Evidence map›Paper›PMID 23448548›Full record

ArticlePopulation health metrics2013

Association of blood lipids, creatinine, albumin, and CRP with socioeconomic status in Malawi.

Iliana V Kohler, Beth J Soldo, Philip Anglewicz, Ben Chilima, Hans-Peter Kohler

Open access · goldAbstract read
In one paragraph

Article in Population health metrics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.0field-weighted citation impact, top 22% of its field
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

7 citing papers in PubMed, 12 citations in OpenAlex.

  1. Trial
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  3. Three common assumptions about inflammation, aging, and health that are probably wrong.Proceedings of the National Academy of Sciences of the United States of America · 2023
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  5. Article
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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 at 3 institutions in 2 countries.

Iliana V KohlerPopulation Studies Center, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA 19104, USA. iliana@pop.upenn.edu.
Beth J Soldo
Philip Anglewicz
Ben Chilima
Hans-Peter Kohler
University City Science Center · USMinistry of Health · MWTulane University · US

Funding

STATISTICAL DATA ENCLAVE COREP30AG012836 · NIA · UNIVERSITY OF PENNSYLVANIA · PI KOHLER, HANS-PETER · 1999 to 2024
$14.0M
Population Science Research Center Development CoreR24HD044964 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI SMITH, HERBERT L. · 2003 to 2017
$9.8M
Sci-Tech Core-Population Dynamics Research CenterP2CHD044964 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI EMILIO ALBERTO PARRADO · 2018 to 2026
$5.3M
NIA NIH HHS P30 AG012836NICHD NIH HHS P2C HD044964NICHD NIH HHS R24 HD044964
6 · The paper itself

Abstract

backgroundThe objective of these analyses is to document the relationship between biomarker-based indicators of health and socioeconomic status (SES) in a low-income African population where the cumulative effects of exposure to multiple stressors on physiological functions and health in general are expected to be highly detrimental for the well-being of individuals.

methodsBiomarkers were collected subsequent to the 2008 round of the Malawi Longitudinal Study of Families and Health (MLSFH), a population-based study in rural Malawi, including blood lipids (total cholesterol, LDL, HDL, ratio of total cholesterol to HDL), biomarkers of renal and liver organ function (albumin and creatinine) and wide-range C-reactive protein (CRP) as a non-specific biomarker for inflammation. These biomarkers represent widely used indicators of health that are individually or cumulatively recognized as risk factors for age-related diseases among prime-aged and elderly individuals. Quantile regressions are used to estimate the age-gradient and the within-day variation of each biomarker distribution. Differences in biomarker levels by socioeconomic status are investigated using descriptive and multivariate statistics.

resultsOverall, the number of significant associations between the biomarkers and socioeconomic measures is very modest. None of the biomarkers significantly varies with schooling. Except for CRP where being married is weakly associated with lower risk of having an elevated CRP level, marriage is not associated with the biomarkers measured in the MLSFH. Similarly, being Muslim is associated with a lower risk of having elevated CRP but otherwise religion does not predict being in the high-risk quartiles of any of the MLSFH biomarkers. Wealth does not predict being in the high-risk quartile of any of the MLSFH biomarkers, with the exception of a weak effect on creatinine. Being overweight or obese is associated with increased likelihood of being in the high-risk quartile for cholesterol, Chol/HDL ratio, and LDL.

conclusionsThe results provide only weak evidence for variation of the biomarkers by socioeconomic indicators in a poor Malawian context. Our findings underscore the need for further research to understand the determinants of health outcomes in a poor low-income context such as rural Malawi.

Identifiers

PMID23448548
PMCPMC3701600
OpenAlexW2133511405

What OpenQuestion holds

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LicenceCC BY
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.