The impact of income definitions on mortality inequalities

Abstract

Income is a strong predictor of adult mortality. Measuring income is not as simple as it may sound. It can be conceptualized at the individual or the household level, with the former better reflecting an individual's earning ability, and the latter better capturing living standards. Furthermore, respondents are often grouped into income categories based on their positions in the income distribution, and this operationalization can be done on the basis of age-specific or total population income distributions. In this study, we look at how four combinations of different conceptualizations (individual vs. household) and operationalizations (age-specific vs. total population) of income can affect mortality inequality estimates. Using Finnish registry data, we constructed period life tables for ages 25+ from 1996 to 2017 by gender and for four income definitions. The results indicated that the slope index of inequality for life expectancy varied by 1.1–5.7 years between income definitions, with larger differences observed for women than for men. The overall age patterns of relative index of inequality for mortality rates yielded by the four definitions were similar, but the levels differed. The period trends across income definitions were consistent for men, but not for women. We conclude that researchers should pay particular attention to the choice of the income definitions when analyzing the association between income and mortality, and when comparing the magnitude of inequality across studies and over time.

Association of income and educational levels with adherence to direct oral anticoagulant therapy in patients with incident atrial fibrillation: A Finnish nationwide cohort study

“…Likewise, differences in the used socioeconomic variables, for example household vs. individual income, may lead to varying results in health inequality research. 26 However, in our study, income and educational levels both had a similar association on the adherence estimates.…”

“…The annual income was capped to a maximum of 100 000 euros to avoid patients’ identifiability due to high incomes. To account for changes in income over time and age, patients were divided into age group and AF diagnosis year specific income quartiles, i.e., each 10‐year age group during each cohort entry year was divided into income quartiles using age group and entry year specific cut‐points 26 . Divisions to income quartiles were performed separately in the implementation and persistence study cohorts.…”

Time Trends and Income Inequalities in Cancer Incidence and Cancer-Free Life Expectancy – a Cancer Site-Specific Analysis of German Health Insurance Data

“…Shi et al. could show that the level of income inequalities in life expectancy is to some extent also influenced by the definition of income ( 55 ). In accordance with this, previous analyses have shown that while the total level of income inequality tends to vary, both household income and individual income are largely reliable to measure health inequalities ( 56 ).…”

Socioeconomic disparities in use of rhythm control therapies in patients with incident atrial fibrillation: A Finnish nationwide cohort study

“…To avoid patients’ identifiability due to high incomes, the annual income was capped to a maximum of 100 000 euros. Since income level is associated with age and the mean income level of patients may vary according to the cohort entry year, the patients were divided into age group and AF diagnosis year specific income quintiles, i.e., each 10-year age group during each cohort entry year was divided into income quintiles using age group and entry year specific cut-points [23] . Since different definitions of income levels may significantly affect the results, sensitivity analysis was performed by using cohort level income quintile cut-points [23] .…”