Modeling and forecasting duration-dependent mortality rates

2015 ◽  
Vol 83 ◽  
pp. 65-81 ◽  
Author(s):  
Marcus C. Christiansen ◽  
Andreas Niemeyer ◽  
Lucia Teigiszerová
Keyword(s):  
Mortality Rates ◽  
Modeling And Forecasting ◽  
Dependent Mortality

Modeling and Forecasting Mortality Rates

2011 ◽  
Author(s):  
Daniel Mitchell ◽  
Patrick L. Brockett ◽  
Rafael Mendoza-Arriaga ◽  
Kumar Muthuraman
Keyword(s):  
Mortality Rates ◽  
Modeling And Forecasting

scholarly journals Assessing joint spatial autocorrelations between mortality rates due to cardiovascular conditions in South Africa

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Timotheus B. Darikwa ◽  
Samuel Manda ◽  
‘Maseka Lesaoana
Keyword(s):  
Heart Failure ◽  
South Africa ◽  
Heart Disease ◽  
Health Outcomes ◽  
Empirical Bayes ◽  
Spatial Clustering ◽  
Mortality Rates ◽  
Noncommunicable Diseases ◽  
Co Morbidity ◽  
Dependent Mortality

South Africa is experiencing an increasing burden of noncommunicable diseases (NCDs). There is evidence of co-morbidity of several NCDs at small geographical areas in the country. However, the extent to which this applies to joint spatial autocorrections of NCDs is not known. The objective of this study was to derive and quantify multivariate spatial autocorrections for NCDrelated mortality in South Africa. The study used mortality attributable to cerebrovascular, ischaemic heart failure and hypertension captured by the country’s Department of Home Affairs for the years 2001, 2007 and 2011. Both univariate and pairwise spatial clustering measures were derived using observed, empirical Bayes smoothed and age-adjusted standardised mortality rates. Cerebrovascular and ischaemic heart co-clustering was significant for the years 2001 and 2011. Cerebrovascular and hypertension co-clustering was significant for the years 2007 and 2011, while hypertension and ischaemic heart co-clustering was significant for the year 2011. Co-clusters of cerebrovascular-ischaemic heart disease are the most profound and located in the south-western part of the country. It was successfully demonstrated that bivariate spatial autocorrelations can be derived for spatially dependent mortality rates as exemplified by mortality rates attributed to three cardiovascular conditions. The identified co-clusters of spatially dependent health outcomes may be targeted for an integrated intervention and monitoring programme.

Estimation of Size Selectivity and Movement Rates from Mark–Recovery Data

1994 ◽  
Vol 51 (3) ◽  
pp. 734-742 ◽  
Author(s):  
Alejandro Anganuzzi ◽  
Ray Hilborn ◽  
John R. Skalski
Keyword(s):  
Mortality Rates ◽  
Fishing Mortality ◽  
Size Selectivity ◽  
Movement Rates ◽  
Return Rates ◽  
Size Dependent ◽  
Pacific Halibut ◽  
Hippoglossus Stenolepis ◽  
Time Specific ◽  
Dependent Mortality

Size selectivity, movement rates among spatial strata, and size-dependent mortality rates were estimated from mark–recovery data of Pacific halibut (Hippoglossus stenolepis). Growth rates, area- and time-specific fishing mortality on fully vulnerable individuals, and tag return rates were assumed known from other data. We obtained similar estimates from a model that considered movement to take place immediately after tagging and a model that assumed that movement takes place once each year. The inability to distinguish between one-time and annual movement is most likely due to the fact that tagged juveniles were not recovered until 3–5 yr later when they became vulnerable to the adult fishery.

A model study with light-dependent mortality rates of copepod stages

2005 ◽  
Vol 56 (3-4) ◽  
pp. 416-434
Author(s):  
Thomas Neumann ◽  
Christine Kremp
Keyword(s):  
Mortality Rates ◽  
Model Study ◽  
Dependent Mortality

An Appraisal of the Size-Dependent Mortality Hypothesis for Larval Fish: Comparison of a Multispecies Study with an Empirical Review

1993 ◽  
Vol 50 (10) ◽  
pp. 2166-2174 ◽  
Author(s):  
Pierre Pepin
Keyword(s):  
Growth Rate ◽  
Larval Fish ◽  
Mortality Rates ◽  
Accurate Information ◽  
Constant Growth Rate ◽  
Size Dependent ◽  
Conception Bay ◽  
Biased Estimates ◽  
Constant Growth ◽  
Dependent Mortality

This study presents an assessment of the size-dependent mortality hypothesis for larval fish from a multispecies survey of Conception Bay, Newfoundland, Canada. Mortality rates are estimated using a length-based method (per millimetre). The results from this survey are consistent with previous studies which indicate that losses decrease with increasing size of fish. However, for each species within this survey, mortality rates are constant. Comparison of mortality rates within species among surveys indicates that as the range of size categories sampled increases, the estimated mortality rates decrease, despite evidence of adequate fit to the length-based model. The findings indicate that previous relationships between size or stage and mortality of larval fish should be reevaluated. Length-based methodology used to estimate mortality rates of larval fish appears to provide biased estimates of this vital characteristic. It is suggested that using size as a proxy for biological age (i.e., assuming a constant growth rate) may be an invalid assumption. Future surveys will need to provide accurate information about the age structure of larvae sampled in order to properly estimate mortality rates.

Estimating larval fish growth under size-dependent mortality: a numerical analysis of bias

2007 ◽  
Vol 64 (3) ◽  
pp. 554-562 ◽  
Author(s):  
Tian Tian ◽  
Øyvind Fiksen ◽  
Arild Folkvord
Keyword(s):  
Growth Rate ◽  
Longitudinal Data ◽  
Growth Rates ◽  
Mortality Rates ◽  
Growth Patterns ◽  
Cross Sectional ◽  
Larval Phase ◽  
Size Dependent ◽  
Recruitment Success ◽  
Dependent Mortality

The early larval phase is characterized by high growth and mortality rates. Estimates of growth from both population (cross-sectional) and individual (longitudinal) data may be biased when mortality is size-dependent. Here, we use a simple individual-based model to assess the range of bias in estimates of growth under various size-dependent patterns of growth and mortality rates. A series of simulations indicate that size distribution of individuals in the population may contribute significantly to bias in growth estimates, but that typical size-dependent growth patterns have minor effects. Growth rate estimates from longitudinal data (otolith readings) are closer to true values than estimates from cross-sectional data (population growth rates). The latter may produce bias in growth estimation of about 0.03 day–1 (in instantaneous, specific growth rate) or >40% difference in some situations. Four potential patterns of size-dependent mortality are tested and analyzed for their impact on growth estimates. The bias is shown to yield large differences in estimated cohort survival rates. High autocorrelation and variance in growth rates tend to increase growth estimates and bias, as well as recruitment success. We also found that autocorrelated growth patterns, reflecting environmental variance structure, had strong impact on recruitment success of a cohort.

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