Association of Socioeconomic Status and Oral Cancer Mortality in Brazil: Temporal Trends and Spatial Distribution Open Access

• The highest oral cancer mortality rates among individuals over 40 years old were in states with elevated socioeconomic levels, mostly located in the Southeast, South, and Midwest regions of Brazil.

• An increasing trend in oral cancer mortality rates was verified in all Brazilian regions during 2010–2019.

• The states with the highest socioeconomic status presented the lowest annual percentage increases in oral cancer mortality rates.

Oral cancer (OC) is among the twenty most common types of cancer, being the seventh most frequent cause of death around the world [1]. Brazil is among the countries with the highest incidence and mortality rates from OC [2]. Regional differences in the distribution of OC cases are attributable to lifestyle, socioeconomic conditions, migratory phenomenon, and dietary patterns of the population [3]. Substance abuse such as tobacco and alcohol, biological factors such as human papillomavirus (HPV), syphilis, oro-dental condition, dietary deficiencies, chronic candidiasis, and other microbiological agents have also been associated with OC [4].

Discussion on how socioeconomic factors can be determinants in the appearance and clinical evolution of OC is not recent, but still represents an issue, especially in developing countries [5]. It is suggested that individuals in socioeconomically vulnerable situations are at increased risk for OC and its complications due to greater exposure to risk factors, limited access to health services, and consequent late diagnosis [6]. A systematic review carried out with 41 case-control studies comprising 15,344 cases of OC reported a significant risk of occurrence of OC associated with a low socioeconomic level, a risk comparable to the risk related to lifestyle [5]. Socioeconomic conditions are recognized as social determinants of health, and directly influence cancer survival rates [7]. Among the socioeconomic indicators that are most commonly used are the Human Development Index (HDI) and the Social Vulnerability Index (SVI), as they include a broad spectrum of aspects related to social development, such as education, income, longevity, urban infrastructure, and vulnerability.

In Latin America, for example, a study showed that HDI was negatively related to mortality due to OC in countries with medium and low HDI, exhibiting an inverse relationship [8]. In Brazil, a study showed that municipalities with more than 50,000 inhabitants and with high HDI were more unequal (Gini >0.4), displaying a lower oral health coverage in primary care (<50%) and a higher cumulative risk of having 1 or more cases (p < 0.001) of hospitalization for OC. A significant correlation between high HDI, larger population size, high Gini index, low coverage of dental care, and high frequency of OC deaths was also reported [9].

The use of temporal analysis techniques combined with spatial statistics allows the incorporation of different variables, mainly demographic and social, in health studies [10], enabling the generation of information on the spatio-temporal dynamics of cancer mortality, the identification of population groups at greater risk of illness and death, and the generation of hypotheses [7, 11]. Studies of this nature are extremely relevant to plan public health actions as they provide information on exposure to risk factors that support the evaluation of existing health services and interventions [12].

However, these estimates alone are not enough to understand the precise magnitude and temporal trend of malignant neoplasms, neither to evaluate interventions against cancer [7]. Thus, this study aimed to explore possible associations between rates of OC mortality and socioeconomic conditions, as expressed by the HDI and SVI, in Brazil, between 2010 and 2019.

We conducted a population-based and ecological-type study using spatial analysis tools, that aimed to assess the relationship between socioeconomic conditions (independent variables) and mortality from OC (dependent variable), in Brazil, between 2010 and 2019. The Brazilian territory is divided into five main regions (North, Northeastern, Midwest, Southern, and South) and 27 Federative Units (FU). According to the Brazilian Institute of Geography and Statistics (IBGE), it has a territorial extension of 8,510,820,623 km², and its population is over 210 million, with a population density of 24.66 people per km². According to the International Monetary Fund’s reports, Brazil is the largest national economy in Latin America and the world’s ninth in nominal gross domestic product. The National Cancer Institute (INCA) estimates that for each year of the last triennium (2020–2022), 15,190 new cases of oral and oropharyngeal cancer will be diagnosed in Brazil.

Socioeconomic conditions were based on HDI, that measures education, longevity, and income of populations, varying from 0 (no human development) to 1 (total human development) [13]; and SVI, composed of variables that assess urban infrastructure, human capital, and labor income [14], varying from 0 (good living conditions) to 1 (bad living conditions). Both indicators were collected from the Institute for Applied Economic Research (IPEA) website [15], selecting the options Brazil, Macro Regions (all 27 FUs), Indexes (HDI, SVI), and Years (2010 to 2019).

