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新冠疫情对高龄老人社会资本与健康的影响以及数字不平等的作用:纵向队列研究

The Impact of the COVID-19 Pandemic on Oldest-Old Social Capital and Health and the Role of Digital Inequalities: Longitudinal Cohort Study.

作者信息

Valla Luca Guido, Rossi Michele, Gaia Alessandra, Guaita Antonio, Rolandi Elena

机构信息

Department of Statistical Sciences, University of Padua, Padua, Italy.

Golgi Cenci Foundation, Abbiategrasso, Italy.

出版信息

J Med Internet Res. 2025 Jan 9;27:e62824. doi: 10.2196/62824.

Abstract

BACKGROUND

During the COVID-19 pandemic, information and communication technology (ICT) became crucial for staying connected with loved ones and accessing health services. In this scenario, disparities in ICT use may have exacerbated other forms of inequality, especially among older adults who were less familiar with technology and more vulnerable to severe COVID-19 health consequences.

OBJECTIVE

This study investigated changes in ICT use, psychological and physical health, and social capital before and after the pandemic among the oldest old population (aged 80 years or older after the pandemic) and explored how internet use influenced these changes.

METHODS

We leveraged data from the InveCe.Ab study, a population-based longitudinal cohort of people born between 1935 and 1939 and living in Abbiategrasso, a municipality on the outskirts of Milan, Italy. Participants underwent multidimensional assessment at baseline (2010) and after 2, 4, 8, and 12 years. We restricted our analysis to cohort members who participated in the last wave (ie, 2022) and who did not have a diagnosis of dementia (n=391). We used linear mixed models to assess the impact of COVID-19 and time on changes in social capital, physical and psychological health, and ICT use in a discontinuity regression design while controlling for age, sex, education, and income satisfaction. Then, we assessed the influence of internet use and its interaction with COVID-19 on these changes.

RESULTS

COVID-19 had a significant impact on social relationships (β=-4.35, 95% CI 6.38 to -2.32; P<.001), cultural activities (β=-.55, 95% CI -0.75 to -0.35; P<.001), cognitive functioning (β=-1.00, 95% CI -1.28 to -0.72; P<.001), depressive symptoms (β=.42, 95% CI 0.10-0.74; P=.009), physical health (β=.07, 95% CI 0.04-0.10; P<.001), and ICT use (β=-.11, 95% CI -0.18 to -0.03; P=.008). Internet use predicts reduced depressive symptoms (β=-.56, 95% CI -1.07 to -0.06; P=.03) over time. The interaction between internet use and COVID-19 was significant for cultural activities (β=-.73, 95% CI -1.22 to -0.24; P=.003) and cognitive functioning (β=1.36, 95% CI 0.67-2.05; P<.001).

CONCLUSIONS

The pandemic had adverse effects on older adults' health and social capital. Contrary to expectations, even ICT use dropped significantly after the pandemic. Internet users maintained higher psychological health regardless of time and COVID-19 status. However, COVID-19 was associated with a steeper decline in cognitive functioning among internet nonusers. Policy makers may develop initiatives to encourage ICT adoption among older adults or strengthen their digital skills.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01345110; https://clinicaltrials.gov/study/NCT01345110.

摘要

背景

在新冠疫情期间,信息通信技术(ICT)对于与亲人保持联系以及获取医疗服务变得至关重要。在这种情况下,ICT使用方面的差距可能加剧了其他形式的不平等,尤其是在那些不太熟悉技术且更容易受到新冠严重健康后果影响的老年人中。

目的

本研究调查了最年长者(疫情后年龄在80岁及以上)在疫情前后ICT使用、心理和身体健康以及社会资本的变化,并探讨了互联网使用如何影响这些变化。

方法

我们利用了InveCe.Ab研究的数据,这是一项基于人群的纵向队列研究,研究对象为1935年至1939年出生且居住在意大利米兰郊区阿比亚泰格拉索市的人群。参与者在基线(2010年)以及2年、4年、8年和12年后接受了多维度评估。我们将分析限制在参与最后一波(即2022年)且未被诊断为痴呆症的队列成员(n = 391)。我们使用线性混合模型,在控制年龄、性别、教育程度和收入满意度的情况下,通过间断回归设计评估新冠疫情和时间对社会资本、身心健康以及ICT使用变化的影响。然后,我们评估了互联网使用及其与新冠疫情的相互作用对这些变化的影响。

结果

新冠疫情对社会关系(β = -4.35,95%置信区间6.38至 -2.32;P <.001)、文化活动(β = -.55,95%置信区间 -0.75至 -0.35;P <.001)、认知功能(β = -1.00,95%置信区间 -1.28至 -0.72;P <.001)、抑郁症状(β =.42,95%置信区间0.10 - 0.74;P =.009)、身体健康(β =.07,95%置信区间0.04 - 0.10;P <.001)和ICT使用(β = -.11,95%置信区间 -0.18至 -0.03;P =.008)产生了显著影响。随着时间的推移,互联网使用预示着抑郁症状的减轻(β = -.56,95%置信区间 -1.07至 -0.06;P =.03)。互联网使用与新冠疫情之间的相互作用在文化活动方面具有显著性(β = -.73,95%置信区间 -1.22至 -0.24;P =.003),在认知功能方面也具有显著性(β = 1.36,95%置信区间0.67 - 2.05;P <.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11e4/11757979/4378a6451758/jmir_v27i1e62824_fig1.jpg

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