Longitudinal models for the progression of disease portfolios in a nationwide chronic heart disease population

Abstract

Background and aim: With multimorbidity becoming increasingly prevalent in the ageing population, addressing the epidemiology and development of multimorbidity at a population level is needed. Individuals subject to chronic heart disease are widely multimorbid, and population-wide longitudinal studies on their chronic disease trajectories are few.

Methods: Disease trajectory networks of expected disease portfolio development and chronic condition prevalences were used to map sex and socioeconomic multimorbidity patterns among chronic heart disease patients. Our data source was all Danish individuals aged 18 years and older at some point in 1995-2015, consisting of 6,048,700 individuals. We used algorithmic diagnoses to obtain chronic disease diagnoses and included individuals who received a heart disease diagnosis. We utilized a general Markov framework considering combinations of chronic diagnoses as multimorbidity states. We analyzed the time until a possible new diagnosis, termed the diagnosis postponement time, in addition to transitions to new diagnoses. We modelled the postponement times by exponential models and transition probabilities by logistic regression models.

Findings: Among the cohort of 766,596 chronic heart disease diagnosed individuals, the prevalence of multimorbidity was 84.36% and 88.47% for males and females, respectively. We found sex-related differences within the chronic heart disease trajectories. Female trajectories were dominated by osteoporosis and male trajectories by cancer. We found sex important in developing most conditions, especially osteoporosis, chronic obstructive pulmonary disease and diabetes. A socioeconomic gradient was observed where diagnosis postponement time increases with educational attainment. Contrasts in disease portfolio development based on educational attainment were found for both sexes, with chronic obstructive pulmonary disease and diabetes more prevalent at lower education levels, compared to higher.

Conclusions: Disease trajectories of chronic heart disease diagnosed individuals are heavily complicated by multimorbidity. Therefore, it is essential to consider and study chronic heart disease, taking into account the individuals' entire disease portfolio.

Fig 1. Disease portfolio development.

Illustration of disease portfolio trajectories following heart disease diagnosis for three individuals.

Fig 2. Diagnosis counts by age for the population of Danish chronic heart disease diagnosed individuals in the period 1995–2015.

The figure shows males (left) and females (right). A diagnosis is counted when the individual obtains the algorithmic chronic disease diagnosis. The diagnoses are ordered, so diagnoses with larger variance in counts are on top.

Fig 3. Heatmap on importance of different variables on transition probabilities to distinct next diagnosis endpoints.

Results were obtained from a backwards selection of covariates where each unique disease portfolio was modelled separately for each next diagnosis endpoint. The importance of one indicates that the term was kept in all disease portfolios. The figure is sorted by sums of importance across endpoints (left to right) and across variables (top to bottom).

Fig 4. Effect of multimorbidity on postponement times.

Estimated mean postponement time of next chronic diagnosis given survival for retired males and females of no education at a set multimorbidity level (left). The mean postponement times are estimated as an average of estimated postponement times for all possible combinations of chronic conditions given the multimorbidity level, weighted by the observed frequency of these combinations. The blue shaded area represents the proportion of HD individuals at risk of a new event. For reference, the right plot represents the same estimates in a model where the new disease event and death are considered a combined event.

Fig 5. Sex-specific disease trajectories contrasting educational attainment.

Disease trajectories starting from the most common triad of diseases for males (left) and females (right) of no education (top) and long education (bottom). Trajectories are constructed for retired individuals with calendar time and age set at the mean levels at t = 0 (70.09 years of age and 2003.37 year time, respectively). The length of the arrows corresponds to the modelled diagnosis postponement time. The width of the arrows corresponds to the modelled probability of obtaining the diagnosis next.

Fig 6. Estimated mean postponement times by educational attainment levels.

The figure presents the mean postponement time of the next chronic diagnosis given survival for retired males (left) and females (right) at various educational levels. Estimations are made starting from the most common triad of diagnoses heart disease, hypertension, and high cholesterol (see Fig 5). The second stage postponement times are a weighted average with weights determined by the transition probabilities of the first stage diagnoses.