Burn deaths in Brazil: a social issue

Abstract

This study explores the relationship between socioeconomic and geographic variables of Brazilian municipalities and deaths caused by thermal (TB) and electrical burns (EB). Using data from the Mortality Information System and municipal indicators from IBGE, the impact of several factors on burn mortality was investigated. The analysis revealed a significant association between mortality and HDI (EB: 2000-2009 - RR=1.4·10⁻³ (95%CI: 3.4·10⁻⁴-5.9·10⁻³), 2010-2019 - RR=1.53·10⁻³ (95%CI: 2.46·10⁻⁴-9.57·10⁻³); TB: 2000-2009 - RR=2.95·10⁻⁶ (95%CI: 7.63·10⁻⁷-1.14·10⁻⁵), 2010-2019 - RR=1.24·10⁻⁷ (95%CI: 1.79·10⁻⁸-8.68·10⁻⁷)), Gini index (EB: 2000-2009 - RR=33.02) (95%CI: 18.43-59.03), 2010-2019 - RR=197.52 (95%CI: 111.2-350.14); TB: 2000-2009 - RR=25.77 (95%CI: 14.68-45.13), 2010-2019 - RR=431.24 (95%CI: 237.24-781.89)), and percentage of the population living in urban areas (EB: 2000-2009 - RR=1.644) (95%CI: 1.253-2.1689), 2010-2019 - RR=1.55 (95%CI: 1.17-2.07); TB: 2000-2009 - RR=2.42 (95%CI: 1.8-3.26), 2010-2019 - RR=2.794 (95%CI: 1.98-3.96)). The results suggest that lower socioeconomic conditions are correlated with higher risks of death by burns, indicating that it is a public health issue linked to social inequalities.

Keywords:
Burns; Health Information Systems; Socioeconomic Factors; Mortality

Resumo

Este estudo explora a relação entre variáveis socioeconômicas e geográficas dos municípios brasileiros e as mortes por queimaduras térmicas (QT) e elétricas (QE). Utilizando dados do Sistema de Informações de Mortalidade e indicadores municipais do IBGE, investigou-se o impacto de diversos fatores na mortalidade por queimaduras. A análise revelou uma associação significativa entre a mortalidade e IDHM (QE: 2000-2009 - RR=1,4·10⁻³ (IC95%: 3,4·10⁻⁴-5,9·10⁻³), 2010-2019 - RR=1,53·10⁻³ (IC95%: 2,46·10⁻⁴-9,57·10⁻³); QT: 2000-2009 - RR=2,95·10⁻⁶ (IC95%: 7,63·10⁻⁷-1,14·10⁻⁵), 2010-2019 - RR=1,24·10⁻⁷ (IC95%: 1,79·10⁻⁸-8,68·10⁻⁷)), índice de Gini (QE: 2000-2009 - RR=33,02 (IC95%: 18,43-59,03), 2010-2019 - RR=197,52 (IC95%: 111,2-350,14); QT: 2000-2009 - RR=25,77 (IC95%: 14,68-45,13), 2010-2019 - RR=431,24 (IC95%: 237,24-781,89)), e percentual da população em áreas urbanas (QE: 2000-2009 - RR=1,644 (IC95%: 1,253-2,1689), e 2010-2019 - RR=1,55 (IC95%: 1,17-2,07); QT: 2000-2009 - RR=2,42 (IC95%: 1,8-3,26), 2010-2019 - RR=2,794 (IC95%: 1,98-3,96)). Os resultados sugerem que condições socioeconômicas inferiores estão correlacionadas com maiores riscos de mortes por queimaduras, indicando que representa um problema de saúde pública interligado às desigualdades sociais.

