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  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Arq Bras Cardiol</journal-id>
      <journal-id journal-id-type="publisher-id">abc</journal-id>
      <journal-title-group>
        <journal-title>Arquivos Brasileiros de Cardiologia</journal-title>
        <abbrev-journal-title abbrev-type="publisher">Arq. Bras. Cardiol.</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0066-782X</issn>
      <issn pub-type="epub">1678-4170</issn>
      <publisher>
        <publisher-name>Sociedade Brasileira de Cardiologia - SBC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id specific-use="scielo-v3" pub-id-type="publisher-id">LRtx9tmQr9bG6dNXYmdWrkM</article-id>
      <article-id specific-use="scielo-v2" pub-id-type="publisher-id">S0066-782X2020000200199</article-id>
      <article-id pub-id-type="doi">10.36660/abc.20180304</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Trends in Mortality Rates from Cardiovascular Disease and Cancer between 2000 and 2015 in the Most Populous Capital Cities of the Five Regions of Brazil</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-2284-8251</contrib-id>
          <name>
            <surname>Martins</surname>
            <given-names>Wolney de Andrade</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
          <xref ref-type="corresp" rid="c1"/>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-4508-256X</contrib-id>
          <name>
            <surname>Rosa</surname>
            <given-names>Maria Luiza Garcia</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-6628-3697</contrib-id>
          <name>
            <surname>Matos</surname>
            <given-names>Ricardo Cardoso de</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Silva</surname>
            <given-names>Willian Douglas de Souza</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-0381-3344</contrib-id>
          <name>
            <surname>Souza</surname>
            <given-names>Erito Marques de</given-names>
            <suffix>Filho</suffix>
          </name>
          <xref ref-type="aff" rid="aff2">2</xref>
          <xref ref-type="aff" rid="aff3">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-0034-4981</contrib-id>
          <name>
            <surname>Jorge</surname>
            <given-names>Antonio Jos&#233; Lagoeiro</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Ribeiro</surname>
            <given-names>Mario Luiz</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Silva</surname>
            <given-names>Eduardo Nani</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution content-type="orgname">Universidade Federal Fluminense</institution>
        <institution content-type="orgdiv1">Programa de P&#243;s-Gradua&#231;&#227;o em Ci&#234;ncias Cardiovasculares</institution>
        <addr-line>
          <city>Niter&#243;i</city>
          <state>RJ</state>
        </addr-line>
        <country country="BR">Brazil</country>
        <institution content-type="original">Universidade Federal Fluminense - Programa de P&#243;s-Gradua&#231;&#227;o em Ci&#234;ncias Cardiovasculares, Niter&#243;i, RJ - Brazil</institution>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution content-type="orgname">Universidade Federal Fluminense</institution>
        <institution content-type="orgdiv1">Departamento de Medicina Cl&#237;nica</institution>
        <addr-line>
          <city>Niter&#243;i</city>
          <state>RJ</state>
        </addr-line>
        <country country="BR">Brazil</country>
        <institution content-type="original">Universidade Federal Fluminense - Departamento de Medicina Cl&#237;nica, Niter&#243;i, RJ - Brazil</institution>
      </aff>
      <aff id="aff3">
        <label>3</label>
        <institution content-type="orgname">Universidade Federal Rural do Rio de Janeiro</institution>
        <addr-line>
          <city>Serop&#233;dica</city>
          <state>RJ</state>
        </addr-line>
        <country country="BR">Brazil</country>
        <institution content-type="original">Universidade Federal Rural do Rio de Janeiro (UFRRJ), Serop&#233;dica, RJ - Brazil</institution>
      </aff>
      <author-notes>
        <corresp id="c1"><bold>Mailing Address: Wolney de Andrade Martins</bold>, Universidade Federal Fluminense - Departamento de medicina cl&#237;nica - Rua Marques do Paran&#225;, 303, 6&#186; Andar. Postal Code 24030-215, Centro, Niter&#243;i, RJ - Brazil. E-mail: <email>wolney_martins@hotmail.com</email></corresp>
        <fn fn-type="con">
          <p>
            <bold>Author contributions</bold>
          </p>
          <p>Conception and design of the research: Martins WA; Acquisition of data: Matos RCC, Silva WDS, Souza Filho EM; Analysis and interpretation of the data: Martins WA, Matos RCC, Silva WDS, Souza Filho EM; Statistical analysis: Rosa MLG, Matos RCC, Silva WDS, Souza Filho EM; Obtaining financing: Martins WA, Matos RCC; Writing of the manuscript: Martins WA, Rosa MLG, Matos RCC, Silva WDS, Souza Filho EM; Critical revision of the manuscript for intellectual content: Martins WA, Rosa MLG, Jorge AJL, Ribeiro ML, Silva EM.</p>
        </fn>
        <fn fn-type="conflict">
          <p>
            <bold>Potential Conflict of Interest</bold>
          </p>
          <p>No potential conflict of interest relevant to this article was reported.</p>
        </fn>
      </author-notes>
      <pub-date publication-format="electronic" date-type="pub">
        <day>20</day>
        <month>03</month>
        <year>2020</year>
      </pub-date>
      <pub-date publication-format="electronic" date-type="collection">
        <month>02</month>
        <year>2020</year>
      </pub-date>
      <volume>114</volume>
      <issue>2</issue>
      <fpage>199</fpage>
      <lpage>206</lpage>
      <history>
        <date date-type="received">
          <day>25</day>
          <month>10</month>
          <year>2018</year>
        </date>
        <date date-type="rev-recd">
          <day>18</day>
          <month>02</month>
          <year>2019</year>
        </date>
        <date date-type="accepted">
          <day>20</day>
          <month>03</month>
          <year>2019</year>
        </date>
      </history>
      <permissions>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/" xml:lang="en">
          <license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited</license-p>
        </license>
      </permissions>
      <abstract>
        <title>Abstract</title>
        <sec>
          <title>Background:</title>
          <p>In many cities around the world, the mortality rate from cancer (CA) has exceeded that from disease of the circulatory system (DCS).</p>
        </sec>
        <sec>
          <title>Objectives:</title>
          <p>To compare the mortality curves from DCS and CA in the most populous capital cities of the five regions of Brazil.</p>
        </sec>
        <sec>
          <title>Methods:</title>
          <p>Data of mortality rates from DCS and CA between 2000 and 2015 were collected from the Mortality Information System of Manaus, Salvador, Goiania, Sao Paulo and Curitiba, and categorized by age range into early (30-69 years) and late (&#8805; 70 years), and by gender of the individuals. Chapters II and IX of the International Classification of Diseases-10 were used for the analysis of causes of deaths. The Joinpoint regression model was used to assess the tendency of the estimated annual percentage change of mortality rate, and the Monte Carlo permutation test was used to detect when changes occurred. Statistical significance was set at 5%.</p>
        </sec>
        <sec>
          <title>Results:</title>
          <p>There was a consistent decrease in early and late mortality from DCS in both genders in the cities studied, except for late mortality in men in Manaus. There was a tendency of decrease of mortality rates from CA in S&#227;o Paulo and Curitiba, and of increase in the rates from CA in Goiania. In Salvador, there was a decrease in early mortality from CA in men and women and an increase in late mortality in both genders.</p>
        </sec>
        <sec>
          <title>Conclusion:</title>
          <p>There was a progressive and marked decrease in the mortality rate from DCS and a maintenance or slight increase in CA mortality in the five capital cities studied. These phenomena may lead to the intersection of the curves, with predominance of mortality from CA (old and new cases).</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="en">
        <title>Keywords:</title>
        <kwd>Cardiovascular Diseases/mortality</kwd>
        <kwd>Coronary Artery Diseases/physiopathology</kwd>
        <kwd>Neoplasms/mortality</kwd>
        <kwd>Epidemiology</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec sec-type="intro">
