RESUMEN
OBJECTIVES: Evidence suggests that environmental support, personality traits, and psychological factors can influence seasonal changes in human mood and behavior, particularly in rural middle-aged women and older people. This study aimed to quantify the associations between personality traits, seasonal affective disorder symptoms, and sun exposure in rural older people. METHODS: This study is a cross-sectional analytical study, the participants were 300 rural older persons from 12 natural villages and 5 geriatric service centers in 4 different cities in Jiangxi Province, China. The Eysenck Personality Questionnaire (EPQ), the Personal Inventory of Depression and Seasonal Affective Disorder (PIDS-SA-SimpChi), and the Sunlight Exposure Scale were used to conduct follow-up interviews throughout the year. Spatial analysis was performed using ArcGIS and Geodetic Probes. The data were analyzed using SPSS 21 and Amos 23.0 mediated models. RESULTS: Rural older people with low sun exposure exhibited higher personality trait scores (p < 0.001). Personality traits were directly associated with seasonal affective disorder symptoms(p < 0.01); Sun exposure mediated this effect in rural older people (p < 0.05). CONCLUSION: High-scoring personalities are more typical of rural older people with low sun exposure, and there is a greater risk of emotional and behavioral instability. Latitudinal differences are not a determinant of SAD. Increased sun exposure is associated with symptom relief. The promotion of light therapy devices in rural areas with low sunlight is warranted.
Asunto(s)
Población Rural , Trastorno Afectivo Estacional , Luz Solar , Humanos , Estudios Transversales , Femenino , Anciano , Masculino , Población Rural/estadística & datos numéricos , China/epidemiología , Trastorno Afectivo Estacional/epidemiología , Trastorno Afectivo Estacional/psicología , Persona de Mediana Edad , Anciano de 80 o más Años , Encuestas y Cuestionarios , PersonalidadRESUMEN
The globe is an organically linked whole, and in the pandemic era, COVID-19 has brought heavy public safety threats and economic costs to humanity as almost all countries began to pay more attention to taking steps to minimize the risk of harm to society from sudden-onset diseases. It is worth noting that in some low- and middle-income areas, where the environment for epidemic detection is complex, the causative and comorbid factors are numerous, and where public health resources are scarce. It is often more difficult than in other areas to obtain timely and effective detection and control in the event of widespread virus transmission, which, in turn, is a constant threat to local and global public health security. Pandemics are preventable through effective disease surveillance systems, with nonpharmacological interventions (NPIs) as the mainstay of the control system, effectively controlling the spread of epidemics and preventing larger outbreaks. However, current state-of-the-art NPIs are not applicable in low- and middle-income areas and tend to be decentralized and costly. Based on a 3-year case study of SARS-CoV-2 preventive detection in low-income areas in south-central China, we explored a strategic model for enhancing disease detection efficacy in low- and middle-income areas. For the first time, we propose an integrated and comprehensive approach that covers structural, social, and personal strategies to optimize the epidemic surveillance system in low- and middle-income areas. This model can improve the local epidemic detection efficiency, ensure the health care needs of more people, reduce the public health costs in low- and middle-income areas in a coordinated manner, and ensure and strengthen local public health security sustainably.