Clinical, social, and occupational determinants of severe preeclampsia: a multifactorial case–control study on maternal health inequities in Peru

Severe preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality, particularly in settings with limited access to quality antenatal care. In Peru, structural inequalities, physically demanding informal labor, and psychosocial stressors may exacerbate the risk of severe preeclampsia; however, local evidence integrating clinical and social determinants is scarce. An unmatched case–control study was conducted at a referral hospital in Ica, Peru, from 2018 to 2023. A total of 720 pregnant women were included: 237 cases with severe preeclampsia and 483 normotensive controls. Data were obtained exclusively from clinical records. Variables were grouped into six determinant domains (sociodemographic, occupational, antenatal access, obstetric history, nutritional status, and psychosocial factors), while perinatal outcomes were analyzed as secondary descriptive measures. Multivariable logistic regression was applied to identify independent factors associated with severe preeclampsia, focusing on etiological determinants rather than clinical prediction. Severe preeclampsia was independently associated with a prior history of preeclampsia (adjusted OR = 5.40; 95% CI: 2.70–10.80), chronic hypertension (OR = 2.35; 95% CI: 1.45–3.82), obesity (OR = 2.05; 95% CI: 1.43–2.95), nulliparity (OR = 1.78; 95% CI: 1.28–2.47), fewer than four antenatal visits (OR = 1.69; 95% CI: 1.23–2.33), high physical workload (OR = 1.41; 95% CI: 1.01–1.96), psychosocial stress (OR = 1.83; 95% CI: 1.32–2.54), and family history of hypertension (OR = 1.47; 95% CI: 1.01–2.14). The regression model showed acceptable overall fit (Hosmer–Lemeshow p = 0.42). This study underscores the multifactorial nature of severe preeclampsia by integrating clinical, occupational, and psychosocial factors associated with the condition. The findings emphasize etiologically relevant associated factors rather than prediction, supporting their relevance for antenatal screening in contexts characterized by labor informality and limited obstetric coverage. These results support socially responsive preventive strategies emphasizing early and comprehensive prenatal care for vulnerable populations.

Authors

Institutions

Publication Details

Journal
BMC Pregnancy and Childbirth
Published
2026-01-21
DOI
https://doi.org/10.1186/s12884-026-08653-w
Primary Topic
Pregnancy and preeclampsia studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical, social, and occupational determinants of severe preeclampsia: a multifactorial case–control study on maternal health inequities in Peru

Víctor Hugo Barrientos-Ramos, Fermín Cáceres-Bellido, Luciana Ybaseta-Soto, Noemí Flores-Hernández et al.
BMC Pregnancy and Childbirth
Pregnancy and preeclampsia studies
article

Clinical, social, and occupational determinants of severe preeclampsia: a multifactorial case–control study on maternal health inequities in Peru

Víctor Hugo Barrientos-Ramos, Fermín Cáceres-Bellido, Luciana Ybaseta-Soto, Noemí Flores-Hernández, Luis Curotto-Palomino, Jorge Ybaseta-Medina, Roberto Munive-Bendezú, Nicolasa Meza-León
article en

Abstract

Severe preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality, particularly in settings with limited access to quality antenatal care. In Peru, structural inequalities, physically demanding informal labor, and psychosocial stressors may exacerbate the risk of severe preeclampsia; however, local evidence integrating clinical and social determinants is scarce. An unmatched case–control study was conducted at a referral hospital in Ica, Peru, from 2018 to 2023. A total of 720 pregnant women were included: 237 cases with severe preeclampsia and 483 normotensive controls. Data were obtained exclusively from clinical records. Variables were grouped into six determinant domains (sociodemographic, occupational, antenatal access, obstetric history, nutritional status, and psychosocial factors), while perinatal outcomes were analyzed as secondary descriptive measures. Multivariable logistic regression was applied to identify independent factors associated with severe preeclampsia, focusing on etiological determinants rather than clinical prediction. Severe preeclampsia was independently associated with a prior history of preeclampsia (adjusted OR = 5.40; 95% CI: 2.70–10.80), chronic hypertension (OR = 2.35; 95% CI: 1.45–3.82), obesity (OR = 2.05; 95% CI: 1.43–2.95), nulliparity (OR = 1.78; 95% CI: 1.28–2.47), fewer than four antenatal visits (OR = 1.69; 95% CI: 1.23–2.33), high physical workload (OR = 1.41; 95% CI: 1.01–1.96), psychosocial stress (OR = 1.83; 95% CI: 1.32–2.54), and family history of hypertension (OR = 1.47; 95% CI: 1.01–2.14). The regression model showed acceptable overall fit (Hosmer–Lemeshow p = 0.42). This study underscores the multifactorial nature of severe preeclampsia by integrating clinical, occupational, and psychosocial factors associated with the condition. The findings emphasize etiologically relevant associated factors rather than prediction, supporting their relevance for antenatal screening in contexts characterized by labor informality and limited obstetric coverage. These results support socially responsive preventive strategies emphasizing early and comprehensive prenatal care for vulnerable populations.

BMC Pregnancy and ChildbirthVol. 26(1)
Saint Aloysius Gonzaga National University (PE)
Openalex Percentile: Top 83%
Pregnancy and preeclampsia studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.