OC-related deaths were collected from the Mortality Information System (SIM) website [16], by selecting Death by place of residence from each FU, the time period (2010–2019), and individuals over 40 years old, since OC rates are distinctly higher over this age group [17]. Cancer codes C00 to C10 (malignant neoplasms of the oral region, including the lip and oropharynx) and C14 (malignant neoplasm of ill-defined sites, which may include the lip, oral cavity, and pharynx) were selected, according to the International Classification of Diseases (ICD-10) [18]. Data on the total resident population of Brazilian states were based on 2010 population census, by selecting “demographic census,” “1.4 spreadsheet – Population in Demographic Censuses, by Major Regions and Federative Units – 1,872/2010,” and the estimates made for the years between censuses (choosing “population estimates,” “Brazil; Major Regions and Federative Units,” and years from 2011 to 2019), available in the IBGE website [19]. Finally, mortality rates from OC were calculated by dividing the number of deaths by the total population residing in the same place and period, standardized at 100,000 inhabitants [20].

Data were tabulated using the software Microsoft Excel 2010 and expressed as tables and graphs. Numerical variables were expressed as mean and standard deviation, besides absolute and relative frequencies. OC mortality rates were evaluated by tertiles of HDI (social and economic development) and SVI (social vulnerability), and also by geographic region. Both HDI and SVI vary from 0 (poor HDI/development and low SVI/vulnerability) to 1 (good HDI/development and high SVI/vulnerability). The indices division in tertiles was made after their extraction from IPEA website. The mean value of the entire period (2010–2019) for each index per state was organized in a descending order and stratified into 3 tertiles (33.3%): first, second, and third, so the first 9 states were classified as high, the second group of 9 as medium, and the last 9 as low, totaling 27 FUs. The indicators were stratified into tertiles to focus on the health disparities throughout Brazil instead of using each index classification and also to facilitate the interpretation of the results [21].

Therefore, in the first group of the HDI (high tertile), the states with greater/good development (0.845–0.765; high tertile) were allocated, followed by those with medium/reasonable (medium tertile) in the second (0.765–0.715), and those with the lowest numbers/poor HDI in the third (0.715–0.671) group (low tertile). For SVI, the states with greater vulnerability were grouped in the first tertile (high tertile; 0.379–0.307), those with medium/reasonable vulnerability were grouped in the second tertile (medium tertile; 0.300–0.256), while the third group was composed of states less vulnerable (low tertile; 0.245–0.140). Then, the mean OC mortality rate per year during the study period (2010–2019) was calculated by tertile/index, to analyze the temporal trend.

Temporal trends were assessed by segmented linear regression (Joinpoint 4.9.0.0 software). The year of occurrence of the event was considered the dependent variable (x axis), and the mean mortality rates from OC were the independent variable, at the state and regional level (y axis). The annual percentage changes (APC) were calculated for each segment, and the average annual percentage changes (AAPC) were calculated for the entire period when there was more than one significant inflection in the studied period. APCs and AAPCs were significant when p < 0.05 and 95% CI did not include zero value. Temporal trends were classified into decreasing (APC– and p < 0.05), increasing (APC+ and p < 0.05), or stable (APC + or – and p > 0.05).

Choropleth maps were elaborated using the raster graphics software Adobe Photoshop CC 2019, displaying HDI, SVI, and OC mortality rates throughout the Brazilian territory. Choropleth maps show the spatial distribution of the HDI, SVI, and mortality rates, classified as high, medium, and low, based on the tertile statistical rule.

The highest OC mortality rates among individuals over 40 years old, in Brazil (from 2010 to 2019), were concentrated in the South and Southeast regions of the country, followed by the Midwest and Northeast regions (Table 1). The FUs with the highest mortality rates of OC (per 100,000 inhabitants) were Espírito Santo (4.51), Rio Grande do Sul (4.10), Paraná (4.06) and Rio de Janeiro (RJ) (3.88), while the lowest rates belonged to Acre (1.01), Maranhão (1.22), Pará (1.34) and Amapá (1.35), during the studied period.

The states with elevated socioeconomic levels were mostly located in the Southeast, South, and Midwest regions and are therefore included in the high tertile of HDI and in the low tertile of SVI. Distrito Federal (0.845), São Paulo (0.819), and Santa Catarina (0.803) had the highest HDI values, while Alagoas (0.671), Maranhão (0.681), and Piauí (0.687) presented lower HDI. Regarding the SVI, Santa Catarina (0.140), Paraná (0.194), and Rio Grande do Sul (0.206) exhibited low vulnerability, while Maranhão (0.379), Acre (0.358), and Amazonas (0.356) exhibited higher SVI (Fig. 1 and Table 1).