Palavras-chave:
Queimaduras; Sistemas de Informação em Saúde; Fatores Socioeconômicos; Mortalidade

Resumen

Este estudio explora la relación entre variables socioeconómicas y geográficas de los municipios brasileños y las muertes por quemaduras térmicas (QT) y eléctricas (QE). Utilizando datos del Sistema de Información de Mortalidad e indicadores municipales del IBGE, se investigó el impacto de diversos factores en la mortalidad por quemaduras. El análisis reveló una asociación significativa entre la mortalidad y el IDHM (QE: 2000-2009 - RR=1,4·10⁻³ (IC95%: 3,4·10⁻⁴-5,9·10⁻³), 2010-2019 - RR=1,53·10⁻³ (IC95%: 2,46·10⁻⁴-9,57·10⁻³); QT: 2000-2009 - RR=2,95·10⁻⁶ (IC95%: 7,63·10⁻⁷-1,14·10⁻⁵), 2010-2019 - RR=1,24·10⁻⁷ (IC95%: 1,79·10⁻⁸-8,68·10⁻⁷)), el índice de Gini (QE: 2000-2009 - RR=33,02 (IC95%: 18,43-59,03), 2010-2019 - RR=197,52 (IC95%: 111,2-350,14); QT: 2000-2009 - RR=25,77 (IC95%: 14,68-45,13), 2010-2019 - RR=431,24 (IC95%: 237,24-781,89)), y el porcentaje de la población en áreas urbanas (QE: 2000-2009 - RR=1,644 (IC95%: 1,253-2,1689), y 2010-2019 - RR=1,55 (IC95%: 1,17-2,07); QT: 2000-2009 - RR=2,42 (IC95%: 1,8-3,26), 2010-2019 - RR=2,794 (IC95%: 1,98-3,96)). Los resultados sugieren que condiciones socioeconómicas desfavorables están correlacionadas con mayores riesgos de muerte por quemaduras, lo que indica que representa un problema de salud pública vinculado a las desigualdades sociales.

Palabras clave:
Quemaduras; Sistemas de Información en Salud; Factores Socioeconómicos; Mortalidad

Introduction

Brazil is a South American continental-size country characterized by marked economic, social, and cultural disparities between its regions. Such diversity implies unique challenges for health policies, which need to be effective, inclusive, and comprehensive for a very heterogeneous population¹. The analysis of socioeconomic and demographic indicators highlights these disparities, evidenced, for example, by the variation of indicators such as the municipal human development index (MHDI) and the Gini index among the different locations. The indicators built from the 2010 Brazilian Census show cities such as São Caetano do Sul in São Paulo with a MHDI of 0.862, similar to that of Hong Kong, Spain, and Denmark in the same year; and the Melgaço, Pará, with a MHDI of 0.418, similar to that of Senegal, Uganda, and Nigeria².

Indicators such as the HDI and the Gini index, among others, are available, built by official sources, and can help to understand the mechanisms involved in the manifestation of public health problems³.

Among the health problems that may be subject to the influence of Brazilian social development factors, burns are highly relevant traumas, as they have significant implications for morbidity and mortality in the population⁴. Burns are complex injuries that require the allocation of several resources and generate implications in different temporal spectra, often with sequelae that can hinder reintegration into society⁴.

One of the dimensions that make burns complex is the different injury mechanisms involved with their epidemiological and clinical characteristics⁴. Despite the importance of this topic, the Brazilian literature on burns offers a significant predominance of descriptive studies on hospitalized patients and lacks population-based studies for a broader understanding of the problem^5,6.

Thus, studies with official databases supported by population counts can contribute to the understanding necessary for a more accurate epidemiological approach to such complex public health issues as burns.

This study seeks to establish relationships between socioeconomic and demographic indicators with burns and their fatal outcomes, an approach of great value for grasping the problem within the complex Brazilian municipalities and with the potential to support the development of coping strategies linked to urban development actions.

Objectives

It aims to analyze the relationship between socioeconomic and geographic variables of Brazilian municipalities and deaths from thermal and electrical burns from 2000 to 2019.

Methods

The Ministry of Health’s official data system, DataSUS, provides publicly accessible data on burns. This platform uses the International Classification of Diseases (ICD-10) and allows for conducting epidemiological studies, especially in the databases of the Mortality Information System (SIM), the Hospital Information System (SIH), and the Outpatient Information System (SIA)⁷. However, the healthcare databases (SIM and SIA) aim to record production and do not contain supplementary healthcare data, while SIM, despite the potential for code recording errors, covers any care in which death occurred⁸.