      <title>Introduction</title>
      <p>Cardio-oncology has emerged as a new area of study and practice, resulting from numerous epidemiological and clinical interactions between diseases of the circulatory system (DCS) and cancer (CA). This interrelationship is supported by the prevalence of common risk factors, population aging, advances in diagnostic and treatment techniques, and cardiovascular injuries secondary to CA treatment.</p>
      <p>One of the common questions in cardio-oncology is where the intersection point between the curves of mortality for DCS and CA will be, i.e., when DCS will become the leading cause of mortality thereafter.<xref ref-type="bibr" rid="B1">1</xref> Circulatory diseases have become the most prevalent causes of death in Brazil, followed by CA, since the decrease in the prevalence of infectious diseases.<xref ref-type="bibr" rid="B2">2</xref><sup>,</sup><xref ref-type="bibr" rid="B3">3</xref> In developed countries, there has been a fall in the mortality from DCS since the mid-1960s,<xref ref-type="bibr" rid="B4">4</xref><sup>,</sup><xref ref-type="bibr" rid="B5">5</xref> and deaths from CA outweigh deaths from DCS.<xref ref-type="bibr" rid="B6">6</xref> In Brazil, there has been a reduction in the rate of mortality from DCS since the 1980s, for both sexes, especially in the South and Southeast regions.<xref ref-type="bibr" rid="B7">7</xref> Concomitantly with this trend, the number of deaths due to CA in Brazil has grown; it went from the fifth to the third cause of death from 1980 to 2000, and today, CA is the second cause of mortality.<xref ref-type="bibr" rid="B8">8</xref></p>
      <p>Cancer is the leading cause of death in half of the United States of America (USA) and in some Western European countries. It has a close relationship with population aging. The drop in mortality from DCS is partly attributed to improved diagnosis and treatment.<xref ref-type="bibr" rid="B9">9</xref><sup>-</sup><xref ref-type="bibr" rid="B11">11</xref> However, both DCS and CA have a complex relationship mediated by several risk factors common to both, like smoking and alcoholism, overweight and obesity, eating pattern, sedentary lifestyle; hypertension, and diabetes mellitus.<xref ref-type="bibr" rid="B12">12</xref><sup>,</sup><xref ref-type="bibr" rid="B13">13</xref></p>
      <p>There are few studies that seek to understand the relationship between CA and DCS in the Brazilian population. Patterns of morbidity and mortality in Brazil have changed over the years. Demographic and epidemiological transitions, differences in access to health care, genetic peculiarities, among other factors, have resulted in the formation of regional population groups with particular characteristics.<xref ref-type="bibr" rid="B14">14</xref> Analysis of temporal trends in mortality based on population data could further clarify the scenario.</p>
      <p>This study aimed to compare early and late mortality, by gender, from CAD and CA between 2000 and 2015 in the most populous capital cities of each Brazilian region and in the country as a whole.</p>
    </sec>
    <sec sec-type="methods">
      <title>Methods</title>
      <p>It was decided to study the mortality rates from DCS and CA in the most populous capital cities, one of each of the five federated states of Brazil. The Federal District was not included. Demographic and mortality data were obtained from the Brazilian Institute of Geography and Statistics (IBGE). The following cities were included: Manaus (Northern region), S&#227;o Paulo (Southeast), Goiania (Central west), Curitiba (South), and Salvador (Northeast).</p>
      <p>Early and late mortality was defined using the age ranges of 30-69 years and &#8805;70 years, according to the definition of early mortality from non-communicable diseases, stratified by gender, adopted by the Brazilian Ministry of Health, and in line with the recommendations published in the United Nations&#8217; World Population Prospects.<xref ref-type="bibr" rid="B14">14</xref></p>
      <p>Data of mortality from 2000 to 2015 were obtained from the Department of Informatics of the Brazilian Unified Health System (DATASUS), and following the International Classification of Diseases (ICD)-10 as follows: mortality from DCS (chapter IX of ICD-10) and specific causes - acute rheumatic fever and chronic rheumatic heart diseases (066), hypertensive diseases (067), ischemic heart diseases (068), acute myocardial infarction (068.1), other forms of heart diseases (069), cerebrovascular diseases (070), atherosclerosis (071), and other and unspecified disorders of the circulatory system (072), and mortality from neoplasms (chapter II of ICD-10) and specific causes: malignant neoplasms, lip, oral cavity and pharynx (032), malignant neoplasms of esophagus (033), malignant neoplasms of stomach (034), malignant neoplasms of colon, rectum and anus (035), malignant neoplasms of liver and intrahepatic bile ducts (036), malignant neoplasms of pancreas (037), malignant neoplasms of larynx (038), malignant neoplasms of trachea, bronchi and lungs (039), malignant neoplasms, skin (040), malignant neoplasm of breast (041), malignant neoplasm of cervix uteri (042), malignant neoplasm of corpus uteri and uterus, part unspecified (043), malignant neoplasm of ovary (044), malignant neoplasm of prostate (045), malignant neoplasm of bladder (046), malignant neoplasm of meninges, brain and other parts of the central nervous system (047), non-Hodgkin's lymphoma (048), multiple myeloma and malignant plasma cell neoplasms (049), leukemia (050), in situ neoplasms, benign neoplasms and neoplasms of uncertain behavior (051), and other malignant neoplasms (052).</p>
      <p>The data were obtained from the computerized databases of the death certificate records of the Brazilian Mortality Information System (Vital Statistics System) and &#8220;Population estimates: city, gender and age 2000-2015 RIPSA IBGE&#8221; (division of Demographic and Socioeconomic sector). All the information was collected from the DATASUS website.<xref ref-type="bibr" rid="B15">15</xref></p>
      <sec>
        <title>Statistical analysis</title>
        <p>To assess the tendency of the estimated annual percentage change (EAPC) of the mortality rate from DCS and CA during the study period, the Joinpoint regression model (joinpoint software version 4.6.0.0 National Cancer Institute, Bethesda, Maryland, EUA)<xref ref-type="bibr" rid="B16">16</xref> was used. The Monte Carlo permutation test was used to detect the years when significant changes in the trends occurred.<xref ref-type="bibr" rid="B17">17</xref><sup>,</sup><xref ref-type="bibr" rid="B18">18</xref> Also, the Poisson distribution was used with the JoinPoint regression model. Assuming such distribution, a maximum of two joinpoints were selected. The software calculates the annual percentage change by the parametric method, with a 95% confidence interval for each segment of trend. The program calculated adjusted mortality rates for sex and age using the standard population based on WHO 2000-2025. Statistical significance was set at 5%.</p>
      </sec>
    </sec>
    <sec sec-type="results">
      <title>Results</title>
      <p><xref ref-type="fig" rid="f1">Figure 1</xref> shows the curves of mortality from DCS and CA in the five capitals studied. For all groups and age ranges, there was a decrease in DCS, more pronounced in early mortality, especially in women, among whom CA is already the leading cause of mortality. A stability trend or a slight increase in mortality rate was found in the CA curves. In late mortality, there was a striking difference between the rates of mortality from DCS and CA, with higher rates of deaths from DCS compared with deaths from CA. Thus, the intersection point of these curves occurs later as compared with early mortality.</p>
      <p>
        <fig id="f1">
          <label>Figure 1</label>
          <caption>
            <title>Trends in mortality rates from cardiovascular disease and cancer in the most populated capital cities of five Brazilian regions, stratified by gender and age group, 2000-2015. Source: DATASUS. Green curve represents cardiovascular diseases and blue curve represents cancer</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-114-02-0199-80304-gf01.tif"/></fig>
      </p>
