Modeling Human Mortality from All Diseases in the Five Most Populated Countries of the European Union

Abstract

Age affects mortality from diseases differently than it affects mortality from external causes, such as accidents. Exclusion of the latter leads to the "all-diseases" category. The age trajectories of mortality from all diseases are studied in the five most populated countries of the EU, and the shape of these 156 age trajectories is investigated in detail. The arithmetic mean of ages where mortality reaches a minimal value is 8.47 years with a 95% confidence interval of [8.08, 8.85] years. Two simple deterministic models fit the age trajectories on the two sides of the mortality minimum. The inverse relationship is valid in all cases prior to this mortality minimum and death rates exactly decreased to three thousandths of its original size during the first 3000 days. After the mortality minimum, the standard Gompertz model fits the data in 63 cases, and the Gompertz model extended by a small quadratic element fits the remaining 93 cases. This analysis indicates that the exponential increase begins before the age of 15 years and that it is overshadowed by non-biological causes. Therefore, the existence of a mechanism switching that would explain the exponential increase in mortality after the age of 35 years is unlikely.

Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 14 European Countries

“…The unit corresponding to the mortality rate is "person-years, " which means the number of years lived by members of the population between ages Ai and Bi. Uncertainty or possible demographic error of Li were discussed in the previous studies in detail (20)(21)(22)(23) and they were assumed to be negligible with the respect to resulting ATM. ATM were assumed to be unknown theoretical curves, and were constructed using the right side of the Equation (1).…”

“…It is usually interpreted as a manifestation of aging and affects all individuals (10)(11)(12)(13)(14)(15)(16)(17)(18). From the statistic point of view, age is a deterministic variable in this relationship and coefficients of determination may be higher than 0.99 (4,(18)(19)(20)(21)(22)(23).…”

“…The historical list of studies on this topic is not big and the models are described more detailed in the Appendix (the relationships [17][18][19][20][21][22][23][24]. The exponential Gompertz model used by Thiele was the first historical attempt (30).…”

Modeling the Age-Associated Decrease in Mortality Rate for Congenital Anomalies of the Central Nervous System Using WHO Metadata From Nine European Countries

“…This increase in mortality rate with age can be attributed to biological processes [e.g., ( 11 – 15 )], and is typically interpreted as a manifestation of aging [e.g., ( 16 – 19 )]. Statistically, age can be considered a deterministic variable in this exponential relationship because coefficients of determination may be higher than 0.99 ( 4 , 6 , 20 ).…”

“…[Specifically, for the nine countries indicated in Table 1 , the proportion of deaths due to congenital anomalies and certain conditions originating during the perinatal period was 96% on the first day, 95% in the first week, and approximately 10% in the age interval of [5, 10) years]. Second, the inverse proportion may also describe the decrease in all-cause mortality with age ( 20 , 21 ). If mortality due to CACNS decreases with age according to an inverse proportion, this may help to explain the overall decrease in mortality with age, although it remains to be determined whether this relationship is merely coincidental.…”