Evaluation of Lee-Carter model to breast cancer mortality prediction in China and Pakistan

Background

Precise breast cancer-related mortality forecasts are required for public health program and healthcare service planning. A number of stochastic model-based approaches for predicting mortality have been developed. The trends shown by mortality data from various diseases and countries are critical to the effectiveness of these models. This study illustrates the unconventional statistical method for estimating and predicting the mortality risk between the early-onset and screen-age/late-onset breast cancer population in China and Pakistan using the Lee-Carter model.

Methods

Longitudinal death data for female breast cancer from 1990 to 2019 obtained from the Global Burden of Disease study database were used to compare statistical approach between early-onset (age group, 25-49 years) and screen-age/late-onset (age group, 50-84 years) population. We evaluated the model performance both within (training period, 1990-2010) and outside (test period, 2011-2019) data forecast accuracy using the different error measures and graphical analysis. Finally, using the Lee-Carter model, we predicted the general index for the time period (2011 to 2030) and derived corresponding life expectancy at birth for the female breast cancer population using life tables.

Results

Study findings revealed that the Lee-Carter approach to predict breast cancer mortality rate outperformed in the screen-age/late-onset compared with that in the early-onset population in terms of goodness of fit and within and outside forecast accuracy check. Moreover, the trend in forecast error was decreasing gradually in the screen-age/late-onset compared with that in the early-onset breast cancer population in China and Pakistan. Furthermore, we observed that this approach had provided almost comparable results between the early-onset and screen-age/late-onset population in forecast accuracy for more varying mortality behavior over time like in Pakistan. Both the early-onset and screen-age/late-onset populations in Pakistan were expected to have an increase in breast cancer mortality by 2030. whereas, for China, it was expected to decrease in the early-onset population.

Conclusion

The Lee-Carter model can be used to estimate breast cancer mortality and so to project future life expectancy at birth, especially in the screen-age/late-onset population. As a result, it is suggested that this approach may be useful and convenient for predicting cancer-related mortality even when epidemiological and demographic disease data sets are limited. According to model predictions for breast cancer mortality, improved health facilities for disease diagnosis, control, and prevention are required to reduce the disease's future burden, particularly in less developed countries.

Excess deaths during the COVID-19 pandemic in Iran

BACKGROUND

The actual number of deaths during the COVID-19 pandemic is expected to be higher than the reported deaths. We aimed to estimate the number of deaths in Iran during the COVID-19 pandemic from December 22, 2019 to March 20, 2022.

METHODS

We compared the number of age- and sex-specific deaths reported by Iran's Bureau of Vital Statistics with the predicted deaths estimated using an improved Lee-Carter model. We estimated the number of all-cause excess deaths in three scenarios, including the baseline scenario (without any undercounting of deaths) and 4% and 8% undercounting of all-cause deaths.

RESULTS

We estimated 282,378 (95% confidence intervals [CI]: 225,439; 341,951) excess deaths in the baseline model. This number was 303,148 (95% CI: 246,417; 357,823) and 308,486 (95% CI: 250,607; 364,417) in the 4% and 8% scenarios, respectively. During the same period, Iran reported 139,610 deaths as being directly related to COVID-19. The ratio of reported COVID-19 deaths to total excess deaths ranged from 45.2% to 49.4% in the various scenarios. Most excess deaths occurred in the baseline scenario in males (157,552 [95% CI: 125,142; 191,265]) and those aged ≥75 years (102,369 [95% CI: 93,894; 111,188]).

CONCLUSIONS

The reported number of COVID-19 deaths was less than half of Iran's estimated number of excess deaths. The results of this study will be helpful for health policymakers' planning, and call for strengthening the timeliness and accuracy of Iran's death registration systems, planning for more accurate monitoring of epidemics, and planning to provide support services for survivors' families.

Mortality forecasting in Colombia from abridged life tables by sex

BACKGROUND

An adequate forecasting model of mortality that allows an analysis of different population changes is a topic of interest for countries in demographic transition. Phenomena such as the reduction of mortality, ageing, and the increase in life expectancy are extremely useful in the planning of public policies that seek to promote the economic and social development of countries. To our knowledge, this paper is one of the first to evaluate the performance of mortality forecasting models applied to abridged life tables.

