Evidence map›Paper›PMID 32606998›Full record

ArticleInternational journal of women's health2020

Individual, Household, and Contextual Factors Influencing the Timing of the First Antenatal Care Attendance in Northwest Ethiopia: A Two-Level Binary Logistic Regression Analysis.

Amanu Aragaw Emiru, Getu Degu Alene, Gurmesa Tura Debelew

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Article in International journal of women's health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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  3. Trends and inequalities in adequate antenatal care coverage among women in Sierra Leone, 2008-2019.Archives of public health = Archives belges de sante publique · 2024
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Amanu Aragaw EmiruDepartment of Reproductive Health and Population Studies, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.ORCID 0000-0002-4652-5596
Getu Degu AleneDepartment of Epidemiology and Biostatistics, College of Medicine and Health Sciences, Bahir Dar University, Bahir, Ethiopia.
Gurmesa Tura DebelewDepartment of Population and Family Health, Faculty of Public Health, Institute of Health, Jimma University, Jimma, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly antenatal visit is critical for the health and well-being of mothers and babies. However, various individual, family level, and contextual factors influence the timely initiation of antenatal care.

objectiveThe aim of this study was to examine individual, household, and community-level factors associated with the timing of first ANC visit among mothers who gave birth in the last twelve months before the survey.

methodsA community-based cross-sectional study was conducted in June 2018. A multistage cluster sampling technique was applied, and a sample of 898 women was considered. Data were collected using a questionnaire and checklist. The analysis was made using SPSS. A multilevel logistic regression with random effects at the kebele level was developed to assess the predictors of late initiation of antenatal care. Odds ratio with 95% confidence intervals was used to measure association while the intra-class correlation coefficient and the median odds ratio were used to measure variations.

resultsOverall, 78.4% (95% CI: 75.6, 80.9) of women started their first ANC in 4 months of gestation or later and significant heterogeneity was observed between clusters. At level 1, women with intended pregnancy (aOR=0.31; 95% CI: 0.12, 0.79), and being knowledgeable about the timing (aOR=0.43; 95% CI: 0.25, 0.75) and pregnancy-related complications (aOR=0.16; 95% CI: 0.10, 0.26) were less likely to delay their first ANC visit. Conversely, the odds of late ANC visit was higher among women with no formal education (aOR=4.08, 95% CI: 2.20, 7.55). Distance to the health facility (aOR=1.04; 95% CI, 1.01-1.08) was the only level-2 significant predictor.

conclusionThe study revealed that late ANC initiation was rampant. Several factors operating at different levels were associated with late ANC visits; yet, the role of individual-level factors was relatively stronger. Hence, awareness creation is essential to the underprivileged community using the available communication networks.

Indexed as

antenatal caredelaylatemultileveltiming

Identifiers

PMID32606998
PMCPMC7308146

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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.