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	<front>
		<journal-meta>
			<journal-id journal-id-type="publisher-id">sn</journal-id>
			<journal-title-group>
				<journal-title>Sociedade &amp; Natureza</journal-title>
				<abbrev-journal-title abbrev-type="publisher">Soc. nat.</abbrev-journal-title>
			</journal-title-group>
			<issn pub-type="epub">1982-4513</issn>
			<issn pub-type="ppub">0103-1570</issn>
			<publisher>
				<publisher-name>Editora da Universidade Federal de Uberlândia - EDUFU</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			<article-id pub-id-type="publisher-id">00005</article-id>
			<article-id pub-id-type="doi">10.14393/SN-v37-2025-73312</article-id>
			<article-categories>
				<subj-group subj-group-type="heading">
					<subject>Artigos</subject>
				</subj-group>
			</article-categories>
			<title-group>
				<article-title>Perfil Geoepidemiológico dos Acidentes por Animais Peçonhentos em Populações Indígenas e Não-Indígenas no Brasil</article-title>
				<trans-title-group xml:lang="en">
					<trans-title>Geoepidemiological Profile of Venomous Animal Incidents in Indigenous and Non-Indigenous Populations in Brazil</trans-title>
				</trans-title-group>
			</title-group>
			<contrib-group>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-7278-0718</contrib-id>
					<name>
						<surname>Polidoro</surname>
						<given-names>Maurício</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/conceptualization/">Conceitualização</role>
					<role content-type="http://credit.niso.org/contributor-roles/data-curation/">curadoria de dados</role>
					<role content-type="http://credit.niso.org/contributor-roles/formal-analysis/">análise de dados</role>
					<role content-type="http://credit.niso.org/contributor-roles/investigation/">pesquisa</role>
					<role content-type="http://credit.niso.org/contributor-roles/methodology/">metodologia</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">redação do manuscrito original</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">redação - revisão e edição</role>
					<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0002-3107-8519</contrib-id>
					<name>
						<surname>Mendonça</surname>
						<given-names>Francisco de Assis</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">redação do manuscrito original</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">redação - revisão e edição</role>
					<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0000-0003-0110-5739</contrib-id>
					<name>
						<surname>Oliveira</surname>
						<given-names>Daniel Canavese de</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">redação do manuscrito original</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">redação - revisão e edição</role>
					<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0001-1831-4724</contrib-id>
					<name>
						<surname>Baniwa</surname>
						<given-names>André</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">redação do manuscrito original</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">redação - revisão e edição</role>
					<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0006-2003-850X</contrib-id>
					<name>
						<surname>Franco</surname>
						<given-names>Claudia Tereza</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">redação do manuscrito original</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">redação - revisão e edição</role>
					<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0009-1828-1713</contrib-id>
					<name>
						<surname>Monteiro</surname>
						<given-names>Suliete Gervásio</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">redação do manuscrito original</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">redação - revisão e edição</role>
					<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
				</contrib>
			</contrib-group>
			<aff id="aff1">
				<label>1</label>
				<institution content-type="original"> Instituto Federal do Rio Grande do Sul - IFRS, Porto Alegre, RS, Brasil. mauricio.polidoro@poa.ifrs.edu.br</institution>
				<institution content-type="orgname">Instituto Federal do Rio Grande do Sul - IFRS</institution>
				<addr-line>
					<named-content content-type="city">Porto Alegre</named-content>
				</addr-line>
				<country country="BR">Brasil</country>
				<email>mauricio.polidoro@poa.ifrs.edu.br</email>
			</aff>
			<aff id="aff2">
				<label>2</label>
				<institution content-type="original"> Universidade Federal do Paraná - UFPR, Curitiba, PR, Brasil. chico@ufpr.br</institution>
				<institution content-type="orgname">Universidade Federal do Paraná - UFPR</institution>
				<addr-line>
					<named-content content-type="city">Curitiba</named-content>
				</addr-line>
				<country country="BR">Brasil</country>
				<email>chico@ufpr.br</email>
			</aff>
			<aff id="aff3">
				<label>3</label>
				<institution content-type="original"> Universidade Federal do Rio Grande do Sul - UFRGS, Porto Alegre, RS, Brasil. daniel.canavese@ufrgs.br</institution>
				<institution content-type="orgname">Universidade Federal do Rio Grande do Sul - UFRGS</institution>
				<addr-line>
					<named-content content-type="city">Porto Alegre</named-content>
				</addr-line>
				<country country="BR">Brasil</country>
				<email>daniel.canavese@ufrgs.br</email>
			</aff>
			<aff id="aff4">
				<label>4</label>
				<institution content-type="original"> Universidade de Brasília - UnB, Brasília, DF, Brasil. andre.fernando@povosindigenas.gov.br</institution>
				<institution content-type="orgname">Universidade de Brasília - UnB</institution>
				<addr-line>
					<named-content content-type="city">Brasília</named-content>
				</addr-line>
				<country country="BR">Brasil</country>
				<email>andre.fernando@povosindigenas.gov.br</email>
			</aff>
			<aff id="aff5">
				<label>5</label>
				<institution content-type="original"> Instituto Etnoambiental e Multicultural Aldeia Verde - IEMAV, Brasília, DF, Brasil. claudiafranco2@gmail.com</institution>
				<institution content-type="normalized">Instituto Etnoambiental e Multicultural Aldeia Verde - IEMAV</institution>
				<addr-line>
					<named-content content-type="city">Brasília</named-content>
				</addr-line>
				<country country="BR">Brasil</country>
				<email>claudiafranco2@gmail.com</email>
			</aff>
			<aff id="aff6">
				<label>6</label>
				<institution content-type="original"> Ministério dos Povos Indígenas, Brasília, DF, Brasil. suliete.bare@povosindigenas.gov.br</institution>
				<institution content-type="normalized">Ministério dos Povos Indígenas</institution>
				<addr-line>
					<named-content content-type="city">Brasília</named-content>
				</addr-line>
				<country country="BR">Brasil</country>
				<email>suliete.bare@povosindigenas.gov.br</email>
			</aff>
			<!--<pub-date date-type="pub" publication-format="electronic">
				<day>23</day>
				<month>01</month>
				<year>2025</year>
			</pub-date>
			<pub-date date-type="collection" publication-format="electronic">
				<year>2025</year>
			</pub-date>-->
			<pub-date pub-type="epub-ppub">
				<year>2025</year>
			</pub-date>
			<volume>37</volume>
			<issue>1</issue>
			<elocation-id>e73312</elocation-id>
			<history>
				<date date-type="received">
					<day>26</day>
					<month>04</month>
					<year>2024</year>
				</date>
				<date date-type="accepted">
					<day>11</day>
					<month>09</month>
					<year>2024</year>
				</date>
				<date date-type="pub">
					<day>12</day>
					<month>12</month>
					<year>2024</year>
				</date>
			</history>
			<permissions>
				<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/" xml:lang="pt">
					<license-p>Este é um artigo publicado em acesso aberto sob uma licença Creative Commons</license-p>
				</license>
			</permissions>
			<abstract>
				<title>Resumo</title>
				<p>Este estudo analisa os acidentes por animais peçonhentos entre 2012 e 2023 a partir dos dados do Sistema de Informação de Agravos de Notificação (SINAN) com enfoque nas diferenças entre populações indígenas e não-indígenas. A metodologia adotada é transversal e ecológica, incorporando análises descritivas estatísticas e a autocorrelação espacial de Moran para examinar padrões geográficos e identificar aglomerados de prevalência. Os resultados revelam a concentração das notificações na região Sudeste. Entre os grupos analisados, a população indígena apresentou a maior taxa de prevalência, com 2.654 casos por 100.000 habitantes, evidenciando uma vulnerabilidade significativa a acidentes por animais peçonhentos. Em relação à faixa etária, crianças e adolescentes indígenas mostraram-se particularmente suscetíveis. Quanto aos agentes causadores, escorpiões lideram entre os não-indígenas (57,3%), enquanto as serpentes são mais prevalentes entre os indígenas (56,6%). Sublinha-se a necessidade de políticas públicas e estratégias de prevenção que considerem as especificidades culturais e ambientais das populações vulnerabilizadas, enfatizando a importância de ações educativas e de saúde pública adaptadas às realidades locais.</p>
			</abstract>
			<trans-abstract xml:lang="en">
				<title>Abstract</title>
				<p>This study analyzes venomous animal incidents between 2012 and 2023 using data from the Notifiable Diseases Information System (SINAN), focusing on differences between Indigenous and non-Indigenous populations. It uses a cross-sectional and ecological methodology, incorporating descriptive statistical analyses and Moran's spatial autocorrelation to examine geographic patterns and identify prevalence clusters. The results reveal a concentration of notifications in the Southeast region. Among the groups analyzed, the Indigenous population had the highest prevalence rate, with 2,654 cases per 100,000 inhabitants, demonstrating significant vulnerability to venomous animal incidents. Regarding age groups, Indigenous children and adolescents were particularly susceptible. Scorpions were the main causative agent among non-Indigenous individuals (57.3%), while snakes were more prevalent among Indigenous groups (56.6%). The study underscores the need for public policies and prevention strategies that consider the cultural and environmental specificities of vulnerable populations, emphasizing the importance of educational and public health actions adapted to local realities.</p>
			</trans-abstract>
			<kwd-group xml:lang="pt">
				<title>Palavras-chave:</title>
				<kwd>População Indígena</kwd>
				<kwd>População Não Indígena</kwd>
				<kwd>Geoepidemiologia</kwd>
				<kwd>Saúde Coletiva</kwd>
			</kwd-group>
			<kwd-group xml:lang="en">
				<title>Keywords:</title>
				<kwd>Indigenous Population</kwd>
				<kwd>Non-Indigenous Population</kwd>
				<kwd>Geoepidemiology</kwd>
				<kwd>Public Health</kwd>
			</kwd-group>
			<counts>
				<fig-count count="2"/>
				<table-count count="5"/>
				<equation-count count="0"/>
				<ref-count count="37"/>
			</counts>
		</article-meta>
	</front>
	<body>
		<sec sec-type="intro">
			<title>INTRODUÇÃO</title>
			<p>Nos últimos anos, acidentes envolvendo animais peçonhentos têm se tornado uma pauta relevante no âmbito da saúde pública, refletindo as intrincadas conexões entre a saúde humana e fatores ambientais, incluindo, mas não se limitando, às mudanças climáticas. A expectativa é que a mudança do clima, aliada a outros fatores como a perda de habitat, leve a um aumento da migração de animais peçonhentos como serpentes, aranhas e insetos como abelhas e formigas (<xref ref-type="bibr" rid="B22">Needleman <italic>et al,</italic> 2018a</xref>). A destruição de habitats naturais, muitas vezes causada pela expansão urbana desordenada e pela conversão de áreas naturais em terrenos agrícolas, força esses animais a migrar para novos territórios, aumentando o risco de interações com humanos. Essas transformações não se restringem apenas aos animais terrestres e incluem espécies marinhas e anfíbios venenosos, como alforrecas, peixes venenosos e rãs tóxicas, que também são afetadas por essas mudanças ambientais (<xref ref-type="bibr" rid="B23">Needleman <italic>et al,</italic> 2018b</xref>). É importante distinguir que animais peçonhentos são aqueles que possuem estruturas especializadas para injetar veneno, como dentes ou ferrões, enquanto animais venenosos são aqueles que liberam toxinas através da pele ou outras partes do corpo quando tocados ou ingeridos. Essas transformações nos padrões climáticos, que incluem o aquecimento global e a modificação dos regimes de chuva, impactam diretamente tanto os animais peçonhentos quanto os venenosos, aumentando os riscos à saúde humana e animal, sobretudo para as populações historicamente vulnerabilizadas, além de trazer implicações significativas para as indústrias, o comércio e o turismo.</p>
			<p>As alterações nos padrões climáticos globais, caracterizadas pelo aquecimento global e pela modificação dos regimes de chuva, têm efeitos diretos sobre o comportamento e a distribuição geográfica de animais peçonhentos (<xref ref-type="bibr" rid="B22">Needleman <italic>et al.,</italic> 2018a</xref>). O aquecimento global provoca alterações nos padrões comportamentais destes animais, como períodos de atividade, reprodução e hibernação (<xref ref-type="bibr" rid="B20">Moreno-Rueda <italic>et al</italic>., 2009</xref>). Isso pode levar a um aumento na frequência de interações entre esses animais e humanos, especialmente em áreas rurais e periurbanas onde a expansão humana invade habitats naturais (<xref ref-type="bibr" rid="B35">Zacarias, Loyola., 2018</xref>). Com o aumento das temperaturas globais, a temporada de atividade de muitos animais peçonhentos pode se estender, levando a um período mais longo durante o ano em que o risco de envenenamento é elevado. Isso é particularmente relevante para regiões que experimentam um aumento significativo nas temperaturas médias e mínimas (<xref ref-type="bibr" rid="B17">Martínez <italic>et al</italic>., 2022</xref>), em especial países do sul global, como o Brasil.</p>
			<p>Neste contexto, este estudo descreve e analisa os acidentes por animais peçonhentos notificados no Sistema de Informações de Agravos de Notificação (SINAN) entre 2013 e 2022, com foco nas populações indígenas. O estudo também identifica padrões de distribuição geográfica e destaca a vulnerabilidade diferenciada dessas populações a esses riscos</p>
			<p>A relevância deste estudo reside na necessidade de compreender os fatores que estão remodelando os riscos associados aos acidentes por animais peçonhentos. Os povos indígenas, que possuem uma conexão profunda com seus territórios tradicionais, são desproporcionalmente afetados, enfrentando não apenas um risco elevado de acidentes, mas também as complicações associadas que afetam suas vidas em nível comunitário, resultando na perda de modos de vida e cultura (<xref ref-type="bibr" rid="B9">Green; Raygorodetsky, 2010</xref>). Assim, torna-se imperativo investigar essas dinâmicas para desenvolver estratégias efetivas de prevenção e mitigação, salvaguardando tanto a saúde pública quanto a integridade cultural e ambiental das populações indígenas.</p>
