Lifespan and Aggregate Size Variables in Specifications of Mortality or Survivorship

Abstract

A specification of mortality or survivorship provides respective explicit details about mortality's or survivorship's relationships with one or more other variables (e.g., age, sex, etc.). Previous studies have discovered and analyzed diverse specifications of mortality or survivorship; these discoveries and analyses suggest that additional specifications of mortality or survivorship have yet to be discovered and analyzed. In consistency with previous research, multivariable limited powered polynomials regression analyses of mortality and survivorship of selected humans (Swedes, 1760–2008) and selected insects (caged medflies) show age-specific, historical-time-specific, environmental-context-specific, and sex-specific mortality and survivorship. These analyses also present discoveries of hitherto unknown lifespan-specific, contemporary-aggregate-size-specific, and lifespan-aggregate-size-specific mortality and survivorship. The results of this investigation and results of previous research help identify variables for inclusion in regression models of mortality or survivorship. Moreover, these results and results of previous research strengthen the suggestion that additional specifications of mortality or survivorship have yet to be discovered and analyzed, and they also suggest that specifications of mortality and survivorship indicate corresponding specifications of frailty and vitality. Furthermore, the present analyses reveal the usefulness of a multivariable limited powered polynomials regression model-building approach. This article shows that much has yet to be learned about specifications of mortality or survivorship of diverse kinds of individuals in diverse times and places.

Tetraeffective causes, mortacauses, and vitacauses of mortality and survivorship

“…These considerations also provide a new paradigm of causes of mortality and survivorship, and enable and promote further scientific research and practical applications 124,125 . These considerations -and the methodology that is employed here and in a previous investigation 123 -could thus prove to be particularly useful because scientific research on causality remains problematic and challenging 35,[126][127][128][129][130] , scientific research on causes of mortality and survivorship remains particularly problematic and challenging 21,[131][132][133][134][135][136][137][138] , and mortality and survivorship and their interrelationships are particularly prone to elicit errors and biases [139][140][141][142][143][144] .…”

“…In [108] I present analyses of 188,087 weighted cases with 79,164,608 events of death or survival of all individuals that were born in Sweden in decennial years 1760 -1930 and died between 1760 and 2008. In [108] I also present analyses of 50,716 weighted cases with 2,211,782 events of death or survival of caged Mediterranean fruit flies, Ceratitis capitata, commonly known as medflies.…”

“…As noted, specifications ∂Y/∂(X p ) = a + bX p -and higher level polynomials -are investigated here in analyses of causes of humans' and medflies' mortality and survivorship. Table 1 presents best-fitting values of coefficients a, b, c, and p for respective Y, X, and humans or medflies based upon the best-fitting models in [108]. For example, Equations 5 -8 and Tables 1 -4 in [108] reveal that each of the best-fitting models include βA1 and βA2 regression coefficients for respective effects of age A p on humans' and medflies' mortality and survivorship, where A denotes age, and where p denotes the respective power coefficient for age.…”

“…Table 1 presents best-fitting values of coefficients a, b, c, and p for respective Y, X, and humans or medflies based upon the best-fitting models in [108]. For example, Equations 5 -8 and Tables 1 -4 in [108] reveal that each of the best-fitting models include βA1 and βA2 regression coefficients for respective effects of age A p on humans' and medflies' mortality and survivorship, where A denotes age, and where p denotes the respective power coefficient for age. Therefore, using a = βA1 and b = 2βA2 yields corresponding partial derivatives ∂Y/∂(A p ) = a + bA p , holding all other variables as known and constant in each respective best-fitting model of humans' and medflies' mortality and survivorship.…”

“…However, employment of models ∂Y/∂(X p ) = a + bX p in previous research on mortality or survivorship is not easy to find. Models ∂Y/∂(X p ) = a + bX p are powered polynomial models [108], they are also fractional polynomial models [109,110], and they are also some of several kinds of Weibull models [13,14,[102][103][104][111][112][113][114][115]. Specifications ∂Y/∂(X p ) = a + bX pand higher level polynomialsare investigated here in further analyses of causes of humans' and medflies' mortality and survivorship.…”