The analysis of age-specific fertility patterns via logistic models

Rueda, Cristina; Alvarez-Esteban, Pedro C.

doi:10.1080/02664760802192999

Cited by 5 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LCMs were originally introduced for mortality rate modelling and are now the dominant approach, but have been applied elsewhere. Within demography, Hyndman [25] and Rueda-Sabater and Alvarez-Esteban [33] used LCMs to forecast the fertility rates, and Cowen [34] fitted LCMs to abortion rates. Kainz et al [35] modelled chronic kidney disease prevalence as rate data, and Yue et al [36] modelled cancer incidence and mortality.…”

Section: Discussionmentioning

confidence: 99%

Forecasting the impact of population ageing on tuberculosis incidence

Dodd

2019

PLoS ONE

View full text Add to dashboard Cite

BackgroundTuberculosis (TB) disease reactivates from distant latent infection or recent (re)infection. Progression risks increase with age. Across the World Health Organisation Western Pacific region, many populations are ageing and have the highest per capita TB incidence rates in older age groups. However, methods for analysing age-specific TB incidence and forecasting epidemic trends while accounting for demographic change remain limited.MethodsWe applied the Lee-Carter models, which were originally developed for mortality modelling, to model the temporal trends in age-specific TB incidence data from 2005 to 2018 in Taiwan. Females and males were modelled separately. We combined our demographic forecasts, and age-specific TB incidence forecasts to project TB incidence until 2035. We compared TB incidence projections with demography fixed in 2018 to projections accounting for demographic change.ResultsOur models quantified increasing incidence rates with age and declining temporal trends. By 2035, the forecast suggests that the TB incidence rate in Taiwan will decrease by 54% (95% Prediction Interval (PI): 45%-59%) compared to 2015, while most age-specific incidence rates will reduce by more than 60%. In 2035, adults aged 65 and above will make up 78% of incident TB cases. Forecast TB incidence in 2035 accounting for demographic change will be 39% (95% PI: 36%-42%) higher than without population ageing.ConclusionsAge-specific incidence forecasts coupled with demographic forecasts can inform the impact of population ageing on TB epidemics. The TB control programme in Taiwan should develop plans specific to older age groups and their care needs.

show abstract

Section: Discussionmentioning

confidence: 99%

Forecasting the impact of population ageing on tuberculosis incidence

Dodd

2019

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…LCMs were originally introduced for mortality rate modelling and are now the dominant approach, but have been applied elsewhere. Within demography, Hyndman [24] and Rueda-Sabater and Alvarez-Esteban [32] used LCMs to forecast the fertility rates, and Cowen [33] fitted LCMs to abortion rates. Kainz et al [34] modelled chronic kidney disease prevalence as rate data, and Yue et al [35] modelled cancer incidence and mortality.…”

Section: Impact Of Demography On Tb Incidencementioning

confidence: 99%

Forecasting the impact of population ageing on tuberculosis incidence

Dodd

2019

Preprint

View full text Add to dashboard Cite

Background: Tuberculosis (TB) disease reactivates from distant latent infection or recent (re)infection. Progression risks increase with age. Across the World Health Organisation Western Pacific region, many populations are ageing and have the highest per capita TB incidence rates in older age groups. However, methods for analysing age-specific TB incidence and forecasting epidemic trends while accounting for demographic change remain limited. Methods: We applied the Lee-Carter models, which were originally developed for mortality modelling, to model the temporal trends in age-specific TB incidence data from 2005 to 2018 in Taiwan. Females and males were modelled separately. We combined our demographic forecasts, and age-specific TB incidence forecasts to project TB incidence until 2035. We compared TB incidence projections with demography fixed in 2018 to projections accounting for demographic change. Results: Our models quantified increasing incidence rates with age and declining temporal trends. By 2035, the forecast suggests that the TB incidence rate in Taiwan will decrease by 54% (95% Prediction Interval (PI): 45%-59%) compared to 2015, while most age-specific incidence rates will reduce by more than 60%. In 2035, adults aged 65 and above will make up 78% of incident TB cases. Forecast TB incidence in 2035 accounting for demographic change will be 39% (95% PI: 36%-42%) higher than without population ageing. Conclusions: Age-specific incidence forecasts coupled with demographic forecasts can inform the impact of population ageing on TB epidemics. The TB control programme in Taiwan should develop plans specific to older age groups and their care needs.

show abstract

“…In literature, there are a variety of parametric fertility models to explain the behaviour of the fertility pattern. The class of models with a certain built-in probability density function includes the Hadwiger model 1 , 2 , Hadwiger mixture model 3 , Coale–Trussell model 4 – 6 , Beta and Gamma models 6 , Pearson Type 1 curve 7 , 8 and Type III curves 9 , P-K model and P-K mixture model 10 , Flexible Generalised skew-Normal model 11 , The scaled Weibull mixture model 12 , logistic model 13 , Skew logistic model 7 , 14 . Other researchers have designed their parametric models using mathematical functions.…”

Section: Introductionmentioning

confidence: 99%

Experiments in modeling recent Indian fertility pattern

Srivastava

Singh

Pandey

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Modelling is a well-established concept for understanding the typical shape and pattern of age-specific fertility. The distribution of India’s age-specific fertility rate (ASFR) is unimodal and positively skewed and is distinct from the ASFR of the developed countries. The existing models (P-K model, Gompertz model, Skew-normal model and G-P model considered here) that were developed, based on the experiences of the developed countries, failed to fit the single-year age-specific fertility pattern for India as a whole and for the six selected states. Our study has proposed four flexible models, to capture the diverse age pattern of fertility, observed in the Indian states. The proposed models were compared in three ways; among themselves, with the original models and with the popular Hadwiger model. The parameters of these proposed models were estimated through the Non-Linear Least Squares Method. To find the model with best fit, we used the corrected version of Akaike’s Information Criterion (AICc). Optimization of the four original models was successfully done. When the model was fitted to the empirical data of the 4th round of the National Family Health Survey conducted in 2015–2016, the results of this study showed that all the four proposed models outperform their corresponding original models and the Hadwiger model. When comparison among the proposed models was done, the Modified Gompertz Model provided the best fit for India, Uttar Pradesh and Gujarat. Whereas, the Modified P-K model gave the best fit for West Bengal, Tripura and Karnataka. The Modified G-P model is the most suitable model for Punjab. Although our proposed models illustrated the fitting of ASFR for India as a whole and the selected six states only, it provides an important tool for the policymakers and the government authorities to project fertility rates and to understand the fertility transitions in India and various other states.

show abstract

The analysis of age-specific fertility patterns via logistic models

Cited by 5 publications

References 23 publications

Forecasting the impact of population ageing on tuberculosis incidence

Forecasting the impact of population ageing on tuberculosis incidence

Forecasting the impact of population ageing on tuberculosis incidence

Experiments in modeling recent Indian fertility pattern

Contact Info

Product

Resources

About