			<p>Neste contexto, este estudo realiza uma análise comparativa do perfil epidemiológico e da distribuição espacial dos acidentes por animais peçonhentos, distinguindo entre populações indígenas e não-indígenas. Busca, nesta direção, identificar padrões de distribuição geográfica e destacar a vulnerabilidade diferenciada dos povos indígenas a esses riscos. Espera-se que, a partir desta análise, possamos avançar no entendimento dos impactos desses acidentes na saúde coletiva e na elaboração de respostas eficientes aos desafios impostos por animais peçonhentos.</p>
		</sec>
		<sec sec-type="methods">
			<title>METODOLOGIA</title>
			<p>Este estudo transversal e ecológico visou investigar a prevalência de acidentes por animais peçonhentos no Brasil durante o período de 2013 a 2022, utilizando registros do Sistema de Informações de Agravos de Notificação (SINAN). Seguindo a orientação do Ministério da Saúde do Brasil (2024), um caso confirmado foi caracterizado por sinais clínicos de envenenamento específicos para a categoria do animal envolvido, sem necessidade da identificação do animal causador.</p>
			<p>Foram incluídos dados de indivíduos de todas as faixas etárias, sexos, e diversas outras variáveis sociodemográficas, conforme disponíveis no SINAN. As variáveis analisadas incluíram dados de notificação individual, como idade, sexo, situação gestacional, raça/cor, e escolaridade; dados residenciais como o município de residência; antecedentes epidemiológicos como o intervalo entre a picada e o atendimento e o local da picada; e dados clínicos, abrangendo manifestações locais e sistêmicas, tipo de acidente, classificação do caso, e desfecho. Foram considerados não-indígenas os casos reportados de raça/cor branca, preta, parda e amarela e os indígenas a declaração raça/cor indígena. As taxas foram calculadas a cada 100 mil habitantes. Dado não válido (<italic>missing</italic>) refere-se a registros em que as informações sobre raça/cor não foram adequadamente preenchidas no sistema, resultando em dados ausentes ou inconsistentes que não puderam ser classificados com precisão.</p>
			<p>A coleta foi realizada através do sistema de transferência de arquivos do DATASUS, complementada por informações demográficas e socioeconômicas do Censo 2010 do Instituto Brasileiro de Geografia e Estatística - IBGE. Os dados foram processados utilizando o software estatístico R, onde foram conduzidas estatísticas descritivas e o teste Qui-quadrado de Pearson para avaliar diferenças entre as populações. Integrou-se uma análise espacial com o índice de autocorrelação espacial de Anselin Moran I, que categorizou a distribuição espacial dos eventos em quatro tipos de associação espacial: Clusters Alto-Alto e Baixo-Baixo, e Outliers Alto-Baixo e Baixo-Alto (onde clusters representam áreas geograficamente próximas com valores similares de uma variável específica, e outliers são áreas com valores discrepantes em comparação com os seus arredores). O <italic>shapefile</italic> resultante da análise espacial LISA foi empregado no software ArcGIS, licenciado pela Universidade Federal do Rio Grande do Sul, para a geração dos mapas.</p>
			<p>O estudo observou os princípios éticos delineados pela Lei Geral de Proteção de Dados Pessoais (Lei nº 13.709/2018) e pela Resolução do Conselho Nacional de Saúde nº 466/2012. Como utilizou dados secundários anonimizados, foi dispensada a avaliação ética por um Comitê de Ética em Pesquisa.</p>
		</sec>
		<sec sec-type="results">
			<title>RESULTADOS</title>
			<p>Na análise das notificações de acidentes por animais peçonhentos (<xref ref-type="fig" rid="f1">Figura 1</xref>) a região Sudeste lidera ao longo do período de 2013 a 2022, seguida da região Nordeste. Na terceira posição encontra-se a região Sul, Norte e Centro-Oeste, respectivamente. O pico de notificações, em todas as regiões, se deu no ano de 2019.</p>
			<p>
				<fig id="f1">
					<label>Figura 1</label>
					<caption>
						<title>Evolução dos casos notificados de acidentes por animais peçonhentos, Grandes Regiões, Brasil, 2013 a 2022</title>
					</caption>
					<graphic xlink:href="1982-4513-sn-37-e73312-gf1.jpg"/>
					<attrib>Fonte: Os autores (2023) a partir de <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</attrib>
				</fig>
			</p>
			<p>Os resultados apresentados na <xref ref-type="table" rid="t1">Tabela 1</xref> mostram que a maioria dos casos reportados envolve indivíduos da raça/cor parda, representando 46,6% do total, seguidos por indivíduos brancos (34,0%) e pretos (5,6%). A menor prevalência foi observada entre indivíduos de raça amarela (0,7%). Notavelmente, a taxa de prevalência mais alta foi registrada entre os indígenas, com 2.654,0 casos por 100.000 habitantes, sugerindo uma vulnerabilidade acentuada desse grupo a acidentes por animais peçonhentos. Por outro lado, casos categorizados como &quot;Ignorado&quot; e &quot;Dado não válido&quot; representam 10,3% e 1,9% do total, respectivamente, indicando lacunas no registro de dados raciais nos sistemas de saúde.</p>
			<p>
				<table-wrap id="t1">
					<label>Tabela 1</label>
					<caption>
						<title>Raça/cor dos casos notificados de acidentes por animais peçonhentos, Brasil, 2013 a 2022</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col/>
							<col/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center">Raça/cor</th>
								<th align="center">N</th>
								<th align="center">%</th>
								<th align="center">Taxa*</th>
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Branca</td>
								<td align="center">762.505</td>
								<td align="center">34,0</td>
								<td align="center">837,4</td>
							</tr>
							<tr>
								<td align="center">Preta</td>
								<td align="center">124.746</td>
								<td align="center">5,6</td>
								<td align="center">859,3</td>
							</tr>
							<tr>
								<td align="center">Amarela</td>
								<td align="center">16.300</td>
								<td align="center">0,7</td>
								<td align="center">782,0</td>
							</tr>
							<tr>
								<td align="center">Parda</td>
								<td align="center">1.045.742</td>
								<td align="center">46,6</td>
								<td align="center">1.271,0</td>
							</tr>
							<tr>
								<td align="center">Indígena</td>
								<td align="center">21.709</td>
								<td align="center">1,0</td>
								<td align="center">2.654,0</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">230.999</td>
								<td align="center">10,3</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">42.039</td>
								<td align="center">1,9</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Total</td>
								<td align="center">2.244.040</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN1">
							<p>Fonte: Os autores (2023) a partir de <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>) e <xref ref-type="bibr" rid="B11">IBGE (2010</xref>).</p>
						</fn>
						<fn id="TFN2">
							<p>* a cada 100 mil habitantes</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>A <xref ref-type="table" rid="t2">Tabela 2</xref> apresenta as informações sobre a população não-indígena e indígena, detalhando-se por sexo e faixa etária. Observa-se uma predominância de casos entre indivíduos do sexo masculino tanto na população não-indígena (55,2% dos casos, com uma taxa de 1.325,5 por 100 mil habitantes) quanto na população indígena (61,6%, taxa de 3.260,5). Já para o sexo feminino, os números são ligeiramente menores entre os não-indígenas (44,8%, taxa de 1.032,8) comparados com indígenas (38,4%, taxa de 2.044,1), indicando uma discrepância significativa na taxa de incidência por 100 mil habitantes entre os sexos e entre indígenas e não indígenas, a qual é estatisticamente significativa (p&lt;0,0001).</p>
			<p>Quanto à faixa etária, os dados revelam que a prevalência de acidentes varia consideravelmente, com as maiores taxas entre os grupos mais jovens e mais velhos na população indígena, destacando uma maior vulnerabilidade ou exposição a animais peçonhentos. Especificamente, crianças menores de 1 ano e adolescentes de 15 a 17 anos entre os indígenas apresentam taxas elevadas de prevalência (2.488,5 e 3.229,7 por 100 mil habitantes, respectivamente). Contrastando, dos não-indígenas, a distribuição etária dos acidentes mostra um padrão diferente, com maiores taxas observadas nas faixas de 18 a 29 anos e 50 a 59 anos.</p>
			<p>
				<table-wrap id="t2">
					<label>Tabela 2</label>
					<caption>
						<title>Total, percentual e taxa a cada 100 mil habitantes do sexo e faixa etária dos casos notificados de acidentes por animais peçonhentos, não-indígenas e indígenas, Brasil, 2013 a 2022</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col span="3"/>
							<col span="3"/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center" rowspan="2">Sexo</th>
								<th align="center" colspan="3">Não-indígenas </th>
								<th align="center" colspan="3">Indígenas </th>
								<th align="center" rowspan="2"><bold>p-valor</bold></th>
							</tr>
							<tr>
								<th align="center">N</th>
								<th align="center">%</th>
								<th align="center">Taxa</th>
								<th align="center">N</th>
								<th align="center">%</th>
								<th align="center">Taxa</th>
								
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Masculino</td>
								<td align="center">1.238.101</td>
								<td align="center">55,2</td>
								<td align="center">1.325,5</td>
								<td align="center">13.365,0</td>
								<td align="center">61,6</td>
								<td align="center">3.260,5</td>
								<td align="center" rowspan="2">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Feminino</td>
								<td align="center">1.005.420</td>
								<td align="center">44,8</td>
								<td align="center">1.032,8</td>
								<td align="center">8.341,0</td>
								<td align="center">38,4</td>
								<td align="center">2.044,1</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">519</td>
								<td align="center">0,02</td>
								<td align="center">-</td>
								<td align="center">3</td>
								<td align="center">0,01</td>
								<td align="center">-</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center"><italic>Faixa Etária</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>Taxa</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>Taxa</italic></td>
								<td align="center"><italic> </italic></td>
							</tr>
							<tr>
								<td align="center">Menor que 1</td>
								<td align="center">31.426</td>
								<td align="center">1,4</td>
								<td align="center">1.158,2</td>
								<td align="center">493</td>
								<td align="center">2,3</td>
								<td align="center">2.488,5</td>
								<td align="center" rowspan="10">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">1 a 4</td>
								<td align="center">80.472</td>
								<td align="center">3,6</td>
								<td align="center">726,1</td>
								<td align="center">844</td>
								<td align="center">3,9</td>
								<td align="center">1.037,1</td>
							</tr>
							<tr>
								<td align="center">5 a 9</td>
								<td align="center">127.933</td>
								<td align="center">5,7</td>
								<td align="center">854,6</td>
								<td align="center">2.043</td>
								<td align="center">9,4</td>
								<td align="center">2.046,3</td>
							</tr>
							<tr>
								<td align="center">10 a 14</td>
								<td align="center">134.603</td>
								<td align="center">6,0</td>
								<td align="center">784,1</td>
								<td align="center">2.552</td>
								<td align="center">11,8</td>
								<td align="center">2.691,2</td>
							</tr>
							<tr>
								<td align="center">15 a 17</td>
								<td align="center">91.719</td>
								<td align="center">4,1</td>
								<td align="center">885,5</td>
								<td align="center">1.659</td>
								<td align="center">7,6</td>
								<td align="center">3.229,7</td>
							</tr>
							<tr>
								<td align="center">18 a 29</td>
								<td align="center">453.456</td>
								<td align="center">20,2</td>
								<td align="center">1.106,5</td>
								<td align="center">5.333</td>
								<td align="center">24,6</td>
								<td align="center">3.144,0</td>
							</tr>
							<tr>
								<td align="center">30 a 39</td>
								<td align="center">350.621</td>
								<td align="center">15,6</td>
								<td align="center">1.183,2</td>
								<td align="center">3.113</td>
								<td align="center">14,3</td>
								<td align="center">2.979,1</td>
							</tr>
							<tr>
								<td align="center">40 a 49</td>
								<td align="center">323.202</td>
								<td align="center">14,4</td>
								<td align="center">1.301,0</td>
								<td align="center">2.346</td>
								<td align="center">10,8</td>
								<td align="center">3.058,9</td>
							</tr>
							<tr>
								<td align="center">50 a 59</td>
								<td align="center">295.919</td>
								<td align="center">13,2</td>
								<td align="center">2.918,2</td>
								<td align="center">1.610</td>
								<td align="center">7,4</td>
								<td align="center">2.983,9</td>
							</tr>
							<tr>
								<td align="center">60 ou mais</td>
								<td align="center">354.681</td>
								<td align="center">15,8</td>
								<td align="center">1.394,7</td>
								<td align="center">1.716</td>
								<td align="center">7,9</td>
								<td align="center">2.091,4</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">8</td>
								<td align="center">-</td>
								<td align="center">-</td>
								<td align="center">-</td>
								<td align="center">0,0</td>
								<td align="center">-</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Total</td>
								<td align="center">2.244.040</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
								<td align="center">21.709</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
								<td align="center">-</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN3">
							<p>Fonte: Os autores (2023) a partir de <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>) e <xref ref-type="bibr" rid="B11">IBGE (2010</xref>).</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>No tocante a escolaridade (<xref ref-type="table" rid="t3">Tabela 3</xref>) é notável que não houve registros de casos entre indivíduos analfabetos em ambos os grupos, sugerindo possíveis lacunas na coleta de dados ou na acessibilidade do sistema de notificação para todas as camadas da população. Entre os não-indígenas, a predominância dos casos recai sobre aqueles com Ensino Fundamental Incompleto, representando 25,6% do total, seguido por indivíduos com Ensino Médio Completo e Ensino Médio Incompleto. Na população indígena, destaca-se também a presença significativa de casos entre pessoas com Ensino Fundamental Incompleto, além de uma notável porcentagem de registros categorizados como &quot;Ignorado&quot; ou &quot;Dado não válido (<italic>missing</italic>)&quot;, indicando imprecisão no preenchimento comprometimento da integridade dos dados reportados.</p>
			<p>Quanto à situação gestacional, observa-se um número relativamente baixo de casos notificados entre gestantes, distribuídos de forma equilibrada pelos três trimestres da gestação em ambas as populações. Contudo, a grande maioria dos registros indica que a maioria das pessoas não estava grávida no momento do acidente, ou que essa informação não se aplicava ao caso, o que pode incluir o sexo masculino ou mulheres não gestantes.</p>
