Worked to Death? A census-based longitudinal study of the relationship between the numbers of hours spent working and mortality risk

Background There has been an increasing interest in the health effects of long
working hours, but little empirical evidence to substantiate early
10 case series suggesting an increased mortality risk. The aim of the
current study is to quantify the mortality risk associated with long
working hours and to see if this varies by employment relations and
conditions of occupation.
Methods A census-based longitudinal study of 414 949 people aged 20-59/64
15 years, working at least 35 h/week, subdivided into four occupational
classes (managerial/professional, intermediate, own account workers,
workers in routine occupations) with linkage to deaths records
over the following 8.7 years. Cox proportional hazards models were
used to examine all-cause and cause-specific mortality risk.
20 Results Overall 9.4% of the cohort worked 55 or more h/week, but this
proportion was greater in the senior management and professional
occupations and in those who were self-employed. Analysis of 4447
male and 1143 female deaths showed that hours worked were
associated with an increased risk of all-cause mortality only for
25 men working for more than 55 or more h/week in routine/semiroutine
occupations [adjusted hazard ratios (adjHR) 1.31: 95%
confidence intervals (CIs) 1.11, 1.55)] compared with their peers
working 35–40 h/week. Their equivalent risk of death from cardiovascular
disease was (adjHR 1.49: 95% CIs 1.10, 2.00).
30 Conclusions These findings substantiate and add to the earlier studies indicating
the deleterious impact of long working hours but also suggest that
the effects are moderated by employment relations or conditions of
occupation. The policy implications of these findings are discussed.

International mortality modelling- an economic perspective

A recent literature has developed on modelling mortality in multiple populations together. The purpose of this paper is to suggest a reason why mortality in different populations may be related based on an economic literature on technology and knowledge diffusion.

Do fertility transitions influence infant mortality declines? Evidence from early modern Germany

The timing and sequencing of fertility transitions and early-life mortality declines in historical Western societies indicate that reductions in sibship (number of siblings) may have contributed to improvements in infant health. Surprisingly, however, this demographic relationship has received little attention in empirical research. We outline the difficulties associated with establishing the effect of sibship on infant mortality and discuss the inherent bias associated with conventional empirical approaches. We offer a solution that permits an empirical test of this relationship while accounting for reverse causality and potential omitted variable bias. Our approach is illustrated by evaluating the causal impact of family size on infant mortality using genealogical data from 13 German parishes spanning the sixteenth, seventeenth, eighteenth, and nineteenth centuries. Overall, our findings do not support the hypothesis that declining fertility led to increased infant survival probabilities in historical populations.

Seasonality of cardiovascular risk factors: an analysis including over 230 000 participants in 15 countries

Objective: To assess the seasonality of cardiovascular risk factors (CVRF) in a large set of population-based studies.

Methods: Cross-sectional data from 24 population-based studies from 15 countries, with a total sample size of 237 979 subjects. CVRFs included Body Mass Index (BMI) and waist circumference; systolic (SBP) and diastolic (DBP) blood pressure; total, high (HDL) and low (LDL) density lipoprotein cholesterol; triglycerides and glucose levels. Within each study, all data were adjusted for age, gender and current smoking. For blood pressure, lipids and glucose levels, further adjustments on BMI and drug treatment were performed.

Results: In the Northern and Southern Hemispheres, CVRFs levels tended to be higher in winter and lower in summer months. These patterns were observed for most studies. In the Northern Hemisphere, the estimated seasonal variations were 0.26 kg/m2 for BMI, 0.6 cm for waist circumference, 2.9 mm Hg for SBP, 1.4 mm Hg for DBP, 0.02 mmol/L for triglycerides, 0.10 mmol/L for total cholesterol, 0.01 mmol/L for HDL cholesterol, 0.11 mmol/L for LDL cholesterol, and 0.07 mmol/L for glycaemia. Similar results were obtained when the analysis was restricted to studies collecting fasting blood samples. Similar seasonal variations were found for most CVRFs in the Southern Hemisphere, with the exception of waist circumference, HDL, and LDL cholesterol.

Conclusions: CVRFs show a seasonal pattern characterised by higher levels in winter, and lower levels in summer. This pattern could contribute to the seasonality of CV mortality.

Depression and mortality: Artifact of measurement and analysis?

