scholarly journals Modelling and Prediction of the Gross Mortality Rate in Ecuador

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Mónica Mite ◽  
Sandra Garcia-Bustos ◽  
Marcela Pincay ◽  
Ana Debón ◽  
Francisco Santoja
Keyword(s):  
Mortality Rate ◽  
Cohort Effect ◽  
Mortality Data ◽  
Arima Models ◽  
Men And Women ◽  
Mortality Forecasting ◽  
Basic Model ◽  
Adjusted Model ◽  
Age Range ◽  
Carter Model

This paper presents the results obtained from the modelling of the mortality data in Ecuador from 1990 to 2010, using the StMoMo library in the open source programming language R. This library was developed based on the Generalized Age-Period-Cohort Models (GAPC), among which is the Lee-Carter model, which has been widely applied in the actuarial area. The gross mortality rate of men and women in an age range of 1 to 85 years was modelled for the data of Ecuador, in the period 1990-2010. Of a total of eight models, two models have been selected because they present a good fit of the data for both genders. The first is the basic model of Lee-Carter and the second, the Plat model, which incorporates the cohort effect. A comparison was made with the two models to determine which one has a better forecast in a horizon of 20 years for specific ages. Both models show and predict the decrease in mortality in Ecuador of both genders, a decrease that is more pronounced, in general, for women at certain ages. In determining the uncertainty of the models, the bootstrap technique was used to define the confidence intervals of the adjusted model. The GAPC and ARIMA models were also compared; the former improve the mortality forecasting.

scholarly journals Stroke Mortality Trends in the Population of Klaipėda From 1994 to 2008

Medicina ◽  
2011 ◽  
Vol 47 (9) ◽  
pp. 512 ◽  
Author(s):  
Henrikas Kazlauskas ◽  
Nijolė Raškauskienė ◽  
Rima Radžiuvienė ◽  
Vinsas Janušonis
Keyword(s):  
Mortality Rate ◽  
Mortality Rates ◽  
Mortality Data ◽  
World Population ◽  
Stroke Mortality ◽  
Middle Aged ◽  
Elderly Men ◽  
Men And Women ◽  
Specific Mortality ◽  
Permanent Residents

The objective of the study was to evaluate the trends in stroke mortality in the population of Klaipėda aged 35–79 years from 1994 to 2008. Material and Methods. Mortality data on all permanent residents of Klaipėda aged 35–79 years who died from stroke in 1994–2008 were gathered for the study. All death certificates of permanent residents of Klaipėda aged 35–79 years who died during 1994–2008 were examined in this study. The International Classification of Diseases (ICD-9 codes 430–436, and ICD-10 codes I60–I64) was used. Sex-specific mortality rates were standardized according to the Segi’s world population; all the mortality rates were calculated per 100 000 population per year. Trends in stroke mortality were estimated using log-linear regression models. Sex-specific mortality rates and trends were calculated for 3 age groups (35–79, 35–64, and 65–79 years). Results. During the entire study period (1994–2008), a marked decline in stroke mortality with a clear slowdown after 2002 was observed. The average annual percent changes in mortality rates for men and women aged 35–79 years were –4.6% (P=0.041) and –6.5% (P=0.002), respectively. From 1994 to 2002, the stroke mortality rate decreased consistently among both Klaipėda men and women aged 35–64 years (20.4% per year, P=0.002, and 14.7% per year, P=0.006, respectively) and in the elderly population aged 65–79 years (13.8% per year, P=0.005; and 12% per year, P=0.019). During 2003–2008, stroke mortality increased by 16.3% per year in middle-aged men (35–64 years), whereas among women (aged 35–64 and 65–79 years) and elderly men (aged 65–79 years), the age-adjusted mortality rate remained relatively unchanged. Conclusions. Among both men and women, the mortality rates from stroke sharply declined between 1994 and 2008 with a clear slowdown in the decline after 2002. Stroke mortality increased significantly among middle-aged men from 2003, while it remained without significant changes among women of the same age and both elderly men and women.

scholarly journals Cardiovascular and all-cause mortality attributable to loneliness in older Swedish men and women.

