Evidence map›Paper›PMID 35586543›Full record

ArticleJournal of health monitoring2018

Selecting and defining indicators for diabetes surveillance in Germany.

Lars Gabrys, Christin Heidemann, Christian Schmidt, Jens Baumert, Andrea Teti, Yong Du, Rebecca Paprott, Thomas Ziese, Winfried Banzer, Michael Böhme and 17 more

Abstract read
In one paragraph

Article in Journal of health monitoring, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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

27 authors.

Lars GabrysRobert Koch Institute, Berlin.
Christin HeidemannRobert Koch Institute, Berlin.
Christian SchmidtRobert Koch Institute, Berlin.
Jens BaumertRobert Koch Institute, Berlin.
Andrea TetiUniversity of Vechta.
Yong DuRobert Koch Institute, Berlin.
Rebecca PaprottRobert Koch Institute, Berlin.
Thomas ZieseRobert Koch Institute, Berlin.
Winfried BanzerGoethe University Frankfurt.
Michael BöhmeBaden-Wuerttemberg Centre for Health, Stuttgart.
Brigitte BorrmannNorth Rhine-Westphalian Centre for Health, Bochum.
Reinhard BusseTechnical University of Berlin.
Michael FreitagCarl von Ossietzky University of Oldenburg.
Bernd HagenCentral Research Institute of Ambulatory Health Care in Germany, Cologne.
Reinhard HollUlm University.
Andrea IcksHeinrich Heine University Düsseldorf.
Matthias KaltheunerInstitute of licensed diabetologists, Leverkusen.
Klaus KochInstitute of Quality and Efficiency in Health Care, Cologne.
Stefanie KümmelInstitute for Applied Quality Improvement and Research in Health Care, Göttingen.
Joseph KuhnBavarian Health and Food Safety Authority, Oberschleißheim.
Oliver KußInstitute for Biometrics and Epidemiology at the German Diabetes Center, Düsseldorf.
Gunter LauxHeidelberg University.
Ingrid SchubertUniversity of Cologne.
Joachim SzecsenyiHeidelberg University.
Til UebelGerman College of General Practitioners and Family Physicians, Berlin.
Daniela ZahnFederal Centre for Health Education, Cologne.
Christa Scheidt-NaveRobert Koch Institute, Berlin.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mainly because of the large number of people affected and associated significant health policy implications, the Robert Koch Institute (RKI) is developing a public health surveillance system using diabetes as an example. In a first step to ensure long-term and comparable data collection and establish efficient surveillance structures, the RKI has defined a set of relevant indicators for diabetes surveillance. An extensive review of the available literature followed by a structured process of consensus provided the basis for a harmonised set of 30 core and 10 supplementary indicators. They correspond to the following four fields of activity: (1) reducing diabetes risk, (2) improving diabetes early detection and treatment, (3) reducing diabetes complications, (4) reducing the disease burden and overall costs of the disease. In future, in addition to the primary data provided by RKI health monitoring diabetes surveillance needs to also consider the results from secondary data sources. Currently, barriers to accessing this data remain, which will have to be overcome, and gaps in the data closed. The RKI intentends to continuously update this set of indicators and at some point apply it also to further chronic diseases with high public health relevance.

Indexed as

DIABETES MELLITUSINDICATORSNCDPUBLIC HEALTHSURVEILLANCE

Identifiers

PMID35586543
PMCPMC8852787

What OpenQuestion holds

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Registered trials

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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.