The premise of multipopulation mortality models is that pooling multiple populations can help identify more stable trends and diminish statistical noise. However, many existing models fail to contextualize mortality trends, treating them as isolated phenomena. This article introduces a comprehensive multipopulation mortality model that incorporates a broad spectrum of economic, environmental, and lifestyle factors to predict mortality trends. The factors are obtained with principal components analysis, extending current models which employ external variables beyond GDP. The model is applied to 33 countries present in the Human Mortality Database, divided into 9 clusters. Expanding the scope of covariates improves model fit for 29 countries out of 33 compared to GDP alone, and consistently outperforms the Li-Lee model. Furthermore, forecasting accuracy surpasses that of the Li-Lee model across various jump-off years and matches or exceeds models limited to GDP as a covariate. This study advances the field by demonstrating that a multipopulation approach, enriched with a wide array of covariates, significantly refines mortality forecasts, challenging the reliance on extrapolative or GDP-only models. It offers actuarial practitioners and policymakers a more nuanced tool for scenario planning, emphasizing the interconnectedness of mortality rates with broader socio-economic and environmental factors.