			<p>
				<table-wrap id="t3">
					<label>Tabela 3</label>
					<caption>
						<title>Escolaridade e situação gestacional dos casos notificados de acidentes por animais peçonhentos, não-indígenas e indígenas, Brasil, 2013 a 2022</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col span="2"/>
							<col span="2"/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center" rowspan="2">Escolaridade</th>
								<th align="center" colspan="2">Não-indígenas</th>
								<th align="center" colspan="2">Indígenas</th>
								<th align="center" rowspan="2">p-valor</th>
							</tr>
							<tr>
								<th align="center">N</th>
								<th align="center">%</th>
								<th align="center">N</th>
								<th align="center">%</th>
								
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Analfabeto</td>
								<td align="center">0</td>
								<td align="center">0</td>
								<td align="center">0</td>
								<td align="center">0</td>
								<td align="center" rowspan="7">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Ensino Fundamental Incompleto</td>
								<td align="center">574.026</td>
								<td align="center">25,6</td>
								<td align="center">6.807</td>
								<td align="center">31,4</td>
							</tr>
							<tr>
								<td align="center">Ensino Fundamental Completo</td>
								<td align="center">115.719</td>
								<td align="center">5,2</td>
								<td align="center">1.029</td>
								<td align="center">4,7</td>
							</tr>
							<tr>
								<td align="center">Ensino Médio Incompleto</td>
								<td align="center">138.662</td>
								<td align="center">6,2</td>
								<td align="center">1.178</td>
								<td align="center">5,4</td>
							</tr>
							<tr>
								<td align="center">Ensino Médio Completo</td>
								<td align="center">281.211</td>
								<td align="center">12,5</td>
								<td align="center">1.202</td>
								<td align="center">5,5</td>
							</tr>
							<tr>
								<td align="center">Educação Superior Incompleta</td>
								<td align="center">26.425</td>
								<td align="center">1,2</td>
								<td align="center">102</td>
								<td align="center">0,5</td>
							</tr>
							<tr>
								<td align="center">Educação Superior Completa</td>
								<td align="center">53.155</td>
								<td align="center">2,4</td>
								<td align="center">114</td>
								<td align="center">0,5</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">579.098</td>
								<td align="center">25,8</td>
								<td align="center">3.481</td>
								<td align="center">16,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Não se aplica</td>
								<td align="center">188.517</td>
								<td align="center">8,4</td>
								<td align="center">2.464</td>
								<td align="center">11,4</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">287.227</td>
								<td align="center">12,8</td>
								<td align="center">5.332</td>
								<td align="center">24,6</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center"><italic>Gestante</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>p valor</italic></td>
							</tr>
							<tr>
								<td align="center">1º Trimestre</td>
								<td align="center">4.808</td>
								<td align="center">0,2</td>
								<td align="center">88</td>
								<td align="center">0,4</td>
								<td align="center" rowspan="5">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">2º Trimestre</td>
								<td align="center">6.927</td>
								<td align="center">0,3</td>
								<td align="center">120</td>
								<td align="center">0,6</td>
							</tr>
							<tr>
								<td align="center">3º Trimestre</td>
								<td align="center">4.940</td>
								<td align="center">0,2</td>
								<td align="center">83</td>
								<td align="center">0,4</td>
							</tr>
							<tr>
								<td align="center">Idade gestacional ignorada</td>
								<td align="center">3.069</td>
								<td align="center">0,1</td>
								<td align="center">73</td>
								<td align="center">0,3</td>
							</tr>
							<tr>
								<td align="center">Não</td>
								<td align="center">567.706</td>
								<td align="center">25,3</td>
								<td align="center">4.293</td>
								<td align="center">19,8</td>
							</tr>
							<tr>
								<td align="center">Não se aplica</td>
								<td align="center">1.513.105</td>
								<td align="center">67,4</td>
								<td align="center">16.620</td>
								<td align="center">76,6</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">143.416</td>
								<td align="center">6,4</td>
								<td align="center">431</td>
								<td align="center">2,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">69</td>
								<td align="center">0,0</td>
								<td align="center">1</td>
								<td align="center">0,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Total</td>
								<td align="center">2.244.040</td>
								<td align="center">100,0</td>
								<td align="center">21.709</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN4">
							<p>Fonte: Os autores (2023) a partir de <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>Nos acidentes identificados (<xref ref-type="table" rid="t4">Tabela 4</xref>), escorpiões são os principais responsáveis pelos acidentes por animais peçonhentos entre os não-indígenas, representando 57,3% dos casos, seguidos por aranhas (14,1%) e serpentes (12,9%). Contrastando, na população indígena, a prevalência de acidentes causados por serpentes é predominantemente alta, atingindo 56,6%.</p>
			<p>Quanto ao local da picada, ambos os grupos mostram uma tendência de picadas nos pés como sendo as mais comuns, especialmente entre os indígenas, onde 44% dos acidentes afetam essa parte do corpo. Isso sugere uma maior exposição dos pés a ambientes onde tais animais são mais prevalentes, provavelmente devido a diferenças nas atividades diárias ou no uso de calçados.</p>
			<p>O intervalo de tempo até o atendimento médico é crítico na gestão de acidentes por animais peçonhentos, com a maioria dos casos recebendo atendimento dentro de 1 a 3 horas após a picada. No entanto, uma proporção notável das notificações, tanto em não-indígenas quanto indígenas, busca atendimento após mais de 24 horas.</p>
			<p>
				<table-wrap id="t4">
					<label>Tabela 4</label>
					<caption>
						<title>Tipo de acidente, local da picada e tempo decorrido do acidente ao atendimento em saúde dos casos notificados de acidentes por animais peçonhentos, não-indígenas e indígenas, Brasil, 2013 a 2022</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col span="2"/>
							<col span="2"/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center" rowspan="2">Tipo de acidente</th>
								<th align="center" colspan="2">Não-indígenas</th>
								<th align="center" colspan="2">Indígenas</th>
								<th align="center" rowspan="2">p-valor</th>
							</tr>
							<tr>
								<th align="center">N</th>
								<th align="center">%</th>
								<th align="center">N</th>
								<th align="center">%</th>
								
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Serpente</td>
								<td align="center">288.474</td>
								<td align="center">12,9</td>
								<td align="center">12.278</td>
								<td align="center">56,6</td>
								<td align="center" rowspan="6">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Aranha</td>
								<td align="center">315.534</td>
								<td align="center">14,1</td>
								<td align="center">1.872</td>
								<td align="center">8,6</td>
							</tr>
							<tr>
								<td align="center">Escorpião</td>
								<td align="center">1.286.801</td>
								<td align="center">57,3</td>
								<td align="center">5.668</td>
								<td align="center">26,1</td>
							</tr>
							<tr>
								<td align="center">Lagarta</td>
								<td align="center">45.824</td>
								<td align="center">2,0</td>
								<td align="center">242</td>
								<td align="center">1,1</td>
							</tr>
							<tr>
								<td align="center">Abelha</td>
								<td align="center">173.550</td>
								<td align="center">7,7</td>
								<td align="center">745</td>
								<td align="center">3,4</td>
							</tr>
							<tr>
								<td align="center">Outros</td>
								<td align="center">93.383</td>
								<td align="center">4,2</td>
								<td align="center">738</td>
								<td align="center">3,4</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">40.367</td>
								<td align="center">1,8</td>
								<td align="center">164</td>
								<td align="center">0,8</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center"><italic>Local da picada</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>p valor</italic></td>
							</tr>
							<tr>
								<td align="center">Cabeça</td>
								<td align="center">140.868</td>
								<td align="center">6,3</td>
								<td align="center">760</td>
								<td align="center">3,5</td>
								<td align="center" rowspan="10">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Braço</td>
								<td align="center">123.145</td>
								<td align="center">5,5</td>
								<td align="center">730</td>
								<td align="center">3,4</td>
							</tr>
							<tr>
								<td align="center">Ante-braço</td>
								<td align="center">64.052</td>
								<td align="center">2,9</td>
								<td align="center">408</td>
								<td align="center">1,9</td>
							</tr>
							<tr>
								<td align="center">Mão</td>
								<td align="center">369.478</td>
								<td align="center">16,5</td>
								<td align="center">2.474</td>
								<td align="center">11,4</td>
							</tr>
							<tr>
								<td align="center">Dedo da mão</td>
								<td align="center">369.590</td>
								<td align="center">16,5</td>
								<td align="center">1.891</td>
								<td align="center">8,7</td>
							</tr>
							<tr>
								<td align="center">Tronco</td>
								<td align="center">120.986</td>
								<td align="center">5,4</td>
								<td align="center">583</td>
								<td align="center">2,7</td>
							</tr>
							<tr>
								<td align="center">Coxa</td>
								<td align="center">87.781</td>
								<td align="center">3,9</td>
								<td align="center">536</td>
								<td align="center">2,5</td>
							</tr>
							<tr>
								<td align="center">Perna</td>
								<td align="center">182.125</td>
								<td align="center">8,1</td>
								<td align="center">2.909</td>
								<td align="center">13,4</td>
							</tr>
							<tr>
								<td align="center">Pé</td>
								<td align="center">532.288</td>
								<td align="center">23,7</td>
								<td align="center">9.557</td>
								<td align="center">44,0</td>
							</tr>
							<tr>
								<td align="center">Dedo do pé</td>
								<td align="center">166.919</td>
								<td align="center">7,4</td>
								<td align="center">1.541</td>
								<td align="center">7,1</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">86.701</td>
								<td align="center">3,9</td>
								<td align="center">318</td>
								<td align="center">1,5</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">107</td>
								<td align="center">0,0</td>
								<td align="center">2</td>
								<td align="center">0,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center"><italic>Tempo decorrido da picada ao atendimento</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>p-valor</italic></td>
							</tr>
							<tr>
								<td align="center">0 a 1 hora</td>
								<td align="center">1.084.890</td>
								<td align="center">48,3</td>
								<td align="center">5.881</td>
								<td align="center">27,1</td>
								<td align="center" rowspan="6">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">1 a 3 horas</td>
								<td align="center">504.527</td>
								<td align="center">22,5</td>
								<td align="center">5.570</td>
								<td align="center">25,7</td>
							</tr>
							<tr>
								<td align="center">3 a 6 horas</td>
								<td align="center">166.709</td>
								<td align="center">7,4</td>
								<td align="center">3.532</td>
								<td align="center">16,3</td>
							</tr>
							<tr>
								<td align="center">6 a 12 horas</td>
								<td align="center">77.266</td>
								<td align="center">3,4</td>
								<td align="center">2.006</td>
								<td align="center">9,2</td>
							</tr>
							<tr>
								<td align="center">12 a 24 horas</td>
								<td align="center">79.610</td>
								<td align="center">3,5</td>
								<td align="center">1.634</td>
								<td align="center">7,5</td>
							</tr>
							<tr>
								<td align="center">mais de 24 horas</td>
								<td align="center">134.575</td>
								<td align="center">6,0</td>
								<td align="center">1.703</td>
								<td align="center">7,8</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">141.055</td>
								<td align="center">6,3</td>
								<td align="center">936</td>
								<td align="center">4,3</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">55.408</td>
								<td align="center">2,5</td>
								<td align="center">447</td>
								<td align="center">2,1</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Total</td>
								<td align="center">2.244.040</td>
								<td align="center">100,0</td>
								<td align="center">21.709</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN5">
							<p>Fonte: Os autores (2023) a partir de <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>No tocante a classificação do caso, a maioria em ambas as populações é classificada como leve, com 82,5% entre os não-indígenas e 62,2% entre os indígenas, indicando que parte dos acidentes resulta em sintomas menos graves. Entretanto, a proporção de casos moderados e graves é significativamente maior entre os indígenas (27,8% e 5,6%, respectivamente), em comparação com os não-indígenas (11,3% e 1,6%).</p>
			<p>A prevalência de complicações locais é maior entre os indígenas (4% dos casos reportam complicações), contrastando com apenas 1,1% nos não-indígenas. Esta diferença dramática pode refletir não apenas a natureza dos acidentes, mas também diferenças no acesso ao cuidado médico imediato e tratamento eficaz, destacando uma questão crítica de saúde pública.</p>
			<p>Dentre as complicações locais especificadas, infecção secundária e necrose extensa são as mais comuns. Notavelmente, a proporção de casos que resultam em amputação, embora baixa, é significativamente maior quando complicações locais são reportadas, indicando a gravidade potencial desses acidentes. As complicações sistêmicas, embora menos frequentes, apresentam uma discrepância, sendo mais prevalentes entre os indígenas.</p>
			<p>A maioria dos casos resulta em cura, com 91,4% entre os não-indígenas e 89,4% entre os indígenas. No entanto, a taxa de mortalidade devido a acidentes por animais peçonhentos é mais alta entre os indígenas (0,6% contra 0,1% nos não-indígenas), uma diferença estatisticamente significativa que sublinha as desigualdades no impacto desses acidentes entre as populações.</p>
			<p>
				<table-wrap id="t5">
					<label>Tabela 5</label>
					<caption>
						<title>Classificação do caso, complicações locais e evolução dos casos notificados de acidentes por animais peçonhentos, não-indígenas e indígenas, Brasil, 2013 a 2022</title>
