Geo-behavioural predictors of diagnosed hypertension in Igbo Ora Area, Oyo State, Nigeria

Olalekan J Taiwo¹, Joshua O Akinyemi², Ayodeji Adebayo³, Oluwafemi A Popoola³, Rufus O Akinyemi^{4

5

6}, Onoja M Akpa², Paul Olowoyo^{7

8}, Akinkunmi P Okekunle⁶, Ezinne O Uvere^{5

6}, Omotolani Titilayo Ajala^{5

6}, Chukwuemeka Nwimo⁵, Olayinka J Adebajo^{5

6}, Adewale E Ayodele^{5

6}, Ayodeji Salami⁹, Oyedunni S Arulogun¹⁰, Olanrewaju Olaniyan¹¹, Richard W Walker¹², Carolyn Jenkins¹³, Bruce Ovbiagele¹⁴, Mayowa Owolabi^{15

16

17

18}

Affiliations

¹ Department of Geography, Faculty of the Social Sciences, University of Ibadan, Ibadan, Oyo State, Nigeria.
² Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
³ Department of Community Medicine, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
⁴ Neuroscience and Ageing Research Unit, IAMRAT, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
⁵ Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
⁶ Department of Medicine, College of Medicine, University of Ibadan, and University College Hospital, Ibadan, Oyo State, Nigeria.
⁷ Department of Medicine, Afe Babalola University, Ado Ekiti, Nigeria.
⁸ Federal Teaching Hospital, Ido-Ekiti, Ekiti State, Nigeria.
⁹ Department of Pathology, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
¹⁰ Department of Health Promotion and Education, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
¹¹ Department of Economics, Faculty of Social Sciences, University of Ibadan, Ibadan, Oyo State, Nigeria.
¹² Population Health Sciences Institute, Newcastle University, Newcastle, UK.
¹³ College of Nursing, Medical University of South Carolina, Charleston, USA.
¹⁴ Northern California Institute for Research and Education, University of California, San Francisco, USA.
¹⁵ Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria. mayowaowolabi@yahoo.com.
¹⁶ Department of Medicine, College of Medicine, University of Ibadan, and University College Hospital, Ibadan, Oyo State, Nigeria. mayowaowolabi@yahoo.com.
¹⁷ Lebanese American University of Beirut, Beirut, Lebanon. mayowaowolabi@yahoo.com.
¹⁸ Blossom Specialist Medical Center, Ibadan, Oyo State, Nigeria. mayowaowolabi@yahoo.com.

PMID: 39910619
PMCID: PMC11796143
DOI: 10.1186/s12889-025-21653-3

Geo-behavioural predictors of diagnosed hypertension in Igbo Ora Area, Oyo State, Nigeria

Olalekan J Taiwo et al. BMC Public Health. 2025.

. 2025 Feb 5;25(1):461.

doi: 10.1186/s12889-025-21653-3.

Authors

Affiliations

¹ Department of Geography, Faculty of the Social Sciences, University of Ibadan, Ibadan, Oyo State, Nigeria.
² Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
³ Department of Community Medicine, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
⁴ Neuroscience and Ageing Research Unit, IAMRAT, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
⁵ Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
⁶ Department of Medicine, College of Medicine, University of Ibadan, and University College Hospital, Ibadan, Oyo State, Nigeria.
⁷ Department of Medicine, Afe Babalola University, Ado Ekiti, Nigeria.
⁸ Federal Teaching Hospital, Ido-Ekiti, Ekiti State, Nigeria.
⁹ Department of Pathology, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
¹⁰ Department of Health Promotion and Education, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria.
¹¹ Department of Economics, Faculty of Social Sciences, University of Ibadan, Ibadan, Oyo State, Nigeria.
¹² Population Health Sciences Institute, Newcastle University, Newcastle, UK.
¹³ College of Nursing, Medical University of South Carolina, Charleston, USA.
¹⁴ Northern California Institute for Research and Education, University of California, San Francisco, USA.
¹⁵ Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria. mayowaowolabi@yahoo.com.
¹⁶ Department of Medicine, College of Medicine, University of Ibadan, and University College Hospital, Ibadan, Oyo State, Nigeria. mayowaowolabi@yahoo.com.
¹⁷ Lebanese American University of Beirut, Beirut, Lebanon. mayowaowolabi@yahoo.com.
¹⁸ Blossom Specialist Medical Center, Ibadan, Oyo State, Nigeria. mayowaowolabi@yahoo.com.

PMID: 39910619
PMCID: PMC11796143
DOI: 10.1186/s12889-025-21653-3

Abstract

Diagnosed hypertension stands out as a prominent global cause of mortality, prompting recent efforts to understand not only treatment options but also determinants across diverse age and occupational groups. However, the literature on the impact of environmental factors on diagnosed hypertension is limited, especially in rural areas with restricted access to health infrastructure. Geographical determinants research has often focused on spatial variations across different units, potentially masking individual environmental contributions. Data on diagnosed hypertension patients and their behaviours were gathered during the ARISE project, complemented by geographical data (elevation, vegetation, road network, population density, and nighttime light exposure) from secondary sources. Spatial patterns were analyzed using the Nearest Neighbour Statistic, Ripley K Function, and Kernel Density Estimation, while Binomial logistic regression identified predictors. Diagnosed hypertension patients exhibit spatial clustering, and are mainly comprised of elderly individuals, residing closer to roads, at higher elevations, in areas with higher population distribution, and with little or no green vegetation. Socio-economic, health-related, behavioural, and environmental factors collectively drive diagnosed hypertension. Spatial clustering of diagnosed hypertension in the Igbo Ora community is localized, indicating potential spatial factors influencing its prevalence. Beyond identified behavioural and medical history factors, geographical elements like nighttime light exposure and normalized vegetation index contribute to the observed clustering. Understanding these dynamics is crucial for targeted interventions in the community.

Keywords: Diagnosed hypertension; Nighttime light; Normalised difference vegetation index.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethics approval was sought for the research before starting. Also, this article does not contain any studies with human participants or animals performed by any of the authors. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

**Fig. 1**
Distribution of self-reported high blood pressure patients in Igbo-Ora and Idere Communities

**Fig. 2**
Ripley-K function of persons with high blood pressure

**Fig. 3**
Kernel density estimation of high blood pressure in Igbo-Ora and Idere Communities, Oyo State, Nigeria

See this image and copyright information in PMC

References

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Geo-behavioural predictors of diagnosed hypertension in Igbo Ora Area, Oyo State, Nigeria

Affiliations

Geo-behavioural predictors of diagnosed hypertension in Igbo Ora Area, Oyo State, Nigeria

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical