Evidence map›Paper›PMID 40022246›Full record

ArticleJournal of health, population, and nutrition2025

Geo-demographic and socioeconomic determinants of diagnosed hypertension among urban dwellers in Ibadan, Nigeria: a community-based study.

Olalekan J Taiwo, Joshua O Akinyemi, Ayodeji Adebayo, Oluwafemi A Popoola, Rufus O Akinyemi, Onoja M Akpa, Paul Olowoyo, Akinkunmi P Okekunle, Ezinne O Uvere, Chukwuemeka Nwimo and 10 more

Abstract read
In one paragraph

Article in Journal of health, population, and nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Olalekan J TaiwoDepartment of Geography, Faculty of the Social Sciences, University of Ibadan, Ibadan, Nigeria.
Joshua O AkinyemiDepartment of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Ayodeji AdebayoDepartment of Community Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Oluwafemi A PopoolaDepartment of Community Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Rufus O AkinyemiNeuroscience and Ageing Research Unit, College of Medicine, IAMRAT, University of Ibadan, Ibadan, Nigeria.
Onoja M AkpaDepartment of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Paul OlowoyoDepartment of Medicine, Afe Babalola University, Ado Ekiti, Nigeria.
Akinkunmi P OkekunleDepartment of Medicine, College of Medicine, University of Ibadan, and University College Hospital, Ibadan, Nigeria.
Ezinne O UvereCenter for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Chukwuemeka NwimoDepartment of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Omotolani Titilayo AjalaCenter for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Olayinka AdebajoCenter for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Adewale E AyodeleCenter for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Ayodeji SalamiDepartment of Pathology, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Oyedunni S ArulogunDepartment of Health Promotion and Education, College of Medicine, University of Ibadan, Ibadan, Nigeria.
Olanrewaju OlaniyanDepartment of Economics, Faculty of Social Sciences, University of Ibadan, Ibadan, Nigeria.
Richard W WalkerPopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
Carolyn JenkinsCollege of Nursing, Medical University of South Carolina, Charleston, USA.
Bruce OvbiageleNorthern California Institute for Research and Education, University of California, San- Francisco, USA. Bruce.Ovbiagele@va.gov.
Mayowa OwolabiCenter for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria. mayowaowolabi@yahoo.com.

Funding

African Rigorous Innovative Stroke Epidemiological Surveillance (ARISES)R01NS115944 · NINDS · COLLEGE OF MEDICINE, UNIVERSITY OF IBADAN · PI OVBIAGELE, BRUCE, OWOLABI, MAYOWA OJO · 2020 to 2025
$2.2M
National Institutes of Health grants supported the study and investigators through the ARISES R01NS115944-01NINDS NIH HHS R01 NS115944
6 · The paper itself

Abstract

backgroundThe relationship between diagnosed high blood pressure (HBP) and proximity to health facilities and noise sources is poorly understood. We investigated the associations between the number of persons diagnosed with HBP at different distance corridors of noise-generating sources (churches, mosques, bus stops, and road networks), and blood pressure monitoring outlets (healthcare facilities and pharmaceutical shops) in Ibadan, Nigeria. In addition, we investigated the likelihood of being diagnosed with HBP using distance from noise-generating sources, distance to blood pressure monitoring outlets, socio-demographic and clinical status of the participants.

methodsWe investigated 13,531 adults from the African Rigorous Innovative Stroke Epidemiological Surveillance (ARISES) study in Ibadan. Using a Geographic Information System (GIS), the locations of healthcare facilities, pharmaceutical shops, bus stops, churches, and mosques were buffered at 100 m intervals, and coordinates of persons diagnosed with HBP were overlaid on the buffered features. The number of persons with diagnosed HBP living at every 100 m interval was estimated. Gender, occupation, marital status, educational status, type of housing, age, and income were used as predictor variables. Analysis was conducted using Spearman rank correlation and binary logistic regression at p < 0.05.

resultsThere was a significant inverse relationship between the number of persons diagnosed with HBP and distance from pharmaceutical shops (r=-0.818), churches (r=-0.818), mosques (r=-0.893) and major roads (r= -0.667). The odds of HBP were higher among the unemployed (AOR = 1.58, 95% CI: 1.11-2.24), currently married (AOR = 1.45, CI: 1.11-1.89), and previously married (1.75, CI: 1.29-2.38). The odds of diagnosed HBP increased with educational level and age group.

conclusionProximity to noise sources, being unemployed and educational level were associated with diagnosed HBP. Reduction in noise generation, transmission, and exposure could reduce the burden of hypertension in urban settings.

Indexed as

HypertensionUrban PopulationAdultAgedFemaleGeographic Information SystemsHealth Services AccessibilityHumansMaleMiddle AgedNigeriaRisk FactorsSociodemographic FactorsSocioeconomic FactorsYoung AdultFaith-based organisationsHigh blood pressureIbadanNoiseSpatial analysis

Identifiers

PMID40022246
PMCPMC11871807

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.