Effect of switch from flash glucose monitoring to flash glucose monitoring with real-time alarms on hypoglycaemia in people with type 1 diabetes mellitus.

We aimed to evaluate the utility of the FreeStyle Libre 2 device for reducing time below range level 1 and level 2 compared with the Freestyle Libre device (without alarms) in people with type 1 diabetes mellitus. We conducted longitudinal observational follow-up study of a cohort of 100 people with type 1 diabetes mellitus who had switched from FreeStyle Libre to FreeStyle Libre 2 as part of routine clinical practice. Three months after switching to FreeStyle Libre 2, compared with results with FreeStyle Libre, there were a significant improvements in time below range level 1 (p = 0.02) and level 2 (p <0.001), time in range (p <0.001), time above range level 1 (p = 0.002), glucose management indicator (p= 0.04) and mean glucose (p= 0.04) during follow-up. Furthermore there was a significant direct association between age and change in TIR with a coefficient of 0.23, and a significant inverse association between age and change in TAR-1 with a coefficient of 0.11. Switching to a flash glucose monitoring system with alarms improves time below range, time in range and coefficient of variation in people with type 1 diabetes mellitus.

[Factors associated with dissatisfaction with pharmacological treatment in patients with type 2 diabetes mellitus: A cross-sectional study]. / Factores asociados a la insatisfacción con el tratamiento farmacológico en pacientes con diabetes mellitus tipo 2: estudio transversal.

BACKGROUND AND OBJECTIVES: Diabetes is a chronic disease with a high impact on both health and Quality of Life Related to Health (QLRH). To evaluate the satisfaction of treatment in patients with type 2 diabetes mellitus through the Diabetes Treatment Satisfaction Questionnaire (DTSQ) and its relationship with sociodemographic variables, with antidiabetic medication and clinical-analytical variables. MATERIALS AND METHODS: This cross-sectional study was conducted in General University Hospital of San Juan de Alicante between September 2016 and December 2017. Two hundred thirty-two patients diagnosed with type 2 diabetes mellitus at least 1 year before inclusion, treated with antidiabetic medication were included. The Spanish version of the DTSQ scale was used to measure satisfaction with treatment. Factors associated with low satisfaction were analyzed by applying the Chi-square test for qualitative variables and Student-T for quantitative variables. To estimate magnitudes of association, logistic models were adjusted. RESULTS: Two hundred thirty-two patients were included in this study. 21.5% of the patients presented low satisfaction with the treatment. Patients who presented low satisfaction with treatment were associated with medications that could cause hypoglycemia (OR: 2.872 [1.195-6.903]), HbA1c levels higher than 7% (OR: 2.260 [1.005-5.083]) and drugs administered by the route oral (OR: 2.749 [1.233-6.131]). CONCLUSIONS: Patients with type 2 diabetes mellitus who had a lower score on the DTSQ questionnaire were associated with medications that produced hypoglycaemia, and with higher levels of HbA1c higher than 7%, and those who took oral medication.

[Analysis of two health attendance models for the non-hospital emergencies in Gipuzkoa: Retrospective study]. / Análisis de dos modelos asistenciales para la atención de las emergencias extrahospitalarias en Guipúzcoa: Estudio retrospectivo.

BACKGROUND AND OBJECTIVE: Out-of-hospital medical emergency services are defined as a functional organization that performs a set of sequential human and material activities. The objective of this study was to compare the mortality of patients attended by the out-of-hospital medical emergency services in 2 neighboring Spanish regions with different models of healthcare transport assistance for emergency care. MATERIAL AND METHOD: Retrospective observational cohort study, done between June 1, 2007 and December 31, 2008 in 2 regions of Gipuzkoa, Alto Deba (AD) and Bajo Deba (BD). The study variables were age, sex and place of exposure (AD/BD), heart rate, blood pressure, initial reason for the call defined by the European Resuscitation Council, unconsciousness and digestive bleeding. 3452 subjects were analyzed. RESULTS: The risk of in situ mortality in BD was 1.31 times higher than in AD (P=.050), that of hospital mortality in BD was 0.71 times lower than in AD (P=.011) and the risk of mortality at one year between counties and the combined mortality (in situ+hospital) did not contribute significant differences. CONCLUSIONS: Mortality (in situ+in-hospital, and one year aftercare) of patients treated by the out-of-hospital emergency medical services in AD (non-medicalized healthcare transport model) was similar to that of the BD region (mixed healthcare transport model).