      <p><xref ref-type="table" rid="t1">Table 1</xref> shows the EAPCs in mortality from DCS and CA in the most populous capital cities of the five regions of Brazil between 2000 and 2015. Between 2000 and 2015, there was a consistent decrease in early and late mortality from DCS, in both sexes, in the most populous capitals, except for late mortality in men in the city of Manaus. The EAPC for mortality from DCS ranged from -6.5% for early mortality in women in S&#227;o Paulo, to 0.3%, for early mortality among men in Curitiba. This variation, however, was not statistically significant, probably resulted from an inversion of the trend, with increased mortality in the last two years analyzed, i.e., 2014 and 2015. The reductions were comparable between genders and more pronounced in early mortality. Interestingly, the coefficients of late mortality from DCS were at least ten times the coefficients of early mortality.</p>
      <table-wrap id="t1">
        <label>Table 1</label>
        <caption>
          <title>Trends in estimated annual percentage change of mortality from diseases of the circulatory system and cancer in the most populated capital cities of the five geographic regions of Brazil, 2015</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
            <col width="10%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" rowspan="3">Capitals</th>
              <th align="center" rowspan="3">&#160;</th>
              <th align="center" colspan="4" style="border-bottom-width:thin;border-bottom-style:solid">Early mortality</th>
              <th align="center" colspan="4" style="border-bottom-width:thin;border-bottom-style:solid">Late mortality</th>
            </tr>
            <tr>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Male</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Female</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Male</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Female</th>
            </tr>
            <tr>
              <th align="center">CA</th>
              <th align="center">DCS</th>
              <th align="center">CA</th>
              <th align="center">DCS</th>
              <th align="center">CA</th>
              <th align="center">DCS</th>
              <th align="center">CA</th>
              <th align="center">DCS</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="left">Manaus</td>
              <td align="center">APC<break/>95%CI</td>
              <td align="center">-0.8<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-1.5;0)</td>
              <td align="center">-2.0<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.9;-1.1)</td>
              <td align="center">0.5<break/>(-0.4;1.3)</td>
              <td align="center">-2.3<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.8;-1.7)</td>
              <td align="center">1.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(0.5;1.7)</td>
              <td align="center">1.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(0.5;1.7)</td>
              <td align="center">-3.0<break/>(-6.6;0.8)</td>
              <td align="center">-0.3<break/>(-0.9;0.3)</td>
            </tr>
            <tr>
              <td align="left">Salvador</td>
              <td align="center">APC<break/>95%CI</td>
              <td align="center">-0.9<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-1.4;-0.4)</td>
              <td align="center">-3.4<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-4.1;-2.7)</td>
              <td align="center">-1.7<break/>(-4.4;1.1)</td>
              <td align="center">-4.3<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-4.9;-3.6)</td>
              <td align="center">0.9<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(0.3;1.5)</td>
              <td align="center">-1.4<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.1;-0.8)</td>
              <td align="center">0.5<break/>(0;1)</td>
              <td align="center">-2.4<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.9;-1.9)</td>
            </tr>
            <tr>
              <td align="left">Goiania</td>
              <td align="center">APC<break/>95%CI</td>
              <td align="center">0.4<break/>(-0.4;1.2)</td>
              <td align="center">-4.0<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-4.5;-3.4)</td>
              <td align="center">0.7<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(0.1;1.4)</td>
              <td align="center">-4.0<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-4.8;-3.2)</td>
              <td align="center">1.2<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(0.4;2)</td>
              <td align="center">-2.3<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-3;-1.7)</td>
              <td align="center">1.4<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(0.7-2.1)</td>
              <td align="center">-2.5<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-3;-2)</td>
            </tr>
            <tr>
              <td align="left">S&#227;o Paulo</td>
              <td align="center">APC<break/>95%CI</td>
              <td align="center">-1.9<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.1;-1.7)</td>
              <td align="center">-2.6<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-3.1;-2.2)</td>
              <td align="center">-0.9<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-1.1;-0.8)</td>
              <td align="center">-2.9<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-3.5;-2.3)</td>
              <td align="center">-1.5<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-1.7;-1.3)</td>
              <td align="center">-3.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-3.3;-2.8)</td>
              <td align="center">-1.0<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-1.3;-0.8)</td>
              <td align="center">-3.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-3.4;-2.8)</td>
            </tr>
            <tr>
              <td align="left">Curitiba</td>
              <td align="center">APC<break/>95%CI</td>
              <td align="center">-3.4<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-4.2;-2.7)</td>
              <td align="center">0.3<break/>(-6.8;7.8)</td>
              <td align="center">-2.2<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.8;-1.5)</td>
              <td align="center">-6.5<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-7.1;-5.9)</td>
              <td align="center">-1.9<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.3;-1.4)</td>
              <td align="center">-4.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-4.5;-3.6)</td>
              <td align="center">-1.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-1.9;-0.3)</td>
              <td align="center">-4.4<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-4.8;-3.9)</td>
            </tr>
            <tr>
              <td align="left">Brazil</td>
              <td align="center">APC<break/>95%CI</td>
              <td align="center">-0.9<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-1.1;-0.7)</td>
              <td align="center">-2.7<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-3.5;-1.9)</td>
              <td align="center">0.1<break/>(-0.1;0.4)</td>
              <td align="center">-2.4<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.6;-2.2)</td>
              <td align="center">0.1<break/>(-0.1;0.3)</td>
              <td align="center">-2.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.5;-1.6)</td>
              <td align="center">0.2<break/>(-0.1;0.4)</td>
              <td align="center">-2.1<xref ref-type="table-fn" rid="TFN1">*</xref><break/>(-2.6;-1.7)</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="TFN1">
            <label>*</label>
            <p>indicates statistically significant association (p &lt; 0.05); CA: cancer; DCS: diseases of the circulatory system; EAPC: estimated annual percentage change; CI: confidence interval</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>Differently from DCS, the coefficients of mortality from CA showed different behaviors by regions, time periods and genders. In general, there was a decreasing trend in the rates of mortality from CA in S&#227;o Paulo and Curitiba in all time periods and sexes. In contrast, there was an increase in the rates of mortality from CA in all periods in Goiania. In Salvador, there was a mixed behavior, characterized by a drop in early mortality in men and women and increment in late mortality in both genders.</p>
      <p>Manaus was a major exception in terms of mortality rate behavior; mortality from DCS decreased in all time periods except for the late mortality in men. On the other hand, early mortality from CA exceeded that from DCS in both men and women.</p>
      <p>Therefore, the determinant of the convergence of the mortality curves seems to be the greater decrease in mortality from CD.</p>
      <p><xref ref-type="table" rid="t2">Table 2</xref> shows the intersection points (years), real or presumed, of the mortality curves from CA and DCS. Regarding early mortality, CA ranks first in males in Goiania, and in women in all the studied cities. In late mortality, however, the intersection of the curves has not occurred in any of the cities yet, and may occur, it he trends continue, from 2026 on.</p>
