The Role of Race in Deaths of Despair in Brazil: is It a White People Problem?

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Raphael Mendonça Guimarães, Karina Cardoso Meira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2273817/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A new paradigm has been discussed regarding the impact of economic cycles on the mortality pattern from specific causes. These causes are called deaths of despair, and they selectively impact specific demographic groups. Also, low- and middle- income countries are most affected due to their economic and social instability. In this sense, the objective of study was to compare the magnitude of disparity in deaths from despair according to sex, age, and race in Brazil. We performed Poisson regression modeling to estimate the magnitude of the association between sex, age group, race, and deaths from despair. We found an association between mortality from despair and male sex (PR=6.15, 95%CI 6.09 – 6.22); emphasis on the age groups from 40 to 49 years old (PR=2.45, 95% CI 2.41 – 2.48) and 50 to 59 years old (PR=2.39, 95% CI 2.36 – 2.43); and brown (PR=1.21, 95% CI 1.20 – 1.22) and black race (PR=1.36, 95% CI 1.34 – 1.37). The present study preliminarily presents the effect of the economic crisis and mortality in the population, with demographic differences. Association with race was opposite to that verified in the original study in US, which suggests that this variable should be analyzed in the light of structural context. Health sciences/Health care/Disease prevention Health sciences/Health care/Public health Mortality – Brazil Economic crisis Demography Health Brazil – Economic policy Figures Figure 1 Introduction Inequities in health and mortality have become an increasingly important topic of study among sociologists, demographers, economists, and social epidemiologists. In recent years, in demography and epidemiology, considerable progress has been made in documenting such inequalities in various contexts, using multiple measures of all-cause and cause-specific health and mortality [ 1 ]. Income inequality is a central theme in health studies today. The development of the models described above, associated with equity issues [ 2 ] and economic development models [ 3 ], has brought to light the need for a deeper discussion about the consequences of heterogeneity in the distribution of income and wealth in the health of the population, despite the difficulty in measuring inequalities [ 4 ] or even in conceptualizing and distinguishing inequalities from inequities [ 5 ]. Two associated demographic trends have generated considerable interest: income stagnation and increased premature mortality from suicides, drug intoxication, and alcoholic liver disease among low-educated non-Hispanic US white people. Indeed, recent increases in premature mortality have coincided with decades of economic decline for less educated and unskilled workers [ 6 ]. Economists interpret these population-level trends to indicate that despair induced by financial stressors is a shared pathway to these causes of death [ 7 ]. The experience in the US shows an increase in mortality in middle-aged, non-Hispanic white men and women [ 8 ]. These combined trends have led to a rise in mortality in young and middle-aged adults. The significant contributors were deaths from unintentional poisoning, opioids, suicide, and alcohol. The underlying hypothesis is that economic insecurity directly affects increasing mortality rates from these specific causes. The term attributed to this group, Deaths of Despair (DoD), is the jargon used in economics and, more recently, in public health to describe deaths attributable to suicide, liver disease, and overdose of drugs [ 9 ] and their associated factors. A pioneering study by Case and Deaton [ 10 ] identified this pattern. Much has been speculated about the mechanism by which these two measures – there is a connection between economic stagnation and death from this group of causes. Despite the phenomenon that occurred among white people (which was later observed in black people), the persistent inequality in health by race calls attention. It raises interesting questions about how causal explanations emerge and what influences the speed with which they are adopted, although these changes occur differently in age groups and sex [ 11 ]. In their most recent report, Case and Deaton [ 8 ] also found a rise in mortality rates in both urban and rural areas. In other words, there is a relationship between an unfavorable economic situation and selective death due to certain conditions. There seems to be an essential difference concerning elementary characteristics of the demographic composition, such as age, sex, and race. The social progress achieved by Brazil at the beginning of the 21st century is undeniable. However, the benefits of social progress are not enjoyed equally. Although there is an average gain in life expectancy, the income concentration and inequality indicators remain high, making Brazil comparable not to developed countries but to countries with greater social fragility and social deprivation, for which race has a critical differential [ 12 ]. Since the beginning of economic stagnation in Brazil in 2011, because of the global financial crisis that began in 2008, Brazil has experienced one of the most severe periods of recession in its recent history [ 13 , 14 ]. Progressively, since 2015, the economic crisis in Brazil has led to an increase in poverty and the implementation of long-term fiscal austerity measures that will substantially reduce spending on social assistance programs as a percentage of the country's GDP over the next 20 years [ 15 ]. The effect of economic crises on health is variable. On the one hand, during periods of economic difficulty, we expect that decreasing investments in health expenditures compromises the quality of services provided, which is reflected in the health indicators of the countries [ 16 ]. This deterioration in health outcomes seems more evident in countries with weaker social protection schemes [ 17 ]. In addition, the need to act on social determinants has led to the development of conceptual frameworks to facilitate understanding of the social processes that impact health. Therefore, we consider it suitable to evaluate the difference in the pattern and level of mortality due to fiscal and economic austerity, analyzing its effect among different subgroups. In this way, we intend to compare the magnitude of the disparity in deaths from despair according to sex, age, and race in Brazil. Results Descriptive statistics from death of despair rates to each group express how different they are. In general, middle-aged black and brown men are more related to this group of causes of death. Still, statistics before and after the beginning of economic stagnation show that the values are systematically lower in the period before 2011, regardless of the explanatory variable, and the difference was statistically significant in all its categories (Table 1). This evidence reinforces that not only the variables sex, age group, and race can be considered explanatory for the occurrence of deaths from despair. Also, that it behaves differently over time, using the trade-off considered as the point beginning of economic stagnation in the country. Descriptive statistics on the death rate from despair before and after the onset of economic stagnation show that the values are systematically lower in the period before 2011 (m=8.69, SD = 0.72 – 2003 to 2011 vs. m=11.62, SD = 0.63 – 2011 to 2018), regardless of the explanatory variable, and the difference was statistically significant in all its categories. This evidence reinforces that not only sex, age group, and race explain the occurrence of deaths from despair, but also that it behaves differently over time, using the trade-off considered as the starting point of economic stagnation in the country. The analysis of Figure 1 suggests that the period after 2011 is not homogeneous concerning the trend of death from despair. However, from a methodological point of view, it is impossible to perform a time series analysis for sequences with few points in time. Therefore, it is impossible to estimate the effect of the economic changes that took place between 2011 and 2018 for each specific phase of the recession and the measures to combat it. However, it is possible to establish these periods and, from the data, explore these time segments as categorical variables. For this, we consider the division between the periods from 2003 to 2007 (before the world crisis), 2008 to 2010 (the period between the world crisis and the beginning of the Brazilian economic stagnation), 2011 to 2014 (period of economic stagnation with attempts to intervene of the State to minimize the deleterious effects), and from 2015 to 2018 (period with economic recession and fiscal austerity measures). First, we observe a difference in the average death rate due to despair in each period, with a progressive increase over time. In addition, it is worth noting that the variability around the mean decreased in the period from 2015 to 2018, showing a more homogeneous behavior of the trend over the periods described. Furthermore, we explored the time effect by introducing all those variables in the Poisson model that included gender, age group, race, and time variable. Results are on Table 2. When adjusting the model with all the variables, the final model showed robustness, confirming the association between the explanatory variables and death from despair. For the gender variable, men have a more significant association with death from despair (RR=6.16, 95% CI 5.92 – 6.41). There is a more significant association for the race variable between brown (RR=1.21, 95% CI 1.18 – 1.25) and black people (RR=1.36, 95% CI 1.29 – 1.43). Finally, for the age group variable, all age groups are associated with death from despair, taking the group between 20 and 29 years old as a reference. We highlight that the most significant associations are in the 30 to 39 age groups (RR=2.46, 95% CI 2.34 – 2.58) and 40 to 49 years (RR=2.40, 95% CI 2.27 – 2.54). In this model, the association with the phases of the economic cycle is more evident: the initial phase of the crisis (2008 – 2011) (RR=1.08, 95% CI 1.04 – 1.13); the initial period of Brazilian economic stagnation (2011 – 2014) (RR=1.10, 95% CI 1.06 – 1.15); and the period of onset of economic recession (2015 – 2018) (RR=1.24, 95% CI 1.20 – 1.30). Furthermore, we observed a gradient in this relationship (p-value of trend <0.001), corroborating a previous result that, even with the increase in the level of trends, there is persistence in the rise in the association effect. Therefore, we consider that the worsening of the crisis interferes in the strength of association with deaths from despair. Discussion Socioeconomic status is a multifaceted concept usually measured by education, income, or occupation. A vast body of empirical research in contemporary developed countries has established a consistent relationship between these measures and health [ 18 , 19 ]. The primary underlying rationale is that differences in mortality are not biological in origin but social. Historical social and political structure changes form an essential backdrop for the discussion of economic systems. The economic crisis brings with it a reflection on the effects of the current economic system and its cycles on the life and health of populations [ 20 ]. In most contemporary societies, there is a gradient of mortality by socioeconomic level for all ages [ 21 ]. Differences in mortality among adults – commonly divided between race, sex, socioeconomic status, and marital status – are of outstanding research and public policy interest. The inverse relationship between socioeconomic status and the level of mortality became more significant after 1960 [ 22 , 23 ]. The literature suggests that people with more significant socioeconomic deprivation are more likely to die, and these differences vary with sex, some socioeconomic indicators, and race [ 24 ]. These changes in understanding the health-disease process meant that, since the 1980s, social inequalities in health have been well documented [ 25 , 26 ] as a fundamental concept in the population and health. public health. Despite changes in the social structure, in the population's pattern of illness and death, the expansion of health and well-being services, and general improvements in living standards and quality of life, these inequalities persisted in the decades following these transformations, and some countries have even increased. Notably, in the 1990s, the Theory of Fundamental Causes was developed by Phelan and Link [ 27 ]. This theory points out why the association between socioeconomic status and health disparities persists over time, especially when component causes associated with the morbidity and mortality profile in situations of socioeconomic deprivation have supposedly been resolved. The theoretical model underlying the theory explains that socioeconomic status incorporates a series of resources in addition to income per se. Still, other dimensions are considered protective of health, such as knowledge, social position, social networks, and power [ 28 ]. It means that even if screening and health intervention techniques improve substantially, groups with lower socioeconomic status have more difficulty obtaining access to them. Therefore, there is a persistence in the health inequality of the population due to a lag in the improvement by social groups. Phelan and Link's Theory posits that a fundamental social cause of health inequalities has four components: the cause influences various health outcomes; the cause affects disease outcomes through various risk factors; the cause involves access to resources that can help to avoid health risks or minimize the consequences of the disease when it occurs, and the association between a root cause and health is reproduced over time through the replacement of intervention mechanisms. To these criteria, socioeconomic status is a fundamental cause of health inequities [ 28 ]. Link and Phelan argue that the root cause theory helps explain why the emphasis on intervening risk factors is ineffective, as it leaves the structural determinants of disease untouched. It means that so-called structural social elements persist over time and maintain health inequalities. In this way, inequities in health do not refer to any differences but those that place certain social groups in persistent situations of disadvantage and discrimination. The literature points to issues such as structural racism, structural gender inequity, and structural heterogeneity (relating to the concept of social class) as edifying several modern societies, including Brazil. In this aspect, it is important to mention them [ 29 – 