Evidence map›Paper›PMID 31226140›Full record

ArticlePloS one2019

An ecological approach to monitor geographic disparities in cancer outcomes.

Jinani Jayasekera, Eberechukwu Onukwugha, Christopher Cadham, Donna Harrington, Sarah Tom, Francoise Pradel, Michael Naslund

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 31% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. A new proactive virtual resource center navigation model identifies patient risk factors to reduce barriers to cancer care during the COVID-19 pandemic.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021
    Article
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

7 authors at 3 institutions in 1 country.

Jinani JayasekeraLombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, United States of America.ORCID 0000-0001-9212-7225
Eberechukwu OnukwughaDepartment of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, Baltimore, MD, United States of America.
Christopher CadhamLombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, United States of America.
Donna HarringtonSchool of Social Work, University of Maryland, Baltimore, MD, United States of America.
Sarah TomDivision of Neurology Clinical Outcomes Research and Population Science (NeuroCORPS), Department of Neurology, Columbia University, New York, NY, United States of America.
Francoise PradelDepartment of Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, Baltimore, MD, United States of America.
Michael NaslundSchool of Medicine, University of Maryland, Baltimore, MD, United States of America.
University of Maryland, Baltimore · USGeorgetown University · USColumbia University · US

Funding

Early life environment and later life dementia, cognition, neuropathology, and reserveK01AG050723 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI TOM, SARAH ELIZABETH · 2016 to 2021
$716k
NIA NIH HHS K01 AG050723
6 · The paper itself

Abstract

backgroundArea-level indices are widely used to assess the impact of socio-environmental characteristics on cancer outcomes. While area-level measures of socioeconomic status (SES) have been previously used in cancer settings, fewer studies have focused on evaluating the impact of area-level health services supply (HSS) characteristics on cancer outcomes. Moreover, there is significant variation in the methods and constructs used to create area-level indices.

methodsIn this study, we introduced a psychometrically-induced, reproducible approach to develop area-level HSS and SES indices. We assessed the utility of these indices in detecting the effects of area-level characteristics on prostate, breast, and lung cancer incidence and stage at diagnosis in the US. The information on county-level SES and HSS characteristics were extracted from US Census, County Business Patterns data and Area Health Resource Files. The Surveillance, Epidemiology, and End Results database was used to identify individuals diagnosed with cancer from 2010 to 2012. SES and HSS indices were developed and linked to 3-year age-adjusted cancer incidence rates. SES and HSS indices empirically summarized the level of employment, education, poverty and income, and the availability of health care facilities and health professionals within counties.

resultsSES and HSS models demonstrated good fit (TLI = 0.98 and 0.96, respectively) and internal consistency (alpha = 0.85 and 0.95, respectively). Increasing SES and HSS were associated with increasing prostate and breast cancer and decreasing lung cancer incidence rates. The results varied by stage at diagnosis and race.

conclusionComposite county-level measures of SES and HSS were effective in ranking counties and detecting gradients in cancer incidence and stage at diagnosis. Thus, these measures provide valuable tools for monitoring geographic disparities in cancer outcomes.

Indexed as

Healthcare DisparitiesNeoplasmsAdolescentAdultAgedAged, 80 and overFemaleGeographyHumansIncidenceMaleMiddle AgedOutcome Assessment, Health CarePrognosisSEER ProgramSocial Environment

Identifiers

PMID31226140
PMCPMC6588275
OpenAlexW2949547499

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.