scholarly journals Recent adverse mortality trends in Scotland: comparison with other high-income countries

2019 ◽  
Author(s):  
Lynda Fenton ◽  
Jon Minton ◽  
Julie Ramsay ◽  
Maria Kaye-Bardgett ◽  
Colin Fischbacher ◽  
...  
Keyword(s):  
Northern Ireland ◽  
Life Expectancy ◽  
Mortality Rates ◽  
Recent Change ◽  
High Income ◽  
Mortality Data ◽  
List Type ◽  
Segmented Regression ◽  
Mortality Trends ◽  
The Usa

AbstractObjectiveGains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland.SettingAustria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA.MethodsWe used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018.ResultsIn 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females.ConclusionLife expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors.Strengths and limitations of this studyThe use of five-year time periods for comparison of life expectancy changes reduces the influence of year-to-year variation on observations.Examining long-term trends addresses concerns that recent life expectancy stalling may be over-emphasised due to notably large gains in the immediately preceding period.The international comparison was limited to the 24 high-income countries for which data were readily available for the relevant period.Analysis of trend data will always be sensitive to the period selected, however segmented regression of the full period of mortality rates available offers an objective method of identifying the timing of a change in trend.

scholarly journals Recent adverse mortality trends in Scotland: comparison with other high-income countries

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e029936 ◽  
Author(s):  
Lynda Fenton ◽  
Jon Minton ◽  
Julie Ramsay ◽  
Maria Kaye-Bardgett ◽  
Colin Fischbacher ◽  
...  
Keyword(s):  
Northern Ireland ◽  
Life Expectancy ◽  
Recent Change ◽  
High Income ◽  
Mortality Trends ◽  
England And Wales ◽  
The Past ◽  
The Usa ◽  
The Mean ◽  
Expectancy Change

ObjectiveGains in life expectancy have faltered in several high-income countries in recent years. Scotland has consistently had a lower life expectancy than many other high-income countries over the past 70 years. We aim to compare life expectancy trends in Scotland to those seen internationally and to assess the timing and importance of any recent changes in mortality trends for Scotland.SettingAustria, Croatia, Czech Republic, Denmark, England and Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland and USA.MethodsWe used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over 5-year periods from 1992 to 2016. Linear regression was used to assess the association between life expectancy in 2011 and mean life expectancy change over the subsequent 5 years. One-break and two-break segmented regression models were used to test the timing of mortality rate changes in Scotland between 1990 and 2018.ResultsMean improvements in life expectancy in 2012–2016 were smallest among women (<2 weeks/year) in Northern Ireland, Iceland, England and Wales, and the USA and among men (<5 weeks/year) in Iceland, USA, England and Wales, and Scotland. Japan, Korea and countries of Eastern Europe had substantial gains in life expectancy over the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 quarter 4 for men and the year to 2014 quarter 2 for women.ConclusionsLife expectancy improvement has stalled across many, but not all, high-income countries. The recent change in the mortality trend in Scotland occurred within the period 2012–2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors.

scholarly journals Is austerity responsible for the recent change in mortality trends across high-income nations? A protocol for an observational study

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e034832 ◽  
Author(s):  
Gerry McCartney ◽  
Lynda Fenton ◽  
Jon Minton ◽  
Colin Fischbacher ◽  
Martin Taulbut ◽  
...  
Keyword(s):  
Government Expenditure ◽  
Mortality Rates ◽  
Recent Change ◽  
High Income ◽  
Sensitivity Analyses ◽  
Service Research ◽  
Social Spending ◽  
Governance System ◽  
Mortality Trends ◽  
Lifespan Variation

IntroductionMortality rates in many high-income countries have changed from their long-term trends since around 2011. This paper sets out a protocol for testing the extent to which economic austerity can explain the variance in recent mortality trends across high-income countries.Methods and analysisThis is an ecological natural experiment study, which will use regression adjustment to account for differences in exposure, outcomes and confounding. All high-income countries with available data will be included in the sample. The timing of any changes in the trends for four measures of austerity (the Alesina-Ardagna Fiscal Index, real per capita government expenditure, public social spending and the cyclically adjusted primary balance) will be identified and the cumulative difference in exposure to these measures thereafter will be calculated. These will be regressed against the difference in the mean annual change in life expectancy, mortality rates and lifespan variation compared with the previous trends, with an initial lag of 2 years after the identified change point in the exposure measure. The role of underemployment and individual incomes as outcomes in their own right and as mediating any relationship between austerity and mortality will also be considered. Sensitivity analyses varying the lag period to 0 and 5 years, and adjusting for recession, will be undertaken.Ethics and disseminationAll of the data used for this study are publicly available, aggregated datasets with no individuals identifiable. There is, therefore, no requirement for ethical committee approval for the study. The study will be lodged within the National Health Service research governance system. All results of the study will be published following sharing with partner agencies. No new datasets will be created as part of this work for deposition or curation.

