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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 OpenAlex
Hanqi Luo, Chelsea Cole, Afrin Jahan, Janet M Peerson, Yi‐An Ko, O. Yaw Addo, Parminder Suchdev, Melissa Young

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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