Evidence map›Paper›PMID 31431905›Full record

ArticleGlobal pediatric health2019

Using Household Socioeconomic Indicators to Predict the Utilization of Maternal and Child Health Services Among Reproductive-Aged Women in Rural Yemen.

Abdullah Nagi Alosaimi, Bright Nwaru, Riitta Luoto, Abdul Wahed Al Serouri, Halima Mouniri

Abstract read
In one paragraph

Article in Global pediatric health, 2019. 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

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

4 citing papers in PubMed.

  1. Article
  2. Research progress of health care in Yemeni children during the war: review.Primary health care research & development · 2022
    Review
  3. Review
  4. 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

5 authors.

Abdullah Nagi AlosaimiUniversity of Tampere, Tampere, Finland.ORCID https://orcid.org/0000-0002-7408-5997
Bright NwaruUniversity of Tampere, Tampere, Finland.
Riitta LuotoUniversity of Tampere, Tampere, Finland.
Abdul Wahed Al SerouriSana'a University, Sana'a, Yemen.
Halima MouniriColumbia University, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using principal component analysis (PCA) and integrating both individual and household factors, we had previously derived and proposed 3 socioeconomic indices (namely, wealth index, educational index, and housing quality index) that can be used to classify rural Yemeni women into different socioeconomic statuses (SES). In the current article, we examined whether the PCA-derived indices can be used to predict the use of maternal and child health care services in rural Yemen. We used data from subnational representative multistage sampling cross-sectional household survey conducted in rural Yemen in 2008-2009 among women (N = 6907) who had given birth. The resulting component scores for each SES index were divided into tertiles. Logistic regression was used to study the associations between the SES indices and 4 indicators of maternal health care use. Higher tertiles of each socioeconomic index increased the likelihood of adequate antenatal care use, delivery assistance, and contraceptive use, but decreased the likelihood of unmet need for contraception. Key maternal health indicators can be determined by socioeconomic indicators. Therefore, in planning maternal and child health interventions, considering disparities of care by socioeconomic factors should be taken into account.

Indexed as

antenatal carecontraceptive usageequitymaternal health caresocioeconomic factorsunmet need for contraceptionYemen

Identifiers

PMID31431905
PMCPMC6686312

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.