The models and data presented here are consistent with the idea that the probability of recovery from major depressive episodes diminishes with increasing episode duration, as suggested by previous studies. Changes that occur over time as episodes unfold may be related to a diminished propensity for recovery. For example, neurotoxicity or a failure of neurogenesis, see reviews [27
] may lead to decreasing hippocampal cellular reserves as episodes get longer. In turn, this may be related to a diminishing propensity for recovery. Similarly, cognitive, behavioural and social changes that occur during depression may become more entrenched and habitual with increasing episode duration.
In more practical terms, these result are consistent with the idea that a large number of depressive episodes occurring in community populations resolve quickly. Since many episodes last only a few weeks, the results seem consistent with the idea that not all major depressive episodes require treatment, an idea endorsed by the NICE guidelines. This conclusion seems even more evident when one considers that treatment generally takes several weeks to be effective. However, a significant qualification applies to this finding. In these models, "prevalent" cases of depression were excluded at the baseline time point for each cycle. This was done so that the frequency of episodes at the end of a cycle would approximate incidence rather than prevalence. Projections beyond the two year time frame must be considered speculative by virtue of the modeling methods employed. However, the tendency of recovery rates to decline with increasing episode duration implies that over time there is likely to be an accumulation of chronic episodes in the population. As such, whereas new episodes appear often to be of brief duration this does not necessarily mean that episodes detected, for example, during screening in primary care are necessarily brief. These may be prevalent cases that have been undetected and may represent longstanding episodes that have accumulated in the population. This distinction may be an important one clinically. In one sense, the results presented here seem consistent with the idea that "watchful waiting" may be a reasonable strategy for mild depression, but they also imply that decisions about how long to wait should be made with reference not to the time of detection or clinical presentation, but rather with reference to the time of onset of an episode.
The fitted values from NPHS are presented in an electronic attachment to this paper, Additional File 2
. The study estimates of incidence and Weibull scale and shape are depicted in the form of a 14 day animation of the NPHS, visually depicting the epidemiology as described by the models. This particular animation incorporates the incidence and duration estimates from the 2000–2002 NPHS cycle. The animation illustrates that even though most new episodes are brief, there is an accumulation of longer episodes in the prevalence pool.
The concept of persistence is central to existing definitions of depressive episodes. The concept of duration is one of the ways in which these definitions attempt to distinguish between episodes that are or are not clinically relevant. The general idea of including persistence in diagnostic definitions receives support from these results in the sense that duration seems strongly related to prognosis, but as the probability of recovery appears to decline as a function of episode duration, the use of any particular time frame, such as the two week reference period used by DSM [29
] and ICD-10 [25
] seems inadequate. The output of the calculator indicates that an episode that has only lasted a few weeks is likely to be associated with a high rate of recovery in the following few weeks, whereas an episode that has lasted for many weeks has a low probability of recovery in the next few weeks. It may be more meaningful to treat episode duration as a dimensional quality at the time of presentation, incorporating epidemiological data into clinical judgments based on this quantity.
These results also have implications for screening in clinical settings. In primary care, active case-finding is often an element of disease management strategies, e.g. [30
], see also a review by Katon . These results suggest that early detection by screening may potentially result in identification of a subgroup with a much better prognosis, and perhaps with a sizable likelihood of recovery even if untreated. Screening measures should be carefully organized and monitored so that they do not cause a diversion of resources away from individuals with greater needs. Such a diversion will not necessarily occur, but could occur if screening resulted primarily in the detection of brief and self-limited episodes. According to the calculator, a person with a three week duration of symptoms has an approximately 40% chance of recovery in the next six weeks, whereas a person with a 23 week duration has less than a 5% chance over the same interval. The risks and benefits of depression screening in various clinical populations will depend not only on the basic features that are usually considered: the prevalence of depression in those populations, the sensitivity and specificity of the measures employed, but also on help-seeking behavior and health systems issues. While traditional screening assessments are based on symptom rating scales or subsets of related questions, items about symptom duration may be more critical than has previously been believed.
The very high probability of recovery in the early weeks of a major depressive episode, combined with the necessity for several weeks of treatment before a response is expected, may suggest that many people with short-lived symptoms do not need active treatment. It is possible, as noted above, that the reasons for the declining recovery rate as episodes get longer is a result of secondary effects of the depression itself. This possibility creates an argument for earlier intervention, rather than expectant management. Further research will be needed to resolve these questions.
The calculator presented in this paper helps to illustrate the importance of episode duration on the probability of recovery in the near future. With the development of more sophisticated episode duration models, it should be possible to develop calculators such as this one into more sophisticated decision-support tools. A step in this direction would involve incorporating other predictors of duration into the calculator's algorithms. However, such efforts need to account for the apparent relationship between episode duration and recovery probability and should not be restricted to estimating average frequencies of recovery or mean episode durations. Furthermore, in view of an apparent change in the probability of recovery over time, classical survival analysis models (particularly those requiring a constant hazard function) are probably not adequate for this task.