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Socioeconomic differentials and disease-free life expectancy of the elderly in Brazil

for there to be more years lived with disease unless there are reductions in the rate
of morbidity onset or increases in the age at onset. The importance for public health
to prevent disease and delay its progression when mortality is declining has been
made clear by the health expectancy model. This indicator provides a yardstick for
measuring the balance achieved between increasing the length of life and increasing
the quality of life.
Thus, measures of healthy life expectancy are important to guide public policy
because they help governments to plan specific health policies. They can also provide
information on the demand for health services, allowing authorities to consider needs
of the population for care in the present and future (Portrait, Maarten, and Degg,
2001). In addition, Bone, Bebbington and Nicolaas (1998) point out that healthy life
expectancy is a good indicator of population health trends and can be used to monitor
the impact of health and social policies, and allows for comparisons between different
populations and subgroups.
Although there are many studies investigating the healthy life expectancy in Brazil
(for example, Campolina, Adami, Santos et al., 2013; Camargos and Gonzaga, 2015;
Camargos, Rodrigues and Machado, 2009; Camargos, Perpétuo and Machado, 2005;
Romero, Leite and Szwarcwald, 2005), there are no studies analyzing the healthy
life expectancy for a specific chronic disease (disease-free life expectancy) by
socioeconomic status and their evolution over time among the elderly.
In this context, the purpose of this study is to investigate whether socioeconomic
status plays a key role in determining life expectancy, with and without a specific
chronic disease, among the elderly. In addition, our working hypothesis is that
changes in the magnitude of disease-free life expectancy will differ when comparing
socioeconomic status by income as opposed to comparing by education level.
Therefore, this study presents and compares estimates of life expectancy with and
without a specific chronic disease among older adult populations in Brazil, for the
years 1998 and 2008, by sex and socioeconomic status.

2 Material and Methods

The study was developed based on data provided by the Pesquisa Nacional por
Amostra de Domicílios (PNAD — Brazilian National Household Survey) from
the Instituto Brasileiro de Geografia e Estatística (IBGE — Brazilian Institute of
Demographic Geography and Statistics), by Sistema de Informação sobre Mortalidade
(MIS — Mortality Information System), and by Life Tables from IBGE (2014). PNAD
is a cross-sectional household interview survey with national coverage, held annually,
in order to obtain information on the household of individuals, migration, education,
labor force, and fertility characteristics. In 1998, PNAD included a health supplement
in its questionnaire, with information to be collected every five years; the 2008 dataset
was the most recent available information. We based our calculations on prevalence
data from the 1998 and 2008 PNAD cross-sectional surveys. The population and
corrected mortality data for underreporting of deaths for 1998 and 2008 were used to
generate the estimates of age-specific mortality rates. To calculate this, we used the
populations of mid-1998 and mid-2008 and total deaths in the respective years. The
estimated population at mid-1998 and mid-2008 was obtained based on the Brazilian
Demographic Census of 2000 and 2010.
The prevalence of chronic disease was estimated on the basis of self-reported
presence of hypertension, diabetes, bronchitis/asthma, and heart disease. The
demographic variables are age (60–64, 65–69, 70–74, 75–79, 80–84, and 85 years
or older) and sex. The socioeconomic variables include household income and
education level (years of school). In Brazil, formal education is organized into first
level (1–8 years of school), second level (9–11 years), and higher. For our analysis, we
categorized by years of education: less than 4 completed years as low education and 11
or more years as high education. For income, we considered total household income by

66 International Journal of Population Studies 2017, Volume 3, Issue 1