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Review

Determinants of diabetes care service utilization among older adults attending outpatient diabetes clinics in tertiary hospitals in Nigeria: A cross-sectional study.

Aug 2026 · Geriatric Nursing · Vol 73, pp. 104256 · 0 citations · 20 references
Medicine

Abstract

BACKGROUND/

Objectives

Older adults face a disproportionately high burden of diabetes mellitus (DM) and related complications, yet healthcare utilization in this demographic remains suboptimal in Nigeria. This study investigated the determinants of diabetes care service utilization (DCSU) among older adults attending outpatient diabetes clinics in Nigerian tertiary hospitals.

Methods

A hospital-based, cross-sectional survey was conducted from August 2024 to April 2025 in 12 tertiary hospitals across Nigeria. A total of 364 older adults (≥60 years) completed a validated questionnaire assessing sociodemographic characteristics and DCSU determinants. Descriptive statistics, chi-square tests, and odds ratio analyses were performed using SPSS version 22, with significance set at p < 0.05.

Results

Among respondents, 74.0% reported that various factors influenced DCSU. Key determinants included service availability (82.7%), suitability (81.0%), adequacy (79.9%), affordability (79.4%), and accessibility (77.7%). Significant associations were found between DCSU and gender (p = 0.02), education (p = 0.01), and religion (p = 0.02) whereas none existed with age (p = 0.83) or place of residence (p = 0.13). Odds ratio analysis indicated positive correlations with gender (OR = 5.88), education (OR = 1.44), and urban residence (OR = 1.73), and negative correlations with advanced age (OR = 0.69) and religious affiliations (OR = 0.86).

Conclusion

DCSU among older adults in Nigeria is shaped by both service-related and sociodemographic factors. Enhancing availability, affordability, and cultural appropriateness of care is essential. These findings can inform age-sensitive policy reforms and health system strengthening to improve diabetes outcomes in older populations across resource-constrained settings.

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