Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model

Ibrahim, Rose Irnawaty; Ngataman, Nuraini; Abrisam, W N A Wan Mohd

doi:10.1088/1742-6596/890/1/012128

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…where 1qx denotes the probability of dying at a yearly interval, and A, B, C, D, E, F, G, and H were the parameters to be estimated. Several studies had agreed that the Heligman-Pollard model fits the Malaysian population considerably well [16–18]. Two variables needed for the UNABR application were age and probability of dying between age x and age x + n from the abridged life tables of Kelantan population mortality data.…”

Section: Methodsmentioning

confidence: 99%

Modelling excess mortality among breast cancer patients in the North East Region of Peninsular Malaysia, 2007–2011: a population-based study

et al. 2019

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BackgroundMeasurement of breast cancer burden and identification of its influencing factors help in the development of public health policy and strategy against the disease. This study aimed to examine the variability of the excess mortality of female breast cancer patients in the North East Region of Peninsular Malaysia.MethodsThis retrospective cohort study was conducted using breast cancer data from the Kelantan Cancer Registry between 2007 and 2011, and Kelantan general population mortality data. The breast cancer cases were followed up for 5 years until 2016. Out of 598 cases, 549 cases met the study criteria and were included in the analysis. Modelling of excess mortality was conducted using Poisson regression.ResultsExcess mortality of breast cancer varied according to age group (50 years old and below vs above 50 years old, Adj. EHR: 1.47; 95% CI: 1.31, 4.09; P = 0.004), ethnicity (Malay vs non-Malay, Adj. EHR: 2.31; 95% CI: 1.11, 1.96; P = 0.008), and stage (stage III and IV vs. stage I and II, Adj. EHR: 5.75; 95% CI: 4.24, 7.81; P < 0.001).ConclusionsPublic health policy and strategy aim to improve cancer survival should focus more on patients presented at age below 50 years old, Malay ethnicity, and at a later stage.

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Section: Methodsmentioning

confidence: 99%

Modelling excess mortality among breast cancer patients in the North East Region of Peninsular Malaysia, 2007–2011: a population-based study

et al. 2019

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Section: Expanding Abridged Life Tablementioning

confidence: 99%

Modelling Excess Mortality Among Breast Cancer Patients in the North East Region of Peninsular Malaysia, 2007-2011: A Population-Based Study

Hanis

Yaacob

Hairon

et al. 2019

Preprint

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Measurement of breast cancer burden and identification of its influencing factors help in the development of public health policy and strategy against the disease. This study aimed to examine the variability of the excess mortality of female breast cancer patients in the North East Region of Peninsular Malaysia. This retrospective cohort study was conducted using breast cancer data from the Kelantan Cancer Registry between 2007 and 2011, and Kelantan general population mortality data. The breast cancer cases were followed up for five years until 2016. Out of 598 cases, 549 cases met the study criteria and were included in the analysis. Modelling of excess mortality was conducted using Poisson regression. Excess mortality of breast cancer varied according to age group (50 years old and below vs above 50 years old, Adj. EHR: 1.47; 95% CI: 1.31, 4.09; P=0.004), ethnicity (Malay vs non-Malay, Adj. EHR: 2.31; 95% CI: 1.11, 1.96; P=0.008), and stage (stage III and IV vs. stage I and II, Adj. EHR: 5.75; 95% CI: 4.24, 7.81; P<0.001). In conclusion, public health policy and strategy aim to improve cancer survival should focus more on patients presented at age below 50 years old, Malay ethnicity, and at a later stage.

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“…Several studies had agreed that the Heligman-Pollard model fits the Malaysian population considerably well. [26][27][28] Two variables needed for the UNABR application were age and probability of dying between age x and age x+n from the abridged life tables of Kelantan population mortality data. A complete life table produced from this application included variables; age in the yearly interval, central mortality rate between age x and age x+1, probability of dying between age x and age x+1, survivors at exact age x and life expectancy.…”

Section: Expanding Abridged Life Tablementioning

confidence: 99%

Net survival differences of breast cancer between stages at diagnosis and age groups in the east coast region of West Malaysia: a retrospective cohort study

et al. 2021

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ObjectiveEstimation of the net survival of breast cancer helps in assessing breast cancer burden at a population level. Thus, this study aims to estimate the net survival of breast cancer at different cancer staging and age at diagnosis in the east coast region of West Malaysia.SettingKelantan, Malaysia.ParticipantsAll breast cancer cases diagnosed in 2007 and 2011 identified from Kelantan Cancer Registry.DesignThis retrospective cohort study used a relative survival approach to estimate the net survival of patients with breast cancer. Thus, two data were needed; breast cancer data from Kelantan Cancer Registry and general population mortality data for Kelantan population.Primary and secondary outcome measuresNet survival according to stage and age group at diagnosis at 1, 3 and 5 years following diagnosis.ResultsThe highest net survival was observed among stage I and II breast cancer cases, while the lowest net survival was observed among stage IV breast cancer cases. In term of age at diagnosis, breast cancer cases aged 65 and older had the best net survival compared with the other age groups.ConclusionThe age at diagnosis had a minimal impact on the net survival compared with the stage at diagnosis. The finding of this study is applicable to other populations with similar breast cancer profile.

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Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model

Cited by 9 publications

References 2 publications

Modelling excess mortality among breast cancer patients in the North East Region of Peninsular Malaysia, 2007–2011: a population-based study

Modelling excess mortality among breast cancer patients in the North East Region of Peninsular Malaysia, 2007–2011: a population-based study

Modelling Excess Mortality Among Breast Cancer Patients in the North East Region of Peninsular Malaysia, 2007-2011: A Population-Based Study

Net survival differences of breast cancer between stages at diagnosis and age groups in the east coast region of West Malaysia: a retrospective cohort study

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