Multistate Markov model application for blood pressure transition among the Chinese elderly population: a quantitative longitudinal study

Abstract

ObjectiveTo explore the transitions of different blood pressure states based on a multistate Markov model among the Chinese elderly population.SettingA community health centre in Xiamen, China.Participants1833 elderly Chinese people.MethodsA multistate Markov model was built based on 5001 blood pressure measurements from 2015 to 2020. Research was conducted to explore the process of hypertension progression, providing information on the transition probability, HR and the mean sojourn time in three blood pressure states, namely normal state, elevated state and hypertensive state.ResultsProbabilities of moving from the normal state to the hypertensive state in the first year were 16.97% (female) and 21.73% (male); they increased dramatically to 47.31% (female) and 51.70% (male) within a 3-year follow-up period. The sojourn time in the normal state was 1.5±0.08 years. Elderly women in the normal state had a 16.97%, 33.30% and 47.31% chance of progressing to hypertension within 1, 2 and 3 years, respectively. The corresponding probabilities for elderly men were 21.73%, 38.56% and 51.70%, respectively. For elderly women starting in the elevated state, the probabilities of developing hypertension were 25.07%, 43.03% and 56.32% in the next 1, 2 and 3 years, respectively; while the corresponding changes for elderly men were 20.96%, 37.65% and 50.86%. Increasing age, body mass index (BMI) and glucose were associated with the probability of developing hypertension from the normal state or elevated state.ConclusionsPreventive actions against progression to hypertension should be conducted at an early stage. More awareness should be paid to elderly women with elevated state and elderly men with normal state. Increasing age, BMI and glucose were critical risk factors for developing hypertension. The derived transition probabilities and sojourn time can serve as a significant reference for making targeted interventions for hypertension progression among the Chinese elderly population.

Non‐homogeneous continuous‐time Markov chain with covariates: Applications to ambulatory hypertension monitoring

“…Most applications of the CTMC in analyzing longitudinal discrete outcomes are limited to stationary models. Several studies used Markov models with stationary transition rates to investigate transition patterns in blood pressure states 28‐30 . However, the stationary rate assumption of the CTMC may not hold true for many applications where the actual transition rate changes over time, including the rate of change for blood pressure 31,32 .…”

“…Several studies used Markov models with stationary transition rates to investigate transition patterns in blood pressure states. [28][29][30] However, the stationary rate assumption of the CTMC may not hold true for many applications where the actual transition rate changes over time, including the rate of change for blood pressure. 31,32 In addition, several covariates are known to affect the status of hypertension and potentially modify the rate at which blood pressure increases or decreases.…”

Investigation of multiple nosocomial infections using a semi-Markov multi- state model

“…The Markov MSM, proposed by Soviet mathematician Markov between 1906 and 1912, is widely used in studying epidemics and chronic diseases (18). According to the assumption of the Markov MSM, future transitions are only influenced by the current state, not by previous history; in other words, there is no aftereffect.…”