OBJECTIVE

Select a mortality model that best describes and forecasts the characteristics of mortality in Colombia when only abridged life tables are available.

DATA AND METHOD

We used Colombian abridged life tables for the period 1973-2005 with data from the Latin American Human Mortality Database. Different mortality models to deal with modeling and forecasting probability of death are presented in this study. For the comparison of mortality models, two criteria were analyzed: graphical residuals analysis and the hold-out method to evaluate the predictive performance of the models, applying different goodness of fit measures.

RESULTS

Only three models did not have convergence problems: Lee-Carter (LC), Lee-Carter with two terms (LC2), and Age-Period-Cohort (APC) models. All models fit better for women, the improvement of LC2 on LC is mostly for central ages for men, and the APC model's fit is worse than the other two. The analysis of the standardized deviance residuals allows us to deduce that the models that reasonably fit the Colombian mortality data are LC and LC2. The major residuals correspond to children's ages and later ages for both sexes.

CONCLUSION

The LC and LC2 models present better goodness of fit, identifying the principal characteristics of mortality for Colombia.Mortality forecasting from abridged life tables by sex has clear added value for studying differences between developing countries and convergence/divergence of demographic changes.

Optimizing the Lee-Carter Approach in the Presence of Structural Changes in Time and Age Patterns of Mortality Improvements

Researchers using the Lee-Carter approach have often assumed that the time-varying index evolves linearly and that the parameters describing the age pattern of mortality decline are time-invariant. However, as several empirical studies suggest, the two assumptions do not seem to hold when the calibration window begins too early. This problem gives rise to the question of identifying the longest calibration window for which the two assumptions hold true. To address this question, we contribute a likelihood ratio-based sequential test to jointly test whether the two assumptions are satisfied. Consistent with the mortality structural changes observed in previous studies, our testing procedure indicates that the starting points of the optimal calibration windows for most populations fall between 1960 and 1990. Using an out-of-sample analysis, we demonstrate that in most cases, models that are estimated to the optimized calibration windows result in more accurate forecasts than models that are fitted to all available data or data beyond 1950. We further apply the proposed testing procedure to data over different age ranges. We find that the optimal calibration windows for age group 0-49 are generally shorter than those for age group 50-89, indicating that mortality at younger ages might have undergone (another) structural change in recent years.

Future healthy life expectancy among older adults in the US: a forecast based on cohort smoking and obesity history

BACKGROUND

In the past three decades, the elderly population in the United States experienced increase in life expectancy (LE) and disability-free life expectancy (LE(ND)), but decrease in life expectancy with disability (LE(D)). Smoking and obesity are two major risk factors that had negative impacts on these trends. While smoking prevalence continues to decline in recent decades, obesity prevalence has been growing and is currently at a high level. This study aims to forecast the healthy life expectancy for older adults aged 55 to 85 in the US from 2011 to 2040, in relation to their smoking and obesity history.

METHODS

First, population-level mortality data from the Human Mortality Database (HMD) and individual-level disability data from the US National Health Interview Survey (NHIS) were used to estimate the transition rates between different health states from 1982 to 2010, using a multi-state life table (MSLT) model. Second, the estimated transition rates were fitted and projected up to 2040, using a modified Lee-Carter model that incorporates cohort smoking and obesity history from NHIS.

RESULTS

Mortality and morbidity for both sexes will continue to decline in the next decades. Relative to 2010, men are expected to have 3.2 years gain in LE(ND) and 0.8 years loss in LE(D). For women, there will be 1.8 years gain in LE(ND) and 0.8 years loss in LE(D). By 2040, men and women are expected to spend respectively 80 % and 75 % of their remaining life expectancy between 55 and 85 disability-free.

CONCLUSIONS

Smoking and obesity have independent negative impacts on both the survival and disability of the US older population in the coming decades, and are responsible for the present and future gender disparity in mortality and morbidity. Overall, the US older population is expected to enjoy sustained health improvements and compression of disability, largely due to decline in smoking.