2.1 Challenge 1: untying individual health forecasting from
gradualist assumptions
A typical view of senescence is that individual health deteriorates with
increasing age. Such a process can be described by mixed-effects
regression methods through an intercept (i.e., baseline measure of
health) and a slope (i.e., rate of health change over time; DiPrete and
Eirich, 2006). Although these methods allow for individual variation in
baseline levels (i.e., random intercepts) and in the rate of change
(e.g., random slopes, quadratic terms, exponential functions), they
operate under the assumption that health changes smoothly and gradually
as individuals age. In doing so, forecasts from these methods do not
align with empirical evidence demonstrating associations between
cumulative disadvantage and late life outcomes, and this modeling
approach may not fully capture within-person variability in health (Fig
1, survey data). In particular, the smoothing of mean health
trajectories across social groups may not capture the effect of the
social environment on individual health and consequent aging because
this approach may underestimate the accumulation of poor health outcomes
in socially disadvantaged persons (Engelman and Jackson 2019; Fig 1,
mixed-effects).
In contrast to the gradualist assumptions of many models, empirical
evidence suggests that human health can show periods of stability,
slight deterioration, and then recovery from health insults (Bolano et
al., 2019; Gill et al., 2010; Guilley et al., 2008; Keown, 2003). As
life progresses through time, individuals may remain in the same health
status (i.e., stasis) or transition among health states before death.
That is, health does not decline gradually and homogeneously with no
reversals. This highlights a crucial aspect of human health dynamics
that accurate forecasting models must capture. In line with this,
Engelman and Jackson (2019) proposed a new approach to health history
forecasting by describing individual health trajectories as a punctuated
equilibrium pattern where individuals experience periods of long-term
stability interrupted by sudden changes in health status or mortality.
The authors argue that while gradual approaches appeal to an intuitive
reasoning about health change, such a modeling choice produces a mean
health change that is not representative of the changes experienced by
most of the individuals comprising the population.
One way to capture such health dynamics are multi-state models
(Namboodiri and Suchindran, 2013; Schoen, 2006) and sequence analysis
(Abbott and Tsay, 2000). These models describe discrete health states
and transition probabilities among these states, and more accurately
capture the health trajectories observed in real populations (Engelman
and Jackson, 2019). We also argue that multi-state models describing
changes across age as a Markov process may also provide a much-needed
reconciliation between deterministic and stochastic processes when
modeling individual health that would be otherwise obscured by the
smoothing of health trajectories in gradualist approaches (Figure 1,
multi-state).