      <table-wrap id="t2">
        <label>Table 2</label>
        <caption>
          <title>Estimated intersection point (year) of the mortality curves from diseases of the circulatory system and cancer in the most populated capital cities of the five geographic regions of Brazil</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="20%"/>
            <col width="20%"/>
            <col width="20%"/>
            <col width="20%"/>
            <col width="20%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" rowspan="3">Locality</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Age range of early mortality</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Age range of late mortality</th>
            </tr>
            <tr>
              <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Male</th>
              <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Female</th>
              <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Male</th>
              <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Female</th>
            </tr>
            <tr>
              <th align="center" colspan="4">Year of intersection</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="left">Manaus</td>
              <td align="center">2009</td>
              <td align="center">1992</td>
              <td align="center">-</td>
              <td align="center">-</td>
            </tr>
            <tr>
              <td align="left">Salvador</td>
              <td align="center">2023</td>
              <td align="center">-</td>
              <td align="center">2031</td>
              <td align="center">2038</td>
            </tr>
            <tr>
              <td align="left">Goiania</td>
              <td align="center">2018</td>
              <td align="center">2000</td>
              <td align="center">2026</td>
              <td align="center">2034</td>
            </tr>
            <tr>
              <td align="left">S&#227;o Paulo</td>
              <td align="center">2071</td>
              <td align="center">2009</td>
              <td align="center">2047</td>
              <td align="center">2051</td>
            </tr>
            <tr>
              <td align="left">Curitiba</td>
              <td align="center">-</td>
              <td align="center">2004</td>
              <td align="center">2032</td>
              <td align="center">2033</td>
            </tr>
            <tr>
              <td align="left">Brazil</td>
              <td align="center">2035</td>
              <td align="center">2015</td>
              <td align="center">2045</td>
              <td align="center">2057</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <attrib>Source: DATASUS, according to chapters II and IX from International Classification of Diseases-10; age of early mortality: between 30 and 60 years old; age of late mortality: older than 70 years</attrib>
        </table-wrap-foot>
      </table-wrap>
      <p><xref ref-type="table" rid="t3">Table 3</xref> shows the three main causes of mortality, stratified by early and late, and by gender, in the most populous capital cities of the five regions. There were differences between the cities, the age ranges and the genders.</p>
      <table-wrap id="t3">
        <label>Table 3</label>
        <caption>
          <title>Three main causes of specific deaths (according to the International Classification of Diseases-10) in the most populated capital cities of the five geographic regions of Brazil, 2015</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
            <col width="9%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" rowspan="2">Gender/Age range</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Manaus</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Salvador</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Goiania</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">S&#227;o Paulo</th>
              <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Curitiba</th>
            </tr>
            <tr>
              <th align="left">Cancer</th>
              <th align="center">DCS</th>
              <th align="center">Cancer</th>
              <th align="center">DCS</th>
              <th align="center">Cancer</th>
              <th align="center">DCS</th>
              <th align="center">Cancer</th>
              <th align="center">DCS</th>
              <th align="center">Cancer</th>
              <th align="center">DCS</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="left" rowspan="3">Female 30-69 years</td>
              <td align="center">Cervix</td>
              <td align="center">CVD</td>
              <td align="center">Breast</td>
              <td align="center">CVD</td>
              <td align="center">Breast</td>
              <td align="center">IHD</td>
              <td align="center">Breast</td>
              <td align="center">IHD</td>
              <td align="center">Breast</td>
              <td align="center">IHD</td>
            </tr>
            <tr>
              <td align="center">Breast</td>
              <td align="center">IHD</td>
              <td align="center">Colon</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">CVD</td>
              <td align="center">Lung</td>
              <td align="center">CVD</td>
              <td align="center">Lung</td>
              <td align="center">CVD</td>
            </tr>
            <tr>
              <td align="center">Lung</td>
              <td align="center">MI</td>
              <td align="center">Lung</td>
              <td align="center">MI</td>
              <td align="center">Cervix</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">MI</td>
            </tr>
            <tr>
              <td align="left" rowspan="3">Female &#8805; 70 years</td>
              <td align="center">Lung</td>
              <td align="center">CVD</td>
              <td align="center">Breast</td>
              <td align="center">CVD</td>
              <td align="center">Breast</td>
              <td align="center">CVD</td>
              <td align="center">Breast</td>
              <td align="center">IHD</td>
              <td align="center">Breast</td>
              <td align="center">CVD</td>
            </tr>
            <tr>
              <td align="center">Cervix</td>
              <td align="center">IHD</td>
              <td align="center">Colon</td>
              <td align="center">IHD</td>
              <td align="center">Colon</td>
              <td align="center">IHD</td>
              <td align="center">Colon</td>
              <td align="center">CVD</td>
              <td align="center">Colon</td>
              <td align="center">IHD</td>
            </tr>
            <tr>
              <td align="center">Breast</td>
              <td align="center">MI</td>
              <td align="center">Lung</td>
              <td align="center">MI</td>
              <td align="center">Lung</td>
              <td align="center">HD</td>
              <td align="center">Lung</td>
              <td align="center">MI</td>
              <td align="center">Lung</td>
              <td align="center">MI</td>
            </tr>
            <tr>
              <td align="left" rowspan="3">Male 30-69 years</td>
              <td align="center">Stomach</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">IHD</td>
            </tr>
            <tr>
              <td align="center">Lung</td>
              <td align="center">CVD</td>
              <td align="center">Prostate</td>
              <td align="center">CVD</td>
              <td align="center">Colon</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">MI</td>
            </tr>
            <tr>
              <td align="center">Larynx</td>
              <td align="center">MI</td>
              <td align="center">Pharynx</td>
              <td align="center">MI</td>
              <td align="center">Pharynx</td>
              <td align="center">CVD</td>
              <td align="center">Stomach</td>
              <td align="center">CVD</td>
              <td align="center">Stomach</td>
              <td align="center">CVD</td>
            </tr>
            <tr>
              <td align="left" rowspan="3">Male &#8805; 70 years</td>
              <td align="center">Prostate</td>
              <td align="center">CVD</td>
              <td align="center">Prostate</td>
              <td align="center">CVD</td>
              <td align="center">Prostate</td>
              <td align="center">CVD</td>
              <td align="center">Prostate</td>
              <td align="center">IHD</td>
