This paper describes the burden of disease for the Brazilian older adults, those aged 60 years or more. In 2010, Brazil had about 207.7 million inhabitants, living in five regions, 26 states and the Federal District. The number of people aged 60 or more years was estimated in 20.590.597, about 10% of the total population . In order to express the diversity among the five regions of the country, we presented the health metrics of the states with the most numerous elderly population within each region. These are São Paulo (4.771.822 older adults, 11.6% of population) in the Southeast (SE) region, Bahia (1.450.007 older adults, 8.2%) in the Northeast (NE) region, Rio Grande do Sul (1.461.480 older adults, 13.7%) in the South (S) region, Pará (534.461 older adults, 7.1%) in the North (N) region, and Goiás (560.451 older adults, 9.4%) in the Central-West (CW) region. Some states have a higher proportion of older adults, such as Rio de Janeiro (SE, 13%), Paraíba (NE, 12%) and Mato Grosso do Sul (CW, 9.8%), but with a lower absolute number .
All estimates, as well as the figures and graphics, were obtained from the Global Burden of Disease 2017, available on the public website of the Institute of Health Metrics and Evaluation (IHME) of the University of Washington. Data points were obtained through the collaboration of Brazil Ministry of Health and IHME . The graphics and figures were extracted from the IHME site, with the elderly designated as in developed countries, 65 + years-old. Since Brazilian legislation classifies as elderly those with 60 + years old, we decided to show the data considering this age range. The Brazilian Institute of Geography and Statistics (IBGE) provided the population estimates based on projections from 2010 census . Data on causes of death came from the Mortality Information System (SIM) of the Ministry of Health. In order to calculate the disease prevalence and injury incidence, population-based studies on the prevalence of diseases in Brazil were comprehensively searched, in addition to information obtained from national databases of morbidity, such as the Hospital Information System (SIH), the Outpatient Information System (SIA), and the Injury Information System (SINAN) [6, 10].
Mortality estimates were corrected for underreporting and garbage codes. In addition to absolute numbers of deaths and age-standardized mortality, the rates of years of life lost (YLLs) expressed the effect of premature deaths by age, sex, year and place. YLLs were obtained by multiplying the number of deaths caused by a disease, in each age group, by the remaining life expectancy at this age, regardless of gender [11, 12]. The estimates on mortality, age-standardized mortality rates and causes of death are available at https://vizhub.healthdata.org/cod/ .
The methods to obtain LE (life expectancy) at birth or any age have been previously reported.  Healthy life expectancy (HALE) summarizes overall population health, accounting for length of life and level of health loss by age using years of life lived with disability (YLDs) estimates and the GBD life tables, as previously described .
The metric YLDs represents morbidity by multiplying the prevalence of each disease-related sequelae by its disability weight [14, 15]. A specific software, DisMod-MR, was used for data processing on Baysean meta-regression models to generate consistent estimates of incidence, prevalence, duration of disease remission and excess risk of death for each disease [14, 15]. The sources of data used are available at: https:/ghdx.healthdata.org/gbd-2017/data-input-sources .
Estimates of disability-adjusted life-years lost (DALYs) were obtained by adding YLLs and YLDs, the burden of disease being a sum of lethal and non-lethal diseases . In this study, the distributions of mortality by the main causes of death and the distribution by DALYs were very similar, given the greater impact of YLLs in this age group (data not shown). Therefore, we will present the main causes of death and the YLDs by place, sex and age groups: 60–64, 65–69, 70–74, 75–79 and 80 + years.
All estimates were drawn 1,000 times, and the 95% uncertainty limits value were defined by 2.5º and 97.5º of the estimated values. The 95% uncertainty intervals (95% UI) include uncertainties of all sources and modeling steps, such as sample size variability of the various sources of data, adjustments to general mortality sources, parameter uncertainty in model estimation, specification of uncertainty for models of causes of death, and different data availability by age, sex, year and location .
The analysis of causes of death or disability comprises different degrees of disaggregation. Level 1 divides diseases into three broad groups (1-communicable diseases, maternal and nutritional diseases, 2- non-communicable diseases and 3-injuries). Level 2 contains 21 groups of diseases, such as cardiovascular diseases, cancers and traffic accidents. Level 3 shows separate causes for 168 diseases, such as chronic renal failure. Level 4, on the other hand, breaks down diseases into further 289, for example, chronic renal failure due to diabetes, and level 5 describes diseases with degrees of severity (879 diseases and their sequelae) . In this study, we use level 1 to compare the metrics between the older adults and the younger population. The burden of diseases was shown at level 4, since more aggregated levels did not express well the differences in disease burden between 2000 and 2017 (data not shown).
The socio-demographic index (SDI) is a composite measure that aggregates the total fertility rate under the age of 25 years, the lag distributed income per capita and average educational attainment of each location . The scores range from zero to one, that is, the lowest income, lowest education and highest fertility, to the highest income, highest education and lowest fecundity. According to the value of the SDI, the sites are classified as high, medium-high, medium, medium-low or low SDI. Overall, Brazil ranked in the medium SDI category in 2015 [6, 10].
The Institutional Review Board of the University of Washington approved the GBD study. There was no need to submit to this research to the local Institutional Review Boards, as data were obtained from a public domain secondary database, without nominal identification, in accordance with Decree No. 7,724, May 16, 2012 and Resolution 510, of April 7, 2016. The Institutional Review Board of the Universidade Federal de Minas Gerais approved the GBD Brazil study, under the protocol CAAE–62803316.7.0000.5149. A consent to participate does not apply, as no individual patient data was collected.