2020 ◽  
Author(s):  
Masuma Novak ◽  
Margda waern ◽  
Lena Johansson ◽  
Anna Zettergren ◽  
Lina Ryden ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Cox Regression ◽  
Mortality Data ◽  
Men And Women ◽  
Face To Face ◽  
Risk Of Mortality ◽  
Increased Risk ◽  
All Cause Mortality ◽  
Adjusted Model

Abstract Background. This study examined whether loneliness predicts cardiovascular- and all-cause mortality in older men and women. Methods. Baseline data from the Gothenburg H70 Birth Cohort Studies, collected during 2000 on 70-year-olds born 1930 and living in Gothenburg were used for analysis (n=524). Mortality data were analyzed until 2012 through Swedish national registers. Results. Perceived loneliness was reported by 17.1% of the men and 30.9% of the women in a face-to-face interview with mental health professional. A total of 142 participants died during the 12-year follow-up period, with 5 334 person-years at risk, corresponding to 26.6 deaths/1000 person-years. Cardiovascular disease accounted for 59.2% of all deaths. The cumulative rates/1000 person-years for cardiovascular mortality were 20.8 (men) and 11.5 (women), and for all-cause mortality 33.8 (men) and 20.5 (women), respectively. In Cox regression models, no significant increased risk of mortality was seen for men with loneliness compared to men without loneliness (cardiovascular mortality HR 1.52, 95% CI 0.78 - 2.96; all-cause HR 1.32, 95% CI 0.77 - 2.28). Increased risk of cardiovascular mortality was observed in women with loneliness compared to those without (HR 2.25 95% CI 1.14 - 4.45), and the risk remained significant in a multivariable-adjusted model (HR 2.42 95% CI 1.04 - 5.65). Conclusions. Loneliness was shown to be an independent predictor of cardiovascular mortality in women. We found no evidence to indicate that loneliness was associated with an increased risk of either cardiovascular- or all-cause mortality in men.

scholarly journals Cardiovascular and all-cause mortality attributable to loneliness in older Swedish men and women

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Masuma Novak ◽  
Margda Waern ◽  
Lena Johansson ◽  
Anna Zettergren ◽  
Lina Ryden ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Cox Regression ◽  
Mortality Data ◽  
Men And Women ◽  
Face To Face ◽  
Risk Of Mortality ◽  
Increased Risk ◽  
All Cause Mortality ◽  
Adjusted Model

Abstract Background This study examined whether loneliness predicts cardiovascular- and all-cause mortality in older men and women. Methods Baseline data from the Gothenburg H70 Birth Cohort Studies, collected during 2000 on 70-year-olds born 1930 and living in Gothenburg were used for analysis (n = 524). Mortality data were analyzed until 2012 through Swedish national registers. Results Perceived loneliness was reported by 17.1% of the men and 30.9% of the women in a face-to-face interview with mental health professional. A total of 142 participants died during the 12-year follow-up period, with 5334 person-years at risk, corresponding to 26.6 deaths/1000 person-years. Cardiovascular disease accounted for 59.2% of all deaths. The cumulative rates/1000 person-years for cardiovascular mortality were 20.8 (men) and 11.5 (women), and for all-cause mortality 33.8 (men) and 20.5 (women), respectively. In Cox regression models, no significant increased risk of mortality was seen for men with loneliness compared to men without loneliness (cardiovascular mortality HR 1.52, 95% CI 0.78–2.96; all-cause HR 1.32, 95% CI 0.77–2.28). Increased risk of cardiovascular mortality was observed in women with loneliness compared to those without (HR 2.25 95% CI 1.14–4.45), and the risk remained significant in a multivariable-adjusted model (HR 2.42 95% CI 1.04–5.65). Conclusions Loneliness was shown to be an independent predictor of cardiovascular mortality in women. We found no evidence to indicate that loneliness was associated with an increased risk of either cardiovascular- or all-cause mortality in men.