Incubation period of COVID-19: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n=792) in the meta-analysis. The heterogeneity (I2 83.0%, p<0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2-6.0) to 6.7 days (95% CI: 6.0-7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

Período de incubación de la COVID-19: revisión sistemática y metaanálisis / Incubation period of COVID-19: A systematic review and meta-analysis

Antecedentes y objetivo El período de incubación de la COVID-19 ayuda a determinar la duración óptima del período de cuarentena y a crear modelos predictivos de curvas de incidencia. Se han reportado resultados variables en recientes estudios y, por ello, el objetivo de esta revisión sistemática es proporcionar una estimación más precisa del período de incubación de la COVID-19. Métodos Se realizó una búsqueda bibliográfica en las bases de datos de Pubmed, Scopus/EMBASE y la Cochrane Library, incluyendo todos los estudios observacionales y experimentales que reportaban un período de incubación y que se habían publicado entre el 1 de enero y el 21 de marzo de 2020. Se estimó la media y el percentil 95 del período de incubación mediante metaanálisis, teniendo en cuenta la heterogeneidad entre los estudios y el análisis con variables moderadoras. Resultados Se incluyeron siete estudios (n = 792) en el metaanálisis. La heterogeneidad (I2 83,0%, p < 0,001) disminuyó significativamente cuando se tuvo en cuenta la calidad del estudio y el modelo estadístico utilizado como variables moderadoras (I2 15%). El período medio de incubación oscilaba entre 5,6 (IC 95%: 5,2 a 6,0) y 6,7 días (IC 95%: 6,0 a 7,4), según el modelo estadístico utilizado. El percentil 95 fue de 12,5 días cuando la edad media de los pacientes era de 60 años, aumentando un día por cada 10 años de edad. Conclusión Según los datos publicados sobre el período de incubación de la COVID-19, el tiempo medio entre la exposición y la aparición de los síntomas clínicos depende del modelo estadístico utilizado y el percentil 95, de la edad media de los pacientes. Se recomienda registrar el sexo y la edad en la recogida de los datos para poder analizar los posibles patrones diferenciales (AU)

Background and objective The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. Methods For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. Results We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. Conclusion Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns (AU)

[Incubation period of COVID-19: A systematic review and meta-analysis]. / Período de incubación de la COVID-19: revisión sistemática y metaanálisis.

BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

Incubation period of COVID-19: A systematic review and meta-analysis. / Período de incubación de la COVID-19: revisión sistemática y metaanálisis.

BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

Período de incubación de la COVID-19: revisión sistemática y metaanálisis / Incubation period of COVID-19: a systematic review and meta-analysis

ANTECEDENTES Y OBJETIVO: El período de incubación de la COVID-19 ayuda a determinar la duración óptima del período de cuarentena y a crear modelos predictivos de curvas de incidencia. Se han reportado resultados variables en recientes estudios y, por ello, el objetivo de esta revisión sistemática es proporcionar una estimación más precisa del período de incubación de la COVID-19. MÉTODOS: Se realizó una búsqueda bibliográfica en las bases de datos de Pubmed, Scopus/EMBASE y la Cochrane Library, incluyendo todos los estudios observacionales y experimentales que reportaban un período de incubación y que se habían publicado entre el 1 de enero y el 21 de marzo de 2020. Se estimó la media y el percentil 95 del período de incubación mediante metaanálisis, teniendo en cuenta la heterogeneidad entre los estudios y el análisis con variables moderadoras. RESULTADOS: Se incluyeron siete estudios (n = 792) en el metaanálisis. La heterogeneidad (I2 83,0%, p < 0,001) disminuyó significativamente cuando se tuvo en cuenta la calidad del estudio y el modelo estadístico utilizado como variables moderadoras (I2 15%). El período medio de incubación oscilaba entre 5,6 (IC 95%: 5,2 a 6,0) y 6,7 días (IC 95%: 6,0 a 7,4), según el modelo estadístico utilizado. El percentil 95 fue de 12,5 días cuando la edad media de los pacientes era de 60 años, aumentando un día por cada 10 años de edad. CONCLUSIÓN: Según los datos publicados sobre el período de incubación de la COVID-19, el tiempo medio entre la exposición y la aparición de los síntomas clínicos depende del modelo estadístico utilizado y el percentil 95, de la edad media de los pacientes. Se recomienda registrar el sexo y la edad en la recogida de los datos para poder analizar los posibles patrones diferenciales

BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns

Waning protection of influenza vaccination during four influenza seasons, 2011/2012 to 2014/2015.