This work was designed as an ecological study of the 5,570 Brazilian municipalities in the decades following the 2000 and 2010 censuses. The dataset was built from two sources: the Mortality Information System (SIM) of DataSUS⁷ and the municipal indicators of the censuses produced by Brazilian Institute of Geography and Statistics (IBGE)¹.

The microdata of the mortality database were retrieved from the SIM, and the annual files in ‘dbc’ format were converted to ‘csv’ format using the Tabwin 4.15 software from the Ministry of Health. The following variables were extracted from the data set obtained: day, month, and year of death; code of the municipality of residence; code of the international classification of diseases with a letter and two digits of the underlying cause of death; and work accident.

Only records whose underlying cause of death contained the following codes were extracted from this database:

• Thermal burns: W35; W36; W38; W39; W40; W92; X00; X01; X02; X03; X04; X05; X06; X08; X09; X10; X11; X12; X13; X14; X15; X16; X17; X18; X19; X30; X75; X76; X77; X88; X96; X97; X98; Y25; Y26; and Y27

• Electrical burns: W85; W86; W87; and X33

• Burns from other causes (chemical, frostbite, radiation): X86; W88; W89; W90; W91; X31; X32; and W93

A new field was created to identify the cause of death due to burns in each record. A second set of data was produced from the IBGE database for municipalities and the 2000 and 2010 censuses, containing the following variables:

• Geographic variables: Municipality code and name; federation unit; latitude; longitude; altitude; Municipal area in square kilometers;

• Socioeconomic variables: Population in 2000 and 2010; Gini index in 2000 and 2010; activity rate among those aged 18 and over in 2000 and 2010; unemployment rate among those aged 18 and over in 2000 and 2010; percentage of employed people in the agricultural sector in 2000 and 2010; percentage of employed people in the mineral extraction sector in 2000 and 2010; percentage of employed people in the manufacturing industry in 2000 and 2010; percentage of employed people in the public utility industrial services sector in 2000 and 2010; percentage of employed people in the construction sector in 2000 and 2010; percentage of people employed in the trade sector in 2000 and 2010; percentage of people employed in the services sector in 2000 and 2010; percentage of the population residing in urban areas in 2000 and 2010; infant mortality in 2000 and 2010; Municipal Human Development Index (MHDI) in 2000 and 2010; dependency ratio in 2000 and 2010; aging rate in 2000 and 2010; illiteracy rate among people aged 15 and over in 2000 and 2010; and percentage of the population aged 18 and over with complete primary education in 2000 and 2010¹.

After organizing the data, a bivariate correlation analysis was performed between the collected variables in search of collinearity, and the following variables were excluded from the analysis due to significant recording errors or collinearity: percentage of people employed in the mineral extraction sector in 2000 and 2010; percentage of people employed in the manufacturing industry in 2000 and 2010; percentage of people employed in the public utility industrial services sector in 2000 and 2010; percentage of people employed in the construction sector in 2000 and 2010; percentage of people employed in the trade sector in 2000 and 2010; and percentage of people employed in the services sector in 2000 and 2010.

Subsequently, the second data set received the calculated fields of number of deaths between 2000 and 2009 and between 2010 and 2019 for deaths from thermal and electrical burns.

After the initial verification of the assumptions, the data were subjected to a robust variance Poisson regression for the outcomes of the number of deaths from thermal burns between 2000 and 2009 and between 2010 and 2019 and the number of deaths from electrical burns between 2000 and 2009 and between 2010 and 2019, in the Jamovi 2.3.26 software and the results were summarized in tables. The historical series was conducted in two periods to correlate with the available 2000 and 2010 Censuses, and the strength of the association was not expressed in order to allow comparability between the variables that are on a decimal scale.

This project was not submitted to ethical review because it is based entirely on publicly available data, per the sole paragraph of the first article of Resolution N° 510/2016 of the National Health Council. The entire project was self-financed by the authors, and no conflict of interest was involved in the production of the article.

Results

From 2010 to 2019, we observed 3.53% (95%CI: 3.33%-3.74%) more deaths than those from 2000 to 2009, and the differences in the proportions between deaths due to thermal and electrical causes decreased (Table 1). The increase was greater in thermal, 4.79% (95%CI: 4.47%-5.11%), than in electrical, 2.13% (1.90%-2.36%). The population difference between the 2000 and 2010 censuses, however, showed an increase of 12.2%.

Table 1
Robust variance Poisson regression for the number of deaths from electrical burns from January 2000 to December 2009.

	2000 to 2019 N - 95% Confidence Interval	2000 to 2009 N - 95% Confidence Interval	2010 to 2019 N - 95% Confidence Interval
Thermal	35,429 - 54.0% (53.6%-54.3%)	17,300 - 53.6% (53.1%-54.2%)	18,129 - 54.3% (53.7%-54.8%)
Electrical	29,790 - 45.4% (45.0%-45.8%)	14,738 - 45.7% (45.1%-46.2%)	15,052 - 45.1% (44.5%-45.6%)
Other	435 - 0.7% (0.6%-0.7%)	219 - 0.7% (0.6%-0.8%)	216 - 0.6% (0.6%-0.7%)
Total	65,654 (100%)	32,257 (100%)	33,397 (100%)

• Source: Authors.

The regression for deaths from electrical causes from 2000 to 2009 showed a model that explained 61% of the data, with statistical significance for all variables except for the municipality. Three of the variables showed a more consistent strength of association: Gini index, percentage of the population living in urban areas, and MHDI. The other variables, despite statistical significance, produced a weak effect on the regression model (Table 2).

Table 2
Robust variance Poisson regression for the number of deaths from electrical burns from January 2000 to December 2009.

	Estimate	SE	95% Exp(B) Confidence Interval			Z	p
			exp(B)	Lower	Upper
Constant	0.217	2.47·10−2	1.242	1.183	1.303	8.79	< .001
Longitude•	1.47·10−2	3.51·10−3	1.015	1.008	1.022	4.19	< .001
Latitude•	1.48·10−2	3.28·10−3	1.015	1.008	1.021	4.51	< .001
Altitude•	-4.60·10−4	6.48·10−3	0.9995	0.999	0.9997	-7.09	< .001
Area (Km²)•	1.51·10−6	2.16·10−6	1	1	1	0.7	0.486
Gini Index*	3.5	0.296	33.02	18.43	59.03	11.78	< .001
Activity rate*†	3.06·10−2	3.6·10−3	1.031	1.024	1.038	8.62	< .001
Unemployment rate*†	-1.02·10−2	4.2·10−3	0.99	0.982	0.998	-2.42	0.015
Percentage of people employed in the agricultural sector*	-2.48·10−2	2.12·10−3	0.975	0.971	0.9796	-11.71	< .001
Percentage of population living in urban areas*	0.497	0.14	1.644	1.253	2.1689	3.55	< .001
MHDI*	-6.558	0.73	1.4·10−3	3.4·10−4	5.9·10−3	-8.98	< .001
Aging rate*	-9.72·10−2	1.347·10−2	0.907	0.884	0.932	-7.22	< .001
Illiteracy rate*‡	1.54·10−2	4.07·10−3	1.016	1.007	1.024	3.8	< .001
Percentage of the population with elementary school*†	8.98·10−2	4.19·10−3	1.094	1.085	1.103	21.45	< .001
Population at the beginning of the period*	3.18·10−7	9.48·10−9	1	1	1	33.53	< .001

• †Among the population aged 18 and over; ‡Among the population aged 15 and over. MHDI: Municipal Human Development Index. Model: Quasi-Poisson; R²: 0.611; Degrees of freedom χ²: 3.417.
• Sources: • IBGE; *2000 Census.