31 ]. “Deaths of despair” are still a recent topic, with little evidence or robust study outside the United States. In this way, any comparison is based on the original studies by Case and Deaton [ 10 ]. Although our research presents an association between deaths from despair and variables such as sex (male) or age group (primarily middle-aged), two characteristics stand out due to the difference in the profile found in the United States: race, whose association was opposite to that found in the United States, and the time effect, considering not only death as a response to the crisis but the intensification of this association thanks to crisis control measures, precisely those that impose greater fiscal austerity on the population. Therefore, we chose to discuss the results considering these two questions. The past 30 years have seen a remarkable growth in scientific evidence examining how racism can adversely affect health. Racism is considered a fundamental cause of adverse health outcomes. However, the manifestation of this relationship needs to be seen, given the similarities and differences between national and cultural contexts [ 32 ]. Structural racism occurs at a societal level and refers to how laws are written or enforced, which benefits the majority and harms racial minorities in accessing opportunities and resources, including social protection mechanisms [ 33 ]. This structural inequality is strongly present in the social determination of the health-disease process [ 34 ]. Racism structurally operates in the capitalist social formation, defining peripheral places of belonging in the social and economic fields [ 35 ]. Its permeability through structures, policies, and practices makes it capable of defining opportunities and values ​​for people and populations [ 36 ]. This view corroborates the pillars of the so-called Structuralist Theory of Development. This theory helps explain why some regions lag in economic development, linking this time lag to distributive inequality within the lagging region and across countries. Raúl Prebisch [ 37 ] describes this phenomenon as a slow and gradual diffusion of progress on an international scale and gives him responsibility for differences in the level of development between countries. Understanding these asymmetric dynamics is central to explaining the persistence of heterogeneity and extreme inequality between nations. More than 50 years after its formulation, the Structuralist Theory of Development still influences economic thinking. More recently, structuralist development macroeconomics has advanced in some of the assumptions of the original theory [ 38 ]. The main instrument for structural transformation lies in the developmental macroeconomic policy [ 39 ]. However, in new developmentalism, the role of the State diminishes, and that of the market increases. Globalization reinforces this model, and, in this sense, economic polarization causes and is a consequence of a delay in the social development of most peripheral countries. For this reason, it is possible that some causal mechanisms observable in countries with a central economy do not occur in the same way in countries with a peripheral economy, as was the case with the relationship between race and deaths from despair. Brazilian statistics have been systematically less favorable to people of color in Brazil for decades [ 40 ]. Although this group represents 56% of the Brazilian population, this aspect is verified in different elements. In education, for example, the illiteracy rate is 9.1% among black people, against 3.9% among white people. Regarding the labor market, the average salary of black is lower than that of white people. The average per capita household income of black and brown people in 2018 was US $ 256.00, and among white was US $ 506.00. The unemployment rate is also higher. In the same year, the unemployment rate among black people was 14.1% and 9.5% among white people [ 41 ]. Interestingly, the discussion of race is one of the main criticisms of the work of Case and Deaton [ 42 ]. In their 2017 follow-up, Case and Deaton compared white North Americans with white Europeans to explore possible explanations for the increase in mortality found in their 2015 analyses. There was something inherent in the “American experience of being white.” It means that it is unique in how social and economic systems are constantly changing. Perhaps it is related to the mortality pattern in this group of middle-aged white men in the United States. However, it does not affect the white population of European, Canadian, or other countries in the same way. Australian or predominantly white [ 8 ]. Critics suggested that Case and Deaton were drawing attention away from more persistent problems, such as consistently higher black death rates. In addition, the most scathing criticisms refer to the collective national response and the attention given to the phenomenon of “deaths of despair”, since the face of disadvantage is now white [ 43 ]. Blacksher [ 44 ], when mentioning this discussion about race, points out that there is some concern that mortality data can be used to justify more help for white people, who in the past ignored the same problems among black people. In the United States, social class appears to be associated with deaths from despair, mainly due to the epidemic of opioid use. Furthermore, it is essential to mention that the social consequences of the opioid epidemic differ from previous drug crises. Part of this can be explained by the fact that the crack crisis in the 1990s was associated with an increase in violent crime, while the opioid crisis occurred against a backdrop of decreasing crime rates [ 45 ]. Classical developmentalism, known as “Latin American structuralism,” coined the expression “center-periphery.” It described the asymmetries of the international order and maintained that industrialization was the way for peripheral economies to benefit from the technical progress of developed economies, progressively increasing – and, therefore, in a delayed way – the standard of living of the population [ 46 , 47 ]. That said, we believe that the eventual differences between this thesis and the original North American study may be due, on the one hand, to the historical difference in the construction of racism and its leading role in the construction of the social structure. On the other hand, it may result from a delay in identifying a social phenomenon and pointing to a possible occurrence of the scenario currently observed in the United States only in a few years or decades. Regarding the temporal gradient, we emphasize that fiscal austerity policies have been used to respond to the economic crisis and fiscal deficit in developed and developing countries. They vary in content, intensity, and implementation schedule; such models advocate reducing public spending. They promote the removal of social investment, the retraction of public assets, and the replacement of the State by the private sector in the provision of certain services linked to social policies [ 48 ]. These austerity measures - reducing social spending and increasing taxation - do more harm to disadvantaged groups. Briefly, we assumed that there is evidence that austerity is regressive, affecting mainly the poor, thus increasing socioeconomic inequalities [ 49 ]. In general, they tend to substantially impact the most vulnerable groups and disadvantaged regions of countries. The literature suggests no immediate changes in the pattern of overall mortality due to the crisis [ 50 ]. We consider it appropriate to say that this lack of association occurs mainly due to the long latency time of chronic diseases. The effect is not immediate, especially in countries at an advanced stage of the epidemiological transition. However, we can see that the effect is significant when stratifying by cause group. This evidence that there is a selectivity to death from specific causes due to economic crises, including short-term effects [ 51 ]. It occurs mainly in peripheral economy countries, which suggests that the change in mortality trends is more evident in places with weaker social protection systems [ 52 ]. In particular, the comparison of expected values ​​at each moment of the economic crisis reveals that the death rates from despair depend on how much the crisis penalizes the population. Furthermore, this effect differentially affects age groups by sex and race [ 53 ]. The evidence is inconclusive, but our findings are consistent with evidence showing a disproportionate increase in deaths from despair – particularly suicide – among men and older, mainly middle-aged groups. As we said earlier, the race attracted attention, and we consider it a claimed proxy for socioeconomic status. We believe it essential to mention that there is previous evidence for Brazil that the recession contributed to the increase in mortality. Furthermore, the same study highlights that spending on health and social protection appeared to mitigate harmful health effects, especially among vulnerable populations. This evidence supports more health and social protection systems [ 54 ]. The persistence of vulnerability associated with the crisis, and above all, the austerity that follows it, changes the trend. We realize this when decomposing the historical series into phases of the economic crisis [ 55 ]. In this regard, the study by Benach et al. [ 56 ] warns of the need to pay special attention to the long-term effects of the recession on mortality inequalities and their variation within countries and the differentials by race, migration status, and severity of the disease. The more recent evidence indicates that not only the period effect of the crisis time frame but its persistence penalizes groups in a very different way. It reaches the most vulnerable groups every time the crisis is prolonged, and government actions preserve the State and not the population. It reinforces our argument that these crises are especially harmful to the health of the most vulnerable groups. When governments do not implement policies to protect these social groups in times of economic difficulties, there is inevitably an acceleration of cumulative disadvantages between them and the more privileged. This relationship occurs since the reduction in income, the increase in unemployment and cuts in the health budget are the main determinants of the rise in mortality [ 57 ]. In this regard, we highlight the study by Suhrcke & Stuckler [ 58 ], whose findings separate the effects of economic crises from government responses. Its main message is that austerity policies may result in independent and far more significant adverse health effects than the economic crisis itself. This evidence is in line with the results of our study, which shows a temporal gradient effect and deaths from despair, possibly due to a deterioration in social protection caused by the crisis and exacerbated by the fiscal austerity measures adopted by Brazil in response to economic instability. Stuckler and Basu [ 59 ] analyze global data over time to show how government policy becomes a matter of life and death during financial crises. The author's starting point is what they call "natural experiments," which represent situations in which several countries adopted economic measures in the face of periods of economic crisis and instability, and what the repercussion of these measures - classified as austerity or security - was in the health of the population [ 60 ]. Stuckler and Basu's work is emphatic when it says that austerity policies devastate the population's health. The analysis of data from Europe and the United States proves that the result of the adoption of this set of austere measures is associated with tens of thousands of suicides and more than one million cases of depression [ 61 ]. The most recent evidence shows that these measures affect those who were already vulnerable before the economic crisis, such as those with precarious jobs or housing or with existing health problems. Furthermore, people living in countries with robust social protection systems can experience less degraded health than those with relatively weaker systems [ 62 ]. Therefore, the combined effects of the economic crisis and structural adjustment policies on health are of most significant interest to Latin American countries. Our study has limitations. Its ecological nature means that the analysis is based on aggregate-level data and does not allow for individual-level inferences. Also, we could not explicitly control migration fluctuations due to missing data. However, we recognize that the net migration in Brazil is not large enough to impact the measures. Furthermore, by controlling for region-specific fixed effects and region-time interactions, we can, to some extent, mitigate this problem. Finally, we emphasize that our study does not establish a causal relationship between economic events and deaths from despair. The results support a context effect. The associations between mortality and macroeconomic indicators, such as unemployment, require bolder designs at the individual level to control the impact of health expenditure, social protection schemes, employment status, and family support networks. Only then will we be able to understand better the mechanisms of the effects of the crisis on health. Despite the similarities with the original study by Case and Deaton, we emphasize the differences concerning race. On the one hand, the forms of dimension and expression of racism in Latin America and Brazil differ from those identified in American culture. That has repercussions on how the social determinants of health emerge, including race. Racism conforms and reproduces itself historically in Brazilian society and institutions, supported by narratives of domination, such as the myth of racial democracy. It produces concealments of its political, social and economic plots, which have a negative impact on the living and health conditions of the black population. We can say that Case and Deaton's findings encourage a more careful analysis of the underexplored concept of "whiteness" and what the white race means for health. Often in demographic and epidemiological studies, we use white people as the reference group for the leading health inferences. Still, the meaning or interpretation of this comparison is not disputed or poorly explained. The increase in the mortality of working-class white men may represent the first marker that traditional paradigms in population health research that consider white people as the universal subject will need to reflect this new demographic reality. For Brazil, I emphasize that the group of black men aged between 30 and 49 years is more vulnerable to despair. In the end, this study concludes that "Deaths of despair" is more than a terminology but constitutes a hypothesis that deserves more robust conceptual