title: Gravity models to classify commuting vs. resident workers. An application to the analysis of residential risk in a contaminated area.
abstract: BACKGROUND
  The analysis of risk for the population residing and/or working in contaminated areas raises the topic of commuting. In fact, especially in contaminated areas, commuting groups are likely to be subject to lower exposure than residents. Only very recently environmental epidemiology has started considering the role of commuting as a differential source of exposure in contaminated areas. In order to improve the categorization of groups, this paper applies a gravitational model to the analysis of residential risk for workers in the Gela petrochemical complex, which began life in the early 60s in the municipality of Gela (Sicily, Italy) and is the main source of industrial pollution in the local area.
  
  
  RESULTS
  A logistic regression model is implemented to measure the capacity of Gela "central location" to attract commuting flows from other sites. Drawing from gravity models, the proposed methodology: a) defines the probability of finding commuters from municipalities outside Gela as a function of the origin's "economic mass" and of its distance from each destination; b) establishes "commuting thresholds" relative to the origin's mass. The analysis includes 367 out of the 390 Sicilian municipalities. Results are applied to define "commuters" and "residents" within the cohort of petrochemical workers. The study population is composed of 5,627 workers. Different categories of residence in Gela are compared calculating Mortality Rate Ratios for lung cancer through a Poisson regression model, controlling for age and calendar period. The mobility model correctly classifies almost 90% of observations. Its application to the mortality analysis confirms a major risk for lung cancer associated with residence in Gela.
  
  
  CONCLUSIONS
  Commuting is a critical aspect of the health-environment relationship in contaminated areas. The proposed methodology can be replicated to different contexts when residential information is lacking or unreliable; however, a careful consideration of the territorial characteristics ("insularity" and its impact on transportation time and costs, in our case) is suggested when specifying the area of application for the mobility analysis.
authors.affiliation: Department of Economics, Statistics, Mathematics and Sociology, University of Messina, Via Tommaso Cannizzaro 278, Messina, Italy.
authors.forename: Guido
authors.initials: G
authors.lastname: Signorino
authors.forename: Roberto
authors.initials: R
authors.lastname: Pasetto
authors.forename: Elisa
authors.initials: E
authors.lastname: Gatto
authors.forename: Massimo
authors.initials: M
authors.lastname: Mucciardi
authors.forename: Marina
authors.initials: M
authors.lastname: La Rocca
authors.forename: Pierpaolo
authors.initials: P
authors.lastname: Mudu
mesh_terms: D000397:Air Pollution / Q000009:adverse effects* / Q000706:statistics & numerical data; D000465:Algorithms; D015331:Cohort Studies; D016001:Confidence Intervals; D004781:Environmental Exposure / Q000009:adverse effects / Q000706:statistics & numerical data; D004787:Environmental Pollution / Q000009:adverse effects / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D006801:Humans; D007558:Italy; D016015:Logistic Models; D015233:Models, Statistical; D016012:Poisson Distribution; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D012802:Sicily; D014186:Transportation / Q000706:statistics & numerical data*; D014195:Travel / Q000706:statistics & numerical data*