BACKGROUND: Concerns have been raised about intraseasonal waning of the protection conferred by influenza vaccination. METHODS: During four influenza seasons, we consecutively recruited individuals aged 18years or older who had received seasonal influenza vaccine and were subsequently admitted to the hospital for influenza infection, asassessed by reverse transcription polymerase chain reaction. We estimated the adjusted odds ratio (aOR) of influenza infection by date of vaccination, defined by tertiles, as early, intermediate or late vaccination. We used a test-negative approach with early vaccination as reference to estimate the aOR of hospital admission with influenza among late vaccinees. We conducted sensitivity analyses by means of conditional logistic regression, Cox proportional hazards regression, and using days between vaccination and hospital admission rather than vaccination date. RESULTS: Among 3615 admitted vaccinees, 822 (23%) were positive for influenza. We observed a lower risk of influenza among late vaccinees during the 2011/2012 and 2014/2015A(H3N2)-dominant seasons: aOR=0.68 (95% CI: 0.47-1.00) and 0.69 (95% CI: 0.50-0.95). We found no differences in the risk of admission with influenza among late versus early vaccinees in the 2012/2013A(H1N1)pdm09-dominant or 2013/2014B/Yamagata lineage-dominant seasons: aOR=1.18 (95% CI: 0.58-2.41) and 0.98 (95% CI: 0.56-1.72). When we restricted our analysis to individuals aged 65years or older, we found a statistically significant lower risk of admission with influenza among late vaccinees during the 2011/2012 and 2014/2015A(H3N2)-dominant seasons: aOR=0.61 (95% CI: 0.41-0.91) and 0.69 (95% CI: 0.49-0.96). We observed 39% (95% CI: 9-59%) and 31% (95% CI: 5-50%) waning of vaccine effectiveness among participants aged 65years or older during the two A(H3N2)-dominant seasons. Similar results were obtained in the sensitivity analyses. CONCLUSION: Waning of vaccine protection was observed among individuals aged 65years old or over in two A(H3N2)-dominant influenza seasons.

Differences in the management of hypertension, diabetes mellitus and dyslipidemia between obesity classes.

We did not find any paper that assessed clinical inertia in obese patients. Therefore, no paper has compared the clinical inertia rates between morbidly and nonmorbidly obese patients. A cross-sectional observational study was carried out. We analysed 8687 obese patients ⩾40 years of age who attended their health-care center for a checkup as part of a preventive program. The outcome was morbid obesity. Secondary variables were as follows: failure in the management of high blood pressure (HBP), high blood cholesterol (HBC) and high fasting blood glucose (HFBG); gender; personal history of hypertension, dyslipidemia, diabetes, smoking and cardiovascular disease; and age (years). We analysed the association between failures and morbid obesity by calculating the adjusted odds ratio (OR). Of 8687 obese patients, 421 had morbid obesity (4.8%, 95% confidence interval (CI): 4.4-5.3%). The prevalence rates for failures were as follows: HBP, 34.7%; HBC, 35.2%; and HFBG, 12.4%. Associated factors with morbid obesity related with failures were as follows: failure in the management of HBP (OR=1.42, 95% CI: 1.15-1.74, P=0.001); failure in the management of HBC (OR=0.73, 95% CI: 0.58-0.91, P=0.004); and failure in the management of HFBG (OR=2.24, 95% CI: 1.66-3.03, P<0.001). Morbidly obese patients faced worse management for HBP and HFBG, and better management for HBC. It would be interesting to integrate alarm systems to avoid this problem.

Effectiveness of influenza vaccination programme in preventing hospital admissions, Valencia, 2014/15 early results.

Preliminary results for the 2014/15 season indicate low to null effect of vaccination against influenza A(H3N2)-related disease. As of week 5 2015, there have been 1,136 hospital admissions, 210 were due to influenza and 98% of subtype A strains were H3. Adjusted influenza vaccine effectiveness was 33% (range: 6-53%) overall and 40% (range: 13% to 59%) in those 65 years and older. Vaccination reduced by 44% (28-68%) the probability of admission with influenza.