scholarly journals Mortality in Tonga over three triennia, 2010–2018

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Carah Figueroa ◽  
Christine Linhart ◽  
Latu Fusimalohi ◽  
Sioape Kupu ◽  
Gloria Mathenge ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Mortality Rates ◽  
Adult Mortality ◽  
Statistical Testing ◽  
International Agency ◽  
Mortality Data ◽  
Local Health ◽  
Mortality Trends ◽  
Substantial Impact ◽  
International Agencies

Abstract Background Tonga is a South Pacific Island country with a population of 100,651 (2016 Census). This study examines Tongan infant mortality rates (IMR), under-five mortality rates (U5MR), adult mortality and life expectancy (LE) at birth from 2010 to 2018 using a recent collation of empirical mortality data over the past decade for comparison with other previously published mortality estimates. Methods Routinely collected mortality data for 2010–2018 from the Ministry of Health, national (Vaiola) hospital, community nursing reports, and the Civil Registry, were consolidated by deterministic and probabilistic linkage of individual death records. Completeness of empirical mortality reporting was assessed by capture-recapture analysis. The reconciled data were aggregated into triennia to reduce stochastic variation, and used to estimate IMR and U5MR (per 1000 live births), adult mortality (15–59, 15–34, 35–59, and 15–64 years), and LE at birth, employing the hypothetical cohort method (with statistical testing). Mortality trends and differences were assessed by Poisson regression. Mortality findings were compared with published national and international agency estimates. Results Over the three triennia in 2010–2018, levels varied minimally for IMR (12–14) and U5MR (15–19) per 1000 births (both ns, p > 0.05), and also for male LE at birth of 64–65 years, and female LE at birth 69–70 years. Cumulated risks of adult mortality were significantly higher in men than women; period mortality increases in 15–59-year women from 18 to 21% were significant (p < 0.05). Estimated completeness of the reconciled data was > 95%. International agencies reported generally comparable estimates of IMR and U5MR, with varying uncertainty intervals; but they reported significantly lower adult mortality and higher LE than the empirical estimates from this study. Conclusions Life expectancy in Tonga over 2010–2018 has remained relatively low and static, with low IMR and U5MR, indicating the substantial impact from premature adult mortality. This analysis of empirical data (> 95% complete) indicates lower LE and higher premature adult mortality than previously reported by international agencies using indirect and modelled methods. Continued integration of mortality recording and data systems in Tonga is important for improving the completeness and accuracy of mortality estimation for local health monitoring and planning.

scholarly journals Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model

2021 ◽  
Vol 18 (4) ◽  
pp. 2204
Author(s):  
Ana Debón ◽  
Steven Haberman ◽  
Francisco Montes ◽  
Edoardo Otranto
Keyword(s):  
Historical Data ◽  
Mortality Rates ◽  
Parametric Model ◽  
Mortality Data ◽  
Block Bootstrap ◽  
Functional Anova ◽  
Model Block ◽  
The Usa ◽  
Data Constraints ◽  
Carter Model

The parametric model introduced by Lee and Carter in 1992 for modeling mortality rates in the USA was a seminal development in forecasting life expectancies and has been widely used since then. Different extensions of this model, using different hypotheses about the data, constraints on the parameters, and appropriate methods have led to improvements in the model’s fit to historical data and the model’s forecasting of the future. This paper’s main objective is to evaluate if differences between models are reflected in different mortality indicators’ forecasts. To this end, nine sets of indicator predictions were generated by crossing three models and three block-bootstrap samples with each of size fifty. Later the predicted mortality indicators were compared using functional ANOVA. Models and block bootstrap procedures are applied to Spanish mortality data. Results show model, block-bootstrap, and interaction effects for all mortality indicators. Although it was not our main objective, it is essential to point out that the sample effect should not be present since they must be realizations of the same population, and therefore the procedure should lead to samples that do not influence the results. Regarding significant model effect, it follows that, although the addition of terms improves the adjustment of probabilities and translates into an effect on mortality indicators, the model’s predictions must be checked in terms of their probabilities and the mortality indicators of interest.

scholarly journals Cancer Mortality Trend in Central Italy: Focus on A “Low Rate of Land Use” Area from 1982 to 2011

