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Background: The adequate allocation of inpatient care resources requires assumptions about the need for health care and how this need will be met. However, in current practice, these assumptions are often based on outdated methods (e.g. Hill-Burton Formula). This study evaluated floating catchment area (FCA) methods, which have been applied as measures of spatial accessibility, focusing on their ability to predict the need for health care in the inpatient sector in Germany.
Methods: We tested three FCA methods (enhanced (E2SFCA), modified (M2SFCA) and integrated (iFCA)) for their accuracy in predicting hospital visits regarding six medical diagnoses (atrial flutter/fibrillation, heart failure, femoral fracture, gonarthrosis, stroke, and epilepsy) on national level in Germany. We further used the closest provider approach for benchmark purposes. The predicted visits were compared with the actual visits for all six diagnoses using a correlation analysis and a maximum error from the actual visits of ± 5%, ± 10% and ± 15%.
Results: The analysis of 229 million distances between hospitals and population locations revealed a high and significant correlation of predicted with actual visits for all three FCA methods across all six diagnoses up to ρ = 0.79 (p < 0.001). Overall, all FCA methods showed a substantially higher correlation with actual hospital visits compared to the closest provider approach (up to ρ = 0.51; p < 0.001). Allowing a 5% error of the absolute values, the analysis revealed up to 13.4% correctly predicted hospital visits using the FCA methods (15% error: up to 32.5% correctly predicted hospital). Finally, the potential of the FCA methods could be revealed by using the actual hospital visits as the measure of hospital attractiveness, which returned very strong correlations with the actual hospital visits up to ρ = 0.99 (p < 0.001).
Conclusion: We were able to demonstrate the impact of FCA measures regarding the prediction of hospital visits in non-emergency settings, and their superiority over commonly used methods (i.e. closest provider). However, hospital beds were inadequate as the measure of hospital attractiveness resulting in low accuracy of predicted hospital visits. More reliable measures must be integrated within the proposed methods. Still, this study strengthens the possibilities of FCA methods in health care planning beyond their original application in measuring spatial accessibility.
Objectives This study wants to assess the cost-effectiveness of unmanned aerial vehicles (UAV) equipped with automated external defibrillators (AED) in out-of-hospital cardiac arrests (OHCA). Especially in rural areas with longer response times of emergency medical services (EMS) early lay defibrillation could lead to a significant higher survival in OHCA.
Participants 3296 emergency medical stations in Germany.
Setting Rural areas in Germany.
Primary and secondary outcome measures Three UAV networks providing 80%, 90% or 100% coverage for rural areas lacking timely access to EMS (ie, time-to-defibrillation: >10 min) were developed using a location allocation analysis. For each UAV network, primary outcome was the cost-effectiveness using the incremental cost-effectiveness ratio (ICER) calculated by the ratio of financial costs to additional life years gained compared with current EMS.
Results Current EMS with 3926 emergency stations was able to gain 1224 life years on annual average in the study area. The UAV network providing 100% coverage consisted of 1933 UAV with average annual costs of €43.5 million and 1845 additional life years gained on annual average (ICER: €23 568). The UAV network providing 90% coverage consisted of 1074 UAV with average annual costs of €24.2 million and 1661 additional life years gained on annual average (ICER: €14 548). The UAV network providing 80% coverage consisted of 798 UAV with average annual costs of €18.0 million and 1477 additional life years gained on annual average (ICER: €12 158).
Conclusion These results reveal the relevant life-saving potential of all modelled UAV networks. Furthermore, all analysed UAV networks could be deemed cost-effective. However, real-life applications are needed to validate the findings.
Background: Health care accessibility is known to differ geographically. With this study we focused on analysing accessibility of general and specialized obstetric units in England and Germany with regard to urbanity, area deprivation and neonatal outcome using routine data.
Methods: We used a floating catchment area method to measure obstetric care accessibility, the degree of urbanization (DEGURBA) to measure urbanity and the index of multiple deprivation to measure area deprivation.
Results: Accessibility of general obstetric units was significantly higher in Germany compared to England (accessibility index of 16.2 vs. 11.6; p < 0.001), whereas accessibility of specialized obstetric units was higher in England (accessibility index for highest level of care of 0.235 vs. 0.002; p < 0.001). We further demonstrated higher obstetric accessibility for people living in less deprived areas in Germany (r = − 0.31; p < 0.001) whereas no correlation was present in England. There were also urban–rural disparities present, with higher accessibility in urban areas in both countries (r = 0.37–0.39; p < 0.001). The analysis did not show that accessibility affected neonatal outcomes. Finally, our computer generated model for obstetric care provider demand in terms of birth counts showed a very strong correlation with actual birth counts at obstetric units (r = 0.91–0.95; p < 0.001).
Conclusion: In Germany the focus of obstetric care seemed to be put on general obstetric units leading to higher accessibility compared to England. Regarding specialized obstetric care the focus in Germany was put on high level units whereas in England obstetric care seems to be more balanced between the different levels of care with larger units on average leading to higher accessibility.
The Education Against Tobacco (EAT) network delivers smoking prevention advice in secondary schools, typically using the mirroring approach (i.e., a "selfie" altered with a face-aging app and shared with a class). In November 2017, however, the German assembly of EAT opted to expand its remit to include nursing students. To assess the transferability of the existing approach, we implemented it with the self-developed face-aging app "Smokerface" (=mixed − methods approach) in six nursing schools. Anonymous questionnaires were used to assess the perceptions of 197 students (age 18–40 years; 83.8% female; 26.4% smokers; 23.3% daily smokers) collecting qualitative and quantitative data for our cross-sectional study. Most students perceived the intervention to be fun (73.3%), but a minority disagreed that their own animated selfie (25.9%) or the reaction of their peers (29.5%) had motivated them to stop smoking. The impact on motivation not to smoke was considerably lower than experienced with seventh graders (63.2% vs. 42.0%; notably, more smokers also disagreed (45.1%) than agreed (23.5%) with this statement. Agreement rates on the motivation not to smoke item were higher in females than in males and in year 2–3 than in year 1 students. Potential improvements included greater focus on pathology (29%) and discussing external factors (26%). Overall, the intervention seemed to be appealing for nursing students
Background: There are several ways to conduct a job task analysis in medical work environments including pencil-paper observations, interviews and questionnaires. However these methods implicate bias problems such as high inter-individual deviations and risks of misjudgement. Computer-based observation helps to reduce these problems. The aim of this paper is to give an overview of the development process of a computer-based job task analysis instrument for real-time observations to quantify the job tasks performed by physicians working in different medical settings. In addition reliability and validity data of this instrument will be demonstrated.
Methods: This instrument was developed in consequential steps. First, lists comprising tasks performed by physicians in different care settings were classified. Afterwards content validity of task lists was proved. After establishing the final task categories, computer software was programmed and implemented in a mobile personal computer. At least inter-observer reliability was evaluated. Two trained observers recorded simultaneously tasks of the same physician.
Results: Content validity of the task lists was confirmed by observations and experienced specialists of each medical area. Development process of the job task analysis instrument was completed successfully. Simultaneous records showed adequate interrater reliability.
Conclusion: Initial results of this analysis supported the validity and reliability of this developed method for assessing physicians' working routines as well as organizational context factors. Based on results using this method, possible improvements for health professionals' work organisation can be identified.