ABSTRACT
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.
Subject(s)
Diabetes Mellitus, Type 2 , Hypoglycemia , Humans , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/drug therapy , Cross-Sectional Studies , Quality of Life , Glycated Hemoglobin , Patient Satisfaction , Hypoglycemic Agents/adverse effects , Hypoglycemia/chemically induced , Hypoglycemia/complications , Hypoglycemia/drug therapyABSTRACT
Introducción y objetivos: Existen modelos de predicción de riesgo cardiovascular en población general, pero no se ha estudiado de modo específico la predicción del riesgo de las mujeres posmenopáusicas. El objetivo de este estudio es conocer los hábitos de vida y las enfermedades crónicas asociados con mayor riesgo cardiovascular en mujeres menopáusicas, así como construir una escala de riesgo. Métodos: Estudio de cohortes retrospectivo de base poblacional cuya fuente de datos es la Encuesta Nacional de Salud de España de 2011. Se incluyó a mujeres de edad ≥ 50 años. Se recogieron las características que mejor definían los hábitos de vida de las mujeres del estudio, así como su estado de salud y los antecedentes médicos declarados por ellas en el momento de la encuesta. Se realizó seguimiento de la mortalidad de las mujeres del estudio desde 2011 hasta 2017. Resultados: Se incluyó a 5.953 mujeres con una media de edad de 66,4±11,4 años. La incidencia de mortalidad cardiovascular en el periodo de seguimiento fue del 4%. Se relacionaron con la mortalidad cardiovascular el consumo de verduras menor de 1 vez/semana (HR=1,758), el tabaquismo (HR=1,816) el exceso de horas de sueño (≥ 9 h/día, HR=1,809) o tener actividad principal diaria sentada la mayor parte del tiempo (HR=2,757). El modelo predictivo presenta un estadístico C «sincero» en muestra de prueba de 0,8407 (IC95%, 0,8025-0,8789). Conclusiones: Hábitos de vida como el consumo de verduras, la actividad principal diaria, las horas de sueño o el tabaquismo son factores de riesgo de mortalidad cardiovascular de gran relevancia entre las mujeres menopáusicas. Se aporta una sencilla escala de riesgo autorreferida a 6 años con elevada capacidad predictiva (AU)
Introduction and objectives: There are models for cardiovascular risk prediction in the general population, but the prediction of risk in postmenopausal women has not been specifically studied. This study aimed to determine the association of lifestyle habits and chronic diseases with cardiovascular risk in menopausal women, as well as to build a risk scale. Methods: Retrospective population-based cohort study using data from the 2011 National Health Survey of Spain as a data source, Women ≥ 50 years were included. The characteristics that best defined the life habits of the study women were collected, as well as their health status and self-reported medical history at the time of the survey. Follow-up data on all-cause mortality were obtained from participants from 2011 to 2017. Results: A total of 5953 women ≥ 50 years of age were included, with a mean age of 66.4 ± 11.4 years. The incidence of cardiovascular mortality in the follow-up period was 4%. Vegetable consumption less than 1 time/week (HR, 1.758), smoking (HR, 1.816) or excess hours of sleep (≥ 9h/day, HR, 1.809), or o have main daily activity sitting most of the time (HR, 2.757) were related to cardiovascular mortality. The predictive model presents an honest C-index in test sample of 0.8407 (95%CI, 0.8025-0.8789). Conclusions: Life habits such as the consumption of vegetables, daily main activity, sleeping hours or smoking are risk factors for cardiovascular mortality of great relevance among menopausal women. A simple 6-year self-reported risk scale with high predictive capacity is provided (AU)
Subject(s)
Humans , Female , Middle Aged , Aged , Cardiovascular Diseases/mortality , Life Style , Menopause , Cohort Studies , Retrospective Studies , Follow-Up Studies , Risk Factors , IncidenceABSTRACT
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).
Subject(s)
Emergencies , Emergency Medical Services , Hospital Mortality , Humans , Resuscitation , Retrospective StudiesABSTRACT
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.
Subject(s)
COVID-19/transmission , Infectious Disease Incubation Period , COVID-19/diagnosis , COVID-19/prevention & control , COVID-19/virology , HumansABSTRACT
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)
Subject(s)
Humans , Infectious Disease Incubation Period , Coronavirus Infections/physiopathology , Coronavirus Infections/transmission , Time FactorsABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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
