Asthma Distribution patterns and their relationship with the urban landscape and social conditions in Newark NJ Authors: Francisco Artigas, Leonard Beilory, Richard Holowczak, Kumar Patel Primary author affiliation: CIMIC - Rutgers University, NJ artigas@cimic.rutgers.edu IHGC 2000 Sunday March 19, 2000

Problem Statement Recent estimates suggest that roughly 50% of school children in the City of Newark suffer from some form of asthma. Similar urban areas across the country exhibit much lower rates. Hospital admissions (asthma related): 110 per 100,000 in Newark 46 per 100,000 in surrounding suburban/rural

Research Objectives Build a robust spatial data-set about asthma cases in Newark. Find spatial correlation between asthma case locations and urban landscape features

Data Sources Admission records from UMDNJ University Hospital 1997-1998 (n = 542 and n = 624) Landsat 5 thermal images (1997) High resolution aerial photographs (1995) Geo-coded street address vector coverage of Newark Census tracts from 1990

Overview of Data 1997 1998 Male 280 305 Female 261 329 Black 485 561 1997 1998 Male 280 305 Female 261 329 Black 485 561 White 2 3 Filipino 0 1 Other 4 35 Unknown 49 34 Age 1997 Min /Max 0 / 82 Average 17 Median 8 Len.of Stay 1997 1998 Min 1 1 Max 24 87 Average 3 3.1 Median 2 2

Analytical Tools ARC/INFO and ARCVIEW MapObjects IDRISI Image processing software SPSS statistics software Wizsoft data mining software

Research Approach Clean and organize asthma case data from UMDNJ University Hospital Generate X and Y coordinates from address lists for 1997 and 1998 Perform cluster analysis Intersect asthma cases with census data Spatial analysis of asthma cases with Landscape texture and features

Assumption Asthma cases are uniformly distributed across: Clusters Streets Landscape texture Socio-economics Asthma cases are uniformly distributed from: Emission focal points

Cluster Analysis Method: Use K-means cluster analysis (K=5, K=10 and K=15) on X, Y coordinates Characteristics of clusters: Size (membership) of clusters Location of cluster centers Cluster migration from year to year Cluster homogeneity

1997 data K=5 Clusters tend to align with Newark Ward boundaries Black - cluster center H UMDNJ Hospital H

1997 data K=15 Clusters tend to align with neighborhood boundaries H UMDNJ Hospital H

1998 data K=5 Clusters tend to align with Newark Ward boundaries Black - cluster center H UMDNJ Hospital H

1998 data K=15 Clusters tend to align with neighborhood boundaries H UMDNJ Hospital H

Cluster Migration ‘97 to ‘98 Rt. 280 Blue: 1997 Cluster centers Red: 1998 Cluster centers Central ward clusters tend to migrate less H UMDNJ Hospital H

Cluster Homogeneity Compare %Race in population with %Race of cases n/a

Cluster Homogeneity n/a n/a

Cluster Homogeneity We expected the number of Asthma Cases to be proportional to the Racial makeup of the clusters However, our data suggests that asthma cases among Blacks are disproportionately higher compared to the racial makeup of the clusters

Spatial analysis Intersection of census data with asthma cases Observation of asthma cases and urban landscape texture Asthma cases at the street level Spatial relationship between diesel fume sources and asthma cases Spatial correlation between urban heat islands (UHI) and asthma cases.

Intersection of census tract information and asthma cases

1997 Cases in terms of Public Assistance Less than half on PA Half on PA More than half on PA Cluster Centers

1998 Cases in terms of Public Assistance Less than half on PA Half on PA More than half on PA Cluster Centers

Landscape Texture “Expect to see more cases in high-density housing areas than in low-density housing areas”

1997 cases in terms of population density Low pop. density Medium pop. density High pop. density Cluster Centers

1998 cases in terms of population density Low pop. density Medium pop. density High pop. density Cluster Centers

Urban Landscape Texture High density housing South Orange Ave. Low density housing

Urban Landscape Texture Low density housing High density housing

Housing Density Effect Cluster centers which had the greatest recruitment of cases occurred in low density neighborhoods in central ward (many vacant lots)

“Sick” Streets “All streets should exhibit a proportional number of cases”

Sick Streets H UMDNJ Hospital H S. 11th St. 1997 Yellow 1998 Green S. Orange Ave. Fairmount Cemetery H

Sick Streets Manufacturing Facility 1997 Yellow 1998 Green S. Orange Ave. Power sub-station

South 11th Street, Newark NJ Power Sub-station at end of S. 11th street Intersection of S. Orange Ave. and South 11th street (facing East)

South 11th street, Newark, NJ Abandoned housing and empty lots next to inhabited structures

South 11th street, Newark, NJ Abandoned lot with light manufacturing facility Adjoining multi-family dwellings (cases from ‘97 and from ‘98)

Sick Streets An unusually high number of asthma cases congregate along specific streets We need to further investigate the impact nearby manufacturing facilities and TRI sites

Spatial Relationship between Diesel Fumes and Asthma Extracted addresses from digital yellow pages of trucking facilities in Newark where trucks are likely to congregate X and Y coordinates were extracted for each facility Trucking facility locations were mapped together with asthma case locations

Diesel fume sources Asthma case

Newark Urban Heat Islands Landsat 5 Thermal Ground level ozone is a photo chemical reaction Greater ozone levels are expected in hotter areas of the city

Newark Urban Heat Islands 1997 asthma cases correlated against urban heat islands

Newark Urban Heat Islands 1998 asthma cases correlated against urban heat islands

Newark Urban Heat Islands

Conclusions Asthma cases tend to congregate in the central ward Great majority of cases are: African American Less than 10 years old Under public assistance From low density neighborhoods (Social dislocation effect, Wallace et. al.)

Conclusions (continued) Cluster centers tend to persist in the central ward and along heavy traffic corridors Some streets in mixed industrial/residential neighborhoods have an unusually high number of asthma cases According to our data (limited number of years and from 1 hospital) we found no significant correlation between diesel fume sources or urban heat islands and asthma

Conclusions (continued) The evidence suggests that the external environmental conditions we studied are not strong indicators of asthma

Future Work Continue to build asthma database for different years and from different hospitals in Newark Incorporate daily and seasonal air quality measurements from monitoring stations to the data set Map TRI sites in Newark Employ more robust statistical tools Investigate temporal relationships (seasons vs. admissions)

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