scholarly journals Forecasting Hungarian mortality rates using the Lee-Carter method

2007 ◽  
Vol 57 (1) ◽  
pp. 21-34 ◽  
Author(s):  
S. Baran ◽  
J. Gáll ◽  
M. Ispány ◽  
G. Pap
Keyword(s):  
Mortality Rates ◽  
Mortality Data ◽  
Good Prediction ◽  
Data Set ◽  
Men And Women ◽  
Carter Model

A modified version of the popular Lee-Carter method (Lee-Carter 1992) is applied to forecast mortality rates in Hungary for the period 2004–2040 on the basis of mortality data between 1949 and 2003 both for men and women. Another case is also considered based on a restricted data set corresponding to the period 1989–2003. The model fitted to the data of the period 1949–2003 forecasts increasing mortality rates for men between ages 45 and 55, pointing out that the Lee-Carter method is hardly applicable for countries where mortality rates exhibit trends as peculiar as in Hungary. However, models fitted to the data for the last 15 years both for men and women forecast decreasing trends similarly to the case of countries where the method was successfully applied. Hence one gets a better fit in this way, however, further concerns suggest that the Lee-Carter model, which is celebrated and widely used in actuarial practice, does not necessarily give sufficiently good prediction.

scholarly journals Why Does Child Mortality Decrease With Age? Modeling the Age-Associated Decrease in Mortality Rate Using WHO Metadata From 25 Countries

2021 ◽  
Vol 9 ◽  
Author(s):  
Josef Dolejs ◽  
Helena Homolková
Keyword(s):  
Mortality Rate ◽  
Child Mortality ◽  
Total Mortality ◽  
Population Heterogeneity ◽  
European Countries ◽  
Mortality Data ◽  
Inverse Proportion ◽  
Child Mortality Rate ◽  
First Year Of Life ◽  
Age Range

Background: Our previous study analyzed the age trajectory of mortality (ATM) in 14 European countries, while this study aimed at investigating ATM in other continents and in countries with a higher level of mortality. Data from 11 Non-European countries were used.Methods: The number of deaths was extracted from the WHO mortality database. The Halley method was used to calculate the mortality rates in all possible calendar years and all countries combined. This method enables us to combine more countries and more calendar years in one hypothetical population.Results: The age trajectory of total mortality (ATTM) and also ATM due to specific groups of diseases were very similar in the 11 non-European countries and in the 14 European countries. The level of mortality did not affect the main results found in European countries. The inverse proportion was valid for ATTM in non-European countries with two exceptions.Slower or no mortality decrease with age was detected in the first year of life, while the inverse proportion model was valid for the age range (1, 10) years in most of the main chapters of ICD10.Conclusions: The decrease in child mortality with age may be explained as the result of the depletion of individuals with congenital impairment. The majority of deaths up to the age of 10 years were related to congenital impairments, and the decrease in child mortality rate with age was a demonstration of population heterogeneity. The congenital impairments were latent and may cause death even if no congenital impairment was detected.

scholarly journals Retiree Mortality Forecasting: A Partial Age-Range or a Full Age-Range Model?

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 69
Author(s):  
Han Lin Shang ◽  
Steven Haberman
Keyword(s):  
Developed Countries ◽  
Mortality Data ◽  
Mortality Forecasting ◽  
The Difference ◽  
Long Term Forecasting ◽  
Using Data ◽  
Mortality Forecasts ◽  
The One ◽  
Age Range

An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can take place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time-series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past.

