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Progress in pediatric asthma surveillance II: geospatial patterns of asthma in Alameda County, California.

Roberts EM, English PB, Wong M, Wolff C, Valdez S, Van den Eeden SK, Ray GT - Prev Chronic Dis (2006)

Bottom Line: Health care use was categorized by type and analyzed by census tract demographic information.Geospatial analysis enabled visualization of this phenomenon; it further detected areas with elevated emergency department visit rates and potentially related environmental hazards in and around communities of concern.Proceedings of group evaluations suggested that the material aided in the translation of data describing spatial variations in health event risk to address specific community experiences and concerns.

View Article: PubMed Central - PubMed

Affiliation: California Department of Health Services, Environmental Health Investigations Branch, Richmond, CA 94804, USA.

ABSTRACT

Introduction: As with many diseases, the epidemic of asthma among children over the past few decades has been shaped by a social and environmental context that is becoming progressively more evident. Commonly used methods for asthma surveillance, however, are based on national rather than local data. The purpose of this study was to develop high-resolution asthma surveillance techniques responsive to the needs of health care professionals and local child health and social justice advocates.

Methods: We assembled a working data set of health care use records from 2001 from public and private sources covering 1.7 million person-months among children younger than 18 years in Alameda County, California. Health care use was categorized by type and analyzed by census tract demographic information. Images of the geographic distribution of health service events were created using density estimation mapping with overlapping 0.5-mile (805-m) radius spatial buffers, and statistical significance (two-tailed P & .05) was estimated using a Monte Carlo simulation algorithm.

Results: High-poverty communities had higher rates of emergency department visits due to asthma than low-poverty communities but had lower rates for indicators of quality primary asthma care. Geospatial analysis enabled visualization of this phenomenon; it further detected areas with elevated emergency department visit rates and potentially related environmental hazards in and around communities of concern. Areas of the county not previously considered to be deeply burdened by asthma were identified as having high emergency department visit rates.

Conclusion: The assembly and high-resolution geospatial analysis of health care use data contributed to a more detailed depiction of pediatric asthma disparities than was previously available to community members, public health professionals, and clinicians. Information generated using these techniques facilitated discussion among stakeholders of the environmental and social contexts of asthma and health disparities in general. Proceedings of group evaluations suggested that the material aided in the translation of data describing spatial variations in health event risk to address specific community experiences and concerns.

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Related in: MedlinePlus

Density estimation method for asthma-related health care use. Distance r is set to 0.5 miles (805 m). Rates for grid points (e.g., A) are calculated for the populations residing within buffers of radius r. To generate continuous, or raster surfaces, nongrid points (e.g., B) are assigned inverse-distance weighted averages of their nearest eight neighboring grid point rates. Data were visualized following the procedure of Rushton and Lolonis (26).
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Figure 2: Density estimation method for asthma-related health care use. Distance r is set to 0.5 miles (805 m). Rates for grid points (e.g., A) are calculated for the populations residing within buffers of radius r. To generate continuous, or raster surfaces, nongrid points (e.g., B) are assigned inverse-distance weighted averages of their nearest eight neighboring grid point rates. Data were visualized following the procedure of Rushton and Lolonis (26).

Mentions: Data were mapped following the procedure of Rushton and Lolonis (26). In brief, a grid of regularly spaced points was calculated for the entire county at 0.5-mile intervals. Overlapping buffers were designated as circles of 0.5-mile radii around each point in the grid (Figure 2). In an effort to minimize representation of unstable rates in thinly populated areas of the county, buffers were included in the analysis if they overlaid enough addresses so that the expected frequency for a given event was four or greater. This criterion resulted in a minimum number of residents ranging from 475 addresses per buffer for the least common event (emergency department visits) to 15 addresses per buffer for the most common event (symptom medication purchasing). The number of buffers used depended on the event. Between 863 and 1195 buffers were included in the analyses.


