[Federal health reporting at the Robert Koch Institute-status quo and current developments]

[Gender-sensitive health indicators for health reporting at the Robert Koch Institute (GBE-GI)-a pilot project as part of the Joint Action PreventNCD project]

Health reporting (GBE) based on gender-sensitive indicators can provide information on gender inequalities, improve health promotion and healthcare, promote gender mainstreaming, and provide relevant information for gender equality measures. To date, there is no set of gender-sensitive health indicators for Federal Health Reporting at the Robert Koch Institute. As part of the Joint Action Prevent Non-Communicable Diseases, gender-sensitive health indicators are to be identified, evaluated, and integrated into the Robert Koch Institute's health reporting website in a pilot project. This is done in three work packages. In a scoping review, the available evidence on gender-sensitive health indicators and theoretical explanations for gender inequality in EU and OECD member states since 2014 will initially be assessed (work package 1). Building on this, identified indicators and explanatory approaches will be collated for evaluation and selection in a structured consensus process as part of a three-stage Delphi approach consisting of a focus group discussion and an online survey (work package 2). The evaluated indicator set will then be integrated into the Robert Koch Institute's health reporting website (work package 3). The results will be disseminated via scientific publications and conference presentations. An English full-text version of this article is available at SpingerLink as Supplementary Information.

[Analysing the Need for Long-Term Care: Potential of Data from Long-Term Care Assessments of the Bavarian Medical Service for Public Health Research and Practice]

BACKGROUND

Despite demographic changes, there is still no systematic and comparable differentiation of nursing care reporting on a small-scale level in Germany, where outpatient long-term care is depicted. This article presents findings of care assessment data of the Medical Service of Bavaria and draws conclusions for future reporting on nursing.

METHODS

For the analysis, anonymised initial long-term care assessments of the Bavarian Medical Service of 2019 were evaluated exemplarily using descriptive methods. The study describes the characteristics of persons with a care level recommendation, the distribution of care level categories, medical diagnoses and degree of independence in the areas of life.

RESULTS

The persons assessed were on average 80 years old. At the time of the initial assessment, the largest proportion of persons with an assigned care level lived in an outpatient setting. Care level (PG) 1 (slight impairment of independence or abilities) was assigned to 35.1% of the insured, PG 2 (considerable impairment) to 43.1%, PG 3 (severe impairment) to 16.6%, PG 4 and 5 (most severe impairment) were each rarely assigned at the time of the initial assessment (3.9% and 1.4%, respectively). Medical diagnoses were dominated by gait and mobility disorders, unspecified dementia, heart failure and senility. In particular, there were impairments in the areas of 'mobility' and 'organisation of everyday life and social contacts'.

CONCLUSIONS

The data available from the German Medical Service may be highly relevant to health research and policy and may provide a basis for planning interventions in long-term care.

Big Data in der Gesundheitsförderung und Prävention

Hintergrund

Die Nutzung großer und vielfältiger Datenmengen (Big Data) kann zur Gewinnung gesundheitsbezogener Erkenntnisse führen. Die Relevanz untermauern aktuelle Erfordernisse, bspw. in Zusammenhang mit der Digitalisierung, der Gesundheitsversorgung in Ausnahmesituationen und der zunehmenden Bedeutung von Personalisierungsprozessen in der Gesundheitsforschung. Das Potenzial von Big Data zur Erforschung vulnerabler Gruppen ist strittig, jedoch vor dem Hintergrund relativ stabiler sozialbedingter gesundheitlicher Ungleichheit besonders relevant.

Ziel der Arbeit

In der Studie wird untersucht, wie Expert*innen im Bereich der Analyse von Gesundheitsdaten das Potenzial von Big Data in der Gesundheitsförderung und Prävention, insbesondere zur Erforschung vulnerabler Gruppen, einschätzen.

Material und Methode

In einer Delphi-Studie wurden Expert*innen in zwei Runden mit einem Onlinefragebogen befragt, um Konsens und Dissens über das Potenzial von Big Data zu identifizieren.

Ergebnisse und Schlussfolgerung

Aus Sicht der Expert*innen birgt Big Data ein Potenzial für die Gesundheitsförderung und Prävention, insbesondere im klinischen Setting und durch die Personalisierung gesundheitsbezogener Maßnahmen. Vor allem Menschen mit seltenen Erkrankungen und ältere Personen könnten durch Big-Data-Analysen profitieren, bspw. durch beschleunigte Diagnoseprozesse oder personalisierte digitale Gesundheitsanwendungen. Uneinig sind sich die Expert*innen über den Umfang, in welchem es Forschungseinrichtungen, Krankenversicherungen oder Unternehmen, erlaubt sein soll, derartige Daten zu nutzen oder zu teilen.