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Semiparametric Modeling of the Spatial Distribution of Occupational Accident Risk in the Casual Labor Market, Piracicaba, Southeast Brazil

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Abstract:

The objective of this study was to estimate the spatial distribution of work accident risk in the informal work market in the urban zone of an industrialized city in southeast Brazil and to examine concomitant effects of age, gender, and type of occupation after controlling for spatial risk variation. The basic methodology adopted was that of a population-based case-control study with particular interest focused on the spatial location of work. Cases were all casual workers in the city suffering work accidents during a one-year period; controls were selected from the source population of casual laborers by systematic random sampling of urban homes. The spatial distribution of work accidents was estimated via a semiparametric generalized additive model with a nonparametric bidimensional spline of the geographical coordinates of cases and controls as the nonlinear spatial component, and including age, gender, and occupation as linear predictive variables in the parametric component. We analyzed 1,918 cases and 2,245 controls between 1/11/2003 and 31/10/2004 in Piracicaba, Brazil. Areas of significantly high and low accident risk were identified in relation to mean risk in the study region (p < 0.01). Work accident risk for informal workers varied significantly in the study area. Significant age, gender, and occupational group effects on accident risk were identified after correcting for this spatial variation. A good understanding of high-risk groups and high-risk regions underpins the formulation of hypotheses concerning accident causality and the development of effective public accident prevention policies.

Keywords: Models; nonlinear models; occupational accidents; risk factors; space; wounds and injury

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1539-6924.2007.00894.x

Affiliations: 1: Department of Mathematical Sciences, School of Engineering, Computer Science & Mathematics, University of Exeter, Exeter, UK. 2: School of Environmental Engineering, Paulista State University, UNESP, Sorocaba SP, Brazil

Publication date: 2007-04-01

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