mapping, with empirical studies that use longitudinal and hierarchical data between individual factors and contextual effects. Methods Data Source It is an ecological study carried out for Brazil with data on deaths between 2003 and 2018. The data came from two sources: for deaths, we considered information from the Datasus Mortality Information System (SIM) by age, sex, race, and cause of death. For the population, we used data from the Brazilian Institute of Geography and Statistics (IBGE) based on population estimates. We used the 2000 and 2010 census populations disaggregated by age group, sex, and race and obtained the populations for the inter-census years 2001 to 2009 by interpolation. Then, with the estimate of the geometric growth rate from the previous period, we calculate the population estimates for the period from 2011 to 2018. Independent variables We consider as independent variables: sex (male and female); age (divided into decennial groups in adulthood, from the '20s, to the last group of over 80's); and race, divided into white, black, and brown people. Our study was particularly interested in racial issues involving black and brown people. In addition, the occurrence of indigenous and Asian people is shallow [ 63 ]. For this reason, we chose to exclude these racial categories. After checking the effect of individual variables, we added time intervals related to economic cycles: we used the period before the 2008 world crisis as a counterfactual scenario (2003 to 2008). We defined the period from 2008 to 2011 as the period whose economic repercussion had not yet been widely noticed in Brazil. Then, the period from 2011 to 2015 corresponds to the period in which there was economic stagnation; and the period from 2015 to 2018, where effectively the scenario was of economic recession and implementation of fiscal austerity measures. We mention that Case and Deaton's original study [ 10 ] adequately describes the American population and deaths from despair not only by sex, age, race, and education. The information on education absent in almost half of the death records in Brazil limits the analysis capacity of the present study for comparison with the original North American study. However, it is essential to consider that, unlike in the United States, the number of Brazilians with higher education is only 16.5% [ 41 ]. This low frequency would potentially create a floor effect, a reduction in intergroup variability due to the high concentration of people in the low education categories. This phenomenon may compromise the property of this indicator to discriminate the populations under comparison. Thus, even if this information were available, it might not be the best criterion for analyzing the Brazilian people. Dependent Variable We used the codes of deaths of despair considering the criteria established by Case and Deaton [ 8 , 10 ]. We performed the categorization of causes of death was performed using the codes of the International Classification of Diseases, 10th revision (ICD-10), considering groups as follows: Accidental or intentional poisoning and poisoning of undetermined intent from drug exposure; drugs in the blood; Drug-induced illnesses; Mental/behavioral disorders due to drugs; Alcohol-induced illnesses; Mental/behavioral disorders due to alcohol; Accidental or intentional poisoning and poisoning of undetermined intent form alcohol exposure; Suicide (for more information relating groups and its codes, see Supplementary File #1). Adjust mortality levels and pattern As it is a very heterogeneous set of municipalities, to ensure data quality, we adjusted mortality estimates for underreporting, age errors, and ill-defined causes [ 64 , 65 ]. We did the adjustment for the effect of underreporting on mortality rates by applying indirect estimation methods to the 2000 and 2010 census data. The methods used were Synthetic Extinct Generation (SEG) and General Growth Balance (General Growth Balance, GGB) in combination [ 66 ] to estimate adult mortality. Secondly, we corrected the mortality data by the methods described for the Mortality Information System (SIM) set of deaths. Based on the duly updated data, we considered only deaths from the above causes. We corrected deaths of despair records by proportional redistribution by year and age group of deaths classified as ill-defined causes, using the methodology proposed by the World Health Organization (WHO) [ 67 ]. For more information concerning adjustment techniques application, see Supplementary File #2). Data analysis We analyzed the death count using Poisson regression to obtain the association between deaths from despair according to sociodemographic and time variables. Since the size of the subpopulations by age group, sex, and race is different over the years of analysis, we used a component (offset) for controlling the number of cases per population in each of the ranges to control the count by quantitative (volume) and qualitative change (distribution of covariates such as sex and age) of the population, In a Poisson regression analysis, the response variable is quantitative, given by the count of successes (deaths from despair), according to this equation: E(Y) = \(\frac{{{\mu }}^{{\text{Y}}_{\text{i}}} {\text{e}}^{-{\mu }}}{{\text{Y}}_{\text{i}}!}\) Where E(Y ) is the conditional expectation of the dependent variable Y; µ = e β 0 + β1Xi ; β i are the parameters. To estimate the parameters, we used the maximum likelihood estimation method according to this formulation: $$\text{L}\left({\beta }\right)= \prod _{\text{i}=1}^{\text{n}}\frac{{\left[{\mu }({\text{X}}_{\text{i}},{\beta })\right]}^{{\text{Y}}_{\text{i}}} \text{e}\text{x}\text{p}\left[-{\mu }({\text{X}}_{\text{i}},{\beta })\right]}{{\text{Y}}_{\text{i}}!}$$ $$\text{l}\text{n}\left(\right(\text{L}\left({\beta }\right)= \sum _{\text{i}=1}^{\text{n}}{\text{Y}}_{\text{i}}\text{l}\text{n}\left[{\mu }\left({\text{X}}_{\text{i}},{\beta }\right)\right]- \sum _{\text{i}=1}^{\text{n}}{\mu }\left({\text{X}}_{\text{i}},{\beta }\right)- \sum _{\text{i}=1}^{\text{n}}\text{l}\text{n}({\text{Y}}_{\text{i}}!)$$ After using numerical methods to estimate β from the derivative of the logarithm of the likelihood function, we find estimators β that can be represented generically, by $${\widehat{{\mu }}}_{\text{i}}={\mu }(\text{X},\widehat{{\beta }})$$ As a way of testing the significance of the parameters, we used the maximum likelihood ratio test. The prevalence coefficient returns the proportion of cases concerning a population or study sample. Through them, we calculate the ratio of prevalence coefficients to estimate the association between the categories of a covariate from a reference category. To compare the frequency in each category of covariates, we used Pearson's Chi-Square and its respective p-value. Similarly, to observe the presence of a gradient between the ordinal categories of stratification, we calculated Chi-Square-trend with the individual p-value. The explanatory variables were tested individually and added to the model through the forward method whenever they presented statistical significance (p < 0.05). Interaction terms were also tested in the model, aggregating the variables two by two and then with triple interaction terms when applicable. We chose the reference categories considering the one with the lowest occurrence of deaths. Then we calculated crude and adjusted prevalence ratios for each variable according to the best model obtained by the maximum likelihood statistics. We also estimated confidence intervals at a significance level of 95%. The adjusted prevalence ratios considered the effect of individual association and the impact of the interaction term together. Finally, the Poisson distribution assumes that the mean and variance are equal. So we obtained diagnoses that reject the possibility of under dispersion (mean greater than variance) or overdispersion (mean less than variance). The regression considered the volume of deaths for the entire period and then for the pre-intervention and post-intervention periods. In this way, we could verify the change in the strength of association associated with the economic crisis and the adoption of fiscal austerity measures. We analyzed data using R software search procedures. Declarations Author contributions (names must be given as initials) RMG contributed to conceptualization, project management, data extraction and analysis, manuscript writing and final review. KCM contributed to conceptualization, manuscript writing and final review Data availability statement Database used for this study is publicly available. The analyzes were carried out using public and universally accessible databases, without identifying people, for whom sensitive data are omitted. Access to mortality data is available at http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obt10uf.def. For more information, please contact [email protected] Competing Interests Statement Authors declare no competing interests. References Barreto, ML. Health inequalities: a global perspective. Ciência & Saúde Coletiva. 22 ( 7) , 2097–2108 (2017). Vieira-da-Silva, LM; Almeida FN. Equidade em saúde: uma análise crítica de conceitos. Cadernos de Saúde Pública , 25 ( Supl . 2 ). s217-s226 (2009). Buss PM, Pellegrini Filho A. A saúde e seus determinantes sociais. Physis: Revista de Saúde Coletiva. 17 ( 1 ), 77–93 (2007). Jannuzzi PM. et al. Construindo mapas de pobreza, serviços públicos e de oportunidades para o Plano Brasil Sem Miséria. Cadernos de Estudos Desenvolvimento Social em Debate. 19 , 78–87 (2014). Braveman P. Health Disparities and Health Equity: Concepts and Measurement. Annual Review of Public Health. 27 , 167–194 (2006). Shanahan L et al. Does Despair Really Kill? A Roadmap for an Evidence-Based Answer. Am J Public Health. 109 ( 6 ), 854–858 (2019). Woolf SH, Schoomaker H. Life Expectancy and Mortality Rates in the United States, 1959–2017. JAMA. 322 ( 20 ),1996–2016 (2019). Case A, Deaton A. Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity . The Brookings Institution, 397–476 (2017). Zeglin RJ, Niemela DRM, Baynard CW. Deaths of Despair in Florida: Assessing the Role of Social Determinants of Health. Health Educ Behav. 46 ( 2 ),329–339 (2019). Case A, Deaton A. Rising Morbidity, and Mortality in Midlife among White Non-Hispanic Americans in the 21st Century. Proceedings of the National Academy of Sciences. 112 ( 49 ), 15078-83 (2015). Simou E, Koutsogourgou E. Effects of the economic crisis on health and healthcare in Greece in the literature from 2009 to 2013: a systematic review. Health Policy. 115 ( 2–3 ), 111–9 (2014). UNDP. Human Development Report 2015 - Work for Human Development . New York. NY: United Nations Development Programme (2015). Oreiro JL. A grande recessão brasileira: diagnóstico e uma agenda de política econômica. Estudos Avançados. 31 ( 89 ), 75–88 (2017). Skoufias E, Nakamura S, Gukovas RM. Safeguarding against a reversal in social gains during the economic crisis in Brazil. Working Paper 112896 . Washington (DC): World Bank (2017). Rasella D et al. Child morbidity and mortality associated with alternative policy responses to the economic crisis in Brazil: A nationwide microsimulation study. PLoS Med . 22 ; 15 ( 5 ),e1002570 (2018). Ruckert A, Labonté R. Health inequities in the age of austerity: The need for social protection policies. Soc Sci Med. 187 , 306–311 (2017). Stuckler D et al. The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis. Lancet. 374 , 315–323 (2009). Deaton A. Health, inequality, and economic development. Journal of Economic Literature. 41 ,113–158 (2013). Torssander J, Erikson R. Stratification, and mortality—A comparison of education, class, status, and income. European Sociological Review. 26 , 465–474 (2010). Bacigalupe A, Escolar-Pujolar A. The impact of economic crises on social inequalities in health: what do we know so far? Int J Equity Health. 25 , 13:52 (2014). Bengsston T, Dribe M, Helgertz J. When Did the Health Gradient Emerge? Social Class and Adult Mortality in Southern Sweden, 1813–2015. Demography. 57 ( 3 ), 953–977 (2020). Link BG, Phelan JC. Social conditions as fundamental causes of disease. Journal of Health and Social Behavior . 35 ( Extra Issue ), 80–94 (1995). Marmot M, Allen JJ. Social determinants of health equity. Am J Public Health. 104 ( Suppl 4 ), S517-9 (2014). Meara ER, Richarrs S, Cutler DM. The gap gets bigger: Changes in mortality and life expectancy, by education, 1981–2000. Health Affairs. 27 , 350–360 (2008). Islamm MM. Social Determinants of Health and Related Inequalities: Confusion and Implications. Front Public Health. 7 ,11 (2019). Kail BL, Spring A, Gayman M. A Conceptual Matrix of the Temporal and Spatial Dimensions of Socioeconomic Status and Their Relationship with Health. J Gerontol B Psychol Sci Soc Sci. 74 ( 1 ), 148–159 (2019). Phelan JC, Link BG, Tehranifar P. Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. Journal of Health and Social Behavior. 51 , S28–S40 (2010). Clouston SAP, Link BG. A retrospective on fundamental cause theory: State of the literature, and goals for the future. Annu Rev Sociol. 47 ( 1 ), 131–156 (2021). Nunes A; et al. Medindo desigualdades em saúde no Brasil . Brasília: OMS/Opas/IPEA; 2001. Menicucci TMG. Regionalização no federalismo brasileiro. Cad Saude Publica. 35 ( Suppl 2 ), e00078419 (2019). Viana ALD, Lozzi FL. Inequalities at the center of the debate. Desigualdades no centro do debate. Cad Saude Publica. 35 ( Suppl 2 ), e00163619 (2019). Williams DR, Lawrance JA, Davis BA. Racism and Health: Evidence and Needed Research. Annu Rev Public Health. 40 ,105–125. 2019. Yearby J. Structural Racism and Health Disparities: Reconfiguring the Social Determinants of Health Framework to Include the Root Cause. The Journal of Law, Medicine & Ethics. 48 ( 3 ), 518–526 (2020). Gee GC, Ford CL. Structural racism and health inequities: Old Issues, New Directions. Du Bois Rev. 8 ( 1 ),115–132. Madeira Z, Gomes DDO. Persistentes desigualdades raciais e resistências negras no Brasil contemporâneo. Serv. Soc. Soc. 133 ,463–479 (2018). Werneck J. Racismo institucional e saúde da população negra. Saude soc. 25 ( 3 ), 535–549 (2016). Prebisch R. O desenvolvimento econômico da América Latina e alguns de seus problemas principais (1949). In: Ricardo Bielschowsky (org.). Cinqüenta anos de pensamento na Cepal . Volume 1, p.78. - Rio de Janeiro, Record. (2000) Bresser-Pereira LC, Oreiro JL, Marconi N. Macroeconomia desenvolvimentista . Rio de Janeiro: Elsevier, 2016. Colistere RP. O desenvolvimentismo cepalino: problemas teóricos e influências no Brasil. Estud. av. 15 ( 41 ), 21–34 (2001). Heringer R. Desigualdades raciais no Brasil: síntese de indicadores e desafios no campo das políticas públicas. Cad de Saúde Pública. 