The severity of SDB was independently associated with greater aggressiveness of cutaneous melanoma, particularly among younger patients.

BackgroundChronic diseases are an increasing threat to people’s health and to the sustainability of health organisations. Despite the need for routine monitoring systems to assess the impact of chronicity in the population and its evolution over time, currently no single source of information has been identified as suitable for this purpose. Our objective was to describe the prevalence of various chronic conditions estimated using routine data recorded by health professionals: diagnoses on hospital discharge abstracts, and primary care prescriptions and diagnoses.MethodsThe ICD-9-CM codes for diagnoses and Anatomical Therapeutic Chemical (ATC) codes for prescriptions were collected for all patients in the Basque Country over 14 years of age (n=1,964,337) for a 12-month period. We employed a range of different inputs: hospital diagnoses, primary care diagnoses, primary care prescriptions and combinations thereof. Data were collapsed into the morbidity groups specified by the Johns Hopkins Adjusted Clinical Groups (ACGs) Case-Mix System. We estimated the prevalence of 12 chronic conditions, comparing the results obtained using the different data sources with each other and also with those of the Basque Health Interview Survey (ESCAV). Using the different combinations of inputs, Standardized Morbidity Ratios (SMRs) for the considered diseases were calculated for the list of patients of each general practitioner. The variances of the SMRs were used as a measure of the dispersion of the data and were compared using the Brown-Forsythe test.ResultsThe prevalences calculated using prescription data were higher than those obtained from diagnoses and those from the ESCAV, with two exceptions: malignant neoplasm and migraine. The variances of the SMRs obtained from the combination of all the data sources (hospital diagnoses, and primary care prescriptions and diagnoses) were significantly lower than those using only diagnoses.ConclusionsThe estimated prevalence of chronic diseases varies considerably depending of the source(s) of information used. Given that administrative databases compile data registered for other purposes, the estimations obtained must be considered with caution. In a context of increasingly widespread computerisation of patient medical records, the complementary use of a range of sources may be a feasible option for the routine monitoring of the prevalence of chronic diseases.

Background: Recent evidence indicates that home telemonitoring of chronic patients reduces the use of healthcare resources. However, further studies exploring this issue are needed in primary care.Objectives: To assess the impact of a primary care-based home telemonitoring intervention for highly unstable chronic patients on the use of healthcare resources.Methods: A one-year follow-up before and after exploratory study, without control group, was conducted. Housebound patients with heart failure or chronic lung disease, with recurrent hospital admissions, were included. The intervention consisted of patient’s self-measurements and responses to a health status questionnaire, sent daily from smartphones to a web-platform (aided by an alert system) reviewed by healthcare professionals. The primary outcome measure was the number of hospital admissions occurring 12 months before and after the intervention. Secondary outcomes were length of hospital stay and number of emergency department attendances. Primary care nurses were mainly in charge of the telemonitoring process and were assisted by the general practitioners when required.Results: For the 28 patients who completed the follow-up (out of 42 included, 13 patients died and 1 discontinued the intervention), a significant reduction in hospitalizations, from 2.6 admissions/patient in the previous year (standard deviation, SD: 1.6) to 1.1 (SD: 1.5) during the one-year telemonitoring follow-up (P <0.001), and emergency department attendances, from 4.2 (SD: 2.6) to 2.1 (SD: 2.6) (P <0.001) was observed. The length of hospital stay was reduced non-significantly from 11.4 to 7.9 days.Conclusion: In this small exploratory study, the primary care-based telemonitoring intervention seemed to have a positive impact decreasing the number of hospital admissions and emergency department attendances.

BackgroundResearch is an essential activity for improving quality and efficiency in healthcare. The objective of this study was to train nurses from the public Basque Health Service (Osakidetza) in critical appraisal, promoting continuous training and the use of research in clinical practice.MethodsThis was a prospective pre-post test study. The InfoCritique course on critical appraisal was translated and adapted. A sample of 50 nurses and 3 tutors was recruited. Educational strategies and assessment instruments were established for the course. A course website was created that contained contact details of the teaching team and coordinator, as well as a course handbook and videos introducing the course. Assessment comprised the administration of questionnaires before and after the course, in order to explore the main intervention outcomes: knowledge acquired and self-learning readiness. Satisfaction was also measured at the end of the course.ResultsOf the 50 health professionals recruited, 3 did not complete the course for personal or work-related reasons. The mean score on the pre-course knowledge questionnaire was 70.5 out of 100, with a standard deviation of 11.96. In general, participants’ performance on the knowledge questionnaire improved after the course, as reflected in the notable increase of the mean score, to 86.6, with a standard deviation of 10.00. Further, analyses confirmed statistically significant differences between pre- and post-course results (p < 0.001). With regard to self-learning readiness, after the course, participants reported a greater readiness and ability for self-directed learning. Lastly, in terms of level of satisfaction with the course, the mean score was 7 out of 10.ConclusionsParticipants significantly improved their knowledge score and self-directed learning readiness after the educational intervention, and they were overall satisfied with the course. For the health system and nursing professionals, this type of course has the potential to provide methodological tools for research, promote a research culture, and encourage critical thinking for evidence-based decision making.

BackgroundAn increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care.The objectives of this study are to evaluate the capability of ACG-PM, DCG-HCC and CRG-based models to predict healthcare costs and identify patients that will be high consumers and to analyse changes to predictive capacity when socio-economic variables are added.MethodsThis cross-sectional study used data of all Basque Country citizens over 14 years of age (n = 1,964,337) collected in a period of 2 years. Data from the first 12 months (age, sex, area deprivation index, diagnoses, procedures, prescriptions and previous cost) were used to construct the explanatory variables. The ability of models to predict healthcare costs in the following 12 months was assessed using the coefficient of determination and to identify the patients with highest costs by means of receiver operating characteristic (ROC) curve analysis.ResultsThe coefficients of determination ranged from 0.18 to 0.21 for diagnosis-based models, 0.17-0.18 for prescription-based and 0.21-0.24 for the combination of both. The observed area under the ROC curve was 0.78-0.86 (identifying patients with a cost higher than P-95) and 0.83-0.90 (P-99). The values of the DCG-HCC models are slightly higher and those of the CRG models are lower, although prescription information could not be used in the latter. On adding previous cost data, differences between the three systems decrease appreciably. Inclusion of the deprivation index led to only marginal improvements in explanatory power.ConclusionThe case-mix systems developed in the USA can be useful in a publicly financed healthcare system with universal coverage to identify people at risk of high health resource consumption and whose situation is potentially preventable through proactive interventions.