Evidence map›Paper›PMID 33564796›Full record

ArticlemedRxiv : the preprint server for health sciences2021

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.

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

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 20 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 2 countries.

Jorge L Cañari-CasañoClima, Latin American Center of Excellence for Climate Change and Health, Universidad Peruana Cayetano Heredia.
Omaira Cochachin-HenostrozaGrupo de Investigación Nutobes, Universidad Nacional Mayor de San Marcos, Lima, Perú.
Oliver A ElorreagaClima, Latin American Center of Excellence for Climate Change and Health, Universidad Peruana Cayetano Heredia.
Gandy Dolores-MaldonadoEscuela Profesional de Nutrición, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Lima, Perú.
Anthony Aquino-RamírezGrupo de Investigación Nutobes, Universidad Nacional Mayor de San Marcos, Lima, Perú.
Sindy Huaman-GilEscuela Profesional de Nutrición, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Lima, Perú.
Juan P Giribaldi-SierraltaEscuela Profesional de Nutrición, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Lima, Perú.
Juan Pablo AparcoCentro Nacional de Alimentación y Nutrición, Instituto Nacional de Salud, Lima, Perú.
Daniel A AntiportaDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Mary E PennyNutrition Research Institute, Lima, Peru.
National University of San Marcos · PEUniversidad Peruana Cayetano Heredia · PEJohns Hopkins University · USUniversidad San Ignacio de Loyola · PE

Funding

Peru Infectious Diseases Epidemiology Research Training ConsortiumD43TW007393 · FIC · UNIVERSIDAD PERUANA CAYETANO HEREDIA · PI Andres G Lescano · 2005 to 2026
$5.9M
FIC NIH HHS D43 TW007393
6 · The paper itself

Abstract

backgroundStay-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.

methodsA 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).

findingsThis 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.

interpretationPeople 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.

Identifiers

PMID33564796
PMCPMC7872391
OpenAlexW3128515377

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.