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Using mortality follow-up of surveys to estimate social inequalities in healthy life years.

ABSTRACT: BACKGROUND: The estimation of healthy life years (HLY) by socio-economic status (SES) requires two types of data: the prevalence of activity limitation by SES generally extracted from surveys and mortality rates by SES generally derived from a linkage between the SES information in population databases (census, register) and mortality records. In some situations, no population-wide databases are available to produce mortality rates by SES, and therefore some alternatives must be explored. This paper assesses the validity of calculating HLY by SES using mortality rates derived from a linkage between surveys and mortality records. METHODS: TWO SURVEYS WERE CHOSEN TO EXPLORE THE VALIDITY OF THE PROPOSED APPROACH: The Belgian Health Interview Survey (HIS) and the Belgian Survey on Income and Living Conditions (SILC). The mortality follow-up of these surveys were used to calculate HLY by educational level at age 25. These HLY were compared with HLY estimates calculated using the mortality follow-up of the 2001 census. The validity of this approach was evaluated against two criteria. First, the HLY calculated using the census and those calculated using the surveys must not be significantly different. Second, survey-based HLY must show significant social inequalities since such inequalities have been consistently reported with census-based HLY. RESULTS: Both criteria were met. First, for each educational category, no statistically significant difference was found when comparing census-based and survey-based HLY estimates. For instance, men in the lowest educational category have shown a HLY of 34 years according to the HIS, and while this figure was 35.5 years according to the census, this difference was not statistically significant. Second, the survey-based HLY have shown a significant social gradient. For instance, men in the highest educational category are expected to live 9.5 more HLY than their counterparts in the lowest educational category based on the HIS estimates, compared with 7.3 HLY based on the census estimates. CONCLUSIONS: This article suggests that using the mortality follow-up of a nationally representative cross-sectional survey is a valid approach to monitor social inequalities in HLY in the absence of population-wide data.

SUBMITTER: Charafeddine R

PROVIDER: S-EPMC4030465 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Using mortality follow-up of surveys to estimate social inequalities in healthy life years.

Charafeddine Rana R Berger Nicolas N Demarest Stefaan S Van Oyen Herman H

Population health metrics 20140512

<h4>Background</h4>The estimation of healthy life years (HLY) by socio-economic status (SES) requires two types of data: the prevalence of activity limitation by SES generally extracted from surveys and mortality rates by SES generally derived from a linkage between the SES information in population databases (census, register) and mortality records. In some situations, no population-wide databases are available to produce mortality rates by SES, and therefore some alternatives must be explored. ...[more]

PMID: 24855457

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Project description:Socioeconomic inequalities in mortality can be explained by different groups of risk factors. However, little is known whether repeated measurement of risk factors can provide better explanation of socioeconomic inequalities in health. Our study examines the extent to which relative educational and income inequalities in mortality might be explained by explanatory risk factors (behavioral, psychosocial, biomedical risk factors and employment) measured at two points in time, as compared to one measurement at baseline.From the Norwegian total county population-based HUNT Study (years 1984-86 and 1995-1997, respectively) 61 513 men and women aged 25-80 (82.5% of all enrolled) were followed-up for mortality in 25 years until 2009, employing a discrete time survival analysis. Socioeconomic inequalities in mortality were observed. As compared to their highest socioeconomic counterparts, the lowest educated men had an OR (odds ratio) of 1.41 (95% CI 1.29-1.55) and for the lowest income quartile OR = 1.59 (1.48-1.571), for women OR = 1.35 (1.17-1.55), and OR = 1.40 (1.28-1.52), respectively. Baseline explanatory variables attenuated the association between education and income with mortality by 54% and 54% in men, respectively, and by 69% and 18% in women. After entering time-varying variables, this attainment increased to 63% and 59% in men, respectively, and to 25% (income) in women, with no improvement in regard to education in women. Change in biomedical factors and employment did not amend the explanation.Addition of a second measurement for risk factors provided only a modest improvement in explaining educational and income inequalities in mortality in Norwegian men and women. Accounting for change in behavior provided the largest improvement in explained inequalities in mortality for both men and women, as compared to measurement at baseline. Psychosocial factors explained the largest share of income inequalities in mortality for men, but repeated measurement of these factors contributed only to modest improvement in explanation. Further comparative research on the relative importance of explanatory pathways assessed over time is needed.

Project description:BackgroundSocial inequalities in mortality persist in high-income countries with universal health care, and the mechanisms by which these inequalities are generated remain unclear. We aimed to examine whether social inequalities were present before or after the onset of adverse health conditions (multimorbidity, frailty, and disability).MethodsOur analysis was based on data from the ongoing Whitehall II cohort study, which enrolled British civil servants aged 35-55 years in 1985-88. Participants were assessed for three indicators of socioeconomic status (education, occupational position, and literacy) at age 50 years. Participants underwent clinical examinations (in 2002-04, 2007-09, 2012-13, and 2015-16) for assessment of frailty (two or more of low physical activity, slow walking speed, poor grip strength, weight loss, and exhaustion) and disability (two or more difficulties in bathing, dressing, going to the toilet, transferring, feeding, and walking). In addition, electronic health records were used to assess the incidence of multimorbidity (two or more of diabetes, coronary heart disease, stroke, chronic obstructive pulmonary disease, depression, arthritis, cancer, dementia, and Parkinson's disease) and mortality. In analyses adjusted for sociodemographic factors, we used multistate models to examine social inequalities in transitions from healthy state to adverse health conditions and subsequently to mortality.FindingsOf 10 308 individuals in the Whitehall II study cohort, 6425 had relevant data available at 50 years and to the end of follow-up on Aug 31, 2017, and were included in our analysis. Participants were followed up for a median of 23·6 years (IQR 19·6-28·9). 1694 (26·4%) of 6425 participants developed multimorbidity, 1733 (27·0%) became frail, 692 (10·8%) had a disability, and 611 (9·5%) died. Multimorbidity (hazard ratio [HR] 4·12 [95% CI 3·41-4·98]), frailty (HR 2·38 [95% CI 1·93-2·93]), and disability (HR 1·73 [95% CI 1·34-2·22]) were associated with increased risk of mortality; these associations were not modified by socioeconomic status. In multistate models, occupation was the socioeconomic status indicator that was most strongly associated with inequalities in the transition from healthy state to multimorbidity (HR 1·54 [95% CI 1·37-1·73]), to frailty (HR 2·08 [95% CI 1·85-2·33]), and to disability (HR 1·44 [95% CI 1·18-1·74]). Socioeconomic status indicators did not affect transitions to mortality in those with multimorbidity, frailty, or disability.InterpretationSocioeconomic status affects the risk of multimorbidity, frailty, and disability, but does not affect the risk of mortality after the onset of these adverse health conditions. Therefore, primary prevention is key to reducing social inequalities in mortality. Of the three adverse health conditions, multimorbidity had the strongest association with mortality, making it a central target for improving population health.FundingUK Medical Research Council; National Institute on Aging, National Institutes of Health; British Heart Foundation.

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Using mortality follow-up of surveys to estimate social inequalities in healthy life years.

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Using mortality follow-up of surveys to estimate social inequalities in healthy life years.

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