In addition, an increasing trend in OC mortality rates was verified in all Brazilian regions (Table 2). The North (APC = 3.9%), Northeast (APC = 2.4%) and Midwest (APC = 2.2%) regions had the highest annual increases (p < 0.05), while Southeast (APC = 1.2%) and South (APC = 1.5%) regions had the lowest annual increases (p < 0.05), during the studied period. Most FUs had increasing (13) and stable (13) trends, while only the state of RJ showed a significant decreasing trend (APC = −1.6%).

Regardless of HDI tertile, a significant increasing trend in the rate of mortality from OC was observed. However, the states in the 1st tertile, exhibiting a good human development, had the lowest significant annual percentage increase (APC = 1.7%), compared to the other tertiles with lower human development indices. Hence, a relevant increasing trend of OC mortality rates was depicted in FUs with low human development rates (p < 0.05). In relation to SVI tertiles, the same temporal pattern was observed. Although all SVI tertiles showed an increasing trend, the 3rd tertile, displaying low vulnerability, exhibited the lowest annual percentage increase (APC = 2.2%), with statistical significance (p < 0.05), as shown in Table 3.

The results showed a concentration of high mortality rates from OC in the South and Southeast regions of Brazil, while the states in the North and Northeast regions had the lowest rates. This disparity may be associated with the fact that OC is more frequent in adults aged 40 years or older and the population in the South and Southeast regions has a higher life expectancy due to the demographic transition observed in recent years [22]. Likewise, another study that analyzed secondary data on OC mortality in Brazil (2005–2014) reported a high spatial autocorrelation (Moran index = 0.648) on the distribution of this rate in the South and Southeast regions. It also compared cancer mortality rates with reference to lifestyle, sociodemographic indicators, consumption of pesticides, presence of comorbidities, use of health services, and food consumption and showed a greater exposure to risk factors in more developed regions, such as HPV infection (p < 0.05), that could be related to the asymmetric profile of OC deaths throughout the country [20].

As for the analysis of the distribution of socioeconomic indices, regional disparities were also observed concerning HDI and SVI indicators, as better social and living conditions were observed in the South and Southeast regions and worse conditions in the North and Northeast regions. This result is consistent with the uneven and biased implementation of public policies that favored the most developed regions, in a historical scope: in the post-colonization period, these places went through the process of metropolization and, in consequence, became large commercial and industrial centers [6].

Overall, the results reported here show a higher concentration of deaths due to OC in regions with better socioeconomic conditions. This scenario is most likely related to an increasing risk of dying from cancer at advanced ages. The level of education, life expectancy, and per capita income in the municipality are also related to a higher rate of OC mortality [6].

A similar finding was found in a study that investigated the longitudinal trends based on the global burden of diseases caused by lip and OCs; this study showed that the primary predictor variable was the HDI, that is, countries with high and medium indexes experienced disproportionate growth in the burden of lip and oral cavity cancer disease burden (from +37.6% to +22.4% in age-standardized disability-adjusted life years) [23]. Furthermore, poor access to cancer control and prevention actions in less developed countries leads to insufficient registration of cases in health information systems. Despite the improvement in health information systems fostered by the Brazilian National Health Information and Informatics Policy (PNIIS/Ordinance No. 589, May 20, 2015), there is still underreporting and sub-records in regions farthest from the largest urban centers [6].

Regular dental appointments are associated with a more precise diagnosis of cancerous lesions and seem to have an important role in this inequality, as the North and Northeast regions have a lower coverage of oral health in primary care [9, 20]. In addition, it is known that the average number of hospitalizations due to OC in Brazil increased after 2004, coinciding with the implementation of the National Oral Health Policy, known as “Brasil Sorridente.” This policy enabled better oral health care organization and defined funding rules, resulting in the improvement of infrastructure in oral health services, increasing the number of services offered and therefore its coverage. Besides, there were only 617 oral health teams in 2003, and almost 26 thousand teams were counted in 5 thousand municipalities in 2017, representing 37% of potential population coverage in that year [24]. The greater dental coverage brought about by this program may have led to an increase in the diagnoses of OC in the Unified Health System (SUS) [9].