2019 ◽  
Vol 16 (4) ◽  
pp. 628 ◽  
Author(s):  
Francesca Santilli ◽  
Stefano Martellucci ◽  
Jennifer Di Pasquale ◽  
Cecilia Mei ◽  
Fabrizio Liberati ◽  
...  
Keyword(s):  
Land Use ◽  
Cancer Mortality ◽  
Central Italy ◽  
Mortality Rates ◽  
Mortality Data ◽  
Mortality Trends ◽  
High Prevalence ◽  
Oncological Diseases ◽  
Total Cancer ◽  
Low Rate

The aim of the present study was to estimate total cancer mortality trends from 1982 to 2011 in a “low rate of land use” province of the Latium region (Rieti, central Italy) characterized by a low degree of urbanization, a high prevalence of elderly, and a low number of births. Mortality data of the studied period, provided by the Italian National Institute of Statistics, were used for calculating standardized cancer mortality rates. Trends in mortality were analyzed using Joinpoint regression analysis. Results showed that total standardized cancer mortality rates decreased in the monitored area over the study period. A comparison with other provinces of the same region evidenced that the studied province presented the lowest cancer mortality. The three systems/apparatuses affected by cancer that mainly influenced cancer mortality in the monitored province were the trachea-bronchus-lung, colorectal-anus, and stomach. These findings could be attributed to the implement of preventive initiatives performed in the early 2000s, to healthier environmental scenario, and to lower levels of carcinogenic pollutants in air, water, and soil matrices. Thus, our results indicate that the studied area could be considered a “healthy” benchmark for studies in oncological diseases.

scholarly journals Trends in Avoidable Mortality in Lithuania During 2001–2008 and Their Impact on Life Expectancy

Medicina ◽  
2011 ◽  
Vol 47 (9) ◽  
pp. 504 ◽  
Author(s):  
Vilius Grabauskas ◽  
Aldona Gaižauskienė ◽  
Skirmantė Sauliūnė ◽  
Rasa Mišeikytė
Keyword(s):  
Life Expectancy ◽  
Mortality Data ◽  
Avoidable Mortality ◽  
Average Life Expectancy ◽  
Mortality Trends ◽  
Annual Percent Change ◽  
Increasing Trend ◽  
Causes Of Deaths ◽  
Changes Over Time ◽  
The Impact

The process of the restructuring of health care system in Lithuania demonstrates the need to continue the monitoring of changes in avoidable mortality. Objective. To assess the level of avoidable mortality as well as its changes over time in Lithuania during 2001–2008 and to define the impact of avoidable mortality on life expectancy. Material and Methods. The mortality data were taken from the Lithuanian Department of Statistics. Twelve avoidable causes of deaths (treatable and preventable) were analyzed. Mortality trends were assessed by computing the average annual percent change (AAPC). The shortening of average life expectancy was computed from survival tables. Results. During the period 2001–2008, the avoidable mortality was increasing more significantly (AAPC 3.0%, P<0.05) than the overall mortality (AAPC 1.7%, P<0.05) in the population aged 5–64 years. The increasing trend was mainly determined by mortality from preventable diseases (AAPC 4.6%, P<0.05). The avoidable causes of death reduced the life expectancy by 1.77 years (preventable by 1.12 and treatable by 0.63 years). Diversity in trends in mortality of different avoidable causes was disclosed. A declining trend in mortality caused by chronic rheumatic heart disease and lung cancer was observed for males (AAPC –22.6% and –2.1%, respectively; P<0.05). However, the mortality caused by liver cirrhosis was increasing for both genders (AAPC 16.1% for males and 17.6% for females, P<0.01) and that caused by tuberculosis – only for females (AAPC 7.8%, P<0.05). Conclusions. An increasing trend in avoidable mortality was observed. Deaths caused by the diseases that could have been prevented had the greatest impact on the increasing mortality and decreasing life expectancy.

scholarly journals Identifying Structural Breaks in Stochastic Mortality Models

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Colin O’Hare ◽  
Youwei Li
Keyword(s):  
Life Expectancy ◽  
Structural Breaks ◽  
Structural Break ◽  
Mortality Rates ◽  
Insurance Companies ◽  
Mortality Data ◽  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Mortality Forecasts

In recent years, the issue of life expectancy has become of utmost importance to pension providers, insurance companies, and government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data to anticipate future life expectancy and hence quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age, and cohort and forecast these trends into the future by using standard statistical methods. These approaches rely on the assumption that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper, we carry out a comprehensive investigation of the presence or of structural breaks in a selection of leading mortality models. We find that structural breaks are present in the majority of cases. In particular, we find that allowing for structural break, where present, improves the forecast result significantly.

