The authors have declared that no competing interests exist.
Chagas disease (CD) is a neglected chronic parasitic infection and a public health problem that is preventable, and has serious complications. In this study, the effects of age, period and birth cohort (APC Effects) on the evolution of the mortality of that disease in Brazil, from 1980–2014, according to sex and geographic region of the country, were analyzed. Mortality forecasts from the years 2015 to 2034 were estimated.
This is an ecological cross-sectional study in which death records and population data were extracted from the DATASUS (Department of Information Technology of the National Health System) website, in age groups from 20–24 years of age to 80 years and over, from 1980 to 2014. The rates were standardized according to age and sex distributions using the direct method. The APC models were estimated using the Bayesian approach, and the INLA (Integrated Nested Laplace Approximations) method was used for parameter inference. Super dispersion of the data was considered, and we included unstructured random terms in the models.
During the analyzed period, there were 178,823 deaths in Brazil (3.85 annual deaths per 100,000 inhabitants). It was found that temporal effects on CD mortality varied by sex and region. In general, there was an increase in mortality rates up to 30 years of age, and the mortality rates were higher between 50 and 64 years of age. On average, men died five years younger than women. Mortality rates were highest in the Central West and Southeast regions. The Central West, Southeast and Southern regions had a reduction over time in the rate of CD deaths between 2000 and 2014. The mortality rate in the Northeast was not statistically different in any period analyzed, while the North had tendency to increase; however, a significant risk increase was only observed between 1995 and 1999. The rate of mortality was high in older birth cohorts. The overall prediction for the next two decades showed a progressive decline in CD mortality, which will be highest among the young. The expected average reduction was 76.1% compared to the last observed period (2010–2014) and the last predicted (2030–2034) period. The average reduction ranged from 86% in the 20–24 age group to 50% in the 80 and over age group.
In the present study, a higher death rate was observed for ages above 30 years, especially 50 to 64 years, and in the older birth cohorts. We believe these results can be related to period effects of vector control actions and preventive and care measures by the health system of Brazil, in addition to demographic changes in the period. The differences among the regions reflect socioeconomic inequities and access to the healthcare systems in the Brazilian population.
Chagas disease (CD) is a neglected chronic parasitic infection of great interest in public health, because it is preventable and has serious complications. In Brazil, despite the success obtained by controlling transmission by the
Chagas disease (CD) is a chronic parasitic infection caused by Trypanosoma cruzi.
It is estimated that between 6 and 7 million people are infected with CD worldwide. Of these, 5 million are in Latin America, where more than 70 million people are at risk of infection and 12,000 die annually [
In Brazil, it is estimated that from 1 to 4.6 million people are currently infected with this parasite, and despite prevention and control advances in the last 40 years, there are still approximately 6,000 deaths per year [
In 2014, Martins-Melo and colleagues estimated an average annual mortality rate per DC of 3.37/100 thousand inhabitants in the country [
Existing studies have shown that the highest mortality rates occur in the Central West, Southeast and South regions, areas of high endemicity in the past and that received a high migratory flow of individuals from endemic regions in the 1950s-60s. On the other hand, the North and Northeast regions, which have the lowest rates of socioeconomic development in the country, show the lowest mortality rates due to CD, with upward or stationary trends, depending on the time period analyzed. These studies also point out that the highest mortality rates occur in men above 60 years of age and include cardiac involvement as the main complication associated with the disease [
Although the aforementioned studies have contributed to the surveillance and knowledge of the epidemiological profile of the mortality burden due to CD in the last 15 years, most studies do not consider the possible influence of birth cohorts on the temporal change in mortality from that CD. Moreover, the few studies that evaluated birth cohort effects in this context has analyzed the effects only in an exploratory way, without considering probabilistic models [
Age, period, and birth cohort are components (APC effects) that can influence, separately or together, the time trend of an event. In the health context, the effect of age may reflect physiological changes that occur in the individual throughout life, increasing or reducing the risk of illness and/or death. The period effect is related to structural changes affecting all age groups simultaneously, such as innovations in diagnostic and therapeutic techniques, expansion of access to health services, and improvements in the certification of deaths. The birth cohort represents the interaction between the effects of age and period, representing the influence of factors of exposure throughout life and possible determinants of disease and/or mortality [
Because Chagas disease in its chronic phase is a long latency disease, responsible for more than 90% of mortalities, it is believed that different birth cohorts will have differences in exposure to risk and protection factors for this disease. Since
This study analyzes secondary data, available for free download on the DATASUS—Departamento de Informática do SUS (Department of Information Technology of the National Health System) website from the Ministry of Health of Brazil. On this website, the city level is the smallest geographic unit available, so it is impossible to identify the individual. Thus, it was not necessary to submit the study to the local ethics committee because all data analyzed were anonymized.
This is an ecological study of the temporal trend of CD mortality rates in Brazil. Brazil has a population of approximately 207.7 million inhabitants. The country is divided into 26 States and one Federal government. The Federation is further grouped into five major regions (North, Northeast, Southeast, South and Central West) with different geographic, economic and cultural characteristics. The best human development indexes are observed in the South. The Southeast, the most populous region, stands out for its labor market. The Central West, although it includes the capital of the country, has an economy focused on agriculture and livestock. The Northeast region has the lowest human development rates. And finally, The North occupies second place for the worst development rates in the country and is characterized by its low population density due to the Amazon forest.
The annual records of deaths and population data from 1980 to 2014, according to sex and Brazilian geographic region, specific for the age groups 0–4 years to 80 years and over, were extracted from DATASUS, a national electronic database from the Ministry of Health of Brazil. The population data, provided by IBGE (Brazilian Institute of Geography and Statistics), could also be downloaded from the DATASUS website.
The last two years available in the system were discarded, because we wanted five-year ranges. The underlying cause of death selected in the Mortality Information System (SIM/DATASUS) was Trypanosomiasis—codes 086 (ICD-9) and B57 (ICD-10). Death certificates contain demographic (age, gender, education, race, marital status, municipality of residence and municipality of occurrence of death) and clinical information (underlying and associated causes of death). It is the physicians' responsibility to complete the death certificates. Until 1995, reference codes were based on the International Classification of Diseases (ICD) in its 9th revision, and after 1996, they were based on the 10th revision of the ICD [
The correction process was used because of the great disparity in information quality among the Brazilian geographic regions, and because during the period under study, there were important improvements in death registry quality in Brazil due to the expansion of health services with the implementation of the Unified Health System (SUS) [
The coverage degree of the external causes of death was close to 100%. Thus, these reports were excluded, considering only "natural" causes of death (natural causes of death = (total of all causes of death) minus (ill-defined causes of death + external causes)). The proportional redistribution of deaths by year and age group was also performed using the following CD correction: underlying CD deaths + (underlying CD deaths / "natural" causes of death). (3) The deaths obtained in steps 1 and 2 were added to the death records initially classified as CD death, per year and age group.
Subsequently, mortality rates per 100,000 inhabitants were calculated according to sex, age group and geographic region and were standardized using the direct method, using the Brazilian population of the 2010 Census as the standard. The average five-year rates per 100,000 inhabitants, crude and standardized, were calculated, as well as the rates for the whole observed period. We decided to use the age groups and annual periods grouped in intervals of five years, a strategy commonly adopted in APC modeling. We also chose to work with age groups older than 20–24 years, considering the excess of zeros in the age groups younger than 20 years. Thus, we considered 13 age groups, 7 periods, and 19 birth cohorts (1900 to 1990).
The classical proposals that model the interest rate as a result of a linear predictor formed by the sum of the temporal components (age, period and cohort) are problematic due to the linear relationship among those three components, making the complete models unidentifiable. Several proposals have been developed in order to solve this problem, but there is still no consensus or definitive solution in the literature [
In a Bayesian context, the APC model is a hierarchical model that incorporates uncertainty about hyper parameters and avoids difficulties arising from the identifiability problem by the application of mildly informative prior distributions. In addition, the Bayesian model is very popular for projection rates because it does not rely on strong parametric assumptions for future values of period and cohort effects. Smoothing priors represents an attractive alternative to maximum likelihood (ML) based approaches when age groups and periods are given for the same time-interval widths and avoids artifacts such as artificial cyclical patterns [
We have age-specific rates in one set of rates for each stratum, sex and geographical region. Each set of rates could be analyzed separately by means of a classical APC model. However, because of similar relevant risk factors, it was beneficial to analyze all sets of rates jointly, treating some sets of time effects as common across strata. The major advantage of using the classical approach in the present study is due to the possibility of statistically testing the difference in effects estimated for the factor levels, such as sex and region, so we would not need to assume a priori that there is a significant difference among these levels, as in the classical method, making it impossible to perform a formal test. Multivariate APC models share sets of time effects (i.e., age effects), while the remaining parameters can be different. The differences in the stratum-specific time effects in multivariate APC models are identifiable and can be interpreted as log relative risks so that the identifiability problem for univariate APC models is avoided [
As a modeling strategy, first, a univariate Bayesian APC model was adjusted to estimate global temporal effects on the risk of CD death in the country [
APC Models | Linear Predictor |
---|---|
Model 1a (1b) | Joint effects for men and women (or geographic regions) |
Model 2a (2b) | Age-specific effects by sex (or geographic regions) |
Model 3a (3b) | Period-specific effects by sex (or geographic regions) |
Model 4a (4b) | Cohort-specific effects by sex (or geographic regions) |
Model 5a (5b) | Age-specific and period-specific effects by sex (or geographic regions) |
Model 6a (6b) | Age-specific and cohort-specific effects by sex (or geographic regions) |
Model 7a (7b) | Period-specific and cohort-specific effects by sex (or geographic regions) |
To ensure the intercept identifiability of
The temporal effects received a first order random-walk (RW1) as priors, which penalize deviations from a model where all parameters are constant (
For the parameter inference, the INLA (Integrated Nested Laplace Approximations) method, an alternative to MCMC (Monte Carlo via Markov Chains), was used in latent Gaussian models [
The Bayesian approach facilitates predictions because strong parametric assumptions are not necessary. Given the samples of the posterior distribution of the fitted model for the whole country, predictions of Chagas death rates, by age groups, were estimated up to 2034 [
In Brazil, from 1980 to 2014, in individuals aged 0–80 and over, 178,823 deaths were classified with Chagas disease as the basic cause. The average of crude mortality rates was 2.70 deaths/100,000 inhabitants, and the average of standardized mortality rates was 3.85 deaths/100,000 inhabitants over the whole period. After correcting by ill-defined causes, with sex and age ignored, there was a 27% increase in the number of deaths due to CD (227,094 deaths), corresponding to 4.28 deaths/100,000 inhabitants (average of crude mortality rates), and an average of standardized mortality rates of 4.85 deaths/100,000 inhabitants.
The averages of five-year standardized mortality rates for the whole country increased. The highest rates occurred in the 1980s, and the lowest rates occurred in the 2000s. The lowest average rate occurred in the last period analyzed (2010–2014) (
Period-specific rates according to age (a) and age-specific rates according to birth cohort (b).
