Evidence map›Paper›PMID 41126498›Full record

ArticleCancer control : journal of the Moffitt Cancer Center

Association of State and Local Social and Public Health Spending With Cancer Incidence and Mortality.

Laura E Newton, Kacie L Dragan, Lucas D Cusimano, Andrew P Loehrer

Abstract read
In one paragraph

Article in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Laura E NewtonDepartment of Surgery, Dartmouth Health, Lebanon, NH, USA.ORCID 0000-0002-3625-1392
Kacie L DraganGeisel School of Medicine, The Dartmouth Institute, Lebanon, NH, USA.
Lucas D CusimanoGeisel School of Medicine at Dartmouth, Hanover, NH, USA.ORCID 0009-0006-1372-6240
Andrew P LoehrerDepartment of Surgery, Dartmouth Health, Lebanon, NH, USA.

Funding

Geospatial drivers of cancer care disparities and their susceptibility to health policy changesK08CA263546 · NCI · DARTMOUTH-HITCHCOCK CLINIC · PI Andrew Phillip Loehrer · 2022 to 2026
$869k
NCI NIH HHS K08 CA263546
6 · The paper itself

Abstract

IntroductionThis study aimed to characterize the association between state and local social spending and incidence and mortality of poverty-associated cancers.MethodsThis cross-sectional cohort study (years 2004-2020) included U.S. adults ages 20-64 years with poverty-associated cancers. The exposure was differential and combined state- and local-level spending on social needs for 50 states. Data from the U.S. Census Bureau Census of Governments was used to determine annual social spending for each state. Deciles of spending were calculated annually. Main outcomes were yearly incidence and mortality of poverty-associated cancers, expressed as rate ratios where the first decile serves as the reference for each of deciles 2-10. Poisson regression models evaluated association of social spending with incidence and mortality of poverty-associated cancers, controlling for secular trends and state fixed effects.ResultsOverall median social spending was $7071dollars per capita (IQR $6373-$8098). By combined state/local social spending, yearly cancer incidence rates were lower in the highest deciles of social spending (rate ratio 0.96 (95% CI 0.92-0.99) and 0.92 (95% CI 0.88-0.96) for 9

Indexed as

Health ExpendituresNeoplasmsPovertyPublic HealthAdultCross-Sectional StudiesFemaleHumansIncidenceMaleMiddle AgedUnited StatesYoung Adultcancer incidencecancer mortalitysocial driverssocial spending

Identifiers

PMID41126498
PMCPMC12553934

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.