              <td align="center">Prostate</td>
              <td align="center">CVD</td>
            </tr>
            <tr>
              <td align="center">Lung</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">IHD</td>
              <td align="center">Lung</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">IHD</td>
            </tr>
            <tr>
              <td align="center">Stomach</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">MI</td>
              <td align="center">Colon</td>
              <td align="center">CVD</td>
              <td align="center">Lung</td>
              <td align="center">MI</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <attrib>Source: DATASUS. DCS: diseases of the circulatory system; CVD: cerebrovascular disease; IHD: ischemic heart diseases; MI: myocardial infarction; HD: hypertensive diseases; for the analysis, MI was considered a separate cause (from ischemic diseases) of death, and the sections 069 (chapter about DCS) and section 052 (chapter about cancer) of the International Classification of Diseases-10 were not included in the ranking of diseases</attrib>
        </table-wrap-foot>
      </table-wrap>
    </sec>
    <sec sec-type="discussion">
      <title>Discussion</title>
      <sec>
        <title>Mortality from DCS and CA in Brazil compared with the world</title>
        <p>In Brazil, the discussion about the increase in cancer mortality is more recent than in European countries and USA, where the epidemiological transition occurred earlier than in Brazil. In Brazil in 2005, 32% of deaths were caused by DCS, followed by cancer (15%). At that time, Rosa et al.<xref ref-type="bibr" rid="B19">19</xref> drew attention to a probable intersection of the curves of mortality from DCS and CA. In the United Kingdom, in 2011, the DCS passed from the first cause of mortality to second position for the first time since the middle of the 20<sup>th</sup> century;<xref ref-type="bibr" rid="B20">20</xref> 29% of the deaths were caused by CA, while 28% by DCS.<xref ref-type="bibr" rid="B21">21</xref> The reduction of mortality from DCS in the United Kingdom was explained by a decrease in the mortality from myocardial infarction, increase of pharmacological and surgical treatments, and decrease of risk factors like smoking.<xref ref-type="bibr" rid="B21">21</xref><sup>-</sup><xref ref-type="bibr" rid="B23">23</xref> Similar situation to Brazil was observed in the USA, where mortality from DCS decreased more than from CA. If this tendency continues, CA will be the leading cause of deaths in 2020.<xref ref-type="bibr" rid="B24">24</xref></p>
        <p>The different stages of growth and development of the Brazilian regions made us make a particularized analysis, since it is difficult to draw a reliable picture of Brazil as a whole. The choice of the most populous capitals came from the assumption of a higher degree of urbanization and its influence on the health of the inhabitants. In general, in the western world, the interception of the mortality curves is caused by a marked decrease of mortality from DCS, especially in more developed countries in terms of socioeconomic development.</p>
      </sec>
      <sec>
        <title>General trend of the curves of mortality from DCS and CA in Brazil</title>
        <p>Analysis of the historical trend of the curves of mortality from DCS and CA reveled an important and sustained decrease of deaths from DCS in the most populous capital cities of each of the five Brazilian regions, except for Manaus. In this city, late mortality from DCS increased in men. Data from Brazil showed that DCS continue the main cause of mortality. However, an analysis of the cities revealed that CA already surpassed DCS as the leading cause of deaths in nearly 10% of the Brazilian cities.<xref ref-type="bibr" rid="B25">25</xref></p>
        <p>The results of the present work suggest two patterns of trends that led to the grouping of the five capitals into two subgroups: in the first subgroup, S&#227;o Paulo and Curitiba, whose pattern is more similar to that of developed countries, i.e., with a significant fall in mortality from DCS, plus maintenance or slight decrease of mortality from CA. In this pattern, convergence of the curves results from the decrease in deaths from DCS. In the second pattern, Goiania, Salvador and Manaus, where there was also a decrease in mortality due to DCS, but less significant, in contrast to a modest increase in mortality from CA. In this second group, the convergence of the curves takes longer to occur. Manaus showed a singular behavior, with increase of late mortality from DCS in males.</p>
        <p>In the Brazilian cities studied, data of 2015 showed that ischemic heart disease and cerebrovascular disease were the main causes of DCS. While individuals in the early age group die more from ischemic heart disease, at late age, mortality from cerebrovascular disease is higher. Between 1996 and 2011, in Brazil, there was a consistent decrease in mortality rate due to cerebrovascular disease in both genders, with differences in the magnitude of decrease between the regions.<xref ref-type="bibr" rid="B26">26</xref> In addition to socioeconomic development, the control of cardiovascular risk factors and a considerable increase (450%) in the access to primary care services, may have contributed to the decrease.<xref ref-type="bibr" rid="B27">27</xref> As observed in developed countries, efforts to diagnosis and treatment of risk factors and comorbidities have probably contributed to the decrease of stroke mortality,<xref ref-type="bibr" rid="B28">28</xref> and hence to the decrease of mortality from DCS.</p>
      </sec>
      <sec>
        <title>Regional trends in the curves of mortality from DCS and CA</title>
        <p>S&#227;o Paulo and Curitiba presented a decrease in early and late mortality in both genders. It could be partly explained by the greater access to the diagnosis and treatment of CA. Chemotherapy and radiotherapy services are more concentrated in the Southern and Southeastern regions of Brazil.<xref ref-type="bibr" rid="B29">29</xref></p>
        <p>In Salvador, it was observed a decrease in early mortality from CA in men. Lung cancer has a high lethality and is the main type of cancer in this population. It is currently the main cause of death among men in North America and Europe and its mortality has significantly increased in Asia, Latin America and Africa.<xref ref-type="bibr" rid="B30">30</xref> In Brazil, adenocarcinoma is the main cause of early mortality among men and is related to the high prevalence of smoking in male sex.<xref ref-type="bibr" rid="B15">15</xref><sup>,</sup><xref ref-type="bibr" rid="B31">31</xref> The decrease in CA mortality in Salvador can be attributed to the public policies for CA prevention during the last decades, and in 2004, Salvador presented the lowest smoking rate in Brazil.<xref ref-type="bibr" rid="B32">32</xref> On the other hand, late mortality from CA has increased among men and women. One hypothesis for such increase among women is the high mortality rates from breast cancer, which represents the leading cause of late mortality in women.<xref ref-type="bibr" rid="B15">15</xref> Mortality rates from breast cancer in the Brazilian population have shown geographic variations, with a trend to stabilization in the southeast, decline in the south and increase in the north, northeast and central-west regions. In the northeast region, between 2000 and 2010, CA mortality increased by 100% in white women, a population subgroup that increased by only 10% in size in the period. In contrast, there was a 183% increase in mortality among black women, with a respective population growth by 58%.<xref ref-type="bibr" rid="B33">33</xref> A possible explanation for the increase in late mortality rates from CA among men in Salvador is the high percentage of Afro-Brazilians living in this city. Prostate cancer is the leading cause of late mortality among men in Brazil<xref ref-type="bibr" rid="B15">15</xref> and a black man has 1.6 of being diagnosed and 2.4 higher odds of dying from prostate cancer than a white man.<xref ref-type="bibr" rid="B34">34</xref></p>
        <p>Manaus greatly differs from the other capital cities regarding the pattern of the mortality curves from DCS and CA. Early mortality from CA significantly increased among women. Cervical cancer is the main type of cancer,<xref ref-type="bibr" rid="B15">15</xref> whose mortality rates increased in the north and northeast regions.<xref ref-type="bibr" rid="B35">35</xref><sup>-</sup><xref ref-type="bibr" rid="B37">37</xref> In Manaus, early mortality decreased whereas late mortality increased among men. The main causes of early death from CA was gastric cancer followed by lung cancer. In Brazil, mortality rates from gastric cancer significantly increased in individuals older than 59 years. In the north region, mortality rates have increased in individuals of both sexes older than 75 years.<xref ref-type="bibr" rid="B37">37</xref> Prostate cancer mortality continues to increase in Brazil, with a vast number of under-reported or late-diagnosed cases.</p>
        <p>In Goiania, early mortality and late mortality from CA increased in both men and women. Early death from CA was mainly caused by breast CA in women. This may be explained by the difficult access to appropriate diagnosis, since only 18% of the mammography machines available in the whole State of Goias belong to the Unified Health System, and 80% of the population living in the state are users of the public health system.<xref ref-type="bibr" rid="B38">38</xref><sup>,</sup><xref ref-type="bibr" rid="B39">39</xref> Another possible reason is the fact that mammograms is performed at relatively late age in Goiania, 49 years old.<xref ref-type="bibr" rid="B40">40</xref></p>
      </sec>
      <sec>
        <title>Differences between sexes in the trends of mortality from DCS and CA</title>
        <p>Early and late mortality rates from DCS were lower in women than in men in all studied capitals and showed a more marked decrease over the years among women than men. One hypothesis for these findings is the fact that women are more adherent to primary healthcare programs for the screening and prevention of diseases.</p>
        <p>With respect to CA, regional differences were found in the incidence of different tumors of varying mortality rates. For example, in Sao Paulo, colon CA ranks the second in incidence and cervical CA is in the fourth position, whereas in Manaus, cervical cancer ranks the first.<xref ref-type="bibr" rid="B37">37</xref></p>
        <p>Breast CA is the most prevalent cancer among women in Brazil and in most of the studied capitals. The treatment may include surgery, chemotherapy, radiotherapy, and hormonal therapy. Despite many advances in the treatment of breast CA, such as the use of immunohistochemical tests and anti-HER-2 agents, the access to these therapies by users of the health public system occurred later, and probably had no effect on the outcome of the patients included in this research.</p>
        <p>Some limitations need to be considered when analyzing the results of this study. Data analyzed in this study were obtained from death certificates, and hence subject to inaccuracy. The diagnosis of CA is confirmed by imaging tests and/or anatomopathological examination, which confer greater reliability. The diagnoses of DCS are essentially established by clinical examination. Also, it is worth mentioning that the results were obtained from populations living in large urban centers; extrapolations to medium- and small-sized cities may not be appropriate, as reproducibility of these data is not necessarily guaranteed. Finally, determinants of mortality and estimates of trends can be influenced by public polices.</p>
      </sec>
    </sec>
    <sec sec-type="conclusions">
      <title>Conclusion</title>
      <p>In general, and considering specific regional exceptions, there was a gradual and marked decrease in mortality rates from DCS in the five Brazilian capital cities studied, whereas mortality rates from CA remained unchanged or showed a slight increase from 2000 to 2015. Such events will lead to the intersection of the mortality curves, with perspective of a predominance of CA (old and new cases) mortality.</p>
    </sec>
  </body>
  <back>
    <fn-group>
      <fn fn-type="supported-by">
        <p>
          <bold>Sources of Funding</bold>
        </p>
        <p>This study was partially funded by FAPERJ.</p>
      </fn>
      <fn fn-type="other">
        <p>
          <bold>Study Association</bold>
        </p>
        <p>This study is not associated with any thesis or dissertation work.</p>
      </fn>
      <fn fn-type="other">
        <p>
          <bold>Ethics approval and consent to participate</bold>
        </p>
        <p>This article does not contain any studies with human participants or animals performed by any of the authors.</p>
      </fn>
    </fn-group>
    <ref-list>
      <title>References</title>
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        <label>1</label>
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            </name>
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              <surname>Mo&#231;o</surname>
              <given-names>ETM</given-names>
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          <article-title>Cardio-Oncologia: o pre&#231;o do envelhecimento</article-title>
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          <volume>25</volume>
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            </name>
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        <label>40</label>
        <mixed-citation>40 Godinho ER, Koch HA. O perfil da mulher que se submete a mamografia em Goi&#226;nia-Uma contribui&#231;&#227;o a &#8220;bases para um programa de detec&#231;&#227;o precoce do c&#226;ncer de mama&#8221;. Radiol Bras. 2002;35(3):139-45.</mixed-citation>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Godinho</surname>
              <given-names>ER</given-names>
            </name>
            <name>
              <surname>Koch</surname>
              <given-names>HA</given-names>
            </name>
          </person-group>
          <article-title>O perfil da mulher que se submete a mamografia em Goi&#226;nia-Uma contribui&#231;&#227;o a &#8220;bases para um programa de detec&#231;&#227;o precoce do c&#226;ncer de mama&#8221;</article-title>
          <source>Radiol Bras</source>
          <year>2002</year>
          <volume>35</volume>
          <issue>3</issue>
          <fpage>139</fpage>
          <lpage>145</lpage>
        </element-citation>
      </ref>
    </ref-list>
  </back>
  <sub-article article-type="translation" id="s1" xml:lang="pt">
    <front-stub>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Artigo Original</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Tend&#234;ncia das Taxas de Mortalidade por Doen&#231;a Cardiovascular e C&#226;ncer entre 2000 e 2015 nas Capitais mais Populosas das Cinco Regi&#245;es do Brasil</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-2284-8251</contrib-id>
          <name>
            <surname>Martins</surname>
            <given-names>Wolney de Andrade</given-names>
          </name>
          <xref ref-type="aff" rid="aff4">1</xref>
          <xref ref-type="corresp" rid="c2"/>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-4508-256X</contrib-id>
          <name>
            <surname>Rosa</surname>
            <given-names>Maria Luiza Garcia</given-names>
          </name>
          <xref ref-type="aff" rid="aff4">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-6628-3697</contrib-id>
          <name>
            <surname>Matos</surname>
            <given-names>Ricardo Cardoso de</given-names>
          </name>
          <xref ref-type="aff" rid="aff5">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Silva</surname>
            <given-names>Willian Douglas de Souza</given-names>
          </name>
          <xref ref-type="aff" rid="aff5">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-0381-3344</contrib-id>
          <name>
            <surname>Souza</surname>
            <given-names>Erito Marques de</given-names>
            <suffix>Filho</suffix>
          </name>
          <xref ref-type="aff" rid="aff5">2</xref>
          <xref ref-type="aff" rid="aff6">3</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Jorge</surname>
            <given-names>Antonio Jos&#233; Lagoeiro</given-names>
          </name>
          <xref ref-type="aff" rid="aff4">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Ribeiro</surname>
            <given-names>Mario Luiz</given-names>
          </name>
          <xref ref-type="aff" rid="aff5">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Silva</surname>
            <given-names>Eduardo Nani</given-names>
          </name>
          <xref ref-type="aff" rid="aff5">2</xref>
        </contrib>
      </contrib-group>
      <aff id="aff4">
        <label>1</label>
        <institution content-type="original">Universidade Federal Fluminense - Programa de P&#243;s-Gradua&#231;&#227;o em Ci&#234;ncias Cardiovasculares, Niter&#243;i, RJ - Brasil</institution>
      </aff>
      <aff id="aff5">
        <label>2</label>
        <institution content-type="original">Universidade Federal Fluminense - Departamento de Medicina Cl&#237;nica, Niter&#243;i, RJ - Brasil</institution>
      </aff>
      <aff id="aff6">
        <label>3</label>
        <institution content-type="original">Universidade Federal Rural do Rio de Janeiro (UFRRJ), Serop&#233;dica, RJ - Brasil</institution>
      </aff>
      <author-notes>
        <corresp id="c2"><bold>Correspond&#234;ncia: Wolney de Andrade Martins</bold>, Universidade Federal Fluminense - Departamento de medicina cl&#237;nica - Rua Marques do Paran&#225;, 303, 6&#186; Andar. CEP 24030-215, Centro, Niter&#243;i, RJ - Brasil. E-mail: <email>wolney_martins@hotmail.com</email> </corresp>
        <fn fn-type="con">
          <p>
            <bold>Contribui&#231;&#227;o dos autores</bold>
          </p>
          <p>Concep&#231;&#227;o e desenho da pesquisa: Martins WA, Rosa MLG; Obten&#231;&#227;o de dados: Matos RCC, Silva WDS, Souza Filho EM; An&#225;lise e interpreta&#231;&#227;o dos dados: Martins WA, Rosa MLG, Matos RCC, Silva WDS, Souza Filho EM; An&#225;lise estat&#237;stica: Rosa MLG, Matos RCC, Silva WDS, Souza Filho EM; Obten&#231;&#227;o de financiamento: Martins WA, Matos RCC; Reda&#231;&#227;o do manuscrito: Martins WA, Rosa MLG, Matos RCC, Silva WDS, Souza Filho EM; Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante: Martins WA, Rosa MLG, Jorge AJL, Ribeiro ML, Silva EM.</p>
        </fn>
        <fn fn-type="conflict">
          <p>
            <bold>Potencial conflito de interesses</bold>
          </p>
          <p>Declaro n&#227;o haver conflito de interesses pertinentes.</p>
        </fn>
      </author-notes>
      <abstract>
        <title>Resumo</title>
        <sec>
          <title>Fundamento:</title>
          <p>Em muitas cidades no mundo, a taxa de mortalidade por c&#226;ncer (CA) ultrapassou aquela por doen&#231;as do aparelho circulat&#243;rio (DAC).</p>
        </sec>
        <sec>
          <title>Objetivos:</title>
          <p>Comparar as curvas de mortalidade por DAC e CA nas capitais mais populosas das cinco regi&#245;es brasileiras.</p>
        </sec>
        <sec>
          <title>M&#233;todos:</title>
          <p>Dados de mortalidade por DAC e CA entre 2000 e 2015 foram coletadas no Sistema de Informa&#231;&#245;es sobre Mortalidade das capitais mais populosas das cinco regi&#245;es do Brasil: Manaus, Salvador, Goi&#226;nia, S&#227;o Paulo e Curitiba. Os dados foram categorizados por faixas et&#225;rias dos indiv&#237;duos em precoce (30-69 anos) e tardia (&#8805;70 anos), e por g&#234;nero. Foram considerados os cap&#237;tulos II e IX da Classifica&#231;&#227;o Internacional de Doen&#231;as-10 para an&#225;lise das causas de &#243;bito. A tend&#234;ncia na estimativa de mudan&#231;a percentual foi calculada pelo modelo de regress&#227;o Joinpoint 4.6.0.0. e a detec&#231;&#227;o das mudan&#231;as das taxas pelo teste de permuta&#231;&#227;o Monte Carlo. N&#237;vel de signific&#226;ncia estat&#237;stica de 5%.</p>
        </sec>
        <sec>
          <title>Resultados:</title>
          <p>Observou-se queda consistente das mortalidades precoce e tardia por DAC, em ambos os g&#234;neros, nas capitais estudadas, com exce&#231;&#227;o da mortalidade tardia em homens em Manaus. Houve tend&#234;ncia de queda das taxas de mortalidade por CA em S&#227;o Paulo e Curitiba, e de aumento da taxa de mortalidade por CA em Goi&#226;nia. Em Salvador, houve queda na mortalidade precoce por CA em homens e mulheres e incremento na mortalidade tardia em ambos os g&#234;neros.</p>
        </sec>
        <sec>
          <title>Conclus&#227;o:</title>
          <p>Houve queda progressiva e expressiva da taxa de mortalidade por DAC nas cinco capitais em oposi&#231;&#227;o &#224; manuten&#231;&#227;o ou discreta eleva&#231;&#227;o da mortalidade por CA. Tais fen&#244;menos concorrem para o cruzamento das curvas com predom&#237;nio da mortalidade por CA (j&#225; ocorrido ou casos novos).</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="pt">
        <title>Palavras-chave:</title>
        <kwd>Doen&#231;as Cardiovasculares/mortalidade</kwd>
        <kwd>Doen&#231;a da Art&#233;ria Coronariana/fisiopatologia</kwd>
        <kwd>Neoplasias/mortalidades</kwd>
        <kwd>Epidemiologia</kwd>
      </kwd-group>
    </front-stub>
    <body>
      <sec sec-type="intro">
        <title>Introdu&#231;&#227;o</title>
        <p>A cardio-oncologia surgiu como nova &#225;rea de estudo e atua&#231;&#227;o fruto das in&#250;meras intera&#231;&#245;es epidemiol&#243;gicas e cl&#237;nicas entre as doen&#231;as do aparelho circulat&#243;rio (DAC) e o c&#226;ncer (CA). Esta inter-rela&#231;&#227;o se sustenta na preval&#234;ncia dos fatores de risco em comum, no envelhecimento das popula&#231;&#245;es, na evolu&#231;&#227;o das t&#233;cnicas de diagn&#243;stico e tratamento e nas les&#245;es cardiovasculares secund&#225;rias ao tratamento do CA.</p>
        <p>Entre as quest&#245;es frequentes em cardio-oncologia, destaca-se qual seria o momento do cruzamento das curvas de mortalidade das DAC e CA no Brasil, com a perda da primazia da primeira.<xref ref-type="bibr" rid="B1">1</xref> As DAC tornaram-se as principais causas de morte no Brasil, seguidas por CA ap&#243;s o decl&#237;nio da preval&#234;ncia das doen&#231;as infecciosas.<xref ref-type="bibr" rid="B2">2</xref><sup>,</sup><xref ref-type="bibr" rid="B3">3</xref> Em pa&#237;ses mais desenvolvidos, observa-se queda na mortalidade por DAC desde os meados da d&#233;cada de 1960,<xref ref-type="bibr" rid="B4">4</xref><sup>,</sup><xref ref-type="bibr" rid="B5">5</xref> e CA superam as doen&#231;as do cora&#231;&#227;o como causa de mortalidade.<xref ref-type="bibr" rid="B6">6</xref> No Brasil, verificou-se redu&#231;&#227;o na taxa de mortalidade por DAC a partir da d&#233;cada de 1980, para ambos os sexos, mais acentuada nas regi&#245;es Sul e Sudeste.<xref ref-type="bibr" rid="B7">7</xref> Concomitante a essa tend&#234;ncia, os &#243;bitos por CA no Brasil cresceram e passaram da quinta causa de morte em 1980 para a terceira em 2000 e, atualmente, representam a segunda causa de mortalidade.<xref ref-type="bibr" rid="B8">8</xref></p>
        <p>O CA &#233; a principal causa dos &#243;bitos em metade dos Estados Unidos da Am&#233;rica (EUA) e em alguns pa&#237;ses da Europa Ocidental e exibe uma &#237;ntima rela&#231;&#227;o com o envelhecimento populacional. A queda da mortalidade por DAC &#233; atribu&#237;da, em parte, pela melhoria no diagn&#243;stico e tratamento.<xref ref-type="bibr" rid="B9">9</xref><sup>-</sup><xref ref-type="bibr" rid="B11">11</xref> No entanto, DAC e CA possuem complexa rela&#231;&#227;o mediada por in&#250;meros fatores de risco em comum, como o tabagismo e o etilismo; o sobrepeso e a obesidade; a dieta; o sedentarismo; a hipertens&#227;o arterial; e o diabetes mellitus. Tamb&#233;m tem rela&#231;&#227;o com horm&#244;nios sexuais, insulina, fatores de sinaliza&#231;&#227;o de crescimento &#8220;insulin-like&#8221; e adipocinas, inflama&#231;&#227;o cr&#244;nica e estresse oxidativo, que ao serem comuns a ambas, dificultam a compreens&#227;o do distanciamento das tend&#234;ncias para os dois grupos de doen&#231;as.<xref ref-type="bibr" rid="B12">12</xref><sup>,</sup><xref ref-type="bibr" rid="B13">13</xref></p>
        <p>Ainda s&#227;o escassos os estudos que buscam compreender as rela&#231;&#245;es entre o CA e as DAC na popula&#231;&#227;o brasileira. O perfil de morbimortalidade no Brasil tem sido alterado ao longo dos anos. Os processos de transi&#231;&#227;o demogr&#225;fica e epidemiol&#243;gica, as diferen&#231;as no acesso aos servi&#231;os de sa&#250;de, as peculiaridades gen&#233;ticas, entre outros fatores, t&#234;m resultado na forma&#231;&#227;o de grupos populacionais regionais com caracter&#237;sticas particulares.<xref ref-type="bibr" rid="B14">14</xref> A an&#225;lise das tend&#234;ncias temporais da mortalidade fundamentadas em dados populacionais pode esclarecer melhor o cen&#225;rio.</p>
        <p>Este trabalho objetivou comparar a mortalidade precoce e tardia, segundo o g&#234;nero, por DAC e CA no per&#237;odo de 2000-2015 nas capitais mais populosas de cada regi&#227;o brasileira e no Brasil como todo.</p>
      </sec>
      <sec sec-type="methods">
        <title>M&#233;todos</title>
        <p>Optou-se por estudar as taxas de mortalidade por DAC e CA em cinco capitais dos estados federativos brasileiros, uma por regi&#227;o, tendo como crit&#233;rio de sele&#231;&#227;o a mais populosa. O Distrito Federal n&#227;o foi inclu&#237;do. Foram coletados dados demogr&#225;ficos e de mortalidade junto ao Instituto Brasileiro de Geografia e Estat&#237;stica (IBGE). Os seguintes munic&#237;pios foram ent&#227;o inclu&#237;dos: Manaus (regi&#227;o norte), S&#227;o Paulo (sudeste), Goi&#226;nia (centro-oeste), Curitiba (sul), e Salvador (nordeste).</p>
