Determining frailty index thresholds for older people across multiple countries in sub-Saharan Africa

Abstract

Background: Despite the increasing attention on frailty as a global public health concern, frailty screening among older people in Sub-Saharan Africa (SSA) continues to rely on instruments and thresholds from high-income countries. These instruments and thresholds may not be useful in SSA due to contextual differences. We explored the development of a frailty threshold for older people in SSA.

Methods: We utilized pooled cross-sectional data from four SSA countries (Kenya, Ghana, Uganda and Côte d'Ivoire) to determine a frailty index threshold for 5527 older people (50 years and above) using a two-step approach. The mean ages of the participants ranged from 62.13 (SD: 9.60) to 74.00 (SD: 9.40) years. The participants were mostly females across the four countries, ranging from 50.1% in Côte d'Ivoire to 65.3% in Kenya. Country-specific frailty thresholds were developed using the Receiver Operating Characteristics (ROC) method. The primary thresholds were further combined into a single threshold using random effects meta-analysis. Subgroup analyses and meta-regression were conducted to explore potential sources of heterogeneity in the pooled frailty threshold.

Results: Here we show the Area Under the Curves from the ROC analyses ranging between 0.91 (CI: 0.89, 0.93) and 0.94 (CI: 0.92, 0.97), with sensitivities ranging from 0.83 to 0.94 and specificities from 0.72 to 0.87. An overall threshold of 0.29 (95% CI: 0.25, 0.33) was obtained after pooled analysis of the country-specific thresholds.

Conclusions: This work demonstrates that using context-specific data can yield valuable insights into frailty thresholds among older people in SSA, enabling more culturally relevant interventions. Effective frailty screening must account for population-level differences, including demographic, health, and socio-cultural factors.

Fig. 1. Receiver operating characteristic (ROC) curves for the frailty index across four sub-Saharan African countries.

Panels represent ROC curves for: a Kenya, b Ghana, c Uganda, and d Côte d’Ivoire. ROC receiver operating characteristic, AUC area under the curve. ROC curves illustrate the diagnostic ability of the frailty index to distinguish between frail and non-frail individuals across different thresholds. The Y-axis represents sensitivity (true positive rate), and the X-axis represents 1–specificity (false positive rate). The closer the curve is to the top-left corner, the better the discriminative ability of the index.

Fig. 2. Forest plot of pooled estimate of optimal frailty index thresholds across four Sub-Saharan African countries.

Effect sizes represent the optimal frailty index thresholds identified through ROC analysis in each country. Boxes represent the point estimates with their 95% confidence intervals; the size of the box reflects the study weight. The diamond indicates the pooled effect size with corresponding 95% CI, calculated using a random-effects REML model. Heterogeneity statistics include τ² (between-study variance), I² (percentage of variation due to heterogeneity), and H² (relative excess in Q over degrees of freedom).

Fig. 3. Forest plot showing subgroup analysis of optimal frailty index thresholds across four Sub-Saharan African countries, grouped by region (West vs. East Africa).

Effect sizes are optimal frailty index thresholds. Squares show point estimates with 95% CIs; diamonds indicate pooled estimates. Results are based on a random-effects REML model. τ², I², and Q test values reflect heterogeneity within and between subgroups.

Fig. 4. Forest plot of frailty thresholds by high and low threshold subgroups.

Effect sizes represent optimal frailty index thresholds. Squares indicate point estimates with 95% confidence intervals; diamonds show pooled estimates. Estimates are based on a random-effects REML model. τ², I², and Q statistics describe within- and between-group heterogeneity.