Evidence for a Human Late Life Mortality Plateau is an Illusion Arising from Bad Data

Mortality rises with age. In fact the very definition of aging is that it is a rise in mortality rate due to intrinsic causes, the accumulation of unrepaired damage and subsequent systems failure. Some years ago it was quite robustly established that, after a certain point, aged flies stop aging in this sense. Their mortality rates remain at a very high plateau, and do not further increase over time. Since then, researchers have crunched the numbers and debated back and forth over whether or not human demographic data shows any signs of a similar phenomenon. The challenge is the sparse, poorly gardened nature of the demographic data for people who pass a century of age. The authors of the paper noted here argue that all of the past evidence for a human mortality plateau emerged precisely because the data is problematic, and that systemic issues with data quality will tend to produce this apparent result.

The age-specific probability of death follows diverse, often species-specific curves. In several species, including humans, rates of mortality increase with age have been observed flattening in advanced old age. In some cases, this late-life mortality deceleration (LLMD) is sufficient to cause a levelling off or plateau in the probability of death at advanced ages. LLMD and late-life mortality plateaus (LLMPs) have been proposed to cause the respective slowing or cessation of biological ageing at advanced ages and, respectively, increase and remove the upper limits of survival in humans.

These findings have led to continuing debate on the biological meaning, magnitude, and importance of LLMDs and LLMPs. Several hypotheses and models have been proposed to explain the observation of LLMPs and LLMDs in diverse taxa, such as population heterogeneity, density effects, and evolutionary theories. In parallel, these observations have led to the development and widespread use [of demographic models, such as the Kannisto old-age-mortality model, that assume a priori the existence of LLMPs.

However, there is evidence that LLMPs can result from diverse statistical errors, such as the pooling of human cohorts, choice of mortality rate metric or time interval, and missing death certification or age-reporting errors. Furthermore, in any species with finite upper limits of life, both random and nonrandom error distributions will necessarily favour the inclusion of younger individuals amongst the oldest survivable age categories, reducing the subsequent probability of death calculated for these ages. As a result, deformation of late-life mortality by biodemographic errors may provide a general explanation of LLMDs and LLMPs.

Therefore, understanding late-life mortality patterns requires consideration of the effect of age-coding errors and whether the late-life patterns of mortality rates in humans may represent combined outcomes of measurement and sampling errors. Here, it is revealed how diverse demographic errors deform the age-specific mortality curve and the hazard rate, causing LLMDs and LLMPs in the absence of other effects. In humans, the error rate of demographic sampling, completeness of birth and death records, and development and income indicators all predict the magnitude of LLMD. Correcting for these factors eliminates LLMDs and LLMPs, suggesting these patterns are caused by sampling and measurement error and not by biological or evolutionary factors.

Link: https://doi.org/10.1371/journal.pbio.2006776

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