BackgroundRepeated and long hospitalizations of cancer patients at the end of life have been suggested as indicators of low quality of palliative care. Comparing the care delivered between different countries with the help of these quality indicators may identify opportunities to improve practice. Our objective is twofold: firstly, to describe the scores for the existing quality indicators “the percentage of time spent in hospital” and “the proportion of adult patients with more than one hospitalization in the last 30 days of life” in populations of cancer patients in four European countries and to see whether these countries met previously defined performance standards; secondly, to assess whether these scores are related to receiving palliative care from their GP.MethodsA mortality follow-back study was conducted, based on data recorded by representative GP networks for samples of cancer patients living at home who died non-suddenly in Belgium (n = 500), the Netherlands (n = 310), Italy (n = 764), and Spain (n = 224).ResultsThe quality indicator score for “the percentage of time spent in hospital” in the last month of life was 14.1% in the Netherlands, 17.7% in Spain, 22.2% in Italy, and 24.6% in Belgium, which means that none of the countries met the performance standard of <10%. For the “proportion of patients with more than one hospitalization in the last 30 days of life”, two countries met the performance standard of <4%: the Netherlands (0.6%) and Italy (3.1%). Spain had a score of 4.0% and Belgium scored 5.4%. When patients received palliative care from their GP, significantly less time was spent in hospital in the last month and fewer hospitalizations took place.ConclusionsEuropean countries differ regarding the frequency and duration of hospitalizations of cancer patients in the last month of life. This reflects country-specific differences in the organization of palliative care and highlights the important role of the GP in palliative care provision.
These results support the importance of duration of ruptured membranes as a risk factor for vertical transmission of HIV and suggest that a diagnosis of AIDS in the mother at the time of delivery may potentiate the effect of duration of ruptured membranes.
Herpes zoster (HZ) mainly affects older adults and immunocompromised individuals and is usually characterized by a unilateral painful skin rash. Its most common complication, postherpetic neuralgia (PHN), may cause chronic debilitating pain lasting for months or years. This study (
ClinicalTrials.gov
Identifier:
NCT01772160
) aimed to estimate the HZ incidence and the proportion of HZ patients with PHN in the Italian population aged 50 years or older.
From 2013 to 2016, HZ-patients were recruited when presenting with acute HZ at 75 reporting general practitioners in Italy, covering 43,875 persons aged ≥50 years. PHN was defined as ‘worst pain’ rated ≥ 3 on the Zoster Brief Pain Inventory persisting or appearing over 90 days after rash onset.
The overall HZ incidence rate per 1000 person-years (PY) was estimated as 6.46 (95% confidence interval [CI]: 5.99–6.95), increasing with age to 9.12/1000 PY (95% CI: 7.50–10.99) in 75–79 year-olds. Among 391 HZ-patients who completed the study, the overall proportion with PHN was 10.23% (95% CI: 7.41–13.67) and the highest proportion was 15.56% (95% CI: 6.49–29.46) for the 75–79 year-olds. Among the 128 patients (32.7%) who reported at least one pre-existing medical condition, 35.9% reported diabetes mellitus and 32.0% reported emotional problems, stress or depression.
The study confirms previous findings that HZ and PHN cause an important clinical burden in older Italian adults. It also confirmed the age-related increasing risk of HZ and PHN.
The objective of this study is to identify a model able to represent the behavior of the historical decision maker (DM) in the management of lake Lugano, during the period 1982-2002. The DM decides every day how much water to release from the lake. We combine hydrological knowledge and machine learning techniques to properly develop the model. As a predictive tool we use lazy learning, namely local linear regression. We setup a daily predictor, which achieves good accuracy, with a mean absolute percentage error around 8.5%. Yet, the behavior of the model is not fully satisfactory during the floods. In fact, from an interview with a domain expert, it appears that the DM can even update the release decision every 6 hours during emergencies. We have therefore developed a refined version of the model, which works with a variable time step: it updates the release decision once a day in normal conditions, and every 6 hours during emergencies. This turns out to be a sensible choice, as the error during emergencies (which represent about 5% of the data set) decreases from 9 to 3 m 3 /sec.
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