scholarly journals Leukemia mortality in children from Latin America: Trends and predictions to 2030

2020 ◽  
Author(s):  
J. Smith Torres-Roman ◽  
Bryan Valcarcel ◽  
Pedro Guerra-Canchari ◽  
Camila Alves Dos Santos ◽  
Isabelle Ribeiro Barbosa ◽  
...  
Keyword(s):  
Latin American ◽  
Mortality Rates ◽  
World Health ◽  
Mortality Data ◽  
Mortality Trends ◽  
Annual Percent Change ◽  
Universal Healthcare ◽  
The World ◽  
Health Organization ◽  
Universal Healthcare Coverage

Abstract Background: Reports suggest that Latin American and Caribbean (LAC) countries have not reduced in leukemia mortality compared to high-income countries. However, updated trends remain largely unknown in the region. Given that leukemia is the leading cause of cancer-related death in LAC children, we evaluated mortality trends in children (0-14y) from 15 LAC countries for the period 2000-2017 and predicted mortality to 2030.Methods: We retrieved cancer mortality data using the World Health Organization Mortality Database. Mortality rates (standardized to the world standard SEGI population) were analyzed for 15 LAC countries. We evaluated the average mortality rates for the last 5 years (2013-2017). Joinpoint regression analysis was used to evaluate leukemia mortality trends and provide an estimated annual percent change (EAPC). Nordpred was utilized for the calculation of predictions until 2030.Results: Between 2013 and 2017, the highest mortality rates were reported in Venezuela, Ecuador, Nicaragua, Mexico, and Peru. Upward mortality trends were reported in Nicaragua (EAPC by 2.9% in boys, and EAPC by 2.0% in girls), and Peru (EAPC by 1.4% in both sexes). Puerto Rico experienced large declines in mortality among both boys (EAPC by −9.7%), and girls (EAPC by −6.0%). Leukemia mortality will increase in Argentina, Ecuador, Guatemala, Panama, Peru, and Uruguay by 2030.Conclusion: Leukemia mortality is predicted to increase in some LAC countries by 2030. Interventions to prevent this outcome should be tailor to reduce the socioeconomic inequalities and ensure universal healthcare coverage.

scholarly journals Tracking Excess Deaths (TRACKED) – an interactive online tool to monitor excess deaths associated with the COVID-19 pandemic in the United Kingdom

2020 ◽  
Vol 5 ◽  
pp. 168 ◽  
Author(s):  
Michael T. C. Poon ◽  
Paul M. Brennan ◽  
Kai Jin ◽  
Jonine D. Figueroa ◽  
Cathie L. M. Sudlow
Keyword(s):  
United Kingdom ◽  
Northern Ireland ◽  
Cause Of Death ◽  
Excess Mortality ◽  
Dynamic Assessment ◽  
Mortality Data ◽  
Mortality Trends ◽  
Online Tool ◽  
The United Kingdom ◽  
Excess Deaths

Background: We aimed to describe trends of excess mortality in the United Kingdom (UK) stratified by nation and cause of death, and to develop an online tool for reporting the most up to date data on excess mortality Methods: Population statistics agencies in the UK including the Office for National Statistics (ONS), National Records of Scotland (NRS), and Northern Ireland Statistics and Research Agency (NISRA) publish weekly mortality data. We used mortality data up to 22nd May in the ONS and the NISRA and 24th May in the NRS. The main outcome measures were crude mortality for non-COVID deaths (where there is no mention of COVID-19 on the death certificate) calculated, and excess mortality defined as difference between observed mortality and expected average of mortality from previous 5 years. Results: There were 56,961 excess deaths, of which 8,986 were non-COVID excess deaths. England had the highest number of excess deaths per 100,000 population (85) and Northern Ireland the lowest (34). Non-COVID mortality increased from 23rd March and returned to the 5-year average on 10th May. In Scotland, where underlying cause mortality data besides COVID-related deaths was available, the percentage excess over the 8-week period when COVID-related mortality peaked was: dementia 49%, other causes 21%, circulatory diseases 10%, and cancer 5%. We developed an online tool (TRACKing Excess Deaths - TRACKED) to allow dynamic exploration and visualisation of the latest mortality trends. Conclusions: Continuous monitoring of excess mortality trends and further integration of age- and gender-stratified and underlying cause of death data beyond COVID-19 will allow dynamic assessment of the impacts of indirect and direct mortality of the COVID-19 pandemic.

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