Background: Previous research demonstrates various associations between depression, cardiovascular disease (CVD) incidence and mortality, possibly as a result of the different methodologies used to measure depression and analyse relationships. This analysis investigated the association between depression, CVD incidence (CVDI) and mortality from CVD (MCVD), smoking related conditions (MSRC), and all causes (MALL), in a sample data set, where depression was measured using items from a validated questionnaire and using items derived from the factor analysis of a larger questionnaire, and analyses were conducted based on continuous data and grouped data.

Methods: Data from the PRIME Study (N=9798 men) on depression and 10-year CVD incidence and mortality were analysed using Cox proportional hazards models.

Results: Using continuous data, both measures of depression resulted in the emergence of positive associations between depression and mortality (MCVD, MSRC, MALL). Using grouped data, however, associations between a validated measure of depression and MCVD, and between a measure of depression derived from factor analysis and all measures of mortality were lost.

Limitations: Low levels of depression, low numbers of individuals with high depression and low numbers of outcome events may limit these analyses, but levels are usual for the population studied.

Conclusions: These data demonstrate a possible association between depression and mortality but detecting this association is dependent on the measurement used and method of analysis. Different findings based on methodology present clear problems for the elucidation and determination of relationships. The differences here argue for the use of validated scales where possible and suggest against over-reduction via factor analysis and grouping.

Comparing the decline in coronary heart disease and stroke mortality in neighbouring countries with different healthcare systems

Objective
To examine age and gender specific trends in coronary heart disease (CHD) and stroke mortality in two neighbouring countries, the Republic of Ireland (ROI) and Northern Ireland (NI). Design Epidemiological study of time trends in CHD and stroke mortality.

Setting/patients
The populations of the ROI and NI, 1985–2010.

Interventions
None.

Main outcome measures
Directly age standardised CHD and stroke mortality rates were calculated and analysed using joinpoint regression to identify years where the slope of the linear trend changed significantly. This was performed separately for specific age groups (25–54, 55–64, 65–74 and 75–84 years) and by gender. Annual percentage change (APC) and 95% CIs are presented.

Results
There was a striking similarity between the two countries, with percentage change between 1985 and 1989 and between 2006 and 2010 of 67% and 69% in
CHD mortality, and 64% and 62% in stroke mortality for the ROI and NI, respectively. However, joinpoint analysis identified differences in the pace of change between the two countries. There was an accelerated pace of decline (negative APC) in mortality for both CHD and stroke in both countries from the mid-1990s (APC ROI −8% (95% CI −9.5 to 6.5) and NI −6.6% (−6.9 to −6.3)), but the accelerated decrease started later for CHD mortality in the ROI. In recent years, a levelling off in CHD mortality was observed in the 25–54 year age group in NI and in stroke mortality for men and women in the ROI.

Conclusions
While differences in the pace of change in mortality were observed at different time points, similar, substantial decreases in CHD and stroke mortality were achieved between 1985 and 1989 and between 2006 and 2010 in the ROI and NI despite important differences in health service structures. There is evidence of a levelling in mortality rates in some groups in recent years.

Is Mortality Spatial or Social?

Mortality modelling for the purposes of demographic forecasting and actuarial pricing is generally done at an aggregate level using national data. Modelling at this level fails to capture the variation in mortality within country and potentially leads to a mis-specification of mortality forecasts for a subset of the population. This can have detrimental effects for pricing and reserving in the actuarial context. In this paper we consider mortality rates at a regional level and analyse the variation in those rates. We consider whether variation in mortality rates within a country can be explained using local economic and social variables. Using Northern Ireland data on mortality and measures of deprivation we identify the variables explaining mortality variation. We create a population polarisation variable and find that this variable is significant in explaining some of the variation in mortality rates. Further, we consider whether spatial and non-spatial models have a part to play in explaining mortality differentials.

Structural Breaks in Mortality Models and their Consequences

In recent years, the issue of life expectancy has become of upmost importance to pension providers, insurance companies and the 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 in order 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 using standard statistical methods. The modeling approaches used failed to capture the effects of any structural change in the trend and, thus, potentially produced incorrect forecasts of future mortality rates. In this paper, we look at a range of leading stochastic models of mortality and test for structural breaks in the trend time series.

Identifying Structural Breaks in Stochastic Mortality Models

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.