18 ( Suppl ), 57–65 (2002). Instituto Brasileiro de Geografia e Estatística (IBGE). Síntese de indicadores sociais: uma análise das condições de vida da população brasileira: 2020 – IBGE, Coordenação de População e Indicadores Sociais, 2020. Case A, Deaton A. Deaths of Despair and the future of capitalism . Princeton: Princeton University Press, 2020 Guo J. If White America is in ‘crisis,’ what have Black Americans been living through?. Washington Post Wonkblog. Last accessed Nov 8, 2022. From: https://www.washingtonpost.com/news/wonk/wp/2017/04/04/if-white-america-is-in-crisis-what-have-black-americans-been-living-through/?utm_term=.7 cd3d55a1444 Blacksher E. Shrinking poor white life spans: Class, race, and health justice. American Journal of Bioethics. 18 ( 10 ), 3–14 (2018). Welhasen C. Precarious Data: Crack, Opioids, and Visualizing a Drug Abuse Epidemic. Online J Public Health Inform. 11 ( 1 ), e315 (2019). Prebisch R. The economic development of Latin America and its principal problems . Nova York: United Nations Department of Economic Affairs/mimeo, 1950 Ocampo JA. Raúl Prebisch and the development agenda at the dawn of the twenty-first century. CEPAL Review. 75 ( 1 ), 25–40 (2001). Paes-Souza R, Schramm JMA, Mendes LVP. Fiscal austerity and the health sector: the cost of adjustments. Ciência & Saúde Coletiva. 24 ( 12 ), 4375–4384 (2019). Figari F, Paulus A, Sutherland H. The Design of Fiscal Consolidation Measures in the European Union: Distributional Effects and Implications for Macroeconomic Recovery . EUROMOD at the Institute for Social and Economic Research, 2015. Palència L et al. Effect of the recent economic crisis on socioeconomic inequalities in mortality in nine urban areas in Europe. Gac Sanit. 34 ( 3 ), 253–260 (2020). Laliotis I, Ioannidis JPA, Stavropoulou C. Total, and cause-specific mortality before and after the onset of the Greek economic crisis: an interrupted time-series analysis. Lancet Public Health. 1 ( 2 ), e56-e65 (2016). Falagas ME, Vouloumanou EK, Mavros MN, Karageorgopoulos DE. Economic crises and mortality: a review of the literature. Int J Clin Pract. 63 ( 8 ), 1128–35 (2009). Alvarez-Galvez J, Suarez-Lledo V, Salvador-Carulla L, Almenara-Barrios J. Structural determinants of suicide during the global financial crisis in Spain: Integrating explanations to understand a complex public health problem. PLoS One. 16 ( 3 ), e0247759 (2021). Hone T et al. Effect of economic recession and impact of health and social protection expenditures on adult mortality: a longitudinal analysis of 5565 Brazilian municipalities. Lancet Glob Health. 7 ( 11 ), e1575-e1583 (2019). Ballester J, Robine JM, Herrmann FR, Rodó X. Effect of the Great Recession on regional mortality trends in Europe. Nat Commun. 10 ( 1 ), 679 (2019). Benach J et al. What do we know about the impact of economic recessions on mortality inequalities? A critical review. Soc Sci Med. 296 ,114733 (2022). Zilidis C, Angelopoulos NV. The impact of economic crisis on mortality due to mental health illnesses. J Public Health (Oxf). 44 ( 1 ),92–99 (2022). Suhrcke M, Stuckler D. Will the recession be bad for our health? It depends. Soc Sci Med. 74 ( 5 ), 647–53 (2012). Stuckler D, Basu S. The Body Economic: Why Austerity Kills . First Edition edition ed. New York: Basic Books, 2013. Guimarães RM. Os impactos das políticas de austeridade nas condições de saúde dos países com algum tipo de crise. Trab. educ. Saúde. 16 ( 1 ), 383–385 (2018). Stuckler D et al. Austerity and health: the impact in the UK and Europe. Eur J Public Health. 27 ( suppl_4 ),18–21 (2017). Riumallo-Herl C et al. Job loss, wealth and depression during the Great Recession in the USA and Europe. Int J Epidemiol. 43 ( 5 ),1508–1517 (2014). Romero DE, Maia L, Muzi J. Tendência e desigualdade na completude da informação sobre raça/cor dos óbitos de idosos no Sistema de Informações sobre Mortalidade no Brasil, entre 2000 e 2015. Cadernos de Saúde Pública. 35 ( 12 ), e00223218 (2019). Timaeus IM. Measurement of adult mortality in less developed countries: a comparative review. Popul Index. 57 ( 4 ), 552–68 (1991). Murray CHJL, Frenk J, Gakidou E. Measuring health inequality: challenges and new directions. In: D. Leon, G. Walt (Eds.). Poverty, inequality, and health . An international perspective. Oxford: Oxford University Press (2001). Dorrington R. The synthetic extinct generations method. Tools for Demographic Estimation. Paris: International Union for the Scientific Study of Population, 2013. Mathers CD et al. Counting the dead and what they died from: an assessment of the global status of cause of death data. Bull World Health Organ. 83 ( 3 ), 171–177 (2005). Tables Table 1: Basic descriptive statistics on mortality rates by sex, age group, and race. Brazil, 2003-2018. Variables Categories Average Specific Mortality Rates (95% CI) Stage of the economic cycle p-value a Before economic stagnation (2003 to 2011) After the economic stagnation (2011 to 2018) Average SD Average SD Age Group 20 to 29 years 5.72 (4.17 - 7.27) 5.03 0.20 6.41 0.46 <0.001 30 to 39 years 12.24 (8.33 - 16.14) 10.33 0.24 14.15 0.32 <0.001 40 to 49 years 23.59 (12.03 - 35.15) 18.00 0.72 29.18 1.62 <0.001 50 to 59 years 25.32 (17.06 - 33.57) 21.43 1.28 29.20 1.36 <0.001 60 to 69 years 26.32 (17.30 - 35.34) 22.06 1.51 30.57 1.32 <0.001 70 to 79 years 25.24 (18.70 - 31.78) 22.49 2.46 30.00 0.71 <0.001 80 years + 28.64 (22.52 - 34.77) 26.52 3.01 30.76 1.27 0.003 Sex Female 3.55 (2.26 - 4.83) 3.01 0.33 4.09 0.37 <0.001 Male 18.93 (7.62 - 30.24) 14.48 1.13 23.38 4.98 <0.001 Race White 15.13 (12.83 - 17.44) 14.10 0.42 16.17 0.58 <0.001 Brown 16.31 (9.87 - 22.75) 13.29 1.44 19.33 0.52 <0.001 Black 19.64 (12.86 - 26.43) 16.48 0.49 22.81 1.62 <0.001 Total 10.16 (6.92 - 13.39) 8.69 0.72 11.63 0.63 <0.001 Legend: SD – standard deviation; a p-value obtained from Student t-test (for dichotomous variables) and ANOVA test for variables with three or more categories. Source: Mortality Information System (SIM/SUS) | National Bureau of Statistics (IBGE) Table 2: Crude and adjusted Poisson Regression Model estimates for deaths from despair. Brazil, 2022. Variable Categories Crude model Adjusted model RR 95% CI p-value RR 95% CI p-value Period 2003 to 2007 1 <0.001 1 0.003 2008 to 2010 1.04 1.02 – 1.05 1.08 1.04 – 1.13 2011 to 2014 1.09 1.08 – 1.11 1.10 1.06 – 1.15 2015 to 2018 1.21 1.20 – 1.23 1.24 1.20 – 1.30 Sex Female 1 <0.001 1 <0.001 Male 6.08 6.02 – 6.16 6.16 5.92 – 6.41 Age Group 20 to 29 years 1 0.001 1 0.001 30 to 39 years 1.52 1.49 – 1.54 1.54 1.47 – 1.62 40 to 49 years 2.33 2.30 – 2.36 2.46 2,34 – 2.58 50 to 59 years 2.24 2.20 – 2.27 2.40 2.27 – 2.54 60 to 69 years 2.13 2.10 – 2.16 2.19 2.10 – 2.30 70 to 79 years 1.97 1.94 – 2.01 2.22 2.08 – 2.39 80 years + 1.80 1.76 – 1.85 2.13 2.02 – 2.26 Race White 1 <0.001 1 <0.001 Brown 1.19 1.18 – 1.21 1.21 1.18 – 1.25 Black 1.41 1.39 – 1.43 1.36 1.29 – 1.43 Source: Mortality Information System (SIM/SUS) | National Bureau of Statistics (IBGE) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2273817","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":153444845,"identity":"91319243-8be2-4501-9a2f-737e690feb79","order_by":0,"name":"Raphael Mendonça Guimarães","email":"data:image/png;base64,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","orcid":"","institution":"University of California at San Diego","correspondingAuthor":true,"prefix":"","firstName":"Raphael","middleName":"Mendonça","lastName":"Guimarães","suffix":""},{"id":153444846,"identity":"39894381-581f-4007-9616-02243be9177a","order_by":1,"name":"Karina Cardoso Meira","email":"","orcid":"","institution":"Federal University of Rio Grande do Norte","correspondingAuthor":false,"prefix":"","firstName":"Karina","middleName":"Cardoso","lastName":"Meira","suffix":""}],"badges":[],"createdAt":"2022-11-15 00:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2273817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2273817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29341027,"identity":"a2b0d77b-9f36-4dea-9324-8c42c1e9e74a","added_by":"auto","created_at":"2022-11-21 15:59:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19097,"visible":true,"origin":"","legend":"\u003cp\u003eDescription of mean and standard deviation of death rates due to despair according to the economic cycle period. Brazil, 2003-2018.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003eMortality Information System\u003cstrong\u003e \u003c/strong\u003e(SIM/SUS) | National Bureau of Statistics (IBGE)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2273817/v1/4773f19c107ebe1360415281.png"},{"id":38593735,"identity":"5f5affea-a7b6-4cad-9a3b-38098e0a2c8e","added_by":"auto","created_at":"2023-06-15 10:59:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":401493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2273817/v1/6c63beee-e034-47a8-800f-2d2c6497a5aa.pdf"},{"id":29341291,"identity":"f793ecfc-786b-4e6b-98d8-222953eded7f","added_by":"auto","created_at":"2022-11-21 16:07:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13891,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2273817/v1/7422f3713374772719870a0a.docx"},{"id":29341028,"identity":"f69b8fa0-c3d4-4bcd-a815-dead8164b0d2","added_by":"auto","created_at":"2022-11-21 15:59:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46772,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2final.docx","url":"https://assets-eu.researchsquare.com/files/rs-2273817/v1/2af2f826c11973984216f0ea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Role of Race in Deaths of Despair in Brazil: is It a White People Problem?\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInequities in health and mortality have become an increasingly important topic of study among sociologists, demographers, economists, and social epidemiologists. In recent years, in demography and epidemiology, considerable progress has been made in documenting such inequalities in various contexts, using multiple measures of all-cause and cause-specific health and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Income inequality is a central theme in health studies today. The development of the models described above, associated with equity issues [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and economic development models [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], has brought to light the need for a deeper discussion about the consequences of heterogeneity in the distribution of income and wealth in the health of the population, despite the difficulty in measuring inequalities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] or even in conceptualizing and distinguishing inequalities from inequities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo associated demographic trends have generated considerable interest: income stagnation and increased premature mortality from suicides, drug intoxication, and alcoholic liver disease among low-educated non-Hispanic US white people. Indeed, recent increases in premature mortality have coincided with decades of economic decline for less educated and unskilled workers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Economists interpret these population-level trends to indicate that despair induced by financial stressors is a shared pathway to these causes of death [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe experience in the US shows an increase in mortality in middle-aged, non-Hispanic white men and women [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These combined trends have led to a rise in mortality in young and middle-aged adults. The significant contributors were deaths from unintentional poisoning, opioids, suicide, and alcohol. The underlying hypothesis is that economic insecurity directly affects increasing mortality rates from these specific causes. The term attributed to this group, Deaths of Despair (DoD), is the jargon used in economics and, more recently, in public health to describe deaths attributable to suicide, liver disease, and overdose of drugs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and their associated factors. A pioneering study by Case and Deaton [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] identified this pattern. Much has been speculated about the mechanism by which these two measures \u0026ndash; there is a connection between economic stagnation and death from this group of causes.\u003c/p\u003e \u003cp\u003eDespite the phenomenon that occurred among white people (which was later observed in black people), the persistent inequality in health by race calls attention. It raises interesting questions about how causal explanations emerge and what influences the speed with which they are adopted, although these changes occur differently in age groups and sex [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In their most recent report, Case and Deaton [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] also found a rise in mortality rates in both urban and rural areas. In other words, there is a relationship between an unfavorable economic situation and selective death due to certain conditions. There seems to be an essential difference concerning elementary characteristics of the demographic composition, such as age, sex, and race.\u003c/p\u003e \u003cp\u003eThe social progress achieved by Brazil at the beginning of the 21st century is undeniable. However, the benefits of social progress are not enjoyed equally. Although there is an average gain in life expectancy, the income concentration and inequality indicators remain high, making Brazil comparable not to developed countries but to countries with greater social fragility and social deprivation, for which race has a critical differential [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Since the beginning of economic stagnation in Brazil in 2011, because of the global financial crisis that began in 2008, Brazil has experienced one of the most severe periods of recession in its recent history [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Progressively, since 2015, the economic crisis in Brazil has led to an increase in poverty and the implementation of long-term fiscal austerity measures that will substantially reduce spending on social assistance programs as a percentage of the country's GDP over the next 20 years [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe effect of economic crises on health is variable. On the one hand, during periods of economic difficulty, we expect that decreasing investments in health expenditures compromises the quality of services provided, which is reflected in the health indicators of the countries [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This deterioration in health outcomes seems more evident in countries with weaker social protection schemes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, the need to act on social determinants has led to the development of conceptual frameworks to facilitate understanding of the social processes that impact health. Therefore, we consider it suitable to evaluate the difference in the pattern and level of mortality due to fiscal and economic austerity, analyzing its effect among different subgroups. In this way, we intend to compare the magnitude of the disparity in deaths from despair according to sex, age, and race in Brazil.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics from death of despair rates to each group express how different they are. In general, middle-aged black and brown men are more related to this group of causes of death. Still, statistics before and after the beginning of economic stagnation show that the values are systematically lower in the period before 2011, regardless of the explanatory variable, and the difference was statistically significant in all its categories (Table 1). This evidence reinforces that not only the variables sex, age group, and race can be considered explanatory for the occurrence of deaths from despair. Also, that it behaves differently over time, using the trade-off considered as the point beginning of economic stagnation in the country. Descriptive statistics on the death rate from despair before and after the onset of economic stagnation show that the values are systematically lower in the period before 2011 (m=8.69, SD = 0.72 \u0026ndash; 2003 to 2011 vs. m=11.62, SD = 0.63 \u0026ndash; 2011 to 2018), regardless of the explanatory variable, and the difference was statistically significant in all its categories. This evidence reinforces that not only sex, age group, and race explain the occurrence of deaths from despair, but also that it behaves differently over time, using the trade-off considered as the starting point of economic stagnation in the country.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis of Figure 1 suggests that the period after 2011 is not homogeneous concerning the trend of death from despair. However, from a methodological point of view, it is impossible to perform a time series analysis for sequences with few points in time. Therefore, it is impossible to estimate the effect of the economic changes that took place between 2011 and 2018 for each specific phase of the recession and the measures to combat it. However, it is possible to establish these periods and, from the data, explore these time segments as categorical variables. For this, we consider the division between the periods from 2003 to 2007 (before the world crisis), 2008 to 2010 (the period between the world crisis and the beginning of the Brazilian economic stagnation), 2011 to 2014 (period of economic stagnation with attempts to intervene of the State to minimize the deleterious effects), and from 2015 to 2018 (period with economic recession and fiscal austerity measures).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; First, we observe a difference in the average death rate due to despair in each period, with a progressive increase over time. In addition, it is worth noting that the variability around the mean decreased in the period from 2015 to 2018, showing a more homogeneous behavior of the trend over the periods described.\u003c/p\u003e\n\u003cp\u003eFurthermore, we explored the time effect by introducing all those variables in the Poisson model that included gender, age group, race, and time variable. \u0026nbsp;Results are on Table 2. When adjusting the model with all the variables, the final model showed robustness, confirming the association between the explanatory variables and death from despair. For the gender variable, men have a more significant association with death from despair (RR=6.16, 95% CI 5.92 \u0026ndash; 6.41). There is a more significant association for the race variable between brown (RR=1.21, 95% CI 1.18 \u0026ndash; 1.25) and black people (RR=1.36, 95% CI 1.29 \u0026ndash; 1.43). Finally, for the age group variable, all age groups are associated with death from despair, taking the group between 20 and 29 years old as a reference. We highlight that the most significant associations are in the 30 to 39 age groups (RR=2.46, 95% CI 2.34 \u0026ndash; 2.58) and 40 to 49 years (RR=2.40, 95% CI 2.27 \u0026ndash; 2.54). In this model, the association with the phases of the economic cycle is more evident: the initial phase of the crisis (2008 \u0026ndash; 2011) (RR=1.08, 95% CI 1.04 \u0026ndash; 1.13); the initial period of Brazilian economic stagnation (2011 \u0026ndash; 2014) (RR=1.10, 95% CI 1.06 \u0026ndash; 1.15); and the period of onset of economic recession (2015 \u0026ndash; 2018) (RR=1.24, 95% CI 1.20 \u0026ndash; 1.30). Furthermore, we observed a gradient in this relationship (p-value of trend \u0026lt;0.001), corroborating a previous result that, even with the increase in the level of trends, there is persistence in the rise in the association effect. Therefore, we consider that the worsening of the crisis interferes in the strength of association with deaths from despair.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSocioeconomic status is a multifaceted concept usually measured by education, income, or occupation. A vast body of empirical research in contemporary developed countries has established a consistent relationship between these measures and health [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The primary underlying rationale is that differences in mortality are not biological in origin but social. Historical social and political structure changes form an essential backdrop for the discussion of economic systems. The economic crisis brings with it a reflection on the effects of the current economic system and its cycles on the life and health of populations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn most contemporary societies, there is a gradient of mortality by socioeconomic level for all ages [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Differences in mortality among adults \u0026ndash; commonly divided between race, sex, socioeconomic status, and marital status \u0026ndash; are of outstanding research and public policy interest. The inverse relationship between socioeconomic status and the level of mortality became more significant after 1960 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The literature suggests that people with more significant socioeconomic deprivation are more likely to die, and these differences vary with sex, some socioeconomic indicators, and race [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese changes in understanding the health-disease process meant that, since the 1980s, social inequalities in health have been well documented [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] as a fundamental concept in the population and health. public health. Despite changes in the social structure, in the population's pattern of illness and death, the expansion of health and well-being services, and general improvements in living standards and quality of life, these inequalities persisted in the decades following these transformations, and some countries have even increased.\u003c/p\u003e \u003cp\u003eNotably, in the 1990s, the Theory of Fundamental Causes was developed by Phelan and Link [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This theory points out why the association between socioeconomic status and health disparities persists over time, especially when component causes associated with the morbidity and mortality profile in situations of socioeconomic deprivation have supposedly been resolved. The theoretical model underlying the theory explains that socioeconomic status incorporates a series of resources in addition to income per se. Still, other dimensions are considered protective of health, such as knowledge, social position, social networks, and power [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It means that even if screening and health intervention techniques improve substantially, groups with lower socioeconomic status have more difficulty obtaining access to them. Therefore, there is a persistence in the health inequality of the population due to a lag in the improvement by social groups.\u003c/p\u003e \u003cp\u003ePhelan and Link's Theory posits that a fundamental social cause of health inequalities has four components: the cause influences various health outcomes; the cause affects disease outcomes through various risk factors; the cause involves access to resources that can help to avoid health risks or minimize the consequences of the disease when it occurs, and the association between a root cause and health is reproduced over time through the replacement of intervention mechanisms. To these criteria, socioeconomic status is a fundamental cause of health inequities [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLink and Phelan argue that the root cause theory helps explain why the emphasis on intervening risk factors is ineffective, as it leaves the structural determinants of disease untouched. It means that so-called structural social elements persist over time and maintain health inequalities. In this way, inequities in health do not refer to any differences but those that place certain social groups in persistent situations of disadvantage and discrimination. The literature points to issues such as structural racism, structural gender inequity, and structural heterogeneity (relating to the concept of social class) as edifying several modern societies, including Brazil. In this aspect, it is important to mention them [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e\u0026ldquo;Deaths of despair\u0026rdquo; are still a recent topic, with little evidence or robust study outside the United States. In this way, any comparison is based on the original studies by Case and Deaton [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although our research presents an association between deaths from despair and variables such as sex (male) or age group (primarily middle-aged), two characteristics stand out due to the difference in the profile found in the United States: race, whose association was opposite to that found in the United States, and the time effect, considering not only death as a response to the crisis but the intensification of this association thanks to crisis control measures, precisely those that impose greater fiscal austerity on the population. Therefore, we chose to discuss the results considering these two questions.\u003c/p\u003e \u003cp\u003eThe past 30 years have seen a remarkable growth in scientific evidence examining how racism can adversely affect health. Racism is considered a fundamental cause of adverse health outcomes. However, the manifestation of this relationship needs to be seen, given the similarities and differences between national and cultural contexts [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Structural racism occurs at a societal level and refers to how laws are written or enforced, which benefits the majority and harms racial minorities in accessing opportunities and resources, including social protection mechanisms [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis structural inequality is strongly present in the social determination of the health-disease process [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Racism structurally operates in the capitalist social formation, defining peripheral places of belonging in the social and economic fields [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Its permeability through structures, policies, and practices makes it capable of defining opportunities and values ​​for people and populations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This view corroborates the pillars of the so-called Structuralist Theory of Development. This theory helps explain why some regions lag in economic development, linking this time lag to distributive inequality within the lagging region and across countries. Ra\u0026uacute;l Prebisch [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] describes this phenomenon as a slow and gradual diffusion of progress on an international scale and gives him responsibility for differences in the level of development between countries. Understanding these asymmetric dynamics is central to explaining the persistence of heterogeneity and extreme inequality between nations.\u003c/p\u003e \u003cp\u003eMore than 50 years after its formulation, the Structuralist Theory of Development still influences economic thinking. More recently, structuralist development macroeconomics has advanced in some of the assumptions of the original theory [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The main instrument for structural transformation lies in the developmental macroeconomic policy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, in new developmentalism, the role of the State diminishes, and that of the market increases. Globalization reinforces this model, and, in this sense, economic polarization causes and is a consequence of a delay in the social development of most peripheral countries. For this reason, it is possible that some causal mechanisms observable in countries with a central economy do not occur in the same way in countries with a peripheral economy, as was the case with the relationship between race and deaths from despair.\u003c/p\u003e \u003cp\u003eBrazilian statistics have been systematically less favorable to people of color in Brazil for decades [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Although this group represents 56% of the Brazilian population, this aspect is verified in different elements. In education, for example, the illiteracy rate is 9.1% among black people, against 3.9% among white people. Regarding the labor market, the average salary of black is lower than that of white people. The average per capita household income of black and brown people in 2018 was US\u003cspan\u003e$\u003c/span\u003e256.00, and among white was US\u003cspan\u003e$\u003c/span\u003e506.00. The unemployment rate is also higher. In the same year, the unemployment rate among black people was 14.1% and 9.5% among white people [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, the discussion of race is one of the main criticisms of the work of Case and Deaton [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In their 2017 follow-up, Case and Deaton compared white North Americans with white Europeans to explore possible explanations for the increase in mortality found in their 2015 analyses. There was something inherent in the \u0026ldquo;American experience of being white.\u0026rdquo; It means that it is unique in how social and economic systems are constantly changing. Perhaps it is related to the mortality pattern in this group of middle-aged white men in the United States. However, it does not affect the white population of European, Canadian, or other countries in the same way. Australian or predominantly white [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCritics suggested that Case and Deaton were drawing attention away from more persistent problems, such as consistently higher black death rates. In addition, the most scathing criticisms refer to the collective national response and the attention given to the phenomenon of \u0026ldquo;deaths of despair\u0026rdquo;, since the face of disadvantage is now white [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Blacksher [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], when mentioning this discussion about race, points out that there is some concern that mortality data can be used to justify more help for white people, who in the past ignored the same problems among black people. In the United States, social class appears to be associated with deaths from despair, mainly due to the epidemic of opioid use. Furthermore, it is essential to mention that the social consequences of the opioid epidemic differ from previous drug crises. Part of this can be explained by the fact that the crack crisis in the 1990s was associated with an increase in violent crime, while the opioid crisis occurred against a backdrop of decreasing crime rates [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClassical developmentalism, known as \u0026ldquo;Latin American structuralism,\u0026rdquo; coined the expression \u0026ldquo;center-periphery.\u0026rdquo; It described the asymmetries of the international order and maintained that industrialization was the way for peripheral economies to benefit from the technical progress of developed economies, progressively increasing \u0026ndash; and, therefore, in a delayed way \u0026ndash; the standard of living of the population [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. That said, we believe that the eventual differences between this thesis and the original North American study may be due, on the one hand, to the historical difference in the construction of racism and its leading role in the construction of the social structure. On the other hand, it may result from a delay in identifying a social phenomenon and pointing to a possible occurrence of the scenario currently observed in the United States only in a few years or decades.\u003c/p\u003e \u003cp\u003eRegarding the temporal gradient, we emphasize that fiscal austerity policies have been used to respond to the economic crisis and fiscal deficit in developed and developing countries. They vary in content, intensity, and implementation schedule; such models advocate reducing public spending. They promote the removal of social investment, the retraction of public assets, and the replacement of the State by the private sector in the provision of certain services linked to social policies [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese austerity measures - reducing social spending and increasing taxation - do more harm to disadvantaged groups. Briefly, we assumed that there is evidence that austerity is regressive, affecting mainly the poor, thus increasing socioeconomic inequalities [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In general, they tend to substantially impact the most vulnerable groups and disadvantaged regions of countries. The literature suggests no immediate changes in the pattern of overall mortality due to the crisis [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. We consider it appropriate to say that this lack of association occurs mainly due to the long latency time of chronic diseases. The effect is not immediate, especially in countries at an advanced stage of the epidemiological transition. However, we can see that the effect is significant when stratifying by cause group. This evidence that there is a selectivity to death from specific causes due to economic crises, including short-term effects [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. It occurs mainly in peripheral economy countries, which suggests that the change in mortality trends is more evident in places with weaker social protection systems [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn particular, the comparison of expected values ​​at each moment of the economic crisis reveals that the death rates from despair depend on how much the crisis penalizes the population. Furthermore, this effect differentially affects age groups by sex and race [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The evidence is inconclusive, but our findings are consistent with evidence showing a disproportionate increase in deaths from despair \u0026ndash; particularly suicide \u0026ndash; among men and older, mainly middle-aged groups. As we said earlier, the race attracted attention, and we consider it a claimed proxy for socioeconomic status. We believe it essential to mention that there is previous evidence for Brazil that the recession contributed to the increase in mortality. Furthermore, the same study highlights that spending on health and social protection appeared to mitigate harmful health effects, especially among vulnerable populations. This evidence supports more health and social protection systems [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe persistence of vulnerability associated with the crisis, and above all, the austerity that follows it, changes the trend. We realize this when decomposing the historical series into phases of the economic crisis [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In this regard, the study by Benach et al. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] warns of the need to pay special attention to the long-term effects of the recession on mortality inequalities and their variation within countries and the differentials by race, migration status, and severity of the disease. The more recent evidence indicates that not only the period effect of the crisis time frame but its persistence penalizes groups in a very different way. It reaches the most vulnerable groups every time the crisis is prolonged, and government actions preserve the State and not the population. It reinforces our argument that these crises are especially harmful to the health of the most vulnerable groups.\u003c/p\u003e \u003cp\u003eWhen governments do not implement policies to protect these social groups in times of economic difficulties, there is inevitably an acceleration of cumulative disadvantages between them and the more privileged. This relationship occurs since the reduction in income, the increase in unemployment and cuts in the health budget are the main determinants of the rise in mortality [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In this regard, we highlight the study by Suhrcke \u0026amp; Stuckler [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], whose findings separate the effects of economic crises from government responses. Its main message is that austerity policies may result in independent and far more significant adverse health effects than the economic crisis itself. This evidence is in line with the results of our study, which shows a temporal gradient effect and deaths from despair, possibly due to a deterioration in social protection caused by the crisis and exacerbated by the fiscal austerity measures adopted by Brazil in response to economic instability.\u003c/p\u003e \u003cp\u003eStuckler and Basu [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] analyze global data over time to show how government policy becomes a matter of life and death during financial crises. The author's starting point is what they call \"natural experiments,\" which represent situations in which several countries adopted economic measures in the face of periods of economic crisis and instability, and what the repercussion of these measures - classified as austerity or security - was in the health of the population [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Stuckler and Basu's work is emphatic when it says that austerity policies devastate the population's health.\u003c/p\u003e \u003cp\u003eThe analysis of data from Europe and the United States proves that the result of the adoption of this set of austere measures is associated with tens of thousands of suicides and more than one million cases of depression [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The most recent evidence shows that these measures affect those who were already vulnerable before the economic crisis, such as those with precarious jobs or housing or with existing health problems. Furthermore, people living in countries with robust social protection systems can experience less degraded health than those with relatively weaker systems [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Therefore, the combined effects of the economic crisis and structural adjustment policies on health are of most significant interest to Latin American countries.\u003c/p\u003e \u003cp\u003eOur study has limitations. Its ecological nature means that the analysis is based on aggregate-level data and does not allow for individual-level inferences. Also, we could not explicitly control migration fluctuations due to missing data. However, we recognize that the net migration in Brazil is not large enough to impact the measures. Furthermore, by controlling for region-specific fixed effects and region-time interactions, we can, to some extent, mitigate this problem. Finally, we emphasize that our study does not establish a causal relationship between economic events and deaths from despair. The results support a context effect. The associations between mortality and macroeconomic indicators, such as unemployment, require bolder designs at the individual level to control the impact of health expenditure, social protection schemes, employment status, and family support networks. Only then will we be able to understand better the mechanisms of the effects of the crisis on health.\u003c/p\u003e \u003cp\u003eDespite the similarities with the original study by Case and Deaton, we emphasize the differences concerning race. On the one hand, the forms of dimension and expression of racism in Latin America and Brazil differ from those identified in American culture. That has repercussions on how the social determinants of health emerge, including race. Racism conforms and reproduces itself historically in Brazilian society and institutions, supported by narratives of domination, such as the myth of racial democracy. It produces concealments of its political, social and economic plots, which have a negative impact on the living and health conditions of the black population.\u003c/p\u003e \u003cp\u003eWe can say that Case and Deaton's findings encourage a more careful analysis of the underexplored concept of \"whiteness\" and what the white race means for health. Often in demographic and epidemiological studies, we use white people as the reference group for the leading health inferences. Still, the meaning or interpretation of this comparison is not disputed or poorly explained. The increase in the mortality of working-class white men may represent the first marker that traditional paradigms in population health research that consider white people as the universal subject will need to reflect this new demographic reality. For Brazil, I emphasize that the group of black men aged between 30 and 49 years is more vulnerable to despair. In the end, this study concludes that \"Deaths of despair\" is more than a terminology but constitutes a hypothesis that deserves more robust conceptual mapping, with empirical studies that use longitudinal and hierarchical data between individual factors and contextual effects.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eData Source\u003c/h2\u003e\n \u003cp\u003eIt is an ecological study carried out for Brazil with data on deaths between 2003 and 2018. The data came from two sources: for deaths, we considered information from the Datasus Mortality Information System (SIM) by age, sex, race, and cause of death. For the population, we used data from the Brazilian Institute of Geography and Statistics (IBGE) based on population estimates. We used the 2000 and 2010 census populations disaggregated by age group, sex, and race and obtained the populations for the inter-census years 2001 to 2009 by interpolation. Then, with the estimate of the geometric growth rate from the previous period, we calculate the population estimates for the period from 2011 to 2018.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003eIndependent variables\u003c/h2\u003e\n \u003cp\u003eWe consider as independent variables: sex (male and female); age (divided into decennial groups in adulthood, from the \u0026apos;20s, to the last group of over 80\u0026apos;s); and race, divided into white, black, and brown people. Our study was particularly interested in racial issues involving black and brown people. In addition, the occurrence of indigenous and Asian people is shallow [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. For this reason, we chose to exclude these racial categories. After checking the effect of individual variables, we added time intervals related to economic cycles: we used the period before the 2008 world crisis as a counterfactual scenario (2003 to 2008). We defined the period from 2008 to 2011 as the period whose economic repercussion had not yet been widely noticed in Brazil. Then, the period from 2011 to 2015 corresponds to the period in which there was economic stagnation; and the period from 2015 to 2018, where effectively the scenario was of economic recession and implementation of fiscal austerity measures.\u003c/p\u003e\n \u003cp\u003eWe mention that Case and Deaton\u0026apos;s original study [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] adequately describes the American population and deaths from despair not only by sex, age, race, and education. The information on education absent in almost half of the death records in Brazil limits the analysis capacity of the present study for comparison with the original North American study. However, it is essential to consider that, unlike in the United States, the number of Brazilians with higher education is only 16.5% [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. This low frequency would potentially create a floor effect, a reduction in intergroup variability due to the high concentration of people in the low education categories. This phenomenon may compromise the property of this indicator to discriminate the populations under comparison. Thus, even if this information were available, it might not be the best criterion for analyzing the Brazilian people.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eDependent Variable\u003c/h2\u003e\n \u003cp\u003eWe used the codes of deaths of despair considering the criteria established by Case and Deaton [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. We performed the categorization of causes of death was performed using the codes of the International Classification of Diseases, 10th revision (ICD-10), considering groups as follows: Accidental or intentional poisoning and poisoning of undetermined intent from drug exposure; drugs in the blood; Drug-induced illnesses; Mental/behavioral disorders due to drugs; Alcohol-induced illnesses; Mental/behavioral disorders due to alcohol; Accidental or intentional poisoning and poisoning of undetermined intent form alcohol exposure; Suicide (for more information relating groups and its codes, see Supplementary File #1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eAdjust mortality levels and pattern\u003c/h2\u003e\n \u003cp\u003eAs it is a very heterogeneous set of municipalities, to ensure data quality, we adjusted mortality estimates for underreporting, age errors, and ill-defined causes [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. We did the adjustment for the effect of underreporting on mortality rates by applying indirect estimation methods to the 2000 and 2010 census data. The methods used were Synthetic Extinct Generation (SEG) and General Growth Balance (General Growth Balance, GGB) in combination [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e] to estimate adult mortality.\u003c/p\u003e\n \u003cp\u003eSecondly, we corrected the mortality data by the methods described for the Mortality Information System (SIM) set of deaths. Based on the duly updated data, we considered only deaths from the above causes. We corrected deaths of despair records by proportional redistribution by year and age group of deaths classified as ill-defined causes, using the methodology proposed by the World Health Organization (WHO) [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. For more information concerning adjustment techniques application, see Supplementary File #2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eWe analyzed the death count using Poisson regression to obtain the association between deaths from despair according to sociodemographic and time variables.\u003c/p\u003e\n \u003cp\u003eSince the size of the subpopulations by age group, sex, and race is different over the years of analysis, we used a component (offset) for controlling the number of cases per population in each of the ranges to control the count by quantitative (volume) and qualitative change (distribution of covariates such as sex and age) of the population, In a Poisson regression analysis, the response variable is quantitative, given by the count of successes (deaths from despair), according to this equation:\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec10\"\u003e\n \u003ch2\u003eE(Y) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{{\\mu }}^{{\\text{Y}}_{\\text{i}}} {\\text{e}}^{-{\\mu }}}{{\\text{Y}}_{\\text{i}}!}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\n \u003cp\u003eWhere\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eE(Y ) is the conditional expectation of the dependent variable Y;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026micro;\u0026thinsp;=\u0026thinsp;e\u003csup\u003e\u0026beta;\u003c/sup\u003e0\u0026thinsp;+\u0026thinsp;\u0026beta;1Xi ;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003ei\u003c/sub\u003e are the parameters.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eTo estimate the parameters, we used the maximum likelihood estimation method according to this formulation:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\text{L}\\left({\\beta }\\right)= \\prod _{\\text{i}=1}^{\\text{n}}\\frac{{\\left[{\\mu }({\\text{X}}_{\\text{i}},{\\beta })\\right]}^{{\\text{Y}}_{\\text{i}}} \\text{e}\\text{x}\\text{p}\\left[-{\\mu }({\\text{X}}_{\\text{i}},{\\beta })\\right]}{{\\text{Y}}_{\\text{i}}!}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Equation\" id=\"Equb\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\text{l}\\text{n}\\left(\\right(\\text{L}\\left({\\beta }\\right)= \\sum _{\\text{i}=1}^{\\text{n}}{\\text{Y}}_{\\text{i}}\\text{l}\\text{n}\\left[{\\mu }\\left({\\text{X}}_{\\text{i}},{\\beta }\\right)\\right]- \\sum _{\\text{i}=1}^{\\text{n}}{\\mu }\\left({\\text{X}}_{\\text{i}},{\\beta }\\right)- \\sum _{\\text{i}=1}^{\\text{n}}\\text{l}\\text{n}({\\text{Y}}_{\\text{i}}!)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAfter using numerical methods to estimate \u0026beta; from the derivative of the logarithm of the likelihood function, we find estimators \u0026beta; that can be represented generically, by\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equc\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$${\\widehat{{\\mu }}}_{\\text{i}}={\\mu }(\\text{X},\\widehat{{\\beta }})$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAs a way of testing the significance of the parameters, we used the maximum likelihood ratio test. The prevalence coefficient returns the proportion of cases concerning a population or study sample. Through them, we calculate the ratio of prevalence coefficients to estimate the association between the categories of a covariate from a reference category.\u003c/p\u003e\n \u003cp\u003eTo compare the frequency in each category of covariates, we used Pearson\u0026apos;s Chi-Square and its respective p-value. Similarly, to observe the presence of a gradient between the ordinal categories of stratification, we calculated Chi-Square-trend with the individual p-value. The explanatory variables were tested individually and added to the model through the forward method whenever they presented statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interaction terms were also tested in the model, aggregating the variables two by two and then with triple interaction terms when applicable. We chose the reference categories considering the one with the lowest occurrence of deaths. Then we calculated crude and adjusted prevalence ratios for each variable according to the best model obtained by the maximum likelihood statistics. We also estimated confidence intervals at a significance level of 95%. The adjusted prevalence ratios considered the effect of individual association and the impact of the interaction term together.\u003c/p\u003e\n \u003cp\u003eFinally, the Poisson distribution assumes that the mean and variance are equal. So we obtained diagnoses that reject the possibility of under dispersion (mean greater than variance) or overdispersion (mean less than variance). The regression considered the volume of deaths for the entire period and then for the pre-intervention and post-intervention periods. In this way, we could verify the change in the strength of association associated with the economic crisis and the adoption of fiscal austerity measures. We analyzed data using R software search procedures.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions (names must be given as initials)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRMG contributed to conceptualization, project management, data extraction and analysis, manuscript writing and final review. KCM contributed to conceptualization, manuscript writing and final review\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabase used for this study is publicly available. The analyzes were carried out using public and universally accessible databases, without identifying people, for whom sensitive data are omitted. Access to mortality data is available at http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obt10uf.def. For more information, please contact [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eBarreto, ML. Health inequalities: a global perspective. Ci\u0026ecirc;ncia \u0026amp; Sa\u0026uacute;de Coletiva. \u003cstrong\u003e22\u003c/strong\u003e(\u003cstrong\u003e7)\u003c/strong\u003e, 2097\u0026ndash;2108 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVieira-da-Silva, LM; Almeida FN. 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Ci\u0026ecirc;ncia \u0026amp; Sa\u0026uacute;de Coletiva. \u003cstrong\u003e24\u003c/strong\u003e(\u003cstrong\u003e12\u003c/strong\u003e), 4375\u0026ndash;4384 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFigari F, Paulus A, Sutherland H. \u003cem\u003eThe Design of Fiscal Consolidation Measures in the European Union: Distributional Effects and Implications for Macroeconomic Recovery\u003c/em\u003e. EUROMOD at the Institute for Social and Economic Research, 2015.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePal\u0026egrave;ncia L et al. Effect of the recent economic crisis on socioeconomic inequalities in mortality in nine urban areas in Europe. Gac Sanit. \u003cstrong\u003e34\u003c/strong\u003e(\u003cstrong\u003e3\u003c/strong\u003e), 253\u0026ndash;260 (2020).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLaliotis I, Ioannidis JPA, Stavropoulou C. Total, and cause-specific mortality before and after the onset of the Greek economic crisis: an interrupted time-series analysis. Lancet Public Health. \u003cstrong\u003e1\u003c/strong\u003e(\u003cstrong\u003e2\u003c/strong\u003e), e56-e65 (2016).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFalagas ME, Vouloumanou EK, Mavros MN, Karageorgopoulos DE. Economic crises and mortality: a review of the literature. Int J Clin Pract. \u003cstrong\u003e63\u003c/strong\u003e(\u003cstrong\u003e8\u003c/strong\u003e), 1128\u0026ndash;35 (2009).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAlvarez-Galvez J, Suarez-Lledo V, Salvador-Carulla L, Almenara-Barrios J. Structural determinants of suicide during the global financial crisis in Spain: Integrating explanations to understand a complex public health problem. PLoS One. \u003cstrong\u003e16\u003c/strong\u003e(\u003cstrong\u003e3\u003c/strong\u003e), e0247759 (2021).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHone T et al. Effect of economic recession and impact of health and social protection expenditures on adult mortality: a longitudinal analysis of 5565 Brazilian municipalities. Lancet Glob Health. \u003cstrong\u003e7\u003c/strong\u003e(\u003cstrong\u003e11\u003c/strong\u003e), e1575-e1583 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBallester J, Robine JM, Herrmann FR, Rod\u0026oacute; X. Effect of the Great Recession on regional mortality trends in Europe. Nat Commun. \u003cstrong\u003e10\u003c/strong\u003e(\u003cstrong\u003e1\u003c/strong\u003e), 679 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBenach J et al. What do we know about the impact of economic recessions on mortality inequalities? A critical review. 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Os impactos das pol\u0026iacute;ticas de austeridade nas condi\u0026ccedil;\u0026otilde;es de sa\u0026uacute;de dos pa\u0026iacute;ses com algum tipo de crise. Trab. educ. Sa\u0026uacute;de. \u003cstrong\u003e16\u003c/strong\u003e(\u003cstrong\u003e1\u003c/strong\u003e), 383\u0026ndash;385 (2018).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eStuckler D et al. Austerity and health: the impact in the UK and Europe. Eur J Public Health. \u003cstrong\u003e27\u003c/strong\u003e(\u003cstrong\u003esuppl_4\u003c/strong\u003e),18\u0026ndash;21 (2017).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRiumallo-Herl C et al. Job loss, wealth and depression during the Great Recession in the USA and Europe. Int J Epidemiol. \u003cstrong\u003e43\u003c/strong\u003e(\u003cstrong\u003e5\u003c/strong\u003e),1508\u0026ndash;1517 (2014).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRomero DE, Maia L, Muzi J. Tend\u0026ecirc;ncia e desigualdade na completude da informa\u0026ccedil;\u0026atilde;o sobre ra\u0026ccedil;a/cor dos \u0026oacute;bitos de idosos no Sistema de Informa\u0026ccedil;\u0026otilde;es sobre Mortalidade no Brasil, entre 2000 e 2015. Cadernos de Sa\u0026uacute;de P\u0026uacute;blica. \u003cstrong\u003e35\u003c/strong\u003e(\u003cstrong\u003e12\u003c/strong\u003e), e00223218 (2019).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTimaeus IM. Measurement of adult mortality in less developed countries: a comparative review. Popul Index. \u003cstrong\u003e57\u003c/strong\u003e(\u003cstrong\u003e4\u003c/strong\u003e), 552\u0026ndash;68 (1991).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMurray CHJL, Frenk J, Gakidou E. Measuring health inequality: challenges and new directions. In: D. Leon, G. Walt (Eds.). \u003cem\u003ePoverty, inequality, and health\u003c/em\u003e. An international perspective. Oxford: Oxford University Press (2001).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDorrington R. \u003cem\u003eThe synthetic extinct generations method.\u003c/em\u003e Tools for Demographic Estimation. Paris: International Union for the Scientific Study of Population, 2013.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMathers CD et al. Counting the dead and what they died from: an assessment of the global status of cause of death data. Bull World Health Organ. \u003cstrong\u003e83\u003c/strong\u003e(\u003cstrong\u003e3\u003c/strong\u003e), 171\u0026ndash;177 (2005).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eBasic descriptive statistics on mortality rates by sex, age group, and race. Brazil, 2003-2018.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Specific Mortality Rates\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"33.9171974522293%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage of the economic cycle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"10.668789808917197%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"38.57142857142857%\"\u003e\n \u003cp\u003eBefore economic stagnation\u003c/p\u003e\n \u003cp\u003e(2003 to 2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"37.5%\"\u003e\n \u003cp\u003eAfter the economic stagnation\u003c/p\u003e\n \u003cp\u003e(2011 to 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"23.571428571428573%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.35714285714285715%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.037383177570092%\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.429906542056074%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.037383177570092%\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.02803738317757%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.4672897196261682%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.554140127388536%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.643312101910828%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.554140127388536%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.165605095541402%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.074349442379184%\"\u003e\n \u003cp\u003e20 to 29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.881040892193308%\"\u003e\n \u003cp\u003e5.72 (4.17 - 7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.921933085501859%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.364312267657992%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.267657992565056%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18587360594795538%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.074349442379184%\"\u003e\n \u003cp\u003e30 to 39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.881040892193308%\"\u003e\n \u003cp\u003e12.24 (8.33 - 16.