Insights into gender-equity in healthcare accessibility in northern Nigeria: descriptive and predictive approaches
Insights into gender-equity in healthcare accessibility in northern Nigeria: descriptive and predictive approaches. Authors: Chika Yinka-Banjo1, Olasupo Ajayi2, Mary Akinyemi3, David Tresner-Kirsch4, and Adekemi Omotubora5 Affiliation: 1 Department of Computer Sciences, University of La...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PAGEPress Publications
2025-07-01
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| Series: | Healthcare in Low-resource Settings |
| Subjects: | |
| Online Access: | https://www.pagepressjournals.org/hls/article/view/13433 |
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| Summary: | Insights into gender-equity in healthcare accessibility in northern Nigeria: descriptive and predictive approaches.
Authors:
Chika Yinka-Banjo1, Olasupo Ajayi2, Mary Akinyemi3, David Tresner-Kirsch4, and Adekemi Omotubora5
Affiliation:
1 Department of Computer Sciences, University of Lagos, Lagos 101017, Nigeria.
2 CAESAR Lab, Queen’s University, Kingston, ON K7L 3N6, Canada.
3 Department of Mathematics & Statistics, Austin Peay State University, Clarksville, TN 37044 USA.
4 Nivi, Inc., 40 Tall Pine Drive, Sudbury, MA 01776 USA and also Department of Computer Science, Brandeis University, 415 South Street, Waltham, MA 02453 USA.
5 Department of Commercial & Industrial Law, University of Lagos, Lagos 101017, Nigeria.
Corresponding author:
Olasupo Ajayi, CAESAR Lab, Department of Electrical and Computer Engineering, Queen’s University, Kingston, K7L 3N6, Ontario, Canada; olasupoajayi@gmail.com, o.ajayi@queensu.ca +1 437-421-8617
• Ethics approval and Consent to participate
Ethics Approval:
• College of Medicine, University of Lagos - Health Research Ethics Committee.
• CMULHREC Number: CMUL/HREC/11/22/1130
Participants Consent:
• Data were collected from respondents who gave their consent to participate. All respondents were presented with a first page on the Nivi app, which clearly stated that the data being collected was for research and analytic purposes and would remain completely anonymous without any form of tracking. All respondents were required to accept these terms and conditions before being allowed to participate in the data collection exercise.
• Data were collected using askNivi, an app specifically designed for healthcare related data collection. Privacy policy, consent, and related information about askNivi can be found at www.nivi.io/privacy-policy
• Availability of data and material
The datasets generated and/or analysed during this study can be found in the Mendeley repository, https://data.mendeley.com/datasets/8gbywtd7bv/2.
• Competing interests
NA
• Funding
United States Agency for International Development (USAID) and the DAI Global, under the ‘Nivi and the University of Lagos (UNILAG): Partnering to Create a Gender-Aware Auditing Tool’ project.
• Authors' contributions
Chika Yinka-Banjo - Principal Investigator, Conceptualization, Methodology.
Olasupo Ajayi - Investigation, Software, Writing – original draft
Mary Akinyemi - Co-investigator, Formal analysis, and validation
David Tresner-Kirsch - Resources, Data collection, Administration
Adekemi Omotubora - Writing reviewing & editing, Adherence to Ethics.
• Acknowledgements
The authors would like to acknowledge Nivi Inc. for the use of their askNivi application platform to administer the questionnaires, as well as the Artificial Intelligence and Robotics Lab (AiRoL) at the University of Lagos, for the use of their space for collaborative meetings.
Insights into gender-equity in healthcare accessibility in northern Nigeria: descriptive and predictive approaches
Chika Yinka-Banjo1, Olasupo Ajayi2, Mary Akinyemi3, David Tresner-Kirsch4, and Adekemi. Omotubora5
1Department of Computer Sciences, University of Lagos, Lagos 101017, Nigeria.
2CAESAR Lab, Queen’s University, Kingston, ON K7L 3N6, Canada.
3Department of Mathematics & Statistics, Austin Peay State University, Clarksville, TN 37044 USA.
4Nivi, Inc., 40 Tall Pine Drive, Sudbury, MA 01776 USA and Department of Computer Science, Brandeis University, 415 South Street, Waltham, MA 02453 USA.
5Department of Commercial & Industrial Law, University of Lagos, Lagos 101017, Nigeria.
Corresponding author: Olasupo Ajayi, o.ajayi@queensu.ca, +1 437-421-8617.
Abstract
Universal health coverage (UHC) aims at ensuring equitable access to healthcare to everyone, irrespective of gender, location, or financial status. Though considerable progress has been made in achieving UHC, a lot remains to be done in under-served, developing, and remote areas of the world. These under-served regions face immense challenges accessing healthcare services, including unavailability of basic medications, expensive / unaffordable drugs, socio-cultural and religious beliefs, and various forms of discrimination. Beyond these, women and girls are still severely disadvantaged in these regions, with child brides and teenage pregnancy being prevalent. This work considered regions in northern Nigeria and carried out analyses on data on healthcare accessibility, while considering individual characteristics (gender inclusive), socio-economic status, and healthcare equity. Descriptive analysis (using Pearson’s and Spearman’s correlation) and predictive analysis (using CATBoost, Random Forest, and Support Vector Machine) were done on the data. The descriptive analysis revealed that women with low income and education levels, and the elderly have a higher chance of accessing healthcare services compared to male and non-binary gender, while the predictive analysis revealed that, using machine learning models, it is possible to predict an individual’s accessibility to healthcare services with up to 81 % accuracy.
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| ISSN: | 2281-7824 |