Spatial enablement to support environmental, demographic, socioeconomics and health data integration and analysis for big cities: A case study with asthma hospitalizations in New York City

Daniele Pala, José Pagán, Enea Parimbelli, Marica T. Rocca, Riccardo Bellazzi, Vittorio Casella

Research output: Contribution to journalArticlepeer-review

Abstract

The percentage of the world's population living in urban areas is projected to increase in the next decades. Big cities are heterogeneous environments in which socioeconomic and environmental differences among the neighborhoods are often very pronounced. Each individual, during his/her life, is constantly subject to a mix of exposures that have an effect on their phenotype but are frequently difficult to identify, especially in an urban environment. Studying how the combination of environmental and socioeconomic factors which the population is exposed to influences pathological outcomes can help transforming public health from a reactive to a predictive system. Thanks to the application of state-of-the-art spatially enabled methods, patients can be stratified according to their characteristics and the geographical context they live in, optimizing healthcare processes and the reducing its costs. Some public health studies focusing specifically on urban areas have been conducted, but they usually consider a coarse spatial subdivision, as a consequence of scarce availability of well-integrated data regarding health and environmental exposure at a sufficient level of granularity to enable meaningful statistical analyses. In this paper, we present an application of highly fine-grained spatial resolution methods to New York City data. We investigated the link between asthma hospitalizations and a combination of air pollution and other environmental and socioeconomic factors. We first performed an explorative analysis using spatial clustering methods that shows that asthma is related to numerous factors whose level of influence varies considerably among neighborhoods. We then performed a Geographically Weighted Regression with different covariates and determined which environmental and socioeconomic factors can predict hospitalizations and how they vary throughout the city. These methods showed to be promising both for visualization and analysis of demographic and epidemiological urban dynamics, that can be used to organize targeted intervention and treatment policies to address the single citizens considering the factors he/she is exposed to. We found a link between asthma and several factors such as PM2.5, age, health insurance coverage, race, poverty, obesity, industrial areas and recycling. This study has been conducted within the PULSE project, funded by the European Commission, briefly presented in this paper.

Original languageEnglish (US)
Article number84
JournalFrontiers in Medicine
Volume6
Issue numberAPR
DOIs
StatePublished - 2019

Keywords

  • Asthma
  • Clustering
  • Data integration
  • Geostatastics
  • Public Health
  • Regression -
  • Spatial enablement

ASJC Scopus subject areas

  • Medicine(all)

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