					</caption>
					<table frame="hsides" rules="groups">
						<colgroup>
							<col/>
							<col span="2"/>
							<col span="2"/>
							<col/>
						</colgroup>
						<thead>
							<tr>
								<th align="center" rowspan="2">Classificação do caso</th>
								<th align="center" colspan="2">Não-indígenas</th>
								<th align="center" colspan="2">Indígenas </th>
								<th align="center" rowspan="2">p-valor</th>
							</tr>
							<tr>
								<th align="center">N</th>
								<th align="center">%</th>
								<th align="center">N</th>
								<th align="center">%</th>
								
							</tr>
						</thead>
						<tbody>
							<tr>
								<td align="center">Leve</td>
								<td align="center">1.851.315</td>
								<td align="center">82,5</td>
								<td align="center">13.511</td>
								<td align="center">62,2</td>
								<td align="center" rowspan="5">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Moderado</td>
								<td align="center">253.361</td>
								<td align="center">11,3</td>
								<td align="center">6.035</td>
								<td align="center">27,8</td>
							</tr>
							<tr>
								<td align="center">Grave</td>
								<td align="center">35.413</td>
								<td align="center">1,6</td>
								<td align="center">1.221</td>
								<td align="center">5,6</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">53.716</td>
								<td align="center">2,4</td>
								<td align="center">417</td>
								<td align="center">1,9</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">50.955</td>
								<td align="center">2,3</td>
								<td align="center">525</td>
								<td align="center">2,4</td>
							</tr>
							<tr>
								<td align="center">Total</td>
								<td align="center">2.244.040</td>
								<td align="center">100,0</td>
								<td align="center">21.709</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center"><italic>Complicações locais</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>p-valor</italic></td>
							</tr>
							<tr>
								<td align="center">Sim</td>
								<td align="center">25.649</td>
								<td align="center">1,1</td>
								<td align="center">858</td>
								<td align="center">4,0</td>
								<td align="center" rowspan="3">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Não</td>
								<td align="center">1.991.079</td>
								<td align="center">88,7</td>
								<td align="center">18.570</td>
								<td align="center">85,5</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">90.599</td>
								<td align="center">4,0</td>
								<td align="center">872</td>
								<td align="center">4,0</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">136.713</td>
								<td align="center">6,1</td>
								<td align="center">1.409</td>
								<td align="center">6,5</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Total</td>
								<td align="center">2.244.040</td>
								<td align="center">100,0</td>
								<td align="center">21.709</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center"><italic>Especificação das complicações locais</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>p-valor</italic></td>
							</tr>
							<tr>
								<td align="center">Amputação*</td>
								<td align="center">323</td>
								<td align="center">1,2</td>
								<td align="center">23</td>
								<td align="center">2,6</td>
								<td align="center" rowspan="6">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Infecção secundária*</td>
								<td align="center">19.692</td>
								<td align="center">76,5</td>
								<td align="center">660</td>
								<td align="center">76,6</td>
							</tr>
							<tr>
								<td align="center">Necrose extensa*</td>
								<td align="center">4.654</td>
								<td align="center">18,1</td>
								<td align="center">121</td>
								<td align="center">14</td>
							</tr>
							<tr>
								<td align="center">Síndrome comportamental*</td>
								<td align="center">1.953</td>
								<td align="center">7,5</td>
								<td align="center">101</td>
								<td align="center">11,7</td>
							</tr>
							<tr>
								<td align="center">Déficit funcional*</td>
								<td align="center">2.870</td>
								<td align="center">11,1</td>
								<td align="center">143</td>
								<td align="center">16,6</td>
							</tr>
							<tr>
								<td align="center">Complicações sistêmicas</td>
								<td align="center">8.020</td>
								<td align="center">0,3</td>
								<td align="center">241</td>
								<td align="center">1,2</td>
							</tr>
							<tr>
								<td align="center"><italic>Evolução do caso</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>N</italic></td>
								<td align="center"><italic>%</italic></td>
								<td align="center"><italic>p-valor</italic></td>
							</tr>
							<tr>
								<td align="center">Cura</td>
								<td align="center">2.049.974</td>
								<td align="center">91,4</td>
								<td align="center">19.402</td>
								<td align="center">89,4</td>
								<td align="center" rowspan="3">&lt; 0,001</td>
							</tr>
							<tr>
								<td align="center">Óbito por acidente por animais peçonhentos</td>
								<td align="center">2.812</td>
								<td align="center">0,1</td>
								<td align="center">124</td>
								<td align="center">0,6</td>
							</tr>
							<tr>
								<td align="center">Óbito por outras causas</td>
								<td align="center">362</td>
								<td align="center">0,0</td>
								<td align="center">14</td>
								<td align="center">0,1</td>
							</tr>
							<tr>
								<td align="center">Ignorado</td>
								<td align="center">63.494</td>
								<td align="center">2,8</td>
								<td align="center">644</td>
								<td align="center">3,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Dado não válido (<italic>missing</italic>)</td>
								<td align="center">127.398</td>
								<td align="center">5,7</td>
								<td align="center">1.525</td>
								<td align="center">7,0</td>
								<td align="center">-</td>
							</tr>
							<tr>
								<td align="center">Total</td>
								<td align="center">2.244.040</td>
								<td align="center">100,0</td>
								<td align="center">21.709</td>
								<td align="center">100,0</td>
								<td align="center">-</td>
							</tr>
						</tbody>
					</table>
					<table-wrap-foot>
						<fn id="TFN6">
							<p><italic>* informações para os casos com “sim” para complicações locais</italic></p>
						</fn>
						<fn id="TFN7">
							<p>Fonte: Os autores (2023) a partir de <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</p>
						</fn>
					</table-wrap-foot>
				</table-wrap>
			</p>
			<p>Relativa à distribuição dos níveis de agrupamento espacial, conforme ilustrado na <xref ref-type="fig" rid="f2">Figura 1</xref>, observa-se uma predominante concentração de clusters Alto-Alto entre a população indígena nos estados do Amazonas, Acre e Mato Grosso, regiões estas que apresentam uma maior proporção de residentes indígenas. Notavelmente, há uma sequência contínua do cluster Baixo-Baixo que se estende desde o nordeste do Paraná até o litoral do Nordeste brasileiro, abrangendo também parte do norte do país. No extremo sul do país, especificamente no Centro-Sul do estado do Rio Grande do Sul, observa-se que aproximadamente metade do território está igualmente sob a influência de um cluster Baixo-Baixo. Quanto à população não indígena, destaca-se um cluster Alto-Alto no litoral do Paraná e, de forma mais dispersa, no estado de São Paulo, do Pará, além de percorrer as regiões sudoeste e noroeste de Minas Gerais e o sul da Bahia. Os estados do Mato Grosso e Rio Grande do Sul são notáveis pela quase completa cobertura de cluster Baixo-Baixo, com a presença de alguns outliers Alto-Baixo.</p>
			<p>
				<fig id="f2">
					<label>Figura 1</label>
					<caption>
						<title>Níveis de agrupamento espacial das notificações de acidentes por animais peçonhentos, indígenas (A) e não-indígenas (B), Brasil, 2013 a 2022</title>
					</caption>
					<graphic xlink:href="1982-4513-sn-37-e73312-gf2.png"/>
					<attrib>Fonte: <xref ref-type="bibr" rid="B12">IBGE (2022</xref>). Elaborado pelos autores (2024).</attrib>
				</fig>
			</p>
		</sec>
		<sec sec-type="discussion">
			<title>DISCUSSÃO</title>
			<p>Nossos resultados destacam uma vulnerabilidade diferenciada aos acidentes por animais peçonhentos entre populações indígenas e não indígenas. Pesquisas indicam que a ocupação desordenada de cidades, a falta de controle de pragas e a inadequada gestão de resíduos contribuem significativamente para a proliferação de escorpiões em áreas urbanas e periurbanas (<xref ref-type="bibr" rid="B36">Zanetta <italic>et al</italic>., 2020</xref>; <xref ref-type="bibr" rid="B2">Cruz <italic>et al</italic>., 1995</xref>; <xref ref-type="bibr" rid="B10">Guerra-Duarte <italic>et al</italic>., 2023</xref>). Esses fatores criam ambientes favoráveis para a sobrevivência e reprodução desses aracnídeos, aumentando a incidência de acidentes com escorpiões, especialmente em regiões densamente povoadas (<xref ref-type="bibr" rid="B27">Olivero <italic>et al</italic>., 2021</xref>).</p>
			<p>A maior prevalência de acidentes causados por serpentes entre os povos indígenas reflete o contato mais frequente dessas comunidades com habitats naturais. Um estudo realizado na Amazônia Ocidental rasileira mostrou que pessoas que vivem em áreas rurais ou florestais são mais propensas a serem afetadas por picadas de serpentes devido à presença e as atividades como o extrativismo e a agricultura (<xref ref-type="bibr" rid="B30">Silva; Colombini Moura-da-Silva; Souza; Monteiro; Bernarde, 2020</xref>; <xref ref-type="bibr" rid="B31">Silva; Fonseca; Silva, Amaral; Ortega; Oliveira; Correa; Oliveira; Monteiro; Bernarde, 2020</xref>). <xref ref-type="bibr" rid="B29">Schneider <italic>et. al</italic>. (2021</xref>) demonstraram que indígenas apresentaram as maiores taxas de exposição a picadas de serpentes (194,3 por 100.000 habitantes), o que é significativamente maior em comparação com outros grupos populacionais.</p>
			<p>A tendência das picadas de serpentes ocorrerem nos pés, especialmente entre populações indígenas, é um reflexo das práticas culturais e das atividades ao ar livre frequentemente realizadas sem proteção e luminosidade adequada (<xref ref-type="bibr" rid="B28">Pierini <italic>et. al.,</italic> 1996</xref>). Em muitos casos, os indígenas e outras populações rurais realizam atividades de subsistência, como caça, coleta ou agricultura, descalços ou usando calçados mínimos, o que aumenta significativamente a exposição a serpentes (<xref ref-type="bibr" rid="B15">Leite <italic>et al</italic>., 2013</xref>; <xref ref-type="bibr" rid="B13">Jayawardana <italic>et al</italic>., 2020</xref>; <xref ref-type="bibr" rid="B33">Venugopalan <italic>et al</italic>., 2021</xref>). Além disso, essas atividades geralmente ocorrem em ambientes onde há uma alta presença de serpentes, como florestas, campos e margens de rios, locais que são habitats naturais desses animais (<xref ref-type="bibr" rid="B6">Eniang <italic>et. al</italic>., 2012</xref>; <xref ref-type="bibr" rid="B31">Silva; Fonseca; Silva; Amaral; Ortega; Oliveira; Correa; Oliveira; Monteiro; Bernarde, 2020</xref>).</p>
			<p>O intervalo de tempo até o atendimento em saúde é um fator crucial na prevenção de complicações graves, especialmente em populações indígenas que enfrentam barreiras geográficas e logísticas significativas. Estudos apontam que a distância e a falta de infraestrutura adequada atrasam o acesso ao tratamento, o que aumenta substancialmente os riscos de saúde (<xref ref-type="bibr" rid="B25">Nguyen <italic>et al</italic>., 2020</xref>). Além disso, questões culturais e a discriminação no atendimento agravam este contexto, dificultando a aceitação e continuidade dos cuidados (<xref ref-type="bibr" rid="B24">Nelson; Wilson, 2018</xref>). A descentralização dos serviços de saúde, aliada à melhoria da infraestrutura em áreas remotas e o desenvolvimento de estratégias interculturais nos serviços, é essencial para mitigar esses riscos e assegurar que os cuidados cheguem de forma eficiente a populações vulnerabilizadas (<xref ref-type="bibr" rid="B14">Juárez-Ramírez <italic>et al</italic>., 2019</xref>). Iniciativas comunitárias, como a construção de infraestrutura de saúde adaptada às necessidades locais, também têm mostrado resultados positivos na ampliação do acesso aos cuidados básicos (<xref ref-type="bibr" rid="B4">Dutta, 2020</xref>).</p>
			<p>A classificação dos casos de acidentes com animais peçonhentos, com uma gravidade acentuada entre as populações indígenas, sinaliza o imperativo de intervenções de saúde coletiva que sejam culturalmente adequadas e efetivamente implementadas (<xref ref-type="bibr" rid="B7">Farias <italic>et al</italic>., 2023</xref>). A alta prevalência de complicações, como infecções secundárias e necrose extensa, nessas populações, corrobora com a demanda de assistência em saúde imediata e adequada, além de um acompanhamento contínuo para o tratamento das complicações (<xref ref-type="bibr" rid="B21">Murta <italic>et al</italic>., 2023</xref>). Estudos demonstram que a descentralização do tratamento com antiveneno para unidades de saúde em serviços de saúde localizados nos territórios indigenas pode reduzir o tempo entre o diagnóstico e o tratamento, melhorando o prognóstico dos envenenamentos e diminuindo as sequelas graves (<xref ref-type="bibr" rid="B19">Monteiro <italic>et al</italic>., 2020</xref>).</p>
			<p>Embora este estudo não investigue os efeitos das mudanças climáticas nas notificações de acidentes com animais peçonhentos, é válido considerar que a alteração dos regimes de temperatura e precipitação pode estar influenciando a distribuição geográfica e o comportamento de algumas espécies de animais peçonhentos (<xref ref-type="bibr" rid="B22">Needleman, 2018a</xref>; <xref ref-type="bibr" rid="B23">2018b</xref>; <xref ref-type="bibr" rid="B1">Bouazza <italic>et al</italic>., 2019</xref>; <xref ref-type="bibr" rid="B16">Martinez <italic>et. al</italic>., 2018</xref>; <xref ref-type="bibr" rid="B17">Martinez <italic>et. al</italic>., 2022</xref>; <xref ref-type="bibr" rid="B18">Martinez <italic>et. al</italic>., 2024</xref>; <xref ref-type="bibr" rid="B35">Zacarias, Loyola., 2018</xref>). Essas hipóteses poderiam ser exploradas em estudos futuros, utilizando modelos preditivos para avaliar como as mudanças climáticas podem alterar os padrões de risco. </p>
			<p>A lacuna na completude dos dados na área da saúde, aliada à necessidade de interoperabilidade com dados ambientais, ressalta a urgência de sistemas de notificação que sejam mais robustos e integrados. Uma integração eficaz é primordial para garantir que as informações de saúde acessíveis para a tomada de decisões baseadas em evidências (<xref ref-type="bibr" rid="B37">Ying <italic>et al</italic>., 2007</xref>). </p>
			<p>A literatura científica indica que a integração de dados de diferentes fontes pode aumentar significativamente a completude dos registros, contribuindo para uma gestão eficiente dos cuidados de saúde (<xref ref-type="bibr" rid="B5">Emran; Leza; Abdullah, 2017</xref>). Além disso, a interoperabilidade entre sistemas de saúde permite não apenas a troca de informações de maneira segura, mas também a melhoria da qualidade dos dados disponíveis para análises epidemiológicas (<xref ref-type="bibr" rid="B3">Dixon <italic>et al</italic>., 2011</xref>). </p>