Suitability of antiplatelet therapy in hypertensive patients.

Antiplatelet therapy (AT) is indicated in hypertensive patients with increased cardiovascular risk. The literature about the adequate or inadequate prescription of AT is scarce. We conducted a prospective descriptive study to quantify therapeutic inertia and non-guideline-recommended prescription (NGRP) of AT (aspirinor clopidogrel or both), and to assess associated factors, calculating the adjusted odds ratios (ORs) from multivariate models. In 2007-2009, 712 primary health-care hypertensive patients in a Spanish region were enrolled. Inertia was defined as the lack of an AT prescription, despite being indicated by guidelines, whereas NGRP was defined as AT prescription when there was no guideline recommendation. We also recorded cardiovascular variables. Inertia and NGRP were quantified for primary and secondary prevention. Of 108 patients in secondary prevention, 53 had inertia (49.1%, 95% confidence interval (CI): 39.6-58.5%). Associated profile: female (OR=0.460, P=0.091), no dyslipidemia (OR=0.393, P=0.048), no coronary heart disease (OR=0.215, P=0.001) and high diastolic blood pressure (OR=1.076, P=0.016). In primary prevention, NGRP was present in 69 of 595 patients (11.6%, 95% CI: 9.0-14.2%). Associated profile: male (OR=1.610, P=0.089), smoking (OR=2.055, P=0.045), dyslipidemia (OR=3.227, P<0.001) and diabetes (OR=2.795, P<0.001). Although certain factors were clearly associated with these phenomena much still remains to be learnt.

Effectiveness of the 2010-2011 seasonal influenza vaccine in preventing confirmed influenza hospitalizations in adults: a case-case comparison, case-control study.

INTRODUCTION: We estimated influenza vaccine effectiveness (IVE) to prevent laboratory-confirmed influenza-related hospitalizations in patients 18 years old or older during the 2010-2011 influenza season. METHODS: We conducted a prospective case-control study in five hospitals, in Valencia, Spain. Study subjects were consecutive emergency hospitalizations for predefined conditions associated with an influenza-like illness episode <8 days before admission. Patients were considered immunized if vaccinated ≥14 days before influenza-like illness onset. Cases were those with a real time reverse transcriptase polymerase chain reaction (RT-PCR) positive for influenza and controls were RT-PCR positive for other respiratory viruses. Adjusted IVE was estimated as 100×(1-adjusted odds ratio). To account for indication bias we computed adjusted IVE for respiratory syncytial virus related hospitalizations. RESULTS: Of 826 eligible hospitalized patients, 102 (12%) were influenza positive and considered cases, and 116 (14%) were positive for other respiratory viruses and considered controls. Adjusted IVE was 54% (95% confidence interval, 11-76%). By subgroup, adjusted IVE was 53% (4-77%) for those with high-risk conditions, 59% (16-79%) for those ≥60 years of age, and, 54% (4-79%) for those ≥60 years of age with high-risk conditions. No influenza vaccine effect was observed against respiratory syncytial virus related hospitalization. CONCLUSION: Influenza vaccination was associated with a significant reduction on the risk of confirmed influenza hospitalization, irrespective of age and high-risk conditions.

[Inter-professional relationship between primary health care (PHC) and secondary care (hospital) in 1992 and 2000. A Delphi study]. / Evolución de la relación entre atención primaria y especializada 1992-2001: estudio Delphi.

AIM: To evaluate the trends in the inter-professional relationship between primary health care (PHC) and secondary care (hospital) at 2 different moments of the health reform, at its start in 1992 and after a phase of consolidation (2001). DESIGN: Observational study based on modified Delphi technique. SETTING: Valencia Community, Spain. PARTICIPANTS: One hundred and ninety six professionals from Valencia Community were selected (103 PH centre administrators, 43 hospital and PC medical directors, and 50 heads of internal medicine or emergency services). RESULTS: One hundred and ninety six questionnaires were sent out, with a response rate of 38%. In PHC problems remained the same, but the following got worse: "lack of motivation" (+1.34), "lack of overall vision of patients" (+1.10), and "overuse of medical services" (+1.06). The existence of non-integrated out-patient specialists got better (-1.32). In hospitals, "lack of overall vision of patients" got worse (+0.51), but in general problems got better, especially in "lack of communication and dialogue" (-1.14). PC increased its demand for "a single computerized clinical record" (+1.50), drawing up of common protocols (+0.86), and periodic rotations of PC doctors through hospitals (+0.85), but bureaucratic referrals to PC (-0.60) and the need for specialists in PC as consultants (-0.36) diminished. In hospitals all solutions showed lower scores, particularly access of PC doctors to monitoring of admitted patients (-2.44) and PC doctors doing hospital cover (-2.30). CONCLUSIONS: Problems and solutions from PHC and hospitals remain the same, but there is a trend to the worse in PHC, whereas in hospitals the trend is more positive.