Period | |||||||||
---|---|---|---|---|---|---|---|---|---|
Subgroup | Rates | 1980–84 | 1985–89 | 1990–94 | 1995–99 | 2000–04 | 2005–09 | 2010–14 | Total |
UCMR |
6,15 | 5,36 | 4,52 | 2,15 | 2,72 | 2,58 | 2,40 | 2,70 | |
USMR |
7,66 | 6,54 | 5,31 | 2,59 | 3,17 | 2,85 | 2,41 | 3,85 | |
Brazil | CCMR |
9,48 | 8,13 | 6,69 | 5,48 | 4,01 | 3,33 | 5,48 | 4,28 |
CSMR |
12,87 | 10,88 | 8,67 | 6,83 | 5,40 | 3,98 | 3,22 | 4,85 | |
UCMR |
2,50 | 2,09 | 1,76 | 1,46 | 1,18 | 0,97 | 0,81 | 1,44 | |
USMR |
3,24 | 3,14 | 2,49 | 2,25 | 1,71 | 1,18 | 0,90 | 1,61 | |
South | CCMR |
3,00 | 2,44 | 1,98 | 1,60 | 1,27 | 1,03 | 0,85 | 1,61 |
CSMR4 | 3,88 | 3,67 | 2,81 | 2,47 | 1,83 | 1,25 | 1,25 | 1,80 | |
UCMR |
6,27 | 5,27 | 4,39 | 3,55 | 2,79 | 2,32 | 1,96 | 3,48 | |
USMR |
9,91 | 8,29 | 6,79 | 5,43 | 4,12 | 3,13 | 2,51 | 3,26 | |
Southeast | CCMR |
7,19 | 5,94 | 4,99 | 4,04 | 3,13 | 2,55 | 2,11 | 3,91 |
CSMR |
11,32 | 9,33 | 7,72 | 6,16 | 4,63 | 3,44 | 2,70 | 4,17 | |
UCMR |
0,33 | 0,49 | 0,59 | 0,50 | 0,47 | 0,47 | 0,55 | 0,50 | |
USMR |
0,50 | 0,79 | 0,98 | 0,96 | 0,88 | 0,79 | 0,84 | 0,84 | |
North | CCMR |
0,48 | 0,74 | 0,90 | 0,72 | 0,64 | 0,57 | 0,67 | 0,66 |
CSMR |
0,72 | 1,19 | 1,51 | 1,39 | 1,20 | 0,95 | 0,96 | 1,12 | |
UCMR |
1,78 | 1,88 | 1,75 | 1,74 | 1,73 | 1,93 | 1,95 | 1,83 | |
USMR |
2,37 | 3,00 | 2,27 | 2,43 | 2,28 | 2,34 | 2,09 | 2,33 | |
Northeast | CCMR |
3,33 | 3,45 | 3,00 | 2,66 | 2,42 | 2,17 | 2,13 | 2,64 |
CSMR |
4,47 | 4,61 | 4,72 | 4,46 | 3,18 | 2,62 | 2,28 | 3,33 | |
UCMR |
26,50 | 21,65 | 16,54 | 10,48 | 9,49 | 7,45 | 12,52 | 11,97 | |
USMR |
40,42 | 32,00 | 23,23 | 18,15 | 15,07 | 12,13 | 8,92 | 16,91 | |
Central West | CCMR |
35,58 | 28,33 | 20,00 | 12,37 | 10,37 | 7,87 | 7,51 | 14,24 |
CSMR |
54,45 | 41,95 | 28,14 | 21,43 | 16,49 | 10,54 | 9,28 | 20,02 | |
UCMR |
7,22 | 6,33 | 5,34 | 1,77 | 3,37 | 3,00 | 2,70 | 3,75 | |
USMR |
9,31 | 7,99 | 6,48 | 9,31 | 4,21 | 9,31 | 2,71 | 4,01 | |
Men | CCMR |
9,48 | 8,13 | 6,69 | 5,48 | 4,01 | 3,33 | 2,93 | 5,25 |
CSMR | 12,21 | 12,62 | 10,10 | 7,96 | 6,24 | 4,60 | 3,68 | 6,08 | |
UCMR |
4,93 | 4,29 | 3,63 | 2,46 | 2,14 | 2,17 | 2,10 | 2,81 | |
USMR |
5,96 | 5,08 | 4,13 | 3,05 | 2,40 | 2,41 | 2,11 | 3,18 | |
Women | CCMR |
6,25 | 5,35 | 4,49 | 2,98 | 4,48 | 2,37 | 2,25 | 3,34 |
CSMR |
7,59 | 6,37 | 5,14 | 3,70 | 4,16 | 2,63 | 2,25 | 4,32 |
1UCMR Uncorrected Crude Mortality Rate.
2USMR Uncorrected Standardized Mortality Rate.
3CCMR Corrected Crude Mortality Rate. Mortality rate after the correction for deaths due to ill-defined cause, with age and sex ignored.
