Geo-behavioural predictors of diagnosed hypertension in Igbo Ora Area, Oyo State, Nigeria
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 hyperte...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-025-21653-3 |
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| Summary: | 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. |
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| ISSN: | 1471-2458 |