Evidence map›Paper›PMID 36410323›Full record

ArticleThe American journal of tropical medicine and hygiene2023

Self-Reported Symptoms at Last Febrile Illness as a Predictor of Treatment-Seeking in Western Kenya: A Cross-Sectional Study.

Valerie Madera-Garcia, Jenna E Coalson, Maja Subelj, Melanie L Bell, Mary H Hayden, Maurice Agawo, Stephen Munga, Kacey C Ernst

Abstract read
In one paragraph

Article in The American journal of tropical medicine and hygiene, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Valerie Madera-GarciaCollege of Public Health, University of Arizona, Tucson, Arizona.
Jenna E CoalsonEck Institute for Global Health, University of Notre Dame, Notre Dame, Indiana.
Maja SubeljNational Institute of Public Health, University of Ljubljana, Ljubljana, Slovenia.
Melanie L BellCollege of Public Health, University of Arizona, Tucson, Arizona.
Mary H HaydenLyda Hill Institute for Human Resilience, University of Colorado, Colorado Springs, Colorado.
Maurice AgawoKenya Medical Research Institute, Kisian Research Station, Kenya.
Stephen MungaKenya Medical Research Institute, Kisian Research Station, Kenya.
Kacey C ErnstCollege of Public Health, University of Arizona, Tucson, Arizona.

Funding

University of Arizona's Initiative for Maximizing Student Development (IMSD)T32GM139779 · NIGMS · UNIVERSITY OF ARIZONA · PI FRANS E TAX · 2022 to 2026
$2.9M
NIGMS NIH HHS T32 GM139779
6 · The paper itself

Abstract

Timely treatment-seeking behavior can reduce morbidity and mortality due to infectious diseases. Patterns of treatment-seeking behavior can differ by access to health care, and perceptions of disease severity and symptoms. We evaluated the association between self-reported symptoms at last illness and the level of treatment-seeking behaviors. We analyzed cross-sectional data from 1,037 participants from the lowlands and highlands of Western Kenya from 2015 using logistic regression models. There was considerable heterogeneity in the symptoms and treatment-seeking behaviors reported among individuals who were febrile at their last illness. A greater number of self-reported categories of symptoms tended to be associated with a higher likelihood of treatment-seeking in both sites. Participants were significantly more likely to seek treatment if they reported fever, aches, and digestive symptoms at last illness than just fever and aches or fever alone, but the frequency of treatment-seeking for fever in combination with aches and respiratory symptoms did not follow a consistent pattern. Among those who sought treatment, most used a formal source, but the patterns were inconsistent across sites and by the number of symptoms categories. Understanding the drivers of treatment-seeking behavior after febrile illness is important to control and treat infectious diseases in Kenya.

Indexed as

Communicable DiseasesPatient Acceptance of Health CareCross-Sectional StudiesFeverHumansKenyaSelf Report

Identifiers

PMID36410323
PMCPMC9833091

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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.