Asunto(s)
Adenoma , Diabetes Insípida , Neoplasias Hipofisarias , Humanos , Neoplasias Hipofisarias/cirugía , Adenoma/cirugía , Factores de Riesgo , Diabetes Insípida/etiología , Complicaciones Posoperatorias/etiología , Estudios Multicéntricos como Asunto , Procedimientos Neuroquirúrgicos/métodos , Procedimientos Neuroquirúrgicos/efectos adversosAsunto(s)
Neoplasias Encefálicas , Glioma , Femenino , Humanos , Embarazo , Neoplasias Encefálicas/cirugía , Craneotomía , Glioma/cirugía , VigiliaRESUMEN
SUMMARY Objective: To quantify the frequency of psychiatric disorders in the outpatient setting in Peru, from 2018 to 2021. Methods: Observational cross-sectional study using outpatient morbidity data from Perú's National Superintendence of Health. Psychiatric diagnoses were identified using ICD-10 codes. The frequency of major psychiatric disorder groups is described by demographic variables. Monthly trends, as well as the number of outpatient visits per 10,000 population at the subnational level, are also presented. Results: Between 2018-2021, psychiatric disorders accounted for 3,142,685 outpatient visits (2.3% of all) in Peru. Across working age groups, anxiety and depressive disorders comprised approximately 6 out of 10 psychiatric outpatient visits in women, and 4 out of 10 in men. Monthly trends showed two peaks in the absolute number of psychiatric outpatient visits during the pre-pandemic period: April-May and September. Provinces in the Highlands had the lowest outpatient visits per 10,000 population. Conclusions: Psychiatric disorders represent a small fraction of the outpatient visits in Peru, with anxiety and depressive disorders as the most frequent. National strategies targeting outpatients with psychiatric disorders should consider demographic differences, monthly trends, and the impact of the COVID-19 pandemic.
RESUMEN Objetivo: Cuantificar la frecuencia de trastornos psiquiátricos en el ámbito ambulatorio en Perú, del 2018 al 2021. Métodos: Estudio observacional transversal utilizando información de la Superintendencia Nacional de Salud del Perú. Los diagnósticos psiquiátricos se identificaron mediante códigos de CIE-10. La frecuencia de trastornos psiquiátricos mayores se describe mediante variables demográficas. También se presentan las tendencias mensuales, así como el número de visitas ambulatorias por cada 10.000 habitantes a nivel subnacional. Resultados: Entre 2018-2021, los trastornos psiquiátricos presentaron 3.142.685 visitas ambulatorias (2,3% del total) en el Perú. En todos los grupos de edad laboral, los trastornos de ansiedad y depresión representaron aproximadamente 6 de cada 10 visitas ambulatorias psiquiátricas en mujeres y 4 de cada 10 en hombres. Las tendencias mensuales mostraron dos picos en el número absoluto de visitas ambulatorias psiquiátricas durante el período prepandémico: abril-mayo y septiembre. Las provincias de la sierra tuvieron la frecuencia más baja de visitas ambulatorias por cada 10.000 habitantes. Conclusiones: Los trastornos psiquiátricos representan una pequeña fracción de las consultas ambulatorias en el Perú, con los trastornos de ansiedad y depresivos como los más frecuentes. Estrategias nacionales en relación a pacientes ambulatorios con trastornos psiquiátricos deben considerar diferencias demográficas, tendencias mensuales y el impacto de la pandemia COVID-19.
RESUMEN
OBJECTIVE: To determine the agreement between the cardiovascular disease (CVD) risk predictions computed with the WHO non-laboratory-based model and laboratory-based model in a nationally representative sample of Peruvian adults. DESIGN: Cross-sectional analysis of a national health survey. METHODS: Absolute CVD risk was computed with the 2019 WHO laboratory-based and non-laboratory-based models. The risk predictions from both models were compared with Bland-Altman plots, Lin's concordance coefficient correlation (LCCC), and kappa statistics, stratified by sex, age, body mass index categories, smoking and diabetes status. RESULTS: 663 people aged 30-59 years were included in the analysis. Overall, there were no substantial differences between the mean CVD risk computed with the laboratory-based model 2.0% (95% CI 1.8% to 2.2%) and the non-laboratory-based model 2.0% (95% CI 1.8% to 2.1%). In the Bland-Altman plots, the limits of agreement were the widest among people with diabetes (-0.21; 4.37) compared with people without diabetes (-1.17; 0.95). The lowest agreement as per the LCCC was also seen in people with diabetes (0.74 (95% CI 0.63 to 0.82)), the same was observed with the kappa statistic (kappa=0.36). In general, agreement between the scores was appropriate in terms of clinical significance. CONCLUSIONS: The absolute cardiovascular predicted risk was similar between the laboratory-based and non-laboratory-based 2019 WHO cardiovascular risk models. Pending validation from longitudinal studies, the non-laboratory-based model (instead of the laboratory-based) could be used when assessing CVD risk in Peruvian population.