		</sec>
		<sec sec-type="conclusions">
			<title>CONCLUSÃO</title>
			<p>Este estudo evidenciou uma vulnerabilidade acentuada das populações indígenas em relação aos acidentes por animais peçonhentos, com destaque para a alta prevalência de picadas de serpentes nesse grupo. Os resultados mostraram que a distribuição geográfica dos acidentes é influenciada por fatores como a proximidade dos habitats naturais e as práticas culturais das populações indígenas, que frequentemente estão expostas a maiores riscos. Além disso, a análise espacial reafirmou a importância de estratégias específicas e territorialmente orientadas para a alocação de recursos em saúde, sobretudo em regiões remotas com alta concentração de acidentes.</p>
			<p>É fundamental que as políticas públicas considerem essas vulnerabilidades ao planejar intervenções que garantam o acesso rápido e eficaz ao tratamento, como a descentralização de unidades de saúde equipadas com antiveneno em áreas indígenas. Os achados desta pesquisa reforçam a necessidade de aprimorar a infraestrutura de saúde e a coleta de dados para otimizar a resposta a esses acidentes e mitigar as disparidades entre as populações indígenas e não-indígenas. </p>
			<p>Para aprofundar a compreensão das dinâmicas subjacentes ao panorama observado, pesquisas futuras devem adotar uma abordagem que inclua análises dos fatores socioambientais e das implicações das mudanças climáticas. Dado que as variações nos padrões de temperatura e precipitação têm o potencial de alterar a distribuição geográfica e o comportamento de animais peçonhentos, é potente explorar como a mudança do clima pode aumentar os riscos para populações vulnerabilizadas. Tais estudos podem contribuir para o estabelecimento de uma base científica sólida que apoie o desenvolvimento de intervenções eficazes e baseadas em estratégias interculturais, além de políticas de saúde que não apenas antecipem, mas também mitiguem os impactos das mudanças climáticas na saúde humana, com especial atenção às comunidades indígenas.</p>
		</sec>
	</body>
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			<article-id pub-id-type="doi">10.14393/SN-v37-2025-73312x</article-id>
			<article-categories>
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					<subject>Papers</subject>
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				<article-title>Geoepidemiological Profile of Venomous Animal Incidents in Indigenous and Non-Indigenous Populations in Brazil</article-title>
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					<role content-type="http://credit.niso.org/contributor-roles/formal-analysis/">Formal Analysis</role>
					<role content-type="http://credit.niso.org/contributor-roles/investigation/">Investigation</role>
					<role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
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					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review &amp; editing</role>
					<xref ref-type="aff" rid="aff9"><sup>3</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0001-1831-4724</contrib-id>
					<name>
						<surname>Baniwa</surname>
						<given-names>André</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - Original Draft</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review &amp; editing</role>
					<xref ref-type="aff" rid="aff10"><sup>4</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0006-2003-850X</contrib-id>
					<name>
						<surname>Franco</surname>
						<given-names>Claudia Tereza</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - Original Draft</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review &amp; editing</role>
					<xref ref-type="aff" rid="aff11"><sup>5</sup></xref>
				</contrib>
				<contrib contrib-type="author">
					<contrib-id contrib-id-type="orcid">0009-0009-1828-1713</contrib-id>
					<name>
						<surname>Monteiro</surname>
						<given-names>Suliete Gervásio</given-names>
					</name>
					<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - Original Draft</role>
					<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review &amp; editing</role>
					<xref ref-type="aff" rid="aff12"><sup>6</sup></xref>
				</contrib>
			</contrib-group>
			<aff id="aff7">
				<label>1</label>
				<institution content-type="original"> Instituto Federal do Rio Grande do Sul - IFRS, Porto Alegre, RS, Brazil. mauricio.polidoro@poa.ifrs.edu.br</institution>
				<institution content-type="orgname">Instituto Federal do Rio Grande do Sul - IFRS</institution>
				<addr-line>
					<city>Porto Alegre</city>
					<state>RS</state>
				</addr-line>
				<country country="BR">Brazil</country>
				<email>mauricio.polidoro@poa.ifrs.edu.br</email>
			</aff>
			<aff id="aff8">
				<label>2</label>
				<institution content-type="original"> Universidade Federal do Paraná - UFPR, Curitiba, PR, Brazil. chico@ufpr.br</institution>
				<institution content-type="orgname">Universidade Federal do Paraná - UFPR</institution>
				<addr-line>
					<city>Curitiba</city>
					<state>PR</state>
				</addr-line>
				<country country="BR">Brazil</country>
				<email>chico@ufpr.br</email>
			</aff>
			<aff id="aff9">
				<label>3</label>
				<institution content-type="original"> Universidade Federal do Rio Grande do Sul - UFRGS, Porto Alegre, RS, Brazil. daniel.canavese@ufrgs.br</institution>
				<institution content-type="orgname">Universidade Federal do Rio Grande do Sul - UFRGS</institution>
				<addr-line>
					<city>Porto Alegre</city>
					<state>RS</state>
				</addr-line>
				<country country="BR">Brazil</country>
				<email>daniel.canavese@ufrgs.br</email>
			</aff>
			<aff id="aff10">
				<label>4</label>
				<institution content-type="original"> Universidade de Brasília - UnB, Brasília, DF, Brazil. andre.fernando@povosindigenas.gov.br</institution>
				<institution content-type="orgname">Universidade de Brasília - UnB</institution>
				<addr-line>
					<city>Brasília</city>
					<state>DF</state>
				</addr-line>
				<country country="BR">Brazil</country>
				<email>andre.fernando@povosindigenas.gov.br</email>
			</aff>
			<aff id="aff11">
				<label>5</label>
				<institution content-type="original"> Instituto Etnoambiental e Multicultural Aldeia Verde - IEMAV, Brasília, DF, Brazil. claudiafranco2@gmail.com</institution>
				<institution content-type="orgname">Instituto Etnoambiental e Multicultural Aldeia Verde - IEMAV</institution>
				<addr-line>
					<city>Brasília</city>
					<state>DF</state>
				</addr-line>
				<country country="BR">Brazil</country>
				<email>claudiafranco2@gmail.com</email>
			</aff>
			<aff id="aff12">
				<label>6</label>
				<institution content-type="original"> Ministério dos Povos Indígenas, Brasília, DF, Brazil. suliete.bare@povosindigenas.gov.br</institution>
				<institution content-type="orgname">Ministério dos Povos Indígenas</institution>
				<addr-line>
					<city>Brasília</city>
					<state>DF</state>
				</addr-line>
				<country country="BR">Brazil</country>
				<email>suliete.bare@povosindigenas.gov.br</email>
			</aff>
			<abstract>
				<title>Abstract</title>
				<p>This study analyzes venomous animal incidents between 2012 and 2023 using data from the Notifiable Diseases Information System (SINAN), focusing on differences between Indigenous and non-Indigenous populations. It uses a cross-sectional and ecological methodology, incorporating descriptive statistical analyses and Moran's spatial autocorrelation to examine geographic patterns and identify prevalence clusters. The results reveal a concentration of notifications in the Southeast region. Among the groups analyzed, the Indigenous population had the highest prevalence rate, with 2,654 cases per 100,000 inhabitants, demonstrating significant vulnerability to venomous animal incidents. Regarding age groups, Indigenous children and adolescents were particularly susceptible. Scorpions were the main causative agent among non-Indigenous individuals (57.3%), while snakes were more prevalent among Indigenous groups (56.6%). The study underscores the need for public policies and prevention strategies that consider the cultural and environmental specificities of vulnerable populations, emphasizing the importance of educational and public health actions adapted to local realities.</p>
			</abstract>
			<kwd-group xml:lang="en">
				<title>Keywords:</title>
				<kwd>Indigenous Population</kwd>
				<kwd>Non-Indigenous Population</kwd>
				<kwd>Geoepidemiology</kwd>
				<kwd>Public Health</kwd>
			</kwd-group>
		</front-stub>
		<body>
			<sec sec-type="intro">
				<title>INTRODUCTION</title>
				<p>In recent years, accidents involving venomous animals have become a relevant topic in public health, reflecting the complex connections between human health and environmental factors, including, but not limited to, climate change. It is anticipated that climate change, alongside other factors (e.g., habitat loss), will lead to increased migration of venomous animals such as snakes, spiders, and insects like bees and ants (<xref ref-type="bibr" rid="B22">Needleman <italic>et al.,</italic> 2018a</xref>). The destruction of natural habitats, often driven by unregulated urban expansion and the conversion of natural areas into agricultural land, forces these animals to migrate to new territories, raising the risk of interactions with humans. These transformations are not restricted to terrestrial animals and include marine species and venomous amphibians, such as jellyfish, poisonous fish, and toxic frogs, which are also affected by environmental changes (<xref ref-type="bibr" rid="B23">Needleman <italic>et al.,</italic> 2018b</xref>). It is important to distinguish that venomous animals possess specialized structures for injecting venom, such as fangs or stingers, while poisonous animals release toxins through their skin or other body parts when touched or ingested. Transformations in climate patterns, including global warming and changes in rainfall regimes, directly impact both venomous and poisonous animals, increasing risks to human and animal health, particularly for historically vulnerable populations, and bringing significant implications for industries, trade, and tourism.</p>
				<p>Changes in global climate patterns, characterized by global warming and altered rainfall regimes, directly affect the behavior and geographic distribution of venomous animals (<xref ref-type="bibr" rid="B22">Needleman <italic>et al.,</italic> 2018a</xref>). Global warming causes changes in the behavioral patterns of these animals, such as periods of activity, reproduction, and hibernation (<xref ref-type="bibr" rid="B20">Moreno-Rueda <italic>et al</italic>., 2009</xref>). That, in turn, can lead to an increase in the frequency of interactions between these animals and humans, especially in rural and peri-urban areas where human expansion encroaches on natural habitats (<xref ref-type="bibr" rid="B35">Zacarias, Loyola, 2018</xref>). As global temperatures rise, the activity season for many venomous animals may lengthen, leading to a more extended yearly period during which the risk of envenomation is elevated. That is particularly relevant for regions experiencing a significant increase in average and minimum temperatures (<xref ref-type="bibr" rid="B17">Martínez <italic>et al</italic>., 2022</xref>), especially in Global South countries like Brazil.</p>
				<p>In this context, this study describes and analyzes accidents involving venomous animals reported in the Sistema de Informação de Agravos de Notificação (SINAN, national SUS system that records notifications of health issues) between 2013 and 2022, focusing on Indigenous populations. It also identifies geographic distribution patterns and highlights differences in how vulnerable these populations are to these risks.</p>
				<p>The relevance of this study lies in the need to understand the factors reshaping the risks associated with venomous animal accidents. Indigenous peoples, who maintain a deep connection to their traditional territories, are disproportionately affected, facing not only an elevated risk of accidents but also the associated complications that affect their lives at a community level, resulting in the loss of livelihood and culture (<xref ref-type="bibr" rid="B9">Green; Raygorodetsky, 2010</xref>). Therefore, it is imperative to investigate these dynamics to develop effective prevention and mitigation strategies, safeguarding public health and Indigenous populations' cultural and environmental integrity.</p>
				<p>With this in mind, this study conducts a comparative analysis of the epidemiological profile and spatial distribution of accidents involving venomous animals, distinguishing between Indigenous and non-Indigenous populations. It seeks to identify geographic distribution patterns and highlight how Indigenous peoples are more vulnerable to these risks. This analysis will advance our understanding of the impacts of these accidents on public health and inform the development of effective responses to the challenges posed by venomous animals.</p>
			</sec>
			<sec sec-type="methods">
				<title>METHODOLOGY</title>
				<p>This cross-sectional and ecological study aimed to investigate the prevalence of venomous animal accidents in Brazil from 2013 to 2022, using records from the SINAN. Following the guidelines of the Brazilian Ministry of Health (2024), a confirmed case was characterized by clinical signs of envenomation specific to the animal category involved without the need to identify the causative animal.</p>
				<p>Data included individuals of all age groups, genders, and other sociodemographic variables as available in SINAN. The variables analyzed included individual notification data, such as age, gender, pregnancy status, race/color, and education level; residential data, such as municipality of residence; epidemiological background, such as the interval between the bite and medical attention and the location of the bite; and clinical data, including local and systemic manifestations, type of accident, case classification, and outcome. Non-Indigenous individuals were classified as those reported under white, black, brown, and yellow (Asian) race/ethnicity categories, while Indigenous individuals were those reported as Indigenous. Rates were calculated per 100,000 inhabitants. Missing data referred to records in which information about race/ethnicity was not adequately filled out in the system, leading to absent or inconsistent data that could not be accurately classified.</p>