Evolución de la relación entre atención primaria y especializada 1992-2001: estudio Delphi / Inter-professional relationship between primary health care (PHC) and secondary care

Objetivo. Valorar la tendencia en la relación de atención primaria (AP) y especializada (hospital) en 2 momentos diferentes de la reforma sanitaria, al inicio (1992) y tras una fase de consolidación (2001). Diseño. Estudio cualitativo basado en la técnica Delphi modificada. Emplazamiento. Comunidad Valenciana. Participantes. Se seleccionó a un total de 196 profesionales de la Comunidad Valenciana (103 coordinadores de AP, 43 directores médicos hospitalarios y de AP, y 50 jefes de servicio de medicina interna/urgencias). Resultados. Se enviaron 196 cuestionarios, con una tasa de respuesta del 38%. Desde AP los problemas se mantienen, con un empeoramiento en la desmotivación del personal sanitario (+1,34), la falta de visión integral del paciente (+1,10) y la masificación asistencial (+1,06), y un mejoría en la presencia de especialistas de ambulatorio no integrados (­1,32). Desde el ámbito hospitalario empeora la falta de visión integral del paciente (+0,51), pero destaca la mejoría generalizada de los problemas, sobre todo la falta de comunicación y diálogo (­1,14). Las soluciones que aumentan su demanda desde AP son una historia clínica única informatizada (+1,50), la elaboración de protocolos comunes (+0,86) y las rotaciones periódicas de los médicos de AP (MAP) por servicios hospitalarios (+0,85), con una disminución de las derivaciones burocráticas a AP (­0,60) y la necesidad de especialistas en AP como consultores (­0,36). Desde el ámbito hospitalario, todas las soluciones disminuyen su valoración y entre ellas destaca facilitar el acceso de MAP para el seguimiento de los pacientes ingresados (­2,44) y la realización de guardias hospitalarias por MAP (­2,30). Conclusiones. Los problemas y las soluciones siguen siendo los mismos que en 1992, pero en AP se observa una tendencia a empeorar y en el ámbito hospitalario se detecta una visión más positiva

Aim. To evaluate the trends in the inter-professional relationship between primary health care (PHC) and secondary care (hospital) at 2 different moments of the health reform, at its start in 1992 and after a phase of consolidation (2001). Design. Observational study based on modified Delphi technique. Setting. Valencia Community, Spain. Participants. One hundred and ninety six professionals from Valencia Community were selected (103 PH centre administrators, 43 hospital and PC medical directors, and 50 heads of internal medicine or emergency services). Results. One hundred and ninety six questionnaires were sent out, with a response rate of 38%. In PHC problems remained the same, but the following got worse: "lack of motivation" (+1.34), "lack of overall vision of patients" (+1.10), and "overuse of medical services" (+1.06). The existence of non-integrated out-patient specialists got better (­1.32). In hospitals, "lack of overall vision of patients" got worse (+0.51), but in general problems got better, especially in "lack of communication and dialogue" (­1.14). PC increased its demand for "a single computerized clinical record" (+1.50), drawing up of common protocols (+0.86), and periodic rotations of PC doctors through hospitals (+0.85), but bureaucratic referrals to PC (­0.60) and the need for specialists in PC as consultants (-0.36) diminished. In hospitals all solutions showed lower scores, particularly access of PC doctors to monitoring of admitted patients (­2.44) and PC doctors doing hospital cover (­2.30). Conclusions. Problems and solutions from PHC and hospitals remain the same, but there is a trend to the worse in PHC, whereas in hospitals the trend is more positive