Evidence map›Paper›PMID 18789142›Full record

ArticleBMC cancer2008

Municipal distribution of ovarian cancer mortality in Spain.

Virginia Lope, Marina Pollán, Beatriz Pérez-Gómez, Nuria Aragonés, Enrique Vidal, Diana Gómez-Barroso, Rebeca Ramis, Javier García-Pérez, Anna Cabanes, Gonzalo López-Abente

Abstract read
In one paragraph

Article in BMC cancer, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Spatial analysis of advanced-stage ovarian cancer mortality in California.American journal of obstetrics and gynecology · 2015
    Observational
  4. Article
  5. 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

10 authors.

Virginia LopeCIBER en Epidemiología y Salud Pública, Madrid, Spain. vicarvajal@isciii.es
Marina Pollán
Beatriz Pérez-Gómez
Nuria Aragonés
Enrique Vidal
Diana Gómez-Barroso
Rebeca Ramis
Javier García-Pérez
Anna Cabanes
Gonzalo López-Abente

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSpain was the country that registered the greatest increases in ovarian cancer mortality in Europe. This study describes the municipal distribution of ovarian cancer mortality in Spain using spatial models for small-area analysis.

methodsSmoothed relative risks of ovarian cancer mortality were obtained, using the Besag, York and Molliè autoregressive spatial model. Standardised mortality ratios, smoothed relative risks, and distribution of the posterior probability of relative risks being greater than 1 were depicted on municipal maps.

resultsDuring the study period (1989-1998), 13,869 ovarian cancer deaths were registered in 2,718 Spanish towns, accounting for 4% of all cancer-related deaths among women. The highest relative risks were mainly concentrated in three areas, i.e., the interior of Barcelona and Gerona (north-east Spain), the north of Lugo and Asturias (north-west Spain) and along the Seville-Huelva boundary (in the south-west). Eivissa (Balearic Islands) and El Hierro (Canary Islands) also registered increased risks.

conclusionWell established ovarian cancer risk factors might not contribute significantly to the municipal distribution of ovarian cancer mortality. Environmental and occupational exposures possibly linked to this pattern and prevalent in specific regions, are discussed in this paper. Small-area geographical studies are effective instruments for detecting risk areas that may otherwise remain concealed on a more reduced scale.

Indexed as

CitiesDemographyFemaleHumansOvarian NeoplasmsRisk FactorsSpain

Identifiers

PMID18789142
PMCPMC2564976

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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.