				<p>Data was collected via the DATASUS file transfer system, supplemented by demographic and socioeconomic information from the 2010 Census (Instituto Brasileiro de Geografia e Estatística - IBGE, the Brazilian Institute of Geography and Statistics). Data processing was performed using the R statistical software, where descriptive statistics and Pearson's Chi-square test were conducted to assess differences between populations. Spatial analysis was integrated using Anselin Moran I's spatial autocorrelation index, categorizing the spatial distribution of events into four types of spatial association: High-High and Low-Low Clusters, and High-Low and Low-High Outliers (where clusters represent geographically proximate areas with similar values of a specific variable, and outliers represent areas with discrepant values compared to their surroundings). To generate the maps, the shapefile resulting from the LISA spatial analysis was processed using the ArcGIS software, licensed by the Universidade Federal do Rio Grande do Sul (Federal University of Rio Grande do Sul in English).</p>
				<p>The study adhered to the ethical principles outlined by the General Data Protection Law (Law No. 13,709/2018) and the Resolution of the National Health Council No. 466/2012. Since it utilized anonymized secondary data, ethical approval by a Research Ethics Committee was waived.</p>
			</sec>
			<sec sec-type="results">
				<title>RESULTS</title>
				<p>In the analysis of notifications of venomous animal accidents (<xref ref-type="fig" rid="f3">Figure 1</xref>), the Southeast region leads from 2013 to 2022, followed by the Northeast region. The South, North, and Central-West regions rank third, fourth, and fifth, respectively. The peak in notifications across all regions occurred in 2019.</p>
				<p>
					<fig id="f3">
						<label>Figure 1</label>
						<caption>
							<title>Evolution of reported cases of venomous animal accidents, Major Regions, Brazil, 2013 to 2022</title>
						</caption>
						<graphic xlink:href="1982-4513-sn-37-e73312-gf3.png"/>
						<attrib>Source: The authors (2023) based on <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</attrib>
					</fig>
				</p>
				<p>The results presented in <xref ref-type="table" rid="t6">Table 1</xref> show that the majority of reported cases involve individuals identified as brown (<italic>pardos</italic>), representing 46.6% of the total, followed by white individuals (34.0%) and black individuals (5.6%). The lowest prevalence was observed among individuals of Asian descent (0.7%). Notably, the highest prevalence rate was recorded among Indigenous peoples, with 2,654.0 cases per 100,000 inhabitants, suggesting a heightened vulnerability of this group to venomous animal accidents. On the other hand, cases categorized as &quot;Ignored&quot; and &quot;Invalid Data&quot; represent 10.3% and 1.9% of the total, respectively, indicating gaps in the recording of racial data in health systems.</p>
				<p>
					<table-wrap id="t6">
						<label>Table 1</label>
						<caption>
							<title>Race/Color of Reported Cases of Venomous Animal Accidents, Brazil, 2013 to 2022</title>
						</caption>
						<table>
							<colgroup>
								<col/>
								<col/>
								<col/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="center">Race/Color</th>
									<th align="center">N</th>
									<th align="center">%</th>
									<th align="center">Rate*</th>
								</tr>
							</thead>
							<tbody>
								<tr>
									<td align="center">White</td>
									<td align="center">762.505</td>
									<td align="center">34,0</td>
									<td align="center">837,4</td>
								</tr>
								<tr>
									<td align="center">Black</td>
									<td align="center">124.746</td>
									<td align="center">5,6</td>
									<td align="center">859,3</td>
								</tr>
								<tr>
									<td align="center">Yellow (Asian</td>
									<td align="center">16.300</td>
									<td align="center">0,7</td>
									<td align="center">782,0</td>
								</tr>
								<tr>
									<td align="center">Brown (<italic>pardo</italic>)</td>
									<td align="center">1.045.742</td>
									<td align="center">46,6</td>
									<td align="center">1.271,0</td>
								</tr>
								<tr>
									<td align="center">Indigenous</td>
									<td align="center">21.709</td>
									<td align="center">1,0</td>
									<td align="center">2.654,0</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">230.999</td>
									<td align="center">10,3</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">42.039</td>
									<td align="center">1,9</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Total</td>
									<td align="center">2.244.040</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN8">
								<p>Source: The authors (2023) based on <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>) and <xref ref-type="bibr" rid="B11">IBGE (2010</xref>).</p>
							</fn>
							<fn id="TFN9">
								<p>*per 100,000 inhabitants</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>
					<xref ref-type="table" rid="t7">Table 2</xref> presents information on the non-Indigenous and Indigenous populations by sex and age group. A predominance of cases is observed among males in the non-Indigenous population (55.2% of cases, at a rate of 1,325.5 per 100,000 inhabitants) and the Indigenous population (61.6%, at a rate of 3,260.5). For females, the numbers are slightly lower among non-Indigenous individuals (44.8%, rate of 1,032.8) compared to Indigenous individuals (38.4%, rate of 2,044.1), indicating a statistically significant (p&lt;0.0001) discrepancy in the incidence rate per 100,000 inhabitants between sexes and between Indigenous and non-Indigenous populations.</p>
				<p>Regarding age groups, the data reveal that the prevalence of accidents varies considerably, with the highest rates observed among younger and older groups in the Indigenous population, highlighting greater vulnerability or exposure to venomous animals. Specifically, Indigenous infants under 1 year and adolescents aged 15 to 17 show elevated prevalence rates (2,488.5 and 3,229.7 per 100,000 inhabitants, respectively). In contrast, among non-Indigenous individuals, the age distribution of accidents shows a different pattern, with higher rates observed between the 18 to 29 and 50 to 59 age groups.</p>
				<p>
					<table-wrap id="t7">
						<label>Table 2</label>
						<caption>
							<title>Total, percentage, and rate per 100,000 inhabitants of sex and age group of reported venomous animal accidents, non-Indigenous and Indigenous populations, Brazil, 2013 to 2022</title>
						</caption>
						<table>
							<colgroup>
								<col/>
								<col span="3"/>
								<col span="3"/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="center" rowspan="2">Sex</th>
									<th align="center" colspan="3">Non-Indigenous</th>
									<th align="center" colspan="3">Indigenous</th>
									<th align="center" rowspan="2"><bold>p-value</bold></th>
								</tr>
								<tr>
									<th align="center">N</th>
									<th align="center">%</th>
									<th align="center">Rate</th>
									<th align="center">N</th>
									<th align="center">%</th>
									<th align="center">Rate</th>
									
								</tr>
							</thead>
							<tbody>
								<tr>
									<td align="center">Male</td>
									<td align="center">1.238.101</td>
									<td align="center">55,2</td>
									<td align="center">1.325,5</td>
									<td align="center">13.365,0</td>
									<td align="center">61,6</td>
									<td align="center">3.260,5</td>
									<td align="center" rowspan="2">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">Female</td>
									<td align="center">1.005.420</td>
									<td align="center">44,8</td>
									<td align="center">1.032,8</td>
									<td align="center">8.341,0</td>
									<td align="center">38,4</td>
									<td align="center">2.044,1</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">519</td>
									<td align="center">0,02</td>
									<td align="center">-</td>
									<td align="center">3</td>
									<td align="center">0,01</td>
									<td align="center">-</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center"><italic>Age Group</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>Rate</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>Rate</italic></td>
									<td align="center"> </td>
								</tr>
								<tr>
									<td align="center">Under 1</td>
									<td align="center">31.426</td>
									<td align="center">1,4</td>
									<td align="center">1.158,2</td>
									<td align="center">493</td>
									<td align="center">2,3</td>
									<td align="center">2.488,5</td>
									<td align="center" rowspan="10">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">1 to 4</td>
									<td align="center">80.472</td>
									<td align="center">3,6</td>
									<td align="center">726,1</td>
									<td align="center">844</td>
									<td align="center">3,9</td>
									<td align="center">1.037,1</td>
								</tr>
								<tr>
									<td align="center">5 to 9</td>
									<td align="center">127.933</td>
									<td align="center">5,7</td>
									<td align="center">854,6</td>
									<td align="center">2.043</td>
									<td align="center">9,4</td>
									<td align="center">2.046,3</td>
								</tr>
								<tr>
									<td align="center">10 to 14</td>
									<td align="center">134.603</td>
									<td align="center">6,0</td>
									<td align="center">784,1</td>
									<td align="center">2.552</td>
									<td align="center">11,8</td>
									<td align="center">2.691,2</td>
								</tr>
								<tr>
									<td align="center">15 to 17</td>
									<td align="center">91.719</td>
									<td align="center">4,1</td>
									<td align="center">885,5</td>
									<td align="center">1.659</td>
									<td align="center">7,6</td>
									<td align="center">3.229,7</td>
								</tr>
								<tr>
									<td align="center">18 to 29</td>
									<td align="center">453.456</td>
									<td align="center">20,2</td>
									<td align="center">1.106,5</td>
									<td align="center">5.333</td>
									<td align="center">24,6</td>
									<td align="center">3.144,0</td>
								</tr>
								<tr>
									<td align="center">30 to 39</td>
									<td align="center">350.621</td>
									<td align="center">15,6</td>
									<td align="center">1.183,2</td>
									<td align="center">3.113</td>
									<td align="center">14,3</td>
									<td align="center">2.979,1</td>
								</tr>
								<tr>
									<td align="center">40 to 49</td>
									<td align="center">323.202</td>
									<td align="center">14,4</td>
									<td align="center">1.301,0</td>
									<td align="center">2.346</td>
									<td align="center">10,8</td>
									<td align="center">3.058,9</td>
								</tr>
								<tr>
									<td align="center">50 to 59</td>
									<td align="center">295.919</td>
									<td align="center">13,2</td>
									<td align="center">2.918,2</td>
									<td align="center">1.610</td>
									<td align="center">7,4</td>
									<td align="center">2.983,9</td>
								</tr>
								<tr>
									<td align="center">60 and over</td>
									<td align="center">354.681</td>
									<td align="center">15,8</td>
									<td align="center">1.394,7</td>
									<td align="center">1.716</td>
									<td align="center">7,9</td>
									<td align="center">2.091,4</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">8</td>
									<td align="center">-</td>
									<td align="center">-</td>
									<td align="center">-</td>
									<td align="center">0,0</td>
									<td align="center">-</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Total</td>
									<td align="center">2.244.040</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
									<td align="center">21.709</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
									<td align="center">-</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN10">
								<p>Source: The authors (2023) based on <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>) and <xref ref-type="bibr" rid="B11">IBGE (2010</xref>).</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>Regarding education level (<xref ref-type="table" rid="t8">Table 3</xref>), it is notable that there were no recorded cases among illiterate individuals in either group, suggesting potential gaps in data collection or accessibility to the notification system for all segments of the population. Among non-Indigenous individuals, the majority of cases are concentrated among people with some primary education, representing 25.6% of the total, followed by individuals with complete secondary education and some secondary education. In the Indigenous population, there is also a significant presence of cases among people with some primary education, as well as a notable percentage of records categorized as &quot;Ignored&quot; or &quot;Invalid Data (missing),&quot; indicating issues with data accuracy and the integrity of the reported information.</p>
				<p>As for pregnancy status, a relatively low number of cases were reported among pregnant individuals, distributed fairly evenly across the three trimesters in both populations. However, the vast majority of records indicate that most individuals were not pregnant at the time of the accident or that this information was not applicable, which may include male individuals or non-pregnant women.</p>
				<p>
					<table-wrap id="t8">
						<label>Tabela 3</label>
						<caption>
							<title>Education level and pregnancy status of reported venomous animal accidents, non-Indigenous and Indigenous populations, Brazil, 2013 to 2022</title>
						</caption>
						<table>
							<colgroup>
								<col/>
								<col span="2"/>
								<col span="2"/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="center" rowspan="2">Education Level</th>
									<th align="center" colspan="2">Non-Indigenous</th>
									<th align="center" colspan="2">Indigenous</th>
									<th align="center" rowspan="2">p-value</th>
								</tr>
								<tr>
									<th align="center">N</th>
									<th align="center">%</th>
									<th align="center">N</th>
									<th align="center">%</th>
									
								</tr>
							</thead>
							<tbody>
								<tr>
									<td align="center">Illiterate</td>
									<td align="center">0</td>
									<td align="center">0</td>
									<td align="center">0</td>
									<td align="center">0</td>
									<td align="center" rowspan="7">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">Some Primary Education</td>
									<td align="center">574.026</td>
									<td align="center">25,6</td>
									<td align="center">6.807</td>
									<td align="center">31,4</td>
								</tr>
								<tr>
									<td align="center">Complete Primary Education</td>
									<td align="center">115.719</td>
									<td align="center">5,2</td>
									<td align="center">1.029</td>
									<td align="center">4,7</td>
								</tr>
								<tr>
									<td align="center">Some Secondary Education</td>
									<td align="center">138.662</td>
									<td align="center">6,2</td>
									<td align="center">1.178</td>
									<td align="center">5,4</td>
								</tr>
								<tr>
									<td align="center">Complete Secondary Education</td>
									<td align="center">281.211</td>
									<td align="center">12,5</td>
									<td align="center">1.202</td>
									<td align="center">5,5</td>
								</tr>
								<tr>
									<td align="center">Some Higher Education</td>
									<td align="center">26.425</td>
