Future inequalities in life expectancy in England and Wales – Authors' reply

Bennett, James L.; Li, Guangquan; Kontis, Vasilis; Foreman, Kyle J; Ezzati, Majid

doi:10.1016/s0140-6736(15)01194-0

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…This is consistent with typical modelling in disease mapping [23] and surveillance studies [24]. Additionally, we are framed in the same perspective as forecasting studies [25,26] and we take advantage of the Bayesian nature of our model to characterise the full uncertainty over the estimate of the rates to predict the weekly trends in 2020. Note that, differently from these, our analysis can only be done retrospectively, as estimating excess mortality requires the comparison of the predicted number of deaths with observed ones.…”

Section: Plos Onesupporting

confidence: 59%

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic

et al. 2020

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In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sexspecific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. NorthWest and NorthEast regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.

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Section: Plos Onesupporting

confidence: 59%

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic

et al. 2020

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“…Some exceptions are a recent projection of life expectancy at birth for different income groups for South Korea [16], projections of remaining life expectancy for socioeconomic groups in Denmark based on an individual affluence index [17], and a projection of life expectancy at age 65 for different education groups for the Netherlands, published five years ago [18]. In addition, there are a few projections by deprivation or wealth index of small areas [19,20], a study that models mortality for socioeconomic groups but does not produce forecasts [21], and a study that models and projects mortality for different socioeconomic groups but does not present forecasts of future life expectancies of these groups [22].…”

Section: Introductionmentioning

confidence: 99%

Future trends of life expectancy by education in the Netherlands

et al. 2022

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Background National projections of life expectancy are made periodically by statistical offices or actuarial societies in Europe and are widely used, amongst others for reforms of pension systems. However, these projections may not provide a good estimate of the future trends in life expectancy of different social-economic groups. The objective of this study is to provide insight in future trends in life expectancies for low, mid and high educated men and women living in the Netherlands. Methods We used a three-layer Li and Lee model with data from neighboring countries to complement Dutch time series. Results Our results point at further increases of life expectancy between age 35 and 85 and of remaining life expectancy at age 35 and age 65, for all education groups in the Netherlands. The projected increase in life expectancy is slightly larger among the high educated than among the low educated. Life expectancy of low educated women, particularly between age 35 and 85, shows the smallest projected increase. Our results also suggest that inequalities in life expectancies between high and low educated will be similar or slightly increasing between 2018 and 2048. We see no indication of a decline in inequality between the life expectancy of the low and high educated. Conclusions The educational inequalities in life expectancy are expected to persist or slightly increase for both men and women. The persistence and possible increase of inequalities in life expectancy between the educational groups may cause equity concerns of increases in pension age that are equal among all socio-economic groups.

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Future inequalities in life expectancy in England and Wales – Authors' reply

Cited by 2 publications

References 6 publications

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic

Future trends of life expectancy by education in the Netherlands

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