Summary Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age‐specific improvement rates, together with a non‐smoothed period effect. Forecasts for the UK are produced by using data from the human mortality database spanning the period 1961–2013. A metric that approximates predictive accuracy is used to estimate weights for the ‘stacking’ of forecasts from models with different points of transition between the GAM and parametric elements. Mortality for males and females is estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations.
Summary.We describe the most recent statistical methodology used to produce the 17th English Life Table, covering the period 2010-2012. Crude mortality rates are smoothed, or graduated, by using a combination of a generalized additive model and low dimensional parametric models.The approach to graduation acknowledges uncertainty, particularly in the highest age groups, by model averaging, using a simplified version of a full Bayesian analysis.
Reliable modelling of the dynamics of cancer morbidity risk is important, not least due to its significant impact on healthcare and related policies. We identify morbidity trends and regional differences in England for all-cancer and type-specific incidence between 1981 and 2016. We use Bayesian modelling to estimate cancer morbidity incidence at various age, year, gender, and region levels. Our analysis shows increasing trends in most rates and marked regional variations that also appear to intensify through time in most cases. All-cancer rates have increased significantly, with the highest increase in East, North West and North East. The absolute difference between the rates in the highest-and lowest-incidence region, per 100,000 people, has widened from 39 (95% CI 33-45) to 86 (78-94) for females, and from 94 (85-104) to 116 (105-127) for males. Lung cancer incidence for females has shown the highest increase in Yorkshire and the Humber, while for males it has declined in all regions with the highest decrease in London. The gap between the highest-and lowestincidence region for females has widened from 47 (42-51) to 94 (88-100). Temporal change in in bowel cancer risk is less manifested, with regional heterogeneity also declining. Prostate cancer incidence has increased with the highest increase in London, and the regional gap has expanded from 33 (30-36) to 76 (69-83). For breast cancer incidence the highest increase has occurred in North East, while the regional variation shows a less discernible increase. The analysis reveals that there are important regional differences in the incidence of all-type and type-specific cancers, and that most of these regional differences become more pronounced over time. A significant increase in regional variation has been demonstrated for most types of cancer examined here, except for bowel cancer where differences have narrowed.
Background We identify socioeconomic disparities by region in cancer morbidity and mortality in England for all-cancer and type-specific cancers, and use incidence data to quantify the impact of cancer diagnosis delays on cancer deaths between 2001–2016. Methods and findings We obtain population cancer morbidity and mortality rates at various age, year, gender, deprivation, and region levels based on a Bayesian approach. A significant increase in type-specific cancer deaths, which can also vary among regions, is shown as a result of delay in cancer diagnoses. Our analysis suggests increase of 7.75% (7.42% to 8.25%) in female lung cancer mortality in London, as an impact of 12-month delay in cancer diagnosis, and a 3.39% (3.29% to 3.48%) increase in male lung cancer mortality across all regions. The same delay can cause a 23.56% (23.09% to 24.30%) increase in male bowel cancer mortality. Furthermore, for all-cancer mortality, the highest increase in deprivation gap happened in the East Midlands, from 199 (186 to 212) in 2001, to 239 (224 to 252) in 2016 for males, and from 114 (107 to 121) to 163 (155 to 171) for females. Also, for female lung cancer, the deprivation gap has widened with the highest change in the North West, e.g. for incidence from 180 (172 to 188) to 272 (261 to 282), whereas it has narrowed for prostate cancer incidence with the biggest reduction in the South West from 165 (139 to 190) in 2001 to 95 (72 to 117) in 2016. Conclusions The analysis reveals considerable disparities in all-cancer and some type-specific cancers with respect to socioeconomic status. Furthermore, a significant increase in cancer deaths is shown as a result of delays in cancer diagnoses which can be linked to concerns about the effect of delay in cancer screening and diagnosis during the COVID-19 pandemic. Public health interventions at regional and deprivation level can contribute to prevention of cancer deaths.
Fertility projections are a key determinant of population forecasts, which are widely used by government policymakers and planners. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model to forecast cohort fertility. Using Hamiltonian Monte Carlo methods and a dataset from the Human Fertility Database, we obtain fertility forecasts for 30 countries. We use scoring rules to quantitatively assess the predictive accuracy of the forecasts; these indicate that our model predicts with an accuracy comparable to that of the best-performing models in the current literature overall, with stronger performance for countries without a recent structural shift. Our findings support the position of hierarchical Bayesian modelling at the forefront of population forecasting methods.
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