Toanalyze the dynamic evolution laws of the quality of life status of the elderly in communities and its influencing factors, so as to provide a reference basis for improving the quality of life of the elderly.

Using the data of the Ma’anshan Healthy Aging Cohort, 2 495 community residents aged ≥ 60 years old in Ma’anshan City were selected. Taking the clustering status of the scores of the World Health Organization Quality of Life-BREF（WHOQOL-BREF）as the main outcome indicator, a multi-state Markov（MSM）model was used to analyze the conversion patterns of the three quality of life statuses, namely low, medium, and high.

For individuals with moderate quality of life, the estimated probabilities of deteriorating to low quality of life, remaining at moderate quality of life, and improving to high quality of life were 0.220, 0.648, and 0.132 respectively in the 1-year observation, and evolved to 0.405, 0.448, and 0.148 respectively within the 5-year observation. The results of the multivariable MSM model analysis revealed the key factors influencing the dynamic transition of the quality of life of the elderly. Age is a negative predictor of the improvement of quality of life. Compared with the 60-69-year-old group, the probability of transition from low quality of life to moderate quality of life in the 70-79-year-old group（HR=0.638, 95% CI:0.484-0.841）and the group aged 80 and above（HR=0.404, 95% CI:0.255-0.641）decreased successively. Conversely, a higher level of education was a significant promoting factor. Compared with the illiterate group, those with primary school/junior high school education（HR=1.467, 95% CI: 1.089-1.975）and those with senior high school/junior college or above education（HR=1.909, 95% CI:1.282-2.844）were more likely to achieve an improvement in quality of life. In addition, having multiple chronic diseases（≥3 types）had a dual negative impact on the quality of life. It reduced the possibility of improving the quality of life（for example, from moderate to high quality of life, HR=0.638, 95% CI:0.417-0.877）and also increased the risk of deteriorating the quality of life（for example, from high to moderate quality of life, HR=1.512, 95% CI:1.156-2.625）. Factors such as marital status, smoking, living alone, and physical exercise also had a significant impact on the transition of the quality of life status.

This study uses a multi-state model to reveal the dynamic evolution law of the quality of life of the elderly. The high-quality-of-life state is unstable, but the low-quality-of-life state is reversible. Prospective intervention strategies should be formulated for high-risk groups to delay the decline process of their quality of life.