									<td align="center">1,2</td>
									<td align="center">102</td>
									<td align="center">0,5</td>
								</tr>
								<tr>
									<td align="center">Complete Higher Education</td>
									<td align="center">53.155</td>
									<td align="center">2,4</td>
									<td align="center">114</td>
									<td align="center">0,5</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">579.098</td>
									<td align="center">25,8</td>
									<td align="center">3.481</td>
									<td align="center">16,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Not applicable</td>
									<td align="center">188.517</td>
									<td align="center">8,4</td>
									<td align="center">2.464</td>
									<td align="center">11,4</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">287.227</td>
									<td align="center">12,8</td>
									<td align="center">5.332</td>
									<td align="center">24,6</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center"><italic>Pregnancy Status</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>p-value</italic></td>
								</tr>
								<tr>
									<td align="center">1st Trimester</td>
									<td align="center">4.808</td>
									<td align="center">0,2</td>
									<td align="center">88</td>
									<td align="center">0,4</td>
									<td align="center" rowspan="5">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">2nd Trimester</td>
									<td align="center">6.927</td>
									<td align="center">0,3</td>
									<td align="center">120</td>
									<td align="center">0,6</td>
								</tr>
								<tr>
									<td align="center">3rd Trimester</td>
									<td align="center">4.940</td>
									<td align="center">0,2</td>
									<td align="center">83</td>
									<td align="center">0,4</td>
								</tr>
								<tr>
									<td align="center">Gestational Age Ignored</td>
									<td align="center">3.069</td>
									<td align="center">0,1</td>
									<td align="center">73</td>
									<td align="center">0,3</td>
								</tr>
								<tr>
									<td align="center">Not pregnant</td>
									<td align="center">567.706</td>
									<td align="center">25,3</td>
									<td align="center">4.293</td>
									<td align="center">19,8</td>
								</tr>
								<tr>
									<td align="center">Not applicable</td>
									<td align="center">1.513.105</td>
									<td align="center">67,4</td>
									<td align="center">16.620</td>
									<td align="center">76,6</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">143.416</td>
									<td align="center">6,4</td>
									<td align="center">431</td>
									<td align="center">2,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">69</td>
									<td align="center">0,0</td>
									<td align="center">1</td>
									<td align="center">0,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Total</td>
									<td align="center">2.244.040</td>
									<td align="center">100,0</td>
									<td align="center">21.709</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN11">
								<p>Source: The authors (2023) based on <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>In the identified accidents (<xref ref-type="table" rid="t9">Table 4</xref>), scorpions are the primary cause of venomous animal accidents among non-Indigenous individuals, representing 57.3% of cases, followed by spiders (14.1%) and snakes (12.9%). In contrast, among the Indigenous population, snake accidents are the most predominant, accounting for 56.6% of cases.</p>
				<p>Regarding the location of the bite, both groups show a tendency for bites on the feet as the most common, especially among Indigenous individuals, where 44% of accidents affect this part of the body. That suggests greater exposure of the feet to environments where such animals are prevalent, likely due to differences in daily activities or footwear use.</p>
				<p>The time to receive medical care is critical in managing venomous animal accidents, with most cases receiving care within 1 to 3 hours after the bite. However, a significant proportion of notifications, both among non-Indigenous and Indigenous individuals, report seeking care over 24 hours after the accident.</p>
				<p>
					<table-wrap id="t9">
						<label>Table 4</label>
						<caption>
							<title>Type of accident, bite location, and time elapsed from accident to health care for reported venomous animal accidents, non-Indigenous and Indigenous populations, Brazil, 2013 to 2022</title>
						</caption>
						<table>
							<colgroup>
								<col/>
								<col span="2"/>
								<col span="2"/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="center" rowspan="2">Type of Accident</th>
									<th align="center" colspan="2">Non-Indigenous</th>
									<th align="center" colspan="2">Indigenous</th>
									<th align="center" rowspan="2">p-value</th>
								</tr>
								<tr>
									<th align="center">N</th>
									<th align="center">%</th>
									<th align="center">N</th>
									<th align="center">%</th>
									
								</tr>
							</thead>
							<tbody>
								<tr>
									<td align="center">Snake</td>
									<td align="center">288.474</td>
									<td align="center">12,9</td>
									<td align="center">12.278</td>
									<td align="center">56,6</td>
									<td align="center" rowspan="6">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">Spider</td>
									<td align="center">315.534</td>
									<td align="center">14,1</td>
									<td align="center">1.872</td>
									<td align="center">8,6</td>
								</tr>
								<tr>
									<td align="center">Scorpion</td>
									<td align="center">1.286.801</td>
									<td align="center">57,3</td>
									<td align="center">5.668</td>
									<td align="center">26,1</td>
								</tr>
								<tr>
									<td align="center">Caterpillar</td>
									<td align="center">45.824</td>
									<td align="center">2,0</td>
									<td align="center">242</td>
									<td align="center">1,1</td>
								</tr>
								<tr>
									<td align="center">Bee</td>
									<td align="center">173.550</td>
									<td align="center">7,7</td>
									<td align="center">745</td>
									<td align="center">3,4</td>
								</tr>
								<tr>
									<td align="center">Others</td>
									<td align="center">93.383</td>
									<td align="center">4,2</td>
									<td align="center">738</td>
									<td align="center">3,4</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">40.367</td>
									<td align="center">1,8</td>
									<td align="center">164</td>
									<td align="center">0,8</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center"><italic>Bite Location</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>p-value</italic></td>
								</tr>
								<tr>
									<td align="center">Head</td>
									<td align="center">140.868</td>
									<td align="center">6,3</td>
									<td align="center">760</td>
									<td align="center">3,5</td>
									<td align="center" rowspan="10">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">Arm</td>
									<td align="center">123.145</td>
									<td align="center">5,5</td>
									<td align="center">730</td>
									<td align="center">3,4</td>
								</tr>
								<tr>
									<td align="center">Forearm</td>
									<td align="center">64.052</td>
									<td align="center">2,9</td>
									<td align="center">408</td>
									<td align="center">1,9</td>
								</tr>
								<tr>
									<td align="center">Hand</td>
									<td align="center">369.478</td>
									<td align="center">16,5</td>
									<td align="center">2.474</td>
									<td align="center">11,4</td>
								</tr>
								<tr>
									<td align="center">Finger</td>
									<td align="center">369.590</td>
									<td align="center">16,5</td>
									<td align="center">1.891</td>
									<td align="center">8,7</td>
								</tr>
								<tr>
									<td align="center">Torso</td>
									<td align="center">120.986</td>
									<td align="center">5,4</td>
									<td align="center">583</td>
									<td align="center">2,7</td>
								</tr>
								<tr>
									<td align="center">Thigh</td>
									<td align="center">87.781</td>
									<td align="center">3,9</td>
									<td align="center">536</td>
									<td align="center">2,5</td>
								</tr>
								<tr>
									<td align="center">Leg</td>
									<td align="center">182.125</td>
									<td align="center">8,1</td>
									<td align="center">2.909</td>
									<td align="center">13,4</td>
								</tr>
								<tr>
									<td align="center">Foot</td>
									<td align="center">532.288</td>
									<td align="center">23,7</td>
									<td align="center">9.557</td>
									<td align="center">44,0</td>
								</tr>
								<tr>
									<td align="center">Toe</td>
									<td align="center">166.919</td>
									<td align="center">7,4</td>
									<td align="center">1.541</td>
									<td align="center">7,1</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">86.701</td>
									<td align="center">3,9</td>
									<td align="center">318</td>
									<td align="center">1,5</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">107</td>
									<td align="center">0,0</td>
									<td align="center">2</td>
									<td align="center">0,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center"><italic>Time Elapsed to Care</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>p-value</italic></td>
								</tr>
								<tr>
									<td align="center">0 to hour</td>
									<td align="center">1.084.890</td>
									<td align="center">48,3</td>
									<td align="center">5.881</td>
									<td align="center">27,1</td>
									<td align="center" rowspan="6">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">1 to 3 hours</td>
									<td align="center">504.527</td>
									<td align="center">22,5</td>
									<td align="center">5.570</td>
									<td align="center">25,7</td>
								</tr>
								<tr>
									<td align="center">3 to 6 hours</td>
									<td align="center">166.709</td>
									<td align="center">7,4</td>
									<td align="center">3.532</td>
									<td align="center">16,3</td>
								</tr>
								<tr>
									<td align="center">6 to 12 hours</td>
									<td align="center">77.266</td>
									<td align="center">3,4</td>
									<td align="center">2.006</td>
									<td align="center">9,2</td>
								</tr>
								<tr>
									<td align="center">12 to 24 hours</td>
									<td align="center">79.610</td>
									<td align="center">3,5</td>
									<td align="center">1.634</td>
									<td align="center">7,5</td>
								</tr>
								<tr>
									<td align="center">More than 24 hours</td>
									<td align="center">134.575</td>
									<td align="center">6,0</td>
									<td align="center">1.703</td>
									<td align="center">7,8</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">141.055</td>
									<td align="center">6,3</td>
									<td align="center">936</td>
									<td align="center">4,3</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">55.408</td>
									<td align="center">2,5</td>
									<td align="center">447</td>
									<td align="center">2,1</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Total</td>
									<td align="center">2.244.040</td>
									<td align="center">100,0</td>
									<td align="center">21.709</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN12">
								<p>Source: The authors (2023) based on <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>Regarding case classification, most cases in both populations are classified as mild, with 82.5% among non-Indigenous individuals and 62.2% among Indigenous individuals, indicating that a significant portion of the accidents result in less severe symptoms. However, the proportion of moderate and severe cases is significantly higher among Indigenous people (27.8% and 5.6%, respectively) compared to non-Indigenous people (11.3% and 1.6%).</p>
				<p>The prevalence of local complications is also higher among Indigenous individuals, with 4% of cases reporting complications, in contrast to only 1.1% among non-Indigenous individuals. This stark difference may reflect not only the nature of the accidents but also disparities in access to immediate medical care and effective treatment, highlighting a critical public health issue.</p>
				<p>Secondary infection and extensive necrosis are the most common among the specified local complications. Notably, the proportion of cases resulting in amputation, though low, is significantly higher when local complications are reported, indicating the potential severity of these accidents. Systemic complications, though less frequent, also show a discrepancy, being more prevalent among Indigenous people.</p>
				<p>Most cases result in recovery, precisely 91.4% for non-Indigenous individuals and 89.4% for Indigenous individuals. However, the mortality rate due to venomous animal accidents is higher among Indigenous people (0.6% compared to 0.1% among non-Indigenous people), a statistically significant difference that underscores the inequalities in the impact of these accidents between the populations.</p>
				<p>
					<table-wrap id="t10">
						<label>Table 5</label>
						<caption>
							<title>Case classification, local complications, and outcomes of reported venomous animal accidents, non-Indigenous and Indigenous populations, Brazil, 2013 to 2022</title>
						</caption>
						<table>
							<colgroup>
								<col/>
								<col span="2"/>
								<col span="2"/>
								<col/>
							</colgroup>
							<thead>
								<tr>
									<th align="center" rowspan="2">Case Classification</th>
									<th align="center" colspan="2">Non-Indigenous</th>
									<th align="center" colspan="2">Indigenous</th>
									<th align="center" rowspan="2">p-value</th>
								</tr>
								<tr>
									<th align="center">N</th>
									<th align="center">%</th>
									<th align="center">N</th>
									<th align="center">%</th>
									
								</tr>
							</thead>
							<tbody>
								<tr>
									<td align="center">Mild</td>
									<td align="center">1.851.315</td>
									<td align="center">82,5</td>
									<td align="center">13.511</td>
									<td align="center">62,2</td>
									<td align="center" rowspan="5">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">Moderate</td>
									<td align="center">253.361</td>
									<td align="center">11,3</td>
									<td align="center">6.035</td>
									<td align="center">27,8</td>
								</tr>
								<tr>
									<td align="center">Severe</td>
									<td align="center">35.413</td>
									<td align="center">1,6</td>
									<td align="center">1.221</td>
									<td align="center">5,6</td>
								</tr>
								<tr>
									<td align="center">Unknown</td>
									<td align="center">53.716</td>
									<td align="center">2,4</td>
									<td align="center">417</td>
									<td align="center">1,9</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">50.955</td>
									<td align="center">2,3</td>
									<td align="center">525</td>
									<td align="center">2,4</td>
								</tr>
