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秘鲁因新冠疫情实施居家令期间粮食不安全状况的社会预测因素。基于网络的横断面调查结果。

Social predictors of food insecurity during the stay-at-home order due to the COVID-19 pandemic in Peru. Results from a cross-sectional web-based survey.

作者信息

Cañari-Casaño Jorge L, Cochachin-Henostroza Omaira, Elorreaga Oliver A, Dolores-Maldonado Gandy, Aquino-Ramírez Anthony, Huaman-Gil Sindy, Giribaldi-Sierralta Juan P, Aparco Juan Pablo, Antiporta Daniel A, Penny Mary E

机构信息

Clima, Latin American Center of Excellence for Climate Change and Health, Universidad Peruana Cayetano Heredia.

Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration, Universidad Peruana Cayetano Heredia.

出版信息

medRxiv. 2021 Mar 31:2021.02.06.21251221. doi: 10.1101/2021.02.06.21251221.

DOI:10.1101/2021.02.06.21251221
PMID:33564796
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7872391/
Abstract

BACKGROUND

Stay-at-home orders and social distancing have been implemented as the primary tools to reduce the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, this approach has indirectly lead to the unemployment of 2·3 million Peruvians, in Lima, Perú alone. As a result, the risk of food insecurity may have increased, especially in low-income families who rely on a daily wage. This study estimates the prevalence of moderate or severe food insecurity (MSFI) and identifies the associated factors that explain this outcome during the stay-at-home order.

METHODS

A cross-sectional web-based survey, with non-probabilistic sampling, was conducted between May 18 and June 30, 2020, during the stay-at-home order in Peru. We used social media advertisements on Facebook to reach 18-59-year-olds living in Peru. MSFI was assessed using the Food Insecurity Experience Scale (FIES). Rasch model methodology requirements were considered, and factors associated with MSFI were selected using stepwise forward selection. A Poisson generalized linear model (Poisson GLM), with log link function, was employed to estimate adjusted prevalence ratios (aPR).

FINDINGS

This analysis is based on 1846 replies. The prevalence of MSFI was 23·2%, and FIES proved to be an acceptable instrument with reliability 0·72 and infit 0·8-1·3. People more likely to experience MSFI were those with low income (less than 255 US$/month) in the pre-pandemic period (aPR 3·77; 95%CI, 1·98-7·16), those whose income was significantly reduced during the pandemic period (aPR 2·27; 95%CI, 1·55-3·31), and those whose savings ran out in less than 21 days (aPR 1·86; 95%CI, 1·43-2·42). Likewise, heads of households (aPR 1·20; 95%CI, 1·00-1·44) and those with probable SARS-CoV2 cases as relatives (aPR 1·29; 95%CI, 1·05-1·58) were at an increased risk of MSFI. Additionally, those who perceived losing weight during the pandemic (aPR 1·21; 95%CI, 1·01-1·45), and increases in processed foods prices (aPR 1·31; 95%CI, 1·08-1·59), and eating less minimally processed food (aPR 1·82; 95%CI, 1·48-2·24) were more likely to experience MSFI.

INTERPRETATION

People most at risk of MSFI were those in a critical economic situation before and during the pandemic. Social protection policies should be reinforced to prevent or mitigate these adverse effects.

摘要

背景

居家令和社交距离措施已被用作减少严重急性呼吸综合征冠状病毒2(SARS-CoV-2)传播的主要手段。然而,仅在秘鲁利马,这种做法就间接导致了230万秘鲁人失业。因此,粮食不安全风险可能增加,尤其是在依靠日薪为生的低收入家庭中。本研究估计了中度或重度粮食不安全(MSFI)的患病率,并确定了在居家令期间解释这一结果的相关因素。

方法

2020年5月18日至6月30日秘鲁实施居家令期间,开展了一项基于网络的横断面调查,采用非概率抽样。我们利用脸书上的社交媒体广告,覆盖居住在秘鲁的18至59岁人群。使用粮食不安全经历量表(FIES)评估MSFI。考虑了拉施模型方法的要求,并采用逐步向前选择法选择与MSFI相关的因素。采用具有对数链接函数的泊松广义线性模型(Poisson GLM)来估计调整患病率比(aPR)。

结果

本分析基于1846份回复。MSFI的患病率为23.2%,FIES被证明是一种可接受的工具,可靠性为0.72,拟合优度为0.8至1.3。更有可能经历MSFI的人群包括:大流行前收入低(每月低于255美元)的人(aPR 3.77;95%CI,1.98至7.16)、大流行期间收入大幅减少的人(aPR 2.27;95%CI,1.55至3.31)以及储蓄在不到21天内耗尽的人(aPR 1.86;95%CI,1.43至2.42)。同样,户主(aPR 1.20;95%CI,1.00至1.44)以及有亲属可能感染SARS-CoV2的人(aPR 1.29;95%CI,1.05至1.58)患MSFI的风险增加。此外,那些在大流行期间感觉体重减轻的人(aPR 1.21;95%CI,1.01至1.45)、加工食品价格上涨的人(aPR 1.31;95%CI,1.08至1.59)以及食用最少加工食品减少的人(aPR 1.82;95%CI,1.48至2.24)更有可能经历MSFI。

解读

最容易出现MSFI的人群是在大流行之前和期间处于经济困境的人。应加强社会保护政策,以预防或减轻这些不利影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b44/8034519/6c0e9b1d3a9a/nihpp-2021.02.06.21251221-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b44/8034519/6c0e9b1d3a9a/nihpp-2021.02.06.21251221-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b44/8034519/6c0e9b1d3a9a/nihpp-2021.02.06.21251221-f0001.jpg

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