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Calculating census tract-based life expectancy in New York state: a generalizable approach

Overview of attention for article published in Population Health Metrics, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)

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Title
Calculating census tract-based life expectancy in New York state: a generalizable approach
Published in
Population Health Metrics, January 2018
DOI 10.1186/s12963-018-0159-3
Pubmed ID
Authors

Thomas O. Talbot, Douglas H. Done, Gwen D. Babcock

Abstract

Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities. Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008-2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years. Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with < 2% of population living below the poverty level had a LE of 82.8 years, while tracts with a poverty level ≥ 25% had a LE of 75.5. We observed differences in LE in border areas, of up to 10.4 years, when excluding or including deaths of study area residents that occurred outside the study area. The range and standard deviation at the county level (77.5-82.8, SD = 1.2 years) were smaller than our final sub-county areas (64.7-92.0, SD = 3.3 years). The correlation between LE and poverty were similar and statistically significant (p < 0.0001) at the county (r = - 0.58) and sub-county level (r = - 0.58). The correlations between LE and percent African-American at the county level were (r = 0.11, p = 0.43) and at the sub-county level (r = - 0.25, p < 0.0001). The proposed approach for geocoding and aggregation of mortality and population data provides a solution for health departments to produce stable empirically-derived LE estimates using data coded to the tract. Reliable estimates within sub-county areas are needed to aid public health officials in focusing preventive health programs in areas where health disparities would be masked by county level estimates.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 36%
Researcher 2 18%
Unspecified 2 18%
Student > Ph. D. Student 1 9%
Professor > Associate Professor 1 9%
Other 1 9%
Readers by discipline Count As %
Unspecified 4 36%
Medicine and Dentistry 2 18%
Biochemistry, Genetics and Molecular Biology 1 9%
Mathematics 1 9%
Nursing and Health Professions 1 9%
Other 2 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 January 2018.
All research outputs
#1,831,792
of 12,427,514 outputs
Outputs from Population Health Metrics
#68
of 280 outputs
Outputs of similar age
#71,059
of 340,426 outputs
Outputs of similar age from Population Health Metrics
#3
of 5 outputs
Altmetric has tracked 12,427,514 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 280 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,426 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.