The OC temporal trends showed that despite the high mortality rates in these regions, the annual increase is higher among the states with lower socioeconomic levels. A similar scenario was reported by Boing et al. [25], between 1970 and 2002, in which the South and Northeast regions displayed an increasing trend in mortality while the other Brazilian regions exhibited a stable trend.

It is important to note that RJ was the only FU that showed a decreasing trend in mortality from OC. Studies have already indicated that almost half of the incidence of OC in Brazil occurs in the Southeast region [6, 20]. RJ is one of the national metropolises, located in this specific region, and has a population of over 6 million. The state also has an extensive network of local government health units, and the headquarters of the National Cancer Institute (INCA) is located in RJ, which is the main reference center for cancer care and research in the country [26]. Another study has already analyzed the temporal trend in OC mortality rates in RJ and showed a reduction of 1.54% per year in the OC mortality rate from 1999 to 2018. At the same time, there was an increasing trend in Family Health Strategy (7.33% per year) and Oral Health Strategy (18.21% per year) coverage, between 2002 and 2018. These health programs favor wide dissemination of information about OC risk factors, signs, and symptoms, aiming at prevention and early diagnosis [27].

In addition, another OC prevention strategy is vaccination against HPV since the virus is carcinogenic and can affect several organs, including the oral cavity. In Brazil, only the states of Pernambuco and RJ maintained a vaccination coverage rate above 15% in girls, between 2016 and 2018 [28]. Regarding RJ, vaccination coverage in girls is 54.76% for the first dose and 33.27% for the second; and in boys, the coverage is 22.45% for the first dose and 14.02% for the second dose. However, these vaccination rates are cumulative [29], and the National HPV Vaccination Program started in 2014, so more time is needed to determine its effect on HPV infection rates and, consequently, on OC mortality rates [28].

It is also important to recognize that OC has a multifactorial etiology that includes endogenous factors, such as genetic predisposition, and external factors, such as habits and lifestyle [4]. The higher number of smokers in the South and Southeast regions of Brazil, for example, may correlate with the high number of OC-associated deaths in these regions [25]. Notwithstanding this, there is an agreement regarding the relationship between OC and the social determinants of health. Dourado Martins et al. [30], in a systematic review that included 21 articles, revealed a strong association between social deprivation, socioeconomic status/income, educational level, and occupation with OC, corroborating the findings described so far.

This study has limitations that must be considered. As this is an ecological study based on secondary data, there is a possibility of bias, related to the quantity and quality of information, that is, underreporting or overreporting of deaths from OC. This study does not have an individualized analysis or detail, and the phenomenon of ecological fallacy can occur if its findings are interpreted at individual levels [10]. Despite these limitations, our study provides relevant data on mortality from OC, which can contribute to the optimization of financial resources, the evaluation and implementation of new health actions, and also the strengthening of public health policies for the most vulnerable population [9]. Multicenter studies may provide more accurate information to guide intervention and control actions for OC in the Brazilian population [9].

Taken together, the data and analyses indicate that despite the advances in the knowledge of the etiological causes related to OC and good visual access to the oral cavity for inspection and follow-up, mortality rates did not show a temporal reduction. OC is a complex disease strongly influenced by behavioral risk factors, which are not fully explained by socioeconomic characteristics of individuals. Public policies should aim to prevent and promote early diagnosis, mainly through oral self-examination, in order to increase survival and cure rates, especially in less favored regions of Brazil.

There was an increasing trend in OC mortality, at a national level, from 2010 to 2019, more accentuated in the regions with the worst social indices (HDI and SVI), as reflected by the highest positive APC in the North and Northeast regions. Policy makers should aim for OC prevention and early diagnosis strategies, especially in vulnerable populations and regions of Brazil, to solve this unacceptable health disparity.

All information was made available to the public, so ethics committee approval was not mandatory.

The authors have no conflicts of interest to declare.

This study did not receive any funding.

Priscila Lima dos Santos and Débora dos Santos Tavares contributed to study conception and design; Priscila Lima dos Santos, Débora dos Santos Tavares, Marcio Bezerra Santos, Natanael Eric Batista Pereira, and Deane Cristina da Rocha Rodrigues de Oliveira contributed to data analysis and interpretation; all authors contributed to drafting, revising the manuscript, and finalizing submission.

Publicly available datasets were used in this study. These can be found in the “Mortality Information System (SIM)” and “ Institute for Applied Economic Research (IPEA).”