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To estimate type 2 diabetes incidence trends by sex and socioeconomic position (SEP) and evaluate trends in SEP-related inequalities in incidence.
Ecological study using ambulatory claims data and regression-based modelling.
All 401 counties in Germany, covering the entire country.
All individuals with statutory health insurance (~85% of the population). Incident cases of type 2 diabetes were identified annually from 2014 to 2019 using the International Statistical Classification of Diseases and Related Health Problems, 10th revision codes.
Incident type 2 diabetes at the county level, adjusted for age and modelled using a mixed negative binomial regression. SEP was measured using the German Index of Socioeconomic Deprivation, and a random intercept accounted for county-level heterogeneity.
The incidence of type 2 diabetes decreased between 2014 and 2017 and plateaued thereafter. Trends were similar between sexes and deprivation levels. The greatest difference was observed between high and low deprivation, with an incidence rate ratio of 1.20 (95% CI: 1.14 to 1.27) among men and 1.21 (95% CI: 1.14 to 1.27) among women in 2014.
There was a positive trend in the decline in age-adjusted type 2 diabetes incidence between 2014 and 2019. However, social inequality persisted with deprived groups at higher risk of type 2 diabetes. The level of inequality was comparable between men and women. Continued monitoring is essential to assess whether these short-term trends persist over time.
Our study aimed to (1) validate the accuracy of nursing mobility documentation and (2) identify the most effective timings for behavioural mapping.
We monitored the mobility of 55 inpatients using behavioural mapping throughout a nursing day shift, comparing the observed mobility levels with the nursing charting in the electronic health record during the same period.
Our results showed a high level of agreement between nursing records and observed mobility, with improved accuracy observed particularly when documentation was at 12 PM or later. Behavioural mapping observations revealed that the most effective timeframe to observe the highest levels of patient mobility was between 10 AM AND 2 PM.
To truly understand patient mobility, comparing nursing charting with methods like behavioural mapping is beneficial. This comparison helps evaluate how well nursing records reflect actual patient mobility and offers insights into the best times for charting to capture peak mobility. While behavioural mapping is a valuable tool for auditing patient mobility, its high resource demands limit its regular use. Thus, determining the most effective times and durations for observations is key for practical implementation in hospital mobility audits.
Nurses are pivotal in ensuring patient mobility in hospitals, an essential element of quality care. Their role involves safely mobilizing patients and accurately charting their mobility levels during each shift. For nursing practice, this research underscores that nurse charting can accurately reflect patient mobility, and highlights that recording the patient's highest level of mobility later in the shift offers a more precise representation of their actual mobility.
Strobe.
No Patient or Public Contribution.
Nurses routinely perform multiple risk assessments related to patient mobility in the hospital. Use of a single mobility assessment for multiple risk assessment tools could improve clinical documentation efficiency, accuracy and lay the groundwork for automated risk evaluation tools.
We tested how accurately Activity Measure for Post-Acute Care (AM-PAC) mobility scores predicted the mobility components of various fall and pressure injury risk assessment tools.
AM-PAC scores along with mobility and physical activity components on risk assessments (Braden Scale, Get Up and Go used within the Hendrich II Fall Risk Model®, Johns Hopkins Fall Risk Assessment Tool (JHFRAT) and Morse Fall Scale) were collected on a cohort of hospitalised patients. We predicted scores of risk assessments based on AM-PAC scores by fitting of ordinal logistic regressions between AM-PAC scores and risk assessments. STROBE checklist was used to report the present study.
AM-PAC scores predicted the observed mobility components of Braden, Get Up and Go and JHFRAT with high accuracy (≥85%), but with lower accuracy for the Morse Fall Scale (40%).
These findings suggest that a single mobility assessment has the potential to be a good solution for the mobility components of several fall and pressure injury risk assessments.
FreshRSS 