Progress in pediatric asthma surveillance II: geospatial patterns of asthma in Alameda County, California.

Roberts EM, English PB, Wong M, Wolff C, Valdez S, Van den Eeden SK, Ray GT - Prev Chronic Dis (2006)

Density estimation method for asthma-related health care use. Distance r is set to 0.5 miles (805 m). Rates for grid points (e.g., A) are calculated for the populations residing within buffers of radius r. To generate continuous, or raster surfaces, nongrid points (e.g., B) are assigned inverse-distance weighted averages of their nearest eight neighboring grid point rates. Data were visualized following the procedure of Rushton and Lolonis (26).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1637800&req=5

Figure 2: Density estimation method for asthma-related health care use. Distance r is set to 0.5 miles (805 m). Rates for grid points (e.g., A) are calculated for the populations residing within buffers of radius r. To generate continuous, or raster surfaces, nongrid points (e.g., B) are assigned inverse-distance weighted averages of their nearest eight neighboring grid point rates. Data were visualized following the procedure of Rushton and Lolonis (26).
Mentions: Data were mapped following the procedure of Rushton and Lolonis (26). In brief, a grid of regularly spaced points was calculated for the entire county at 0.5-mile intervals. Overlapping buffers were designated as circles of 0.5-mile radii around each point in the grid (Figure 2). In an effort to minimize representation of unstable rates in thinly populated areas of the county, buffers were included in the analysis if they overlaid enough addresses so that the expected frequency for a given event was four or greater. This criterion resulted in a minimum number of residents ranging from 475 addresses per buffer for the least common event (emergency department visits) to 15 addresses per buffer for the most common event (symptom medication purchasing). The number of buffers used depended on the event. Between 863 and 1195 buffers were included in the analyses.

Bottom Line: Health care use was categorized by type and analyzed by census tract demographic information.Geospatial analysis enabled visualization of this phenomenon; it further detected areas with elevated emergency department visit rates and potentially related environmental hazards in and around communities of concern.Proceedings of group evaluations suggested that the material aided in the translation of data describing spatial variations in health event risk to address specific community experiences and concerns.

View Article: PubMed Central - PubMed

Affiliation: California Department of Health Services, Environmental Health Investigations Branch, Richmond, CA 94804, USA.

ABSTRACT

Introduction: As with many diseases, the epidemic of asthma among children over the past few decades has been shaped by a social and environmental context that is becoming progressively more evident. Commonly used methods for asthma surveillance, however, are based on national rather than local data. The purpose of this study was to develop high-resolution asthma surveillance techniques responsive to the needs of health care professionals and local child health and social justice advocates.

Methods: We assembled a working data set of health care use records from 2001 from public and private sources covering 1.7 million person-months among children younger than 18 years in Alameda County, California. Health care use was categorized by type and analyzed by census tract demographic information. Images of the geographic distribution of health service events were created using density estimation mapping with overlapping 0.5-mile (805-m) radius spatial buffers, and statistical significance (two-tailed P & .05) was estimated using a Monte Carlo simulation algorithm.

Results: High-poverty communities had higher rates of emergency department visits due to asthma than low-poverty communities but had lower rates for indicators of quality primary asthma care. Geospatial analysis enabled visualization of this phenomenon; it further detected areas with elevated emergency department visit rates and potentially related environmental hazards in and around communities of concern. Areas of the county not previously considered to be deeply burdened by asthma were identified as having high emergency department visit rates.

Conclusion: The assembly and high-resolution geospatial analysis of health care use data contributed to a more detailed depiction of pediatric asthma disparities than was previously available to community members, public health professionals, and clinicians. Information generated using these techniques facilitated discussion among stakeholders of the environmental and social contexts of asthma and health disparities in general. Proceedings of group evaluations suggested that the material aided in the translation of data describing spatial variations in health event risk to address specific community experiences and concerns.

Show MeSH
Related in: MedlinePlus