4CSMR Corrected Standardized Mortality Rate. Standardized mortality rate after the correction for deaths with ill-defined cause, with age and sex ignored.
The highest mortality rates were observed for males, with a rate of 6.08 deaths/100,000 males in the period, a rate 1.61 times higher than the female rate (4.32 deaths/100,000 females). Regarding the effects of age, period and cohort, mortality rates by sex were higher, on average, among men for all temporal effects, except in the generation of the 1920s, when the mortality rate in women was higher. For women, the rate was 56.82 (1920–1924) and 41.39 (1925–1929), while for men it was 46.61 (1920–1924) and 40.61 (1925–1929) deaths/100,000 individuals (
Standardized rates of Chagas disease mortality according to the observed effects of age (a,b), period (c,d), and birth cohort (e,f) by sex (first column) and geographic region (second column).
The observed temporal evolution of the rates in the geographic regions, according to the effects of age, period and cohort, showed a heterogeneous pattern. The highest average rate occurred in the Central West region (20.02 deaths/100,000 inhabitants), and the lowest rate occurred in the North region (1.12 deaths/100,000 inhabitants) (
The univariate APC model estimated in Brazil showed that the age-specific death risk for CD ranged from 0.37 to 1.85, and it showed a tendency of increasing with age. People from 20 to 29 years of age had the lowest rate (Estimated 0 < RRR < 1), and people from 50 and 64 years of age had the highest rate (Estimated RRR > 1) (
Estimated mortality rates from Chagas disease and credibility intervals (95%) for the effects of age (a, b), period (c, d), and birth cohort (e, f) in Brazil from 1980–2014.
Based on the
Estimated mortality rates for Chagas disease and credibility intervals (95%) according to sex, for the temporal effects of age (a, b, c) and period (c, d, e) in Brazil from 1980–2014.
Fitted models considering heterogeneity by sex |
|||||||
Models | 1a | 2a | 3a | 4a | 5a | 6a | 7a |
Log Score | 6.032 | 5.716 | 6.033 | 5.864 | 5.594 | 5.795 | 5.704 |
DIC | 349.76 | 324.457 | 349.858 | 334.636 | 318.462 | 330.890 | 332.539 |
Fitted models considering heterogeneity by geographic regions |
|||||||
Modelo | 1b | 2b | 3b | 4b | 5b | 6b | 7b |
Log Score | 5.334 | 5.251 | 5.171 | 5.074 | 4.836 | 4.730 | 4.630 |
DIC | 920.728 | 928.478 | 877.157 | 922.776 | 847.647 | 809.50 | 755.13 |
For the Brazilian regions (models ·b), the best fitted model considered period effect and cohort effect specifics by region.
Estimated rates of mortality due to Chagas disease and intervals of credibility (95%) according to the effects of period (first line) and birth cohort (second line) for different Brazilian regions from 1980–2014.
The predictions of Chagas death rates in Brazil for the next 20 years (2015–2034) indicate a progressive reduction in death rates for all age groups, with a higher reduction in younger age groups that progressively decreases with age (
Adjusted age-specific rates for Chagas disease mortality and confidence intervals (95%) according to age groups, projected from 2030–2034 (predictions from the vertical line) in Brazil.
Although many social transformations have occurred over the years in endemic and non-endemic regions for Chagas disease (CD), it continues to be an important problem for public health in Brazil with new challenges and epidemiological scenarios such as oral transmission and decentralized health systems. In addition, there are difficulties regarding the production and distribution of therapeutic drugs, specialized medical knowledge, and appropriate access for the diagnosis and treatment of disease [
The coverage quality of information systems has an important role in health surveillance, especially in monitoring mortality. However, the information quality of death registries in Brazil, in addition to regional disparities in access to health services, can lead to underestimated mortality rates, even with improved coverage over the years [
We verified that the most important factor associated with the temporal evolution of Chagas mortality in Brazil was the birth cohort, which is generally not considered in temporal trend studies. This evidence suggests that, in the last three decades, the deaths reported due to CD mainly occurred in older generations. These people were more commonly exposed to the main vector responsible for transmission and were infected at a time when therapeutic drugs and diagnoses were less advanced.
