IT   EN

Ultimi tweet

Poor socioeconomic circumstances are associated with the loss of more than two years of life

Having poor socioeconomic conditions -- such as a lower occupational position -- can take away 2.1 years of life on average from a person. This is the conclusion of a study published in The Lancet by LIFEPATH, a project funded by the European Commission, which investigates the biological pathways underlying social differences in healthy ageing.

Low socioeconomic conditions are almost as deadly as smoking, having diabetes, or being physically inactive. Smoking is associated with the loss of 4.8 years of life; diabetes, 3.9; and physical inactivity, 2.4. High alcohol intake can take away one year of life.

This is the first study ever to compare life expectancy among people of different socioeconomic status, and cross-correlate it with six other major known risk factors like smoking and diabetes. These other six factors are already included in World Health Organization global mortality reduction strategy. Socioeconomic status is not.

“We were surprised to find that poor social and economic circumstances seem to kill people at the same rate as powerful risk factors such as smoking, obesity, and hypertension. Because these circumstances are modifiable, they should be included in the list of risk factors targeted by global health strategies,” argues Silvia Stringhini, lead author of the study. She is a researcher at Lausanne University Hospital in Switzerland.

International leading-edge analysis with reliable data

LIFEPATH researchers gathered and analysed data from 48 independent cohort studies from the United Kingdom, Italy, United States, Australia, Portugal, Switzerland and France. The lives were examined of over 1.7 million adults in total. Socioeconomic status was measured by their last known occupational title, and participants were followed for an average of 13 years. Statistics obtained were then compared to those of six risk factors included in the WHO “25x25 plan” for global health.

“Education, income, and work are known to affect health, but few studies have examined how important these socioeconomic factors actually are. This is the reason why we decided to compare the importance of socioeconomic factors as determinants of health with six major risk factors targeted in global health strategies for the reduction of premature mortality,” says Mika Kivimaki, a professor at University College London, who is one of the two senior authors of the study.

Social risk factors versus individual risks factors

Low socioeconomic status is one of the strongest predictors of premature mortality worldwide, but health policymakers often do not consider it a risk factor to target. Socioeconomic circumstances and their consequences are modifiable by policies at the local, national, and international levels. Changing “upstream factors” such as earned income tax credits, occupation, or early childhood education, is more likely to have an impact, compared to changing “downstream” interventions like smoking cessation assistance or dietary advice. This is because focusing on downstream factors favours privileged persons, who can more easily change their habits.

“Socioeconomic status is important because it is a summary measure of lifetime exposures to hazardous circumstances and behaviours that goes beyond the risk factors for noncommunicable diseases that policies usually address,” says Paolo Vineis, professor at Imperial College London and head of LIFEPATH. “The main aim of our consortium is to understand the biological pathways through which social inequalities lead to health inequalities in order to provide evidence for public health institutions and policymakers.”

 

About LIFEPATH

LIFEPATH is an EU-funded project aimed at providing updated, relevant, and innovative evidence for the relationship between social disparities and healthy ageing, to lay ground for the development of future health policies and strategies. LIFEPATH experts develop original study designs that integrate social science approaches with biology and big data analysis, using existing population cohorts and omics measurements.

Commenta questo articolo:

*
Il tuo indirizzo email non sarà visibile agli altri utenti.
Il commento sarà pubblicato solo previa approvazione del webmaster.