Uncertainty in Mortality Forecasting: An Extension to the Classical Lee-Carter Approach

2009 ◽  
Vol 39 (1) ◽  
pp. 137-164 ◽  
Author(s):  
Johnny Siu-Hang Li ◽  
Mary R. Hardy ◽  
Ken Seng Tan
Keyword(s):  
Individual Differences ◽  
Selection Criteria ◽  
Financial Risk ◽  
Goodness Of Fit ◽  
Mortality Data ◽  
Model Structure ◽  
Death Rates ◽  
Mortality Forecasting ◽  
Mortality Improvement ◽  
Carter Model

AbstractTraditionally, actuaries have modeled mortality improvement using deterministic reduction factors, with little consideration of the associated uncertainty. As mortality improvement has become an increasingly significant source of financial risk, it has become important to measure the uncertainty in the forecasts. Probabilistic confidence intervals provided by the widely accepted Lee-Carter model are known to be excessively narrow, due primarily to the rigid structure of the model. In this paper, we relax the model structure by considering individual differences (heterogeneity) in each age-period cell. The proposed extension not only provides a better goodness-of-fit based on standard model selection criteria, but also ensures more conservative interval forecasts of central death rates and hence can better reflect the uncertainty entailed. We illustrate the results using US and Canadian mortality data.

scholarly journals NEIGHBOURING PREDICTION FOR MORTALITY

2021 ◽  
pp. 1-30
Author(s):  
Chou-Wen Wang ◽  
Jinggong Zhang ◽  
Wenjun Zhu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mortality Rate ◽  
Prediction Model ◽  
Mortality Data ◽  
Nonlinear Structure ◽  
Mortality Forecasting ◽  
Mortality Models ◽  
Forecasting Performance ◽  
Deep Learning Model

ABSTRACT We propose a new neighbouring prediction model for mortality forecasting. For each mortality rate at age x in year t, mx,t, we construct an image of neighbourhood mortality data around mx,t, that is, Ꜫ mx,t (x1, x2, s), which includes mortality information for ages in [x-x1, x+x2], lagging k years (1 ≤ k ≤ s). Combined with the deep learning model – convolutional neural network, this framework is able to capture the intricate nonlinear structure in the mortality data: the neighbourhood effect, which can go beyond the directions of period, age, and cohort as in classic mortality models. By performing an extensive empirical analysis on all the 41 countries and regions in the Human Mortality Database, we find that the proposed models achieve superior forecasting performance. This framework can be further enhanced to capture the patterns and interactions between multiple populations.

scholarly journals Evaluation of the Kou-Modified Lee-Carter Model in Mortality Forecasting: Evidence from French Male Mortality Data

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 123 ◽  
Author(s):  
Marie Angèle Cathleen Alijean ◽  
Jason Narsoo
Keyword(s):  
Mortality Data ◽  
Male Mortality ◽  
Forecasting Accuracy ◽  
Mortality Forecasting ◽  
Mortality Models ◽  
Double Exponential ◽  
Jump Diffusion Model ◽  
Mortality Forecasts ◽  
Death Data ◽  
Carter Model

Mortality forecasting has always been a target of study by academics and practitioners. Since the introduction and rising significance of securitization of risk in mortality and longevity, more in-depth studies regarding mortality have been carried out to enable the fair pricing of such derivatives. In this article, a comparative analysis is performed on the mortality forecasting accuracy of four mortality models. The methodology employs the Age-Period-Cohort model, the Cairns-Blake-Dowd model, the classical Lee-Carter model and the Kou-Modified Lee-Carter model. The Kou-Modified Lee-Carter model combines the classical Lee-Carter with the Double Exponential Jump Diffusion model. This paper is the first study to employ the Kou model to forecast French mortality data. The dataset comprises death data of French males from age 0 to age 90, available for the years 1900–2015. The paper differentiates between two periods: the 1900–1960 period where extreme mortality events occurred for French males and the 1961–2015 period where no significant jump is observed. The Kou-modified Lee-Carter model turns out to give the best mortality forecasts based on the RMSE, MAE, MPE and MAPE metrics for the period 1900–1960 during which the two World Wars occurred. This confirms that the consideration of jumps and leptokurtic features conveys important information for mortality forecasting.

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