Evidence map›Paper›PMID 38845562›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2024

Socioeconomic Disparities and the Prevalence of Antimicrobial Resistance.

Lauren N Cooper, Alaina M Beauchamp, Tanvi A Ingle, Marlon I Diaz, Abdi D Wakene, Chaitanya Katterpalli, Tony Keller, Clark Walker, Seth Blumberg, Sanjat Kanjilal and 7 more

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Risk factors for polymicrobial klebsiella pneumoniae bloodstream infection: a retrospective cohort study using the antimicrobial resistance monitoring database.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Spatial pattern and risk factors of resistance to important antibiotics amongAntimicrobial stewardship & healthcare epidemiology : ASHE · 2026
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Epidemiology of bacterial and fungal infections among level IV neonatal units in North America.Journal of perinatology : official journal of the California Perinatal Association · 2025
    Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Lauren N CooperClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID 0000-0003-4000-9192
Alaina M BeauchampPeter O'Donnell Jr School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Tanvi A IngleUT Southwestern Medical School, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Marlon I DiazPaul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas, USA.
Abdi D WakeneClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Chaitanya KatterpalliDepartment of CareConnect Reporting, Texas Health Resources, Arlington, Texas, USA.
Tony KellerDepartment of CareConnect Reporting, Texas Health Resources, Arlington, Texas, USA.
Clark WalkerDepartment of CareConnect Reporting, Texas Health Resources, Arlington, Texas, USA.
Seth BlumbergDivision of Hospital Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, USA.
Sanjat KanjilalDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA.ORCID 0000-0002-1221-5725
Jonathan H ChenStanford Center for Biomedical Informatics Research, Division of Hospital Medicine, Clinical Excellence Research Center, Department of Medicine, Stanford University, Stanford, California, USA.
Alexander P RadunskyDivision of Infectious Diseases and Geographic Medicine, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Zachary M MostDepartment of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA.
John J HannaClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Trish M PerlPeter O'Donnell Jr School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Christoph U LehmannClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Richard J MedfordClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Funding

UT Southwestern Center for Translational MedicineUL1TR003163 · NCATS · UT SOUTHWESTERN MEDICAL CENTER · PI TOTO, ROBERT DANIEL · 2021 to 2025
$39.3M
UT Southwestern Center for Translational Medicine (UL1/KL2/TL1)UL1TR001105 · NCATS · UT SOUTHWESTERN MEDICAL CENTER · PI TOTO, ROBERT DANIEL · 2013 to 2017
$24.5M
Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant BacteriaR01AI178121 · NIAID · STANFORD UNIVERSITY · PI JONATHAN H. CHEN · 2023 to 2026
$3.1M
CDC HHS U01CK000590NCATS NIH HHS UL1 TR001105NCATS NIH HHS UL1 TR003163NCEZID CDC HHS U01 CK000590NIAID NIH HHS R01 AI178121NIH HHS 1R01AI178121Texas Health Resources Clinical Scholars Program
6 · The paper itself

Abstract

backgroundThe increased prevalence of antimicrobial-resistant (AMR) infections is a significant global health threat, resulting in increased disease, deaths, and costs. The drivers of AMR are complex and potentially impacted by socioeconomic factors. We investigated the relationships between geographic and socioeconomic factors and AMR.

methodsWe collected select patient bacterial culture results from 2015 to 2020 from electronic health records of 2 expansive healthcare systems within the Dallas-Fort Worth, Texas, metropolitan area. Among individuals with electronic health records who resided in the 4 most populous counties in Dallas-Fort Worth, culture data were aggregated. Case counts for each organism studied were standardized per 1000 persons per area population. Using residential addresses, the cultures were geocoded and linked to socioeconomic index values. Spatial autocorrelation tests identified geographic clusters of high and low AMR organism prevalence and correlations with established socioeconomic indices.

resultsWe found significant clusters of AMR organisms in areas with high levels of deprivation, as measured by the area deprivation index (ADI). We found a significant spatial autocorrelation between ADI and the prevalence of AMR organisms, particularly for AmpC β-lactamase and methicillin-resistant Staphylococcus aureus, with 14% and 13%, respectively, of the variability in prevalence rates being attributable to their relationship with the ADI values of the neighboring locations.

conclusionsWe found that areas with a high ADI are more likely to have higher rates of AMR organisms. Interventions that improve socioeconomic factors such as poverty, unemployment, decreased access to healthcare, crowding, and sanitation in these areas of high prevalence may reduce the spread of AMR.

Indexed as

Anti-Bacterial AgentsSocioeconomic FactorsAdolescentAdultAgedBacterial InfectionsChildChild, PreschoolDrug Resistance, BacterialFemaleHumansMaleMethicillin-Resistant Staphylococcus aureusMiddle AgedPrevalenceSocioeconomic Disparities in HealthAnti-Bacterial Agentsantimicrobial resistanceantimicrobial-resistant organismsarea deprivation indexsocial vulnerability indexsociodemographic factors

Identifiers

PMID38845562
PMCPMC11650857

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.