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e10.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.921933085501859%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e14.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.364312267657992%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.267657992565056%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18587360594795538%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.074349442379184%\"\u003e\n \u003cp\u003e40 to 49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.881040892193308%\"\u003e\n \u003cp\u003e23.59 (12.03 - 35.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.921933085501859%\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e29.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.364312267657992%\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.267657992565056%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18587360594795538%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.074349442379184%\"\u003e\n \u003cp\u003e50 to 59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.881040892193308%\"\u003e\n \u003cp\u003e25.32 (17.06 - 33.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e21.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.921933085501859%\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e29.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.364312267657992%\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.267657992565056%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18587360594795538%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.074349442379184%\"\u003e\n \u003cp\u003e60 to 69 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.881040892193308%\"\u003e\n \u003cp\u003e26.32 (17.30 - 35.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e22.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.921933085501859%\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.364312267657992%\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.267657992565056%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18587360594795538%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.074349442379184%\"\u003e\n \u003cp\u003e70 to 79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.881040892193308%\"\u003e\n \u003cp\u003e25.24 (18.70 - 31.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e22.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.921933085501859%\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.364312267657992%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.267657992565056%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18587360594795538%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"20.074349442379184%\"\u003e\n \u003cp\u003e80 years +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.881040892193308%\"\u003e\n \u003cp\u003e28.64 (22.52 - 34.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e26.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.921933085501859%\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.152416356877323%\"\u003e\n \u003cp\u003e30.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.364312267657992%\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"12.267657992565056%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.18587360594795538%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e3.55 (2.26 - 4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e18.93 (7.62 - 30.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e14.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e23.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e15.13 (12.83 - 17.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e14.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e16.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e16.31 (9.87 - 22.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e13.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e19.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e19.64 (12.86 - 26.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e16.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e22.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"14.331210191082803%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.197452229299362%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"23.88535031847134%\"\u003e\n \u003cp\u003e10.16 (6.92 - 13.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.643312101910828%\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.554140127388536%\"\u003e\n \u003cp\u003e11.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.165605095541402%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e SD \u0026ndash; standard deviation; \u003csup\u003ea\u003c/sup\u003e p-value obtained from Student t-test (for dichotomous variables) and ANOVA test for variables with three or more categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Mortality Information System\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(SIM/SUS) | National Bureau of Statistics (IBGE)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Crude and adjusted Poisson Regression Model estimates for deaths from despair. Brazil, 2022.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"18.26923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"33.17307692307692%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"33.17307692307692%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.526570048309178%\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.256038647342994%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.285024154589372%\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.497584541062803%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.24%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.24%\"\u003e\n \u003cp\u003e2003 to 2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.27710843373494%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003e2008 to 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.244979919678714%\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.670682730923694%\"\u003e\n \u003cp\u003e1.02 \u0026ndash; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.04417670682731%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.8714859437751%\"\u003e\n \u003cp\u003e1.04 \u0026ndash; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.27710843373494%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003e2011 to 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.244979919678714%\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.670682730923694%\"\u003e\n \u003cp\u003e1.08 \u0026ndash; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.04417670682731%\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.8714859437751%\"\u003e\n \u003cp\u003e1.06 \u0026ndash; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.27710843373494%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003e2015 to 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.244979919678714%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.670682730923694%\"\u003e\n \u003cp\u003e1.20 \u0026ndash; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.04417670682731%\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.8714859437751%\"\u003e\n \u003cp\u003e1.20 \u0026ndash; 1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.24%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.24%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.27710843373494%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.244979919678714%\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.670682730923694%\"\u003e\n \u003cp\u003e6.02 \u0026ndash; 6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.04417670682731%\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.8714859437751%\"\u003e\n \u003cp\u003e5.92 \u0026ndash; 6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.24%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.24%\"\u003e\n \u003cp\u003e20 to 29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.35820895522388%\"\u003e\n \u003cp\u003e30 to 39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.930348258706468%\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"21.890547263681594%\"\u003e\n \u003cp\u003e1.49 \u0026ndash; 1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.681592039800995%\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"22.139303482587064%\"\u003e\n \u003cp\u003e1.47 \u0026ndash; 1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.35820895522388%\"\u003e\n \u003cp\u003e40 to 49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.930348258706468%\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"21.890547263681594%\"\u003e\n \u003cp\u003e2.30 \u0026ndash; 2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.681592039800995%\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"22.139303482587064%\"\u003e\n \u003cp\u003e2,34 \u0026ndash; 2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.35820895522388%\"\u003e\n \u003cp\u003e50 to 59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.930348258706468%\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"21.890547263681594%\"\u003e\n \u003cp\u003e2.20 \u0026ndash; 2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.681592039800995%\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"22.139303482587064%\"\u003e\n \u003cp\u003e2.27 \u0026ndash; 2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.35820895522388%\"\u003e\n \u003cp\u003e60 to 69 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.930348258706468%\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"21.890547263681594%\"\u003e\n \u003cp\u003e2.10 \u0026ndash; 2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.681592039800995%\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"22.139303482587064%\"\u003e\n \u003cp\u003e2.10 \u0026ndash; 2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.35820895522388%\"\u003e\n \u003cp\u003e70 to 79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.930348258706468%\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"21.890547263681594%\"\u003e\n \u003cp\u003e1.94 \u0026ndash; 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.681592039800995%\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"22.139303482587064%\"\u003e\n \u003cp\u003e2.08 \u0026ndash; 2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"28.35820895522388%\"\u003e\n \u003cp\u003e80 years +\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.930348258706468%\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"21.890547263681594%\"\u003e\n \u003cp\u003e1.76 \u0026ndash; 1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.681592039800995%\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"22.139303482587064%\"\u003e\n \u003cp\u003e2.02 \u0026ndash; 2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.24%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.36%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.24%\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.96%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.96%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.8%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.12%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.28%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"10.08%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.27710843373494%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.244979919678714%\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.670682730923694%\"\u003e\n \u003cp\u003e1.18 \u0026ndash; 1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.04417670682731%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.8714859437751%\"\u003e\n \u003cp\u003e1.18 \u0026ndash; 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.27710843373494%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.89156626506024%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.244979919678714%\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.670682730923694%\"\u003e\n \u003cp\u003e1.39 \u0026ndash; 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.04417670682731%\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.8714859437751%\"\u003e\n \u003cp\u003e1.29 \u0026ndash; 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Mortality Information System\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(SIM/SUS) | National Bureau of Statistics (IBGE)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mortality – Brazil, Economic crisis, Demography, Health, Brazil – Economic policy","lastPublishedDoi":"10.21203/rs.3.rs-2273817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2273817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA new paradigm has been discussed regarding the impact of economic cycles on the mortality pattern from specific causes. These causes are called deaths of despair, and they selectively impact specific demographic groups. Also, low- and middle- income countries are most affected due to their economic and social instability. In this sense, the objective of study was to compare the magnitude of disparity in deaths from despair according to sex, age, and race in Brazil. We performed Poisson regression modeling to estimate the magnitude of the association between sex, age group, race, and deaths from despair. We found an association between mortality from despair and male sex (PR=6.15, 95%CI 6.09 – 6.22); emphasis on the age groups from 40 to 49 years old (PR=2.45, 95% CI 2.41 – 2.48) and 50 to 59 years old (PR=2.39, 95% CI 2.36 – 2.43); and brown (PR=1.21, 95% CI 1.20 – 1.22) and black race (PR=1.36, 95% CI 1.34 – 1.37). The present study preliminarily presents the effect of the economic crisis and mortality in the population, with demographic differences. Association with race was opposite to that verified in the original study in US, which suggests that this variable should be analyzed in the light of structural context.\u003c/p\u003e","manuscriptTitle":"The Role of Race in Deaths of Despair in Brazil: is It a White People Problem?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-21 15:59:51","doi":"10.21203/rs.3.rs-2273817/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1011d1f5-12b6-4af8-91bd-29d07e4b35f5","owner":[],"postedDate":"November 21st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17128683,"name":"Health sciences/Health care/Disease prevention"},{"id":17128684,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2023-06-15T10:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-21 15:59:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2273817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2273817","identity":"rs-2273817","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}