Relative contributions of geographic, socioeconomic, and lifestyle factors to quality of life, frailty, and mortality in elderly

Abstract

Background: To date, few studies address disparities in older populations specifically using frailty as one of the health outcomes and examining the relative contributions of individual and environmental factors to health outcomes.

Methodology/principal findings: Using a data set from a health survey of 4,000 people aged 65 years and over living in all regions of Hong Kong, we examined regional variations in self-rated health, frailty, and four-year mortality, and analyzed the relative contributions of lifestyle, socioeconomic status, and geographical location of residence to these outcomes using path analysis. We hypothesize that lifestyle, socioeconomic status, and regional characteristics directly and indirectly through interactions contribute to self-rated physical and psychological health, frailty, and four-year mortality. District variations directly affect self-rated physical health, and also exert an effect through socioeconomic position as well as lifestyle factors. Socioeconomic position in turn directly affects self-rated physical health, as well as indirectly through lifestyle factors. A similar pattern of interaction is observed for self-rated mental health, frailty, and mortality, although there are differences in different lifestyle factors and district associations. Lifestyle factors also directly affect physical and mental components of health, frailty, and mortality. The magnitude of direct district effect is comparable to those of lifestyle and socioeconomic position.

Conclusions/significance: We conclude that district variations in health outcomes exist in the Hong Kong elderly population, and these variations result directly from district factors, and are also indirectly mediated through socioeconomic position as well as lifestyle. Provision and accessibility to health services are unlikely to play a significant role. Future studies on these district factors would be important in reducing health disparities in the older population.

Figure 1. Recruitment flow chart.

Figure 2. Risk variation in self-rated physical health (SF-12 physical) across districts.

Figure 3. Risk variation in self-rated mental health (SF-12 mental) across districts.

Figure 4. Risk variation in frailty (Log Frailty Index) across districts.

Figure 5. Risk variation in mortality across districts.

Figure 6. Path analysis model of SF12-physical.

Figure 7. Path analysis model of SF12-mental.

Figure 8. Path analysis model of frailty index (log transformed).

Figure 9. Path analysis model of Death.