Education-related inequalities in cause-specific mortality: first estimates for Australia using individual-level linked census and mortality data

Trends and projections of cause-specific premature mortality in Australia to 2044: a statistical modelling study

Background

Long-term projections of premature mortality (defined as deaths age <75 years) help to inform decisions about public health priorities. This study aimed to project premature mortality rates in Australia to 2044, and to estimate numbers of deaths and potential years of life lost (PYLL) due to premature mortality overall and for 59 causes.

Methods

We examined the past trends in premature mortality rates using Australian mortality data by sex, 5-year age group and 5-year calendar period up to 2019. Cigarette smoking exposure data (1945-2019) were included to project lung cancer mortality. Age-period-cohort or generalised linear models were developed and validated for each cause to project premature mortality rates to 2044.

Findings

Over the 25-year period from 1990-1994 to 2015-2019, there was a 44.4% decrease in the overall age-standardised premature mortality rate. This decline is expected to continue, from 162.4 deaths/100,000 population in 2015-2019 to 141.7/100,000 in 2040-2044 (12.7% decrease). Despite declining rates, total numbers of premature deaths are projected to increase by 22.8%, rising from 272,815 deaths in 2015-2019 to 334,894 deaths in 2040-2044. This is expected to result in 1.58 million premature deaths over the 25-year period 2020-2044, accounting for 24.5 million PYLL. Of the high-level cause categories, cancer is projected to remain the most common cause of premature death in Australia by 2044, followed by cardiovascular disease, external causes (including injury, poisoning, and suicide), and respiratory diseases.

Interpretation

Despite continuously declining overall premature mortality rates, the total number of premature deaths in Australia is projected to remain substantial, and cancer will continue to be the leading cause. These projections can inform the targeting of public health efforts and can serve as benchmarks against which to measure the impact of future interventions. They emphasise the ongoing importance of accelerating the prevention, early detection, and treatment of key health conditions.

Funding

No funding was provided for this study.

Changes in longevity inequality by education among OECD countries before the COVID-19 pandemic

BACKGROUND

Disparities in life expectancy between socioeconomic groups are one of the main challenges for health policy, and their reduction over time is an important policy objective.

METHODS

Observational study using routinely registered data on mortality around 2011 and 2016 by sex, age, educational attainment level, and cause of death in 13 member countries of the Organization for Economic Cooperation and Development (OECD). The main outcome measures are life expectancy by education at the ages of 25 and 65 in 2011 and 2016.

RESULTS

Between 2011 and 2016, the life expectancy gap has increased by 0·2 years among men and 0·3 years among women from 13 available countries. The United States recorded one the largest increases in the absolute life expectancy gap, 1·3 years for women and 1·1 years for men respectively.

CONCLUSION

Inequality in longevity has increased in over half of the countries surveyed and starkly so in the United States in a context of deteriorating health.

TRIAL REGISTRATION

Not applicable.

National estimates of occupation-related inequalities in all-cause mortality using linked Census-mortality data

OBJECTIVE

This report aims to provide national estimates of occupation-related inequalities in all-cause mortality for Australian residents aged 25-64 years.

METHOD

Data came from the 2016 Census linked to Deaths Registrations, available via the Multi-Agency Data Integration Project. Using negative binomial regression, we estimated age-adjusted relative and absolute inequalities in all-cause mortality rates in the 13 months following Census according to occupation, defined using the Australian and New Zealand Standard Classification of Occupations (eight major groups), using managers as the reference group.

RESULTS

Among 10.8M people, there were 20,987 deaths. Age-adjusted mortality rates were lowest among managers and professionals and were generally highest for manual occupations, for example, among men, relative risks (RR) for labourers ranged across age groups from 1.44 (95% CI 1.19-1.75, age 54-64) to 2.99 (1.93-4.65, age 25-34); among women, the RR for machine operators and drivers were 3.95 (1.39-11.21 in age 25-24 and 2.73 (1.66-4.49) in age 45-54, but there was relatively little variation by occupation in women aged 35-44 and 55-64. Around one in five deaths (23% for men, 17% for women) were associated with being in an occupation other than manager.

CONCLUSION

These findings highlight that there is benefit in documenting national mortality inequalities according to occupation in addition to other measures of socioeconomic position. They provide further insights into socioeconomic inequalities in mortality.

IMPLICATIONS FOR PUBLIC HEALTH

Methods that aim to reduce mortality for those in manual occupations, particularly among young men, will reduce inequalities and improve population health.

Inequalities in life expectancy in Australia according to education level: a whole-of-population record linkage study

Background

Life expectancy in Australia is amongst the highest globally, but national estimates mask within-country inequalities. To monitor socioeconomic inequalities in health, many high-income countries routinely report life expectancy by education level. However in Australia, education-related gaps in life expectancy are not routinely reported because, until recently, the data required to produce these estimates have not been available. Using newly linked, whole-of-population data, we estimated education-related inequalities in adult life expectancy in Australia.

Methods

Using data from 2016 Australian Census linked to 2016-17 Death Registrations, we estimated age-sex-education-specific mortality rates and used standard life table methodology to calculate life expectancy. For men and women separately, we estimated absolute (in years) and relative (ratios) differences in life expectancy at ages 25, 45, 65 and 85 years according to education level (measured in five categories, from university qualification [highest] to no formal qualifications [lowest]).

Results

Data came from 14,565,910 Australian residents aged 25 years and older. At each age, those with lower levels of education had lower life expectancies. For men, the gap (highest vs. lowest level of education) was 9.1 (95 %CI: 8.8, 9.4) years at age 25, 7.3 (7.1, 7.5) years at age 45, 4.9 (4.7, 5.1) years at age 65 and 1.9 (1.8, 2.1) years at age 85. For women, the gap was 5.5 (5.1, 5.9) years at age 25, 4.7 (4.4, 5.0) years at age 45, 3.3 (3.1, 3.5) years at 65 and 1.6 (1.4, 1.8) years at age 85. Relative differences (comparing highest education level with each of the other levels) were larger for men than women and increased with age, but overall, revealed a 10–25 % reduction in life expectancy for those with the lowest compared to the highest education level.

Conclusions

Education-related inequalities in life expectancy from age 25 years in Australia are substantial, particularly for men. Those with the lowest education level have a life expectancy equivalent to the national average 15–20 years ago. These vast gaps indicate large potential for further gains in life expectancy at the national level and continuing opportunities to improve health equity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-021-01513-3.