Profiles of depressive symptoms in Peru: An 8-year analysis in population-based surveys

抑郁症状 泊松回归 人口 医学 萧条(经济学) 潜在类模型 人口学 观察研究 精神科 内科学 焦虑 环境卫生 统计 数学 社会学 经济 宏观经济学
作者
David Villarreal‐Zegarra,Sharlyn Otazú-Alfaro,Piero Segovia-Bacilio,Jackeline García-Serna,C Mahony Reátegui-Rivera,G. J. Meléndez‐Torres
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:333: 384-391
标识
DOI:10.1016/j.jad.2023.04.078
摘要

Profiles of depressive symptoms have been described due to heterogeneity in symptomatology and presentation. In our study, we estimate depressive symptom profiles and relate these symptom profiles to risk factors in the Peruvian population. We carried out an observational study based on the Peruvian Demographic and Health Survey (2014-2022). Men and women aged 15 years and older living in urban and rural areas in all regions of Peru were included. The Patient Health Questionnaire-9 was used to define depressive symptom profiles. We estimated latent class models to define the profiles and performed a Poisson regression analysis to determine the associated factors. A total of 259,655 participants were included. The three-class model was found to be the most appropriate, and the classes were defined according to the severity of depressive symptoms (moderate-severe symptoms, mild symptoms, and without depressive symptoms). Also, it was found that the three classes identified have not changed during the years of evaluations, presenting very similar prevalence over the years. In addition, women are more likely than men to belong to a class with more severe depressive symptoms; and the older the age, the higher the probability of belonging to a class with greater severity of depressive symptoms. Our study found that at the population level in Peru, depressive symptoms are grouped into three classes according to the intensity of the symptomatology present (no symptoms, mild symptoms and moderate-severe symptoms).

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Hello应助温柔的灵萱采纳,获得10
刚刚
所所应助hui采纳,获得10
刚刚
十七发布了新的文献求助10
刚刚
Akim应助不吃青菜采纳,获得10
刚刚
哈佛发布了新的文献求助10
刚刚
iex777完成签到 ,获得积分10
刚刚
Twonej应助accelia采纳,获得30
刚刚
wise111发布了新的文献求助10
1秒前
1秒前
zty完成签到,获得积分10
1秒前
眼睛大鸭子完成签到,获得积分10
1秒前
张孟翰发布了新的文献求助10
1秒前
xrzxlxj613814完成签到,获得积分10
2秒前
2秒前
小九发布了新的文献求助10
2秒前
Rebekah发布了新的文献求助80
3秒前
science发布了新的文献求助10
4秒前
4秒前
5秒前
Ww发布了新的文献求助10
6秒前
6秒前
小光同学完成签到,获得积分10
7秒前
7秒前
Jasper应助丰富的灵枫采纳,获得10
7秒前
唐水之完成签到,获得积分10
8秒前
8秒前
所所应助乐正如彤采纳,获得10
8秒前
9秒前
gzy完成签到,获得积分10
9秒前
9秒前
9秒前
唐水之发布了新的文献求助10
10秒前
11秒前
Chemistry完成签到 ,获得积分10
11秒前
奔波儿霸发布了新的文献求助10
11秒前
liwenqiang完成签到,获得积分10
11秒前
CipherSage应助捏个小雪团采纳,获得10
12秒前
科研通AI6.1应助waaliyh采纳,获得10
12秒前
丘比特应助哈佛采纳,获得10
12秒前
张孟翰完成签到,获得积分10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Burger's Medicinal Chemistry, Drug Discovery and Development, Volumes 1 - 8, 8 Volume Set, 8th Edition 1800
Cronologia da história de Macau 1600
Contemporary Debates in Epistemology (3rd Edition) 1000
International Arbitration Law and Practice 1000
文献PREDICTION EQUATIONS FOR SHIPS' TURNING CIRCLES或期刊Transactions of the North East Coast Institution of Engineers and Shipbuilders第95卷 1000
BRITTLE FRACTURE IN WELDED SHIPS 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 计算机科学 化学工程 生物化学 物理 复合材料 内科学 催化作用 物理化学 光电子学 细胞生物学 基因 电极 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6160241
求助须知:如何正确求助?哪些是违规求助? 7988465
关于积分的说明 16604681
捐赠科研通 5268562
什么是DOI,文献DOI怎么找? 2811078
邀请新用户注册赠送积分活动 1791264
关于科研通互助平台的介绍 1658124