↓ Skip to main content

Self-reported general health, physical distress, mental distress, and activity limitation by US county, 1995-2012

Overview of attention for article published in Population Health Metrics, April 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
9 X users

Citations

dimensions_citation
49 Dimensions

Readers on

mendeley
97 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Self-reported general health, physical distress, mental distress, and activity limitation by US county, 1995-2012
Published in
Population Health Metrics, April 2017
DOI 10.1186/s12963-017-0133-5
Pubmed ID
Authors

Laura Dwyer-Lindgren, Johan P. Mackenbach, Frank J. van Lenthe, Ali H. Mokdad

Abstract

Metrics based on self-reports of health status have been proposed for tracking population health and making comparisons among different populations. While these metrics have been used in the US to explore disparities by sex, race/ethnicity, and socioeconomic position, less is known about how self-reported health varies geographically. This study aimed to describe county-level trends in the prevalence of poor self-reported health and to assess the face validity of these estimates. We applied validated small area estimation methods to Behavioral Risk Factor Surveillance System data to estimate annual county-level prevalence of four measures of poor self-reported health (low general health, frequent physical distress, frequent mental distress, and frequent activity limitation) from 1995 and 2012. We compared these measures of poor self-reported health to other population health indicators, including risk factor prevalence (smoking, physical inactivity, and obesity), chronic condition prevalence (hypertension and diabetes), and life expectancy. We found substantial geographic disparities in poor self-reported health. Counties in parts of South Dakota, eastern Kentucky and western West Virginia, along the Texas-Mexico border, along the southern half of the Mississippi river, and in southern Alabama generally experienced the highest levels of poor self-reported health. At the county level, there was a strong positive correlation among the four measures of poor self-reported health and between the prevalence of poor self-reported health and the prevalence of risk factors and chronic conditions. There was a strong negative correlation between prevalence of poor self-reported health and life expectancy. Nonetheless, counties with similar levels of poor self-reported health experienced life expectancies that varied by several years. Changes over time in life expectancy were only weakly correlated with changes in the prevalence of poor self-reported health. This analysis adds to the growing body of literature documenting large geographic disparities in health outcomes in the United States. Health metrics based on self-reports of health status can and should be used to complement other measures of population health, such as life expectancy, to identify high need areas, efficiently allocate resources, and monitor geographic disparities.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 97 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 19%
Student > Ph. D. Student 15 15%
Researcher 12 12%
Student > Bachelor 10 10%
Student > Doctoral Student 6 6%
Other 10 10%
Unknown 26 27%
Readers by discipline Count As %
Social Sciences 15 15%
Medicine and Dentistry 14 14%
Psychology 10 10%
Nursing and Health Professions 7 7%
Mathematics 4 4%
Other 18 19%
Unknown 29 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 07 June 2017.
All research outputs
#5,651,998
of 22,965,074 outputs
Outputs from Population Health Metrics
#157
of 391 outputs
Outputs of similar age
#88,859
of 309,828 outputs
Outputs of similar age from Population Health Metrics
#4
of 12 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 391 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one has gotten more attention than average, scoring higher than 59% 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 309,828 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.