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Record W4282842146 · doi:10.1093/cdn/nzac067.044

Prevalence and Predictors of Inflammation in Pregnant Women: Multi-Country Analysis From BRINDA Project

2022· article· en· W4282842146 on OpenAlexaff
Hanqi Luo, Chelsea Cole, Afrin Jahan, Janet M Peerson, Yi‐An Ko, O. Yaw Addo, Parminder Suchdev, Melissa Young

Bibliographic record

VenueCurrent Developments in Nutrition · 2022
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsNutrition International
Fundersnot available
KeywordsPregnancyMedicineInflammationSocioeconomic statusSubclinical infectionGestational ageBiomarkerC-reactive proteinInternal medicinePhysiologyEnvironmental healthPopulationBiology

Abstract

fetched live from OpenAlex

Limited data exist on the prevalence and predictors of inflammation during pregnancy. We aimed to characterize the inflammatory pattern and predictors of subclinical inflammation across pregnancy using multi-country analysis. The Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project compiled 17 datasets of pregnant women (n = 14,077) from 15 countries in both high- and low-income settings. Datasets were included if at least one inflammation biomarker (C-reactive protein, CRP, or α-1-acid glycoprotein, AGP) were collected. We estimated the prevalence of any subclinical inflammation (defined as CRP >5 mg/L or AGP >1 g/L), examined AGP and CRP patterns throughout pregnancy, and assessed the relationship between inflammation and covariates such as maternal age, gestational age, socioeconomic, and water and sanitation factors for each dataset. The prevalence of inflammation varied from 16.6% in Afghanistan to < 1% in Vietnam using elevated AGP and from 52.9% in the US to 7.6% in Vietnam using elevated CRP. Inflammation was common but varied across datasets: >40% in 5 datasets, 20–40% in 6 datasets, 10−< 20% in 5 datasets and < 10% in one dataset. AGP decreased with increasing gestational age (P < 0.01 in all seven datasets with gestational age information); however, the magnitude of decrease in AGP varied by country. In contrast, CRP showed an inconsistent pattern by gestational age. In multivariable models, the predictors of inflammation included age, trimester, urban or rural residence, socioeconomic status, improved sanitation, improved drinking water, lactating and smoking status, although strengths of association differed by dataset. Although there was considerable heterogeneity in the prevalence of inflammation, inflammation was common across pregnancy in diverse settings. AGP, a measure of long-term inflammation, decreased across pregnancy in all countries, whereas the pattern for CRP was inconsistent. The relationship between socioeconomic and health factors and inflammation varied across countries. Bill & Melinda Gates Foundation, Centers for Disease Control and Prevention, Eunice Kennedy Shriver National Institute of Child Health and Human Development, HarvestPlus, and the United States Agency for International Development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.291
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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