Evidence map›Paper›PMID 39754089›Full record

ArticleBMC public health2025

Predictors of self-reported hypertension among women of reproductive age in North Dakota.

Corey A Day, Grace Njau, Matthew Schmidt, Agricola Odoi

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Corey A DayDepartment of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA.
Grace NjauDivision of Special Projects & Health Analytics, North Dakota Department of Health, Bismarck, ND, USA.
Matthew SchmidtDepartment of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA.
Agricola OdoiDepartment of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA. aodoi@utk.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnderstanding the risk factors of hypertension among women of reproductive age (18-44 years) is important for guiding health programs aimed at reducing the burden of hypertensive disorders in this population. Therefore, the objective of this study was to investigate predictors of self-reported hypertension among women of reproductive age in North Dakota.

methodsBehavioral Risk Factor Surveillance System data for the years 2017, 2019, and 2021 were obtained from North Dakota Department of Health and Human Services. A conceptual model was used to identify potential predictors of hypertension including sociodemographic characteristics, behavioral factors, chronic health conditions, and healthcare access. A multivariable binary logistic regression model was then used to identify significant predictors of hypertension. The predictive ability of the final model was assessed using a Receiver Operating Characteristic (ROC) curve and area under the curve (AUC).

resultsThe odds of hypertension were significantly higher among women of reproductive age who reported frequent mental distress (odds ratio [OR] = 2.0, 95% confidence interval [CI] = 1.3-3.3), resided in a primary care health professional shortage area (OR = 1.8, 95% CI = 1.2-2.6), were obese (OR = 2.6, 95% CI = 1.7-4.1) and were 35-44 years old (OR = 2.3, 95% CI = 1.6-3.4), relative to their counterparts who did not have frequent mental distress, did not reside in a health professional shortage area, had a normal body mass index, and were 18-34 years old, respectively. Additionally, the odds of hypertension were lower among women who did not have a checkup within the last year compared to those who did have a checkup within the last year (OR = 0.6, 95% CI = 0.4-0.9). The AUC of the final model was 0.68.

conclusionsThere is evidence that frequent mental distress and disparities in healthcare access or utilization are predictors of hypertension among women of reproductive age in North Dakota. Further research is warranted to determine whether improved mental health can reduce the risk of hypertension in this population. Public health officials may consider promoting hypertension awareness and control programs in areas with limited access to healthcare professionals.

Indexed as

HypertensionSelf ReportAdolescentAdultBehavioral Risk Factor Surveillance SystemFemaleHealth Services AccessibilityHumansNorth DakotaRisk FactorsYoung AdultBehavioral risk factor surveillance systemConceptual modelHypertensionImputationLogistic regressionNorth DakotaPredictorsReproductive age womenRisk factorsUnited States of AmericaUSA

Identifiers

PMID39754089
PMCPMC11697968

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Registered trials

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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.