Evidence map›Paper›PMID 24915157›Full record

ArticlePloS one2014

Treatment pattern of type 2 diabetes differs in two German regions and with patients' socioeconomic position.

Teresa Tamayo, Heiner Claessen, Ina-Maria Rückert, Werner Maier, Michaela Schunk, Christine Meisinger, Andreas Mielck, Rolf Holle, Barbara Thorand, Maria Narres and 9 more

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Observational
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  5. Observational
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  7. Article
  8. Educational Inequality in Obesity-Related Mortality in Korea.Journal of Korean medical science · 2017
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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

19 authors.

Teresa TamayoInstitute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University, Düsseldorf, Germany.
Heiner ClaessenInstitute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University, Düsseldorf, Germany.
Ina-Maria RückertInstitute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Werner MaierInstitute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Germany.
Michaela SchunkInstitute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Germany.
Christine MeisingerInstitute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Andreas MielckInstitute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Germany.
Rolf HolleInstitute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Germany.
Barbara ThorandInstitute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.
Maria NarresInstitute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University, Düsseldorf, Germany.
Susanne MoebusInstitute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.
Amir-Abbas MahabadiDepartment of Cardiology, West-German Heart Center, University of Duisburg-Essen, Essen, Germany.
Noreen PundtInstitute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.
Bastian KroneInstitute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.
Uta SlomianyInstitute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.
Raimund ErbelDepartment of Cardiology, West-German Heart Center, University of Duisburg-Essen, Essen, Germany.
Karl-Heinz JöckelInstitute for Medical Informatics, Biometry, and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany.
Wolfgang RathmannInstitute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University, Düsseldorf, Germany.
Andrea IcksInstitute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University, Düsseldorf, Germany; Department of Public Health, Faculty of Medicine, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes treatment may differ by region and patients' socioeconomic position. This may be particularly true for newer drugs. However, data are highly limited.

methodsWe examined pooled individual data of two population-based German studies, KORA F4 (Cooperative Health Research in the Region of Augsburg, south), and the HNR (Heinz Nixdorf Recall study, west) both carried out 2006 to 2008. To ascertain the association between region and educational level with anti-hyperglycemic medication we fitted poisson regression models with robust error variance for any and newer anti-hyperglycemic medication, adjusting for age, sex, diabetes duration, BMI, cardiovascular disease, lifestyle, and insurance status.

resultsThe examined sample comprised 662 participants with self-reported type 2 diabetes (KORA F4: 83 women, 111 men; HNR: 183 women, 285 men). The probability to receive any anti-hyperglycemic drug as well as to be treated with newer anti-hyperglycemic drugs such as insulin analogues, thiazolidinediones, or glinides was significantly increased in southern compared to western Germany (prevalence ratio (PR); 95% CI: 1.12; 1.02-1.22, 1.52;1.10-2.11 respectively). Individuals with lower educational level tended to receive anti-hyperglycemic drugs more likely than their better educated counterparts (PR; 95% CI univariable: 1.10; 0.99-1.22; fully adjusted: 1.10; 0.98-1.23). In contrast, lower education was associated with a lower estimated probability to receive newer drugs among those with any anti-hyperglycemic drugs (PR low vs. high education: 0.66; 0.48-0.91; fully adjusted: 0.68; 0.47-0.996).

conclusionsWe found regional and individual social disparities in overall and newer anti-hyperglycemic medication which were not explained by other confounders. Further research is needed.

Indexed as

AgedDiabetes Mellitus, Type 2FemaleGermanyHumansHyperglycemiaHypoglycemic AgentsMaleSocioeconomic FactorsHypoglycemic Agents

Identifiers

PMID24915157
PMCPMC4051778

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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.