The 2010-2019 period evidenced regression for deaths from electrical causes that explained 58.9% of the data, with statistical significance for all variables. Again, the Gini index, the percentage of the population living in urban areas, and the MHDI showed consistent strength of association in contrast to the weaker association of the other variables (Table 3).

Table 3
Robust variance Poisson regression for the number of deaths from electrical burns from January 2010 to December 2019.

	Estimate	SE	95% Exp(B) Confidence Interval			Z	p
			exp(B)	Lower	Upper
Constant	0.297	2.32·10−2	1.35	1.29	1.41	12.82	< .001
Longitude•	2.34·10−2	3.13·10−3	1.024	1.017	1.03	7.46	< .001
Latitude•	2.61·10−2	3.19·10−3	1.026	1.02	1.033	8.19	< .001
Altitude•	-6.03·10−4	6.59·10−5	0.9994	0.999	0.9995	-9.14	< .001
Areas (Km²)•	4.93·10−6	1.55·10−6	1	1	1	3.18	0.001
Gini Index*	5.29	0.292	197.52	111.2	350.14	18.07	< .001
Activity rate*†	2.76·10−2	3.43·10−3	1.027	1.02	1.034	7.76	< .001
Unemployment rate*†	-463·10−2	6.08·10−3	0.955	0.943	0.966	-7.62	< .001
Percentage of people employed in the agricultural sector*	-2.21·10−2	2.18·10−3	0.978	0.974	0.9824	-10.1	< .001
Percentage of population living in urban areas*	0.441	0.146	1.55	1.17	2.07	3.02	0.003
MHDI*	-6.4798	0.934	1.53·10−3	2.46·10−4	9.57·10−3	-6.94	< .001
Aging rate*	-8.52·10−2	1.14·10−2	0.918	0.898	0.939	-7.45	< .001
Illiteracy rate*‡	2.16·10−2	4.46·10−3	1.022	1.013	1.031	4.84	< .001
Percentage of the population with elementary school*†	7.59·10−2	4.44·10−3	1.079	1.069	1.088	17.1	< .001
Population at the beginning of the period*	3.15·10−7	8.85·10−9	1	1	1	35.53	< .001

• †Among the population aged 18 and over; ‡Among the population aged 15 and over. MHDI: Municipal Human Development Index. Model: Quasi-Poisson; R²: 0.589; Degrees of freedom χ²: 3.224.
• Sources: • IBGE; *2010 Census.

The same three variables showed a strong association with deaths from thermal burns in 2000-2009 and 2010-2019, with statistical significance. However, we could also find an association between deaths from this burn mechanism and the percentage of the population with complete elementary education (Tables 4 and 5).

Table 4
Robust variance Poisson regression for the number of deaths from thermal burns from January 2000 to December 2009.

	Estimate	SE	95% Exp(B) Confidence Interval			Z	p
			exp(B)	Lower	Upper
Constant	0.152	2.41·10−2	1.16	1.11	1.22	6.3	< .001
Longitude•	3.02·10−2	3.79·10−3	1.031	1.023	1.038	7.97	< .001
Latitude•	-4.29·10−2	3.39·10−3	0.958	0.952	0.964	-12.66	< .001
Altitude•	4.11·10−4	5.29·10−5	1	1	1.001	7.77	< .001
Areas (Km²)•	1. 25·10−5	1.94·10−6	1	1	1	6.47	< .001
Gini Index*	3.25	0.286	25.77	14.68	45.13	11.34	< .001
Activity rate*†	4.371·10−2	3.54·10−3	1.045	1.037	1.052	12.34	< .001
Unemployment rate*†	9.77·10−3	4.05·10−3	1.01	1.002	1.018	2.41	0.016
Percentage of people employed in the agricultural sector*	-2.446·10−2	2.10·10−3	0.976	0.972	0.98	-11.64	< .001
Percentage of population living in urban areas*	0.884	0.151	2.42	1.8	3.26	5.84	< .001
MHDI*	-12.73	0.6901	2.95·10−6	7.63·10−7	1.14·10−5	-18.45	< .001
Aging rate*	1.97·10−3	0.01226	1.002	0.978	1.026	0.16	0.873
Illiteracy rate*‡	8.75·10−3	0.00421	1.009	1	1.017	2.08	0.038
Percentage of the population with elementary school*†	0.122	0.00399	1.129	1.121	1.138	30.5	< .001
Population at the beginning of the period*	3.07·10−7	6.78E-09	1	1	1	45.36	< .001

• †Among the population aged 18 and over; ‡Among the population aged 15 and over. MHDI: Municipal Human Development Index. Model: Quasi-Poisson; R²: 0.704; Degrees of freedom χ²: 2.997.
• Sources: • IBGE; *2000 Census.