								<tr>
									<td align="center">Total</td>
									<td align="center">2.244.040</td>
									<td align="center">100,0</td>
									<td align="center">21.709</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center"><italic>Local Complications</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>p-value</italic></td>
								</tr>
								<tr>
									<td align="center">Yes</td>
									<td align="center">25.649</td>
									<td align="center">1,1</td>
									<td align="center">858</td>
									<td align="center">4,0</td>
									<td align="center" rowspan="3">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">No</td>
									<td align="center">1.991.079</td>
									<td align="center">88,7</td>
									<td align="center">18.570</td>
									<td align="center">85,5</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">90.599</td>
									<td align="center">4,0</td>
									<td align="center">872</td>
									<td align="center">4,0</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">136.713</td>
									<td align="center">6,1</td>
									<td align="center">1.409</td>
									<td align="center">6,5</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Total</td>
									<td align="center">2.244.040</td>
									<td align="center">100,0</td>
									<td align="center">21.709</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center"><italic>Specification of Local Complications</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>p-value</italic></td>
								</tr>
								<tr>
									<td align="center">Amputation*</td>
									<td align="center">323</td>
									<td align="center">1,2</td>
									<td align="center">23</td>
									<td align="center">2,6</td>
									<td align="center" rowspan="6">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">Secondary Infection*</td>
									<td align="center">19.692</td>
									<td align="center">76,5</td>
									<td align="center">660</td>
									<td align="center">76,6</td>
								</tr>
								<tr>
									<td align="center">Extensive Necrosis*</td>
									<td align="center">4.654</td>
									<td align="center">18,1</td>
									<td align="center">121</td>
									<td align="center">14</td>
								</tr>
								<tr>
									<td align="center">Behavioral Syndrome*</td>
									<td align="center">1.953</td>
									<td align="center">7,5</td>
									<td align="center">101</td>
									<td align="center">11,7</td>
								</tr>
								<tr>
									<td align="center">Functional Deficit*</td>
									<td align="center">2.870</td>
									<td align="center">11,1</td>
									<td align="center">143</td>
									<td align="center">16,6</td>
								</tr>
								<tr>
									<td align="center">Systemic Complications</td>
									<td align="center">8.020</td>
									<td align="center">0,3</td>
									<td align="center">241</td>
									<td align="center">1,2</td>
								</tr>
								<tr>
									<td align="center"><italic>Case Outcome</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>N</italic></td>
									<td align="center"><italic>%</italic></td>
									<td align="center"><italic>p-value</italic></td>
								</tr>
								<tr>
									<td align="center">Recovered</td>
									<td align="center">2.049.974</td>
									<td align="center">91,4</td>
									<td align="center">19.402</td>
									<td align="center">89,4</td>
									<td align="center" rowspan="3">&lt; 0,001</td>
								</tr>
								<tr>
									<td align="center">Death by venomous animal accident</td>
									<td align="center">2.812</td>
									<td align="center">0,1</td>
									<td align="center">124</td>
									<td align="center">0,6</td>
								</tr>
								<tr>
									<td align="center">Death by other causes</td>
									<td align="center">362</td>
									<td align="center">0,0</td>
									<td align="center">14</td>
									<td align="center">0,1</td>
								</tr>
								<tr>
									<td align="center">Ignored</td>
									<td align="center">63.494</td>
									<td align="center">2,8</td>
									<td align="center">644</td>
									<td align="center">3,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Invalid Data (missing)</td>
									<td align="center">127.398</td>
									<td align="center">5,7</td>
									<td align="center">1.525</td>
									<td align="center">7,0</td>
									<td align="center">-</td>
								</tr>
								<tr>
									<td align="center">Total</td>
									<td align="center">2.244.040</td>
									<td align="center">100,0</td>
									<td align="center">21.709</td>
									<td align="center">100,0</td>
									<td align="center">-</td>
								</tr>
							</tbody>
						</table>
						<table-wrap-foot>
							<fn id="TFN13">
								<p>*Information for cases with &quot;yes&quot; for local complications.</p>
							</fn>
							<fn id="TFN14">
								<p>Source: The author (2023) based on <xref ref-type="bibr" rid="B32">SINAN (c2024</xref>).</p>
							</fn>
						</table-wrap-foot>
					</table-wrap>
				</p>
				<p>Regarding the distribution of spatial clustering levels, as illustrated in <xref ref-type="fig" rid="f4">Figure 1</xref>, there is a predominant concentration of High-High clusters among the Indigenous population in the states of Amazonas, Acre, and Mato Grosso, regions with a higher proportion of Indigenous residents. Notably, there is a continuous sequence of Low-Low clusters extending from the northeast of Paraná to the northeastern coast of Brazil, also encompassing parts of the northern region. In the extreme south of the country, specifically in the south-central region of Rio Grande do Sul, approximately half of the territory is also influenced by a Low-Low cluster.</p>
				<p>For the non-Indigenous population, a High-High cluster is observed along the coast of Paraná and, in a more dispersed manner, in the state of São Paulo, Pará, as well as in the southwestern and northwestern regions of Minas Gerais and southern Bahia. The states of Mato Grosso and Rio Grande do Sul are notable for their almost complete coverage by Low-Low clusters, with some High-Low outliers.</p>
				<p>
					<fig id="f4">
						<label>Figure 1</label>
						<caption>
							<title>Level of spatial clustering for reported venomous animal accidents, Indigenous (A) and non-Indigenous (B), Brazil, 2013 to 2022</title>
						</caption>
						<graphic xlink:href="1982-4513-sn-37-e73312-gf4.png"/>
					</fig>
				</p>
			</sec>
			<sec sec-type="discussion">
				<title>DISCUSSION</title>
				<p>Our results highlight a differentiated vulnerability to venomous animal accidents between Indigenous and non-Indigenous populations. Research indicates that unregulated urban expansion, lack of pest control, and inadequate waste management significantly contribute to the proliferation of scorpions in urban and peri-urban areas (<xref ref-type="bibr" rid="B36">Zanetta <italic>et al</italic>., 2020</xref>; <xref ref-type="bibr" rid="B2">Cruz <italic>et al</italic>., 1995</xref>; <xref ref-type="bibr" rid="B10">Guerra-Duarte <italic>et al</italic>., 2023</xref>). These factors create favorable environments for the survival and reproduction of these arachnids, increasing the incidence of scorpion accidents, especially in densely populated regions (<xref ref-type="bibr" rid="B27">Olivero <italic>et al</italic>., 2021</xref>).</p>
				<p>The higher prevalence of snakebite accidents among Indigenous peoples reflects the more frequent contact of these communities with natural habitats. A study conducted in the Western Brazilian Amazon showed that people living in rural or forested areas are more likely to be affected by snakebites due to the presence of snakes and activities such as extractivism and agriculture (<xref ref-type="bibr" rid="B30">Silva; Colombini Moura-da-Silva; Souza; Monteiro; Bernarde, 2020</xref>; <xref ref-type="bibr" rid="B31">Silva; Fonseca; Silva, Amaral; Ortega; Oliveira; Correa; Oliveira; Monteiro; Bernarde, 2020</xref>). <xref ref-type="bibr" rid="B29">Schneider <italic>et al</italic>. (2021</xref>) demonstrated that Indigenous people have the highest rates of exposure to snakebites (194.3 per 100,000 inhabitants), significantly higher compared to other population groups.</p>
				<p>The tendency for snakebites to occur on the feet, particularly among Indigenous people, reflects cultural practices and outdoor activities often carried out by this population without proper protection and adequate lighting (<xref ref-type="bibr" rid="B28">Pierini <italic>et. al.,</italic> 1996</xref>). In many cases, Indigenous and other rural populations engage in subsistence activities such as hunting, gathering, or farming while barefoot or using minimal footwear, which significantly increases their exposure to snakes (<xref ref-type="bibr" rid="B15">Leite <italic>et al</italic>., 2013</xref>; <xref ref-type="bibr" rid="B13">Jayawardana <italic>et al</italic>., 2020</xref>; <xref ref-type="bibr" rid="B33">Venugopalan <italic>et al</italic>., 2021</xref>). Moreover, these activities typically occur in environments with a high presence of snakes, such as forests, fields, and riverbanks, which are natural habitats for these animals (<xref ref-type="bibr" rid="B6">Eniang <italic>et. al</italic>., 2012</xref><italic>;</italic><xref ref-type="bibr" rid="B31">Silva; Fonseca; Silva; Amaral; Ortega; Oliveira; Correa; Oliveira; Monteiro; Bernarde, 2020</xref>).</p>
				<p>The time to access healthcare is a crucial factor in preventing severe complications, particularly for Indigenous populations who face significant geographical and logistical barriers. Studies indicate that distance and the lack of adequate infrastructure delay access to treatment, substantially increasing health risks (<xref ref-type="bibr" rid="B25">Nguyen <italic>et al</italic>., 2020</xref>). Additionally, cultural issues and discrimination in healthcare further hinder admittance and care continuity (<xref ref-type="bibr" rid="B24">Nelson; Wilson, 2018</xref>). Decentralizing, improving infrastructure in remote areas, and developing intercultural strategies in healthcare services are essential to mitigate these risks and ensure that care reaches vulnerable populations efficiently (<xref ref-type="bibr" rid="B14">Juárez-Ramírez <italic>et al</italic>., 2019</xref>). Community initiatives like building healthcare infrastructure adapted to local needs have also shown positive results in expanding access to primary care (<xref ref-type="bibr" rid="B4">Dutta, 2020</xref>).</p>
				<p>The classification of venomous animal accident cases, with increased severity among Indigenous populations, underscores the urgent need for culturally appropriate and effective public health interventions (<xref ref-type="bibr" rid="B7">Farias <italic>et al</italic>., 2023</xref>). The high prevalence of complications, such as secondary infections and extensive necrosis in these populations, highlights the need for immediate and adequate healthcare, along with continuous follow-up to treat complications (<xref ref-type="bibr" rid="B21">Murta <italic>et al</italic>., 2023</xref>). Studies demonstrate that decentralizing antivenom treatment to local healthcare units within Indigenous territories can reduce the time between diagnosis and treatment, improving the prognosis of envenomation and reducing severe sequelae (<xref ref-type="bibr" rid="B19">Monteiro <italic>et al</italic>., 2020</xref>).</p>
				<p>Although this study does not explore the effects of climate change on the reporting of venomous animal accidents, it is worth considering that changes in temperature and precipitation regimes may influence the geographic distribution and behavior of some venomous species (<xref ref-type="bibr" rid="B22">Needleman, 2018a</xref>; <xref ref-type="bibr" rid="B23">2018b</xref>; <xref ref-type="bibr" rid="B1">Bouazza <italic>et al</italic>., 2019</xref>; <xref ref-type="bibr" rid="B16">Martinez <italic>et. al</italic>., 2018</xref>; <xref ref-type="bibr" rid="B17">Martinez <italic>et. al</italic>., 2022</xref>; <xref ref-type="bibr" rid="B18">Martinez <italic>et. al</italic>., 2024</xref>; <xref ref-type="bibr" rid="B35">Zacarias, Loyola., 2018</xref>). Future studies could explore these hypotheses using predictive models to assess how climate change may alter risk patterns.</p>
				<p>The gap in data completeness in the healthcare sector, combined with the need for interoperability with environmental data, highlights the urgency for more robust and integrated reporting systems. Effective integration is crucial to ensure accessible health information to support evidence-based decision-making (<xref ref-type="bibr" rid="B37">Ying <italic>et al</italic>., 2007</xref>).</p>
				<p>Scientific literature suggests that integrating data from various sources can significantly improve the completeness of records, contributing to more efficient healthcare management (<xref ref-type="bibr" rid="B5">Emran <italic>et. al</italic>., 2017</xref>). Furthermore, interoperability between healthcare systems enables not only the secure exchange of information but also enhances the quality of available data for epidemiological analysis (<xref ref-type="bibr" rid="B3">Dixon <italic>et al</italic>., 2011</xref>).</p>
			</sec>
			<sec sec-type="conclusions">
				<title>CONCLUSION</title>
				<p>This study highlighted the heightened vulnerability of Indigenous populations to venomous animal accidents, particularly the high prevalence of snakebites within this group. The results demonstrated that the geographic distribution of accidents is influenced by factors such as proximity to natural habitats and the cultural practices of Indigenous populations, who are often exposed to greater risks. Additionally, the spatial analysis underscored the importance of targeted, territory-based strategies for allocating healthcare resources, especially in remote regions with a high concentration of accidents.</p>
				<p>Public policies must account for these vulnerabilities when planning interventions to ensure rapid and effective access to treatment, such as the decentralization of healthcare units equipped with antivenom in Indigenous areas. The findings of this research emphasize the need to improve healthcare infrastructure and data collection to optimize the response to these accidents and mitigate disparities between Indigenous and non-Indigenous populations.</p>
				<p>To further comprehend the dynamics underlying this context, future research should adopt analyses of socio-environmental factors and the implications of climate change. Given that variations in temperature and precipitation patterns have the potential to alter the geographic distribution and behavior of venomous animals, it is crucial to explore how climate change may increase risks for vulnerable populations. Such studies could contribute to establishing a solid scientific foundation to support the development of effective and interculturally based interventions, as well as health policies that not only anticipate but also mitigate the impacts of climate change on human health, with particular attention to Indigenous communities.</p>
			</sec>
		</body>
	</sub-article>-->
</article>