Evidence map›Paper›PMID 27501962›Full record

ArticleBMC public health2016

Material deprivation and health: a longitudinal study.

Anne Grete Tøge, Ruth Bell

Abstract read
In one paragraph

Article in BMC public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

2 authors.

Anne Grete TøgeDepartment of Social Work, Child Welfare and Social Policy, Faculty of Social Sciences, Oslo and Akershus University College of Applied Sciences, P.O. box 4, St. Olavs plass, NO-0130, Oslo, Norway. anne-grete.toge@hioa.no.
Ruth BellDepartment of Epidemiology & Public Health, Faculty of Population Health Sciences, University College London, 1-19 Torrington Place, London, WC1E 7HB, Great Britain, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDoes material deprivation affect the consequences of ill health? Answering this question requires that we move beyond the effects of income. Longitudinal data on material deprivation, longstanding illness and limiting longstanding illness enables investigations of the effects of material deprivation on risk of limiting longstanding illness. This study investigates whether a shift from affording to not affording a car predicts the probability of limiting longstanding ill (LLSI).

methodsThe 2008-2011 longitudinal panel of Statistics on Income, Social Inclusion and Living Conditions (EU-SILC) is utilised. Longitudinal fixed effects logit models are applied, using LLSI as dependent variable. Transition from affording a car to not affording a car is used as a proxy for material deprivation. All models are controlled for whether the person becomes longstanding ill (LSI) as well as other time-variant covariates that could affect the results.

resultsThe analysis shows a statistically significant increased odds ratio of LLSI when individuals no longer can afford a car, after controlling for confounders and LSI in the previous year (1.129, CI = 1.022-1.248). However, when restricting the sample to observations where respondents report longstanding illness the results are no longer significant (1.032, CI = 0.910-1.171).

conclusionThe results indicate an individual level effect of material deprivation on LLSI, suggesting that material resources can affect the consequences of ill health.

Indexed as

Health StatusMaternal DeprivationChildChronic DiseaseFemaleHealth SurveysHumansLongitudinal StudiesMaleMiddle AgedOdds RatioPovertyFixed effectsHealthLimiting longstanding illness (LLSI)Longstanding illness (LSI)Social exclusion

Identifiers

PMID27501962
PMCPMC4977874

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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.