背景:尽管使用eHealth有潜在的好处,电子健康使用中存在社会人口统计学差异,这可能会进一步扩大卫生公平差距。文献一直表明年龄和教育与电子健康的使用有关,而种族和族裔差异的调查结果喜忧参半。然而,以前的差距可能已经缩小,因为由于COVID-19大流行,每个人的医疗保健互动都转向基于网络的模式。
目的:本研究旨在提供对社会人口统计学差异的最新研究,这些差异导致与使用eHealth进行3个时间点的信息查找相关的健康公平性差距。
方法:本研究的数据来自全国代表性的2018年(n=3504),2020年(n=3865),和2022年(n=6252)健康信息国家趋势调查的时间点。使用Logistic回归来回归电子健康在种族和族裔信息搜索中的使用,性别,年龄,教育,收入,健康状况,和调查年份。考虑到年龄与因变量的一致关联,分析按年龄队列分层(千禧一代,X代,婴儿潮一代,和沉默的一代)来比较相似年龄的个体。
结果:对于千禧一代,作为女性,获得一些大学或大学学位,报告年收入50,000-74,999美元或>75,000美元与使用电子健康寻求信息有关。对于X代,作为女性,获得了某种大学或大学学位,报告年收入为50,000-74,999美元或>75,000美元,自我报告的健康状况更好,在2022年完成调查(vs2018;比值比[OR]1.80,95%CI1.11-2.91)与使用eHealth寻求信息相关。对于婴儿潮一代来说,作为女性,年纪大了,获得高中学位,获得一些大学或大学学位,报告年收入为50,000-74,999美元或>75,000美元,并在2020年(OR1.56,95%CI1.15-2.12)和2022年(OR4.04,95%CI2.77-5.87)完成调查与使用eHealth寻求信息相关。在沉默的一代中,年纪大了,获得一些大学或大学学位,报告年收入为50,000-74,999美元或>75,000美元,并在2022年完成调查(OR5.76,95%CI3.05-10.89)与使用eHealth寻求信息相关。
结论:随着时间的推移,婴儿潮一代可能在使用eHealth寻求信息方面取得了最大的收获。种族和种族的发现,或缺乏,可能表明种族和族裔差异的减少。基于性别的差异,教育,所有年龄组的收入保持一致。这与关注社会经济地位较低的个体的健康差异文献相一致,最近,与女性相比,男性寻求医疗保健的可能性较小。
BACKGROUND: Despite the potential benefits of using eHealth, sociodemographic disparities exist in eHealth use, which threatens to further widen health equity gaps. The literature has consistently shown age and education to be associated with eHealth use, while the findings for racial and ethnic disparities are mixed. However, previous disparities may have narrowed as health care interactions shifted to web-based modalities for everyone because of the COVID-19 pandemic.
OBJECTIVE: This
study aims to provide an updated examination of sociodemographic disparities that contribute to the health equity gap related to using eHealth for information seeking using 3 time points.
METHODS: Data for this
study came from the nationally representative 2018 (n=3504), 2020 (n=3865), and 2022 (n=6252) time points of the Health Information National Trends Survey. Logistic regression was used to regress the use of eHealth for information seeking on race and ethnicity, sex, age, education, income, health status, and year of survey. Given the consistent association of age with the dependent variable, analyses were stratified by age cohort (millennials, Generation X, baby boomers, and silent generation) to compare individuals of similar age.
RESULTS: For millennials, being female, attaining some college or a college degree, and reporting an annual income of US $50,000-$74,999 or >US $75,000 were associated with the use of eHealth for information seeking. For Generation X, being female, having attained some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, better self-reported health, and completing the survey in 2022 (vs 2018; odds ratio [OR] 1.80, 95% CI 1.11-2.91) were associated with the use of eHealth for information seeking. For baby boomers, being female, being older, attaining a high school degree, attaining some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, and completing the survey in 2020 (OR 1.56, 95% CI 1.15-2.12) and 2022 (OR 4.04, 95% CI 2.77-5.87) were associated with the use of eHealth for information seeking. Among the silent generation, being older, attaining some college or a college degree, reporting an annual income of US $50,000-$74,999 or >US $75,000, and completing the survey in 2022 (OR 5.76, 95% CI 3.05-10.89) were associated with the use of eHealth for information seeking.
CONCLUSIONS: Baby boomers may have made the most gains in using eHealth for information seeking over time. The race and ethnicity findings, or lack thereof, may indicate a reduction in racial and ethnic disparities. Disparities based on sex, education, and income remained consistent across all age groups. This aligns with health disparities literature focused on individuals with lower socioeconomic status, and more recently on men who are less likely to seek health care compared to women.