The CD death rate was low (0 < RRR < 1) for people born after 1960. The lower mortality after the 1960s may also be a consequence of the migration process that occurred in Brazil, especially in the 1950s and 1960s, when large population groups moved from rural areas with a high risk of infection to urban areas [
These study results also reinforce the success of the National Chagas Disease Control Program. After the control measures were implemented, the younger generations were less exposed to infection by this parasite. Thus, a high prevalence of infection by this parasite in older people is expected due to the blockade of infection in the younger cohorts who live in areas that were previously endemic [
We showed that the death risk was lower between 1995 and 2004, possibly as a result of the decrease in classical vector transmission in endemic areas from 1990 onward, when intergovernmental programs were proposed in order to combat the proliferation of the disease [
In contrast to previous studies, we observed a trend of increasing mortality due to CD from 2005 onward, although the trend was not statistically significant. This shows that there was no the expected decline in the death risk after the discontinuation of
It was observed that the CD mortality rate increased with increasing age up to 30 years and that the death risk was higher for people aged 50–64 years. Some studies, particularly population-based studies in cohorts with long follow-up periods, point to the aging pattern of the Chagas population and to the increase in ages in the higher age groups [
We found that in all periods, the mortality rates were higher in males than females, which is similar to other studies [
We also verified that the temporal effects of age and period varied according to gender. Men had a higher death risk between the ages of 40 and 59 years, and women had a higher death risk at age 50 and older. In addition, the risk for both sexes was high and significant after 2005, although it was higher among men in the period from 1995 to 2004. Nóbrega et al. (2014) observed that the majority of deaths by CD (85%) occurred in men over 60 years of age, and the main cause of death was cardiac involvement [
The highest mortality rates were observed in the Central West and Southeast regions. This depicts T. cruzi infection in the past decades because the mortality rate reflects a large proportion of patients with chronic Chagas disease [
Drummond and Marcopito (2006) found that the mortality coefficient between 1981 and 1998 declined in the Southeast, South and Central West regions, which was different from the North and Northeast [
The North and Northeast regions have been considered areas of low incidence; however, several studies have shown a trend of ascending mortality by CD in these areas [
In the Northern region, it is believed that the increased mortality trend is due to the improvement in the coverage and quality of death records, which is associated with increased surveillance due to the increase in oral transmission in the 2000s. In addition, the migratory flow to this region in the 1970s and 1980s played an important role in CD deaths, since more than 80% of deaths in this region between 1981 and 1998 occurred in persons born in other states of the country [
The Amazon region, which corresponds to most of the Northern region of Brazil, was not considered a priority area for CD prevention and control since it had a low prevalence of the disease and low morbimortality [
We included age-specific rates in one set of rates for each stratum, sex and geographical region that could be analyzed separately by means of a classical APC model. However, because of similar relevant risk factors, it was beneficial to analyze all sets of rates jointly, treating some sets of time effects as common across strata. Multivariate APC models share sets of time effects, while the remaining parameters can be different. The differences in stratum-specific time effects in multivariate APC models are identifiable and can be interpreted as log relative risks so that the identifiability problem for univariate APC models is avoided [
Counterpoints to this work are the information quality and the mortality information system coverage. In the present study, we performed death records correction for ill-defined causes while ignoring sex and age, but we did not correct the underreporting of deaths, and therefore, the Chagas disease mortality rates may be underestimated. In addition to that, the available death information does not discriminate between the acute and chronic phases of the disease, which have had different predominance over time in Brazil. Epidemiological surveillance focuses on the acute phase of the disease, which is the unique phase of compulsory notification in the country, so the mortality rates are always underestimated. We also have not considered the covariables sex and residence region in the predicted rates. In future works, we should consider inequalities due to race and smaller spatial units, such as federated units and municipalities. Additionally, the quality of the fitted models should be retrospectively assessed by cross-validation methods.
Our findings are in line with the reduction of exposure to
(ZIP)