Table 5
Robust variance Poisson regression for the number of deaths from thermal burns from January 2010 to December 2019.

	Estimate	SE	95% Exp(B) Confidence Interval			Z	p
			exp(B)	Lower	Upper
Constant	0.156	2.592·10−2	1.169	1.111	1.23	6.04	< .001
Longitude•	3.47·10−2	3.8·10−3	1.035	1.028	1.043	9.14	< .001
Latitude•	-3.73·10−2	3.52·10−3	0.963	0.957	0.97	-10.62	< .001
Altitude•	2.54·10−4	5.69·10−5	1	1	1	4.47	< .001
Areas (Km²)•	1.40·10−5	1.63·10−6	1	1	1	8.55	< .001
Gini Index*	6.067	0.304	431.24	237.24	781.89	19.94	< .001
Activity rate*†	3.39·10−2	3.91·10−3	1.034	1.027	1.042	8.66	< .001
Unemployment rate*†	-2.93·10−2	6.89·10−3	0.971	0.958	0.984	-4.26	< .001
Percentage of people employed in the agricultural sector*	-2.91·10−2	2.52·10−3	0.971	0.967	0.976	-11.53	< .001
Percentage of population living in urban areas*	1.028	0.177	2.794	1.98	3.96	5.81	< .001
MHDI*	-15.9	0.99	1.24·10−7	1.79·10−8	8.68·10−7	-16.06	< .001
Aging rate*	4.58·10−3	1.14·10−2	1.005	0.982	1.027	0.4	0.687
Illiteracy rate*‡	2.93·10−3	5.53·10−3	1.003	0.992	1.014	0.53	0.596
Percentage of the population with elementary school*†	0.112	4.78·10−3	1.118	1.108	1.129	23.42	< .001
Population at the beginning of the period*	2.84·10−7	6.82·10−9	1	1	1	41.61	< .001

• †Among the population aged 18 and over; ‡Among the population aged 15 and over. MHDI: Municipal Human Development Index. Model: Quasi-Poisson; R²: 0.681; Degrees of freedom χ²: 6.404.
• Sources: • IBGE; *2010 Census.

All variables were statistically significant in both periods, except for the aging and the illiteracy rates for deaths from thermal burns (Tables 4 and 5). The mathematical prediction model seemed to explain the dependent variable slightly better in the second period than in the first for both mechanisms (Tables 2 to 5).

Finally, when analyzing the mean monthly mortalities per 1 million inhabitants due to burns for each state in Brazil from 2010 to 2019, Rio de Janeiro (1.8: 1.617-1.983) and Maranhão (1.775: 1.483-2.058) were above the national average, while Minas Gerais (1.058: 0.933-1.183) and São Paulo (0.95: 0.867-1.033) were below. The other Federation Units did not show statistically significant differences regarding the average (data not shown).

Discussion

A total of 2,791,718 deaths from external causes⁷ were officially reported during the period studied. Thus, the 65,654 deaths from burns correspond to only 2.35% of these deaths, which may mean that the importance of this issue is underestimated (Table 1). However, deaths from burns are only the final phase of the natural history of this type of trauma, with a high risk of sequelae and incapacity for work in most cases that do not result in death⁹.

The best estimates for the incidence of burns use studies from hospitalization units that are difficult to compare with the original population base^6,9, which makes the study of deaths from burns more reliable because it uses a unified and qualified national database: the SIM, despite the potential to underestimate the injury events, in cases of error in indicating the ICD consistently to the point of not being noticed by the validation processes by the state health secretariats⁸.

Although the situation involved in the dynamics of deaths from burns over the twenty years studied may have altered, there was no significant change in the proportions of deaths from electrical and thermal burns (Table 1), and the results obtained by this study were consistent (Tables 2 to 5).

In the four mathematical models constructed, most of the variables studied showed statistical significance. However, many of them had weak association strength, suggesting that these variables require significant variations to produce any noticeable effect (Tables 2 to 5). This was the case for the geographic variables (latitude, longitude, altitude, and area), activity rate, unemployment rate, percentage of employed people in the agricultural sector, aging rate, illiteracy rate, and population at the beginning of the period.

The Gini index and the MHDI evidenced great strength of association in all mathematical models constructed (Tables 2 to 5). Both variables are important social indicators, and this result suggests that the social components involved in the context of the municipality may be relevant social determinants for severe events involving thermal or electrical burns.

The literature review for this article was unsuccessful in finding ecological or population-based studies associating social variables with burns or their fatal outcomes. Despite this, studies were found with case series from specialized services suggesting a relationship between the individual’s social variables and burn events that led to hospitalizations, especially in burns involving children and other vulnerable individuals^10-15.

The studies found were committed to using hospitalization cases to estimate population incidence and, therefore, had important limitations in testing hypotheses of association between these social variables with reliable representation^10-15. The results found by this study may suggest greater security in establishing this association.

The Gini index is a statistical strategy for summarizing how equitably a resource is distributed in a population¹⁶. In the case of the indicator produced by the IBGE, per capita household income is employed¹, which makes it a measure of income concentration. The closer to one, the more concentrated the income is in the hands of a few individuals, and the closer to zero, the more balanced the income distribution is^1,16. The results obtained seem to point to a higher risk of deaths associated with income concentration in the municipality.

The interpretation of the Gini index should be cautious and consider that municipalities with greater equality in income distribution may have very different mean incomes¹⁶. In the case of this study, the association occurred only for income concentration, and it is impossible to make statements about the mean or median income value; thus, municipalities with the same value in the indicator may have very different mean or median incomes. In any case, income concentration is recognized as a relevant social problem and a determinant of health for several conditions, including external causes¹⁷.

The MHDI is a composite indicator formed by three dimensions of development: longevity, education, and income. Therefore, its variations must consider the trade-offs between its three axes¹⁸. The results obtained indicate a greater association between deaths from burns and lower human development indices in all mathematical models constructed. This effect appears to be plausible, although it is impossible to separate the contribution of each dimension.

The municipal structure associated with the best MHDI results includes complex urban management apparatuses such as education, health, transportation, security, and social support. When this structure is adjusted to the needs of the location, one can expect better results regarding life expectancy, education, and economic conditions of the population, but collaterally, this structure could address complex traumas such as burns, reducing their lethality¹⁹.

It therefore seems reasonable to assume that burns may constitute public health problems resulting from failures in the development of today’s society, inflicting death on the most vulnerable segment of the population. Suppose the effect is extended to burns and not only deaths. In that case, we should also consider that the potential consequences may include high costs to the individual and society due to functional losses, but also cruel stigmatization.

In December 2022, the Ministry of Health recognized burns in Brazil for the first time as a problem to be monitored and produced a specific epidemiological bulletin for this purpose⁴. Although this bulletin also focused on deaths from burns, its argument maintains that these numbers may represent only a small fraction of burn events, which in turn assume a broad spectrum of severity and consequences⁴.

Thus, we can generalize with relative certainty the association between low socioeconomic conditions and burns based on the results of this study. However, several studies worldwide with case series of hospitalizations due to burns also show this association, although based on participants who had already been hospitalized^20-23.

The regression model for deaths from electrical burns also showed a significant association with the percentage of the population living in urban areas (Tables 2 and 3). This effect may be associated with the urban energy distribution apparatus.

The Brazilian Association for Awareness of Electricity Dangers (ABRACOPEL) produces an annual report on accidents involving the electrical grid and, in 2019, at the end of the period studied, it evidenced accidents involving the overhead grid as the primary mechanism causing death for these accidents, probably primarily associated with energy theft and handling of the grid without proper qualification²⁴.

In the case of deaths from thermal burns, the association with the percentage of the population in urban areas was even more significant (Tables 4 and 5). In a study conducted with patients admitted to Tygerberg Hospital in Cape Town, South Africa, Cloake et al.²⁵ suggest that previous migratory movements that promoted an increase in the population in substandard sociodemographic conditions in urban centers may have produced a similar effect. In general, populations living in poverty in large cities tend to have less time to take care of home safety, besides having a greater risk of being exposed to work accidents in unhealthy environments²⁶.

Still, regarding deaths from thermal burns, a significant positive association was observed with the percentage of the population with complete elementary school level. Although the Brazilian literature finds a negative association between educational variables and burns^27-29, this study’s finding may have a complex explanation that may be biased by not differentiating between people who have only elementary school level and those with secondary and higher education. It is also impossible to rule out the hypothesis that complete elementary school level opens access to higher-risk occupations.

Finally, the monthly means of mortality from burns per million inhabitants from both causes by region can be analyzed. In this context, the Southeast drew attention compared the others (Thermal: 54.4 95%CI: 53.5-55.2; Electric: 22.9 95%CI: 22.4-23.5), followed by the Northeast (Thermal: 17.1 95%CI: 16.7-17.5; Electric: 25.2 95%CI: 24.7-25.6), South (Thermal: 7.5 95%CI: 7.3-7.7; Electric: 4.7 95%CI: 4.6-4.8), North (Thermal: 1.1 95%CI: 1.0-1.2; Electric: 2.5 95%CI: 2.4-2.6) and Midwest (Thermal: 1.5 95%CI: 1.4-1.6; Electric: 1.9 95%CI: 1.9-2.0), considering both decades aggregated. This indicates opportunities for future research exploring the data discussed regionally (data not shown).

This study’s main limitation is that the object was defined by death, which does not allow for safer inferences regarding burn events. However, other limitations should be considered, such as the result of the regression models, which explained 58.9% to 70.4% of the data, suggesting that other unused variables may have a significant influence on the effects studied, including confounding variables. There are also limitations related to the actions of state health secretariats in verifying and recording deaths and qualifying records, which can introduce different types of measurement bias into the database used. In this sense, errors in completing data consistently with other information may go unnoticed during data consolidation and affect the results. While these possibilities are relevant, the consistent findings in two different decades, mainly scientifically plausible, seem to suggest that they can be considered valid with reasonable certainty.

Final considerations

Based on the above, we can conclude that this study offers credible evidence to recognize burns in Brazil as one of the many complex problems involving socioeconomic dimensions and support future studies that could indicate strategies to address this public health problem within the framework of the Unified Health System. The results of the analyses relating deaths from burns to the MHDI, Gini Index, and Urbanization Rate reinforce the impacts of socioeconomic characteristics on burns. Therefore, we can affirm that burns and their sequelae should be understood as yet another of the cruel and complex consequences of inequalities in modern Brazil.

Despite the limitations mentioned above, the results provide relevant discussions and indicate new research opportunities. Future studies can explore mortality relationships by geographic region of the country, exploring the homogeneous characteristics by state with a greater number of municipalities, such as São Paulo, Rio de Janeiro, and Minas Gerais. Additionally, other analyses comparing mortality from burns and geographic and socioeconomic indicators can be developed using the same method.

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• Data availability statement
  The data sources used in the research are indicated in the body of the article.

• Chief editors:
  Maria Cecília de Souza Minayo, Romeu Gomes, Antônio Augusto Moura da Silva, Vania de Matos Fonseca

Data availability

The data sources used in the research are indicated in the body of the article.

Publication Dates

• Publication in this collection
  10 Nov 2025
• Date of issue
  Nov 2025

History

• Received
  15 Mar 2024
• Accepted
  22 May 2025
• Published
  24 May 2025