforts.