Improving an outreach service by analyzing the relationship of health information disparities to socioeconomic indicators using geographic information systems

Socha, Yvonne M.; Oelschlegel, Sandra; Vaughn, Cynthia J.; Earl, Martha

doi:10.3163/1536-5050.100.3.014

Cited by 4 publications

(2 citation statements)

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“…To learn more about the population using CAPHIS, researchers conducted a study in 2012 analyzing use of the service, residency of those people, and demographic data (e.g., poverty, disability) associated with the requestors' geographical locations [3]. Demographic data were obtained through the US Census Bureau.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the authors analyzed the internal CAPHIS database to identify the locations from which patrons were requesting information, specifically focusing on rural counties or zip codes with high rates of poverty. We examined the zip codes and counties of patrons who used the service but did not previously use it in the 2012 study [3]. We also examined the data to determine where to conduct future outreach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of library relocation and marketing: examining zip codes and health disparities to serve consumers in East Tennessee

Petersen

Earl

Grabeel

2020

jmla

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Background: In 2014, the Preston Medical Library underwent a radical change, moving from an academic office building to the main floor of a regional medical center. While the library had previously served the public, health information requests have substantially increased in volume due to the new location. Researchers analyzed request data to see if the service’s reach has expanded to counties that previously had not used the service, to see which counties have requested the most health information, and to ascertain whether more requests are from counties with higher poverty rates.Case Presentation: Each health information request is logged with the subject nature and patron contact information. Consumer health request data were downloaded from the library database. Names and other identifying data were removed. Request forms were sorted and reviewed by zip code and county, comparing number of requests as well as poverty levels. Tableau was utilized to create maps, visually showing patron concentrations and poverty levels.Conclusions: There were 3,141 health information requests from September 21, 2014, to May 31, 2019. The majority of requests were from local counties. Requests were also received from counties that had not been previously reached and counties with elevated poverty levels. Collecting data on patron interactions is not only critical for institutional reporting, but also for community outreach. Understanding that data require taking additional steps to filter the information, assess local demographics, and customize library services. Researchers anticipate being able to better tailor services to the community based on the results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%