Spatial identification of potential health hazards: a systematic areal search approach

Abstract

Background and aims: Large metropolitan areas often exhibit multiple morbidity hotspots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In this study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns.

Methods: The morbidity effect of a particular health hazard is expected to weaken with distance. To account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate this approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, which is characterized by multiple health hazards.

Results: In our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the main health hazard was identified as a spot in the industrial zone, which hosts several petrochemical facilities.

Conclusion: The proposed approach does not require extensive input information and can be used as a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns.

Keywords: Disease hotspots; Multivariate regression analysis; Receptor-oriented models; Source-oriented models; Systematic search approach; Wind adjustment.

Fig. 1

Input morbidity patterns (left panel) and risk source estimates (right panel). Notes: Green dots in the left panel diagrams indicate morbidity observations with the size of each dot set proportional to the morbidity prevalence rate observed in a given location; the triangle and the solid line in the left panel diagrams indicate predefined sources of environmental pollution (see text for explanations)

Fig. 2

Map of the GHMA study area, showing residential buildings, main industrial facilities (1–5) and thoroughfare roads

Fig. 3

Changes in NHL and lung cancer incidence rates (per 100,000) as a function of distance from industrial facility 5 (see Fig. 2)

Fig. 4

Risk source assessment for lung cancer (left panel) and NHL cancer (right panel) by uncontrolled (a, b) and controlled regressions (c, d). Note: Black triangles mark the points, distances to which are used in the regression models reported in Tables 1 and 2