        <p>Para defini&#231;&#227;o de mortalidade precoce e tardia, estabeleceu-se as faixas et&#225;rias de 30-69 anos e &gt;70 anos respectivamente, estratificadas por idade, segundo a defini&#231;&#227;o de mortalidade precoce por doen&#231;a cr&#244;nica n&#227;o transmiss&#237;vel adotada pelo Minist&#233;rio da Sa&#250;de e em conson&#226;ncia com as recomenda&#231;&#245;es publicadas na <italic>World Population Prospects</italic> da Organiza&#231;&#227;o das Na&#231;&#245;es Unidas.<xref ref-type="bibr" rid="B14">14</xref></p>
        <p>O presente estudo utilizou o DATASUS como fonte de dados para a mortalidade de 2000 a 2015 nos cap&#237;tulos das DAC - cap&#237;tulo IX da Classifica&#231;&#227;o Internacional das Doen&#231;as (CID)-10 - e suas causas espec&#237;ficas dentro do cap&#237;tulo: Febre reum&#225;tica aguda e doen&#231;a reum&#225;tica cr&#244;nica do cora&#231;&#227;o (066), Doen&#231;as hipertensivas (067), Doen&#231;as isqu&#234;micas do cora&#231;&#227;o (068), Infarto agudo do mioc&#225;rdio (068.1), Outras doen&#231;as card&#237;acas (069), Doen&#231;as cerebrovasculares (070), Aterosclerose (071), e Restante de doen&#231;as do aparelho circulat&#243;rio (072). Utilizou-se tamb&#233;m mortalidade no cap&#237;tulo das neoplasias - CID 10 cap&#237;tulo II - e suas causas espec&#237;ficas dentro do cap&#237;tulo: Neoplasia maligna do l&#225;bio, cavidade oral e faringe (032), Neoplasia maligna de es&#244;fago (033), Neoplasia maligna de est&#244;mago (034), Neoplasia maligna de col&#243;n, reto e &#226;nus (035), Neoplasia maligna do f&#237;gado e vias biliares intra-hep&#225;ticas (036), Neoplasia maligna do p&#226;ncreas (037), Neoplasia maligna de laringe (038), Neoplasia maligna da traqueia, br&#244;nquios e pulm&#245;es (039), Neoplasia maligna da pele (040), Neoplasia maligna da mama (041), Neoplasia maligna do colo de &#250;tero (042), Neoplasia maligna de corpo e partes n&#227;o especificadas do &#250;tero (043), Neoplasia maligna de ov&#225;rio (044), Neoplasia maligna da pr&#243;stata (045), Neoplasia maligna da bexiga (046), Neoplasia maligna da meninge, enc&#233;falo e outras partes do SNC (047), Linfoma n&#227;o-Hodgkin (048), Mieloma m&#250;ltiplo e neoplasia maligna de plasm&#243;citos (049), Leucemia (050), Neoplasias <italic>in situ</italic>, benignas e comportamentos incertos (051), e Restante das neoplasias malignas (052).</p>
        <p>Os dados foram obtidos dos bancos de dados informatizados dos registros de atestados de &#243;bito do Sistema de Informa&#231;&#245;es sobre Mortalidade (Setor de Estat&#237;sticas Vitais) e &#8220;Estimativas da popula&#231;&#227;o: munic&#237;pio, sexo e idade 2000-2015 RIPSA IBGE&#8221; (Setor Demogr&#225;ficas e Socioecon&#244;micas). Todas as informa&#231;&#245;es foram coletadas na p&#225;gina do DATASUS.<xref ref-type="bibr" rid="B15">15</xref></p>
        <sec>
          <title>An&#225;lise estat&#237;stica</title>
          <p>Para avaliar a tend&#234;ncia na mudan&#231;a percentual anual estimada (EAPC) da taxa da mortalidade por DAC e CA durante o per&#237;odo estudado foi utilizado o modelo de regress&#227;o Joinpoint 4.6.0.0 (<italic>National Cancer Institute</italic>, Bethesda, Maryland, EUA).<xref ref-type="bibr" rid="B16">16</xref> Utilizou-se um teste de permuta&#231;&#227;o denominado Monte Carlo para detectar os anos quando houve mudan&#231;as significantes na tend&#234;ncia das taxas.<xref ref-type="bibr" rid="B17">17</xref><sup>,</sup><xref ref-type="bibr" rid="B18">18</xref> Al&#233;m disso, a distribui&#231;&#227;o de Poisson foi utilizada juntamente com o modelo de regress&#227;o Joinpoint. Assumindo tal distribui&#231;&#227;o, foi selecionado n&#250;mero m&#225;ximo de dois joinpoints. O programa calcula a mudan&#231;a percentual anual pelo m&#233;todo param&#233;trico, com intervalo de confian&#231;a de 95% para cada segmento de tend&#234;ncia. A partir desse software foram calculadas taxas ajustadas de mortalidade para sexo e faixa et&#225;ria utilizando a popula&#231;&#227;o padr&#227;o baseada na OMS 2000-2025. Considerou-se signific&#226;ncia estat&#237;stica o n&#237;vel de 5%.</p>
        </sec>
      </sec>
      <sec sec-type="results">
        <title>Resultados</title>
        <p>A <xref ref-type="fig" rid="f2">Figura 1</xref> mostra as curvas para a mortalidade por DAC e CA nas cinco capitais estudadas. Observa-se que para todos os grupos e faixas et&#225;rias, houve queda das DAC, mais acentuada na mortalidade precoce, especialmente no sexo feminino, em que o CA j&#225; &#233; a principal causa de mortalidade. Nas curvas de CA houve uma tend&#234;ncia &#224; estabilidade ou leve aumento na taxa de mortalidade. Na mortalidade tardia, nota-se uma faixa bem ampla na diferen&#231;a entre a taxa de mortalidade por CA e DAC, em que as DAC t&#234;m taxas muito elevadas comparadas &#224;quelas do CA, levando os cruzamentos dessas curvas serem mais tardios em rela&#231;&#227;o &#224; mortalidade precoce.</p>
        <p>
          <fig id="f2">
            <label>Figura 1</label>
            <caption>
              <title>Tend&#234;ncias das taxas de mortalidade por doen&#231;as do aparelho circulat&#243;rio e c&#226;ncer nas capitais mais populosas das cinco regi&#245;es brasileiras, estratificada por sexo e faixa et&#225;ria, 2000-2015. Fonte: DATASUS. Curva verde indica doen&#231;as do aparelho circulat&#243;rio e a azul, c&#226;ncer.</title>
            </caption>
            <graphic xlink:href="1678-4170-abc-114-02-0199-80304-gf01-pt.tif"/></fig>
        </p>
        <p>A <xref ref-type="table" rid="t4">Tabela 1</xref> apresenta as EAPCs na mortalidade por DAC e CA para as capitais mais populosas das cinco regi&#245;es brasileiras entre 2000 e 2015. Entre 2000 e 2015, observou-se uma queda consistente da mortalidade precoce e tardia por DAC, em ambos os g&#234;neros, nas capitais mais populosas, &#224; exce&#231;&#227;o da mortalidade tardia em homens, na cidade de Manaus. A EAPC para a mortalidade por DAC oscilou de -6,5% para mortalidade precoce em mulheres do munic&#237;pio de S&#227;o Paulo a 0,3%, para a mortalidade precoce em homens em Curitiba. Tal mudan&#231;a n&#227;o foi estatisticamente significativa e, provavelmente, resultou de uma invers&#227;o da tend&#234;ncia, com aumento da mortalidade nos dois &#250;ltimos anos analisados, ou seja, 2014 e 2015. As quedas foram compar&#225;veis entre os g&#234;neros e foi mais acentuada para a mortalidade precoce. &#201; interessante ressaltar que os coeficientes de mortalidade tardia para DAC foram pelo menos dez vezes os coeficientes de mortalidade precoce.</p>
        <table-wrap id="t4">
          <label>Tabela 1</label>
          <caption>
            <title>Tend&#234;ncia de mortalidade por cap&#237;tulo para as capitais mais populosas das cinco regi&#245;es brasileiras, ano 2015</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
              <col width="10%"/>
            </colgroup>
            <thead>
              <tr>
                <th align="left" rowspan="3">Capital</th>
                <th align="center" rowspan="3">&#160;</th>
                <th align="center" colspan="4" style="border-bottom-width:thin;border-bottom-style:solid">Mortalidade precoce</th>
                <th align="center" colspan="4" style="border-bottom-width:thin;border-bottom-style:solid">Mortalidade tardia</th>
              </tr>
              <tr>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Masculino</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Feminino</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Masculino</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Feminino</th>
              </tr>
              <tr>
                <th align="center">CA</th>
                <th align="center">DAC</th>
                <th align="center">CA</th>
                <th align="center">DAC</th>
                <th align="center">CA</th>
                <th align="center">DAC</th>
                <th align="center">CA</th>
                <th align="center">DAC</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left">Manaus</td>
                <td align="center">APC<break/>IC 95%</td>
                <td align="center">-0,8<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-1,5;0)</td>
                <td align="center">-2,0<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,9;-1,1)</td>
                <td align="center">0,5<break/>(-0,4;1,3)</td>
                <td align="center">-2,3<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,8;-1,7)</td>
                <td align="center">1,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(0,5;1,7)</td>
                <td align="center">1,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(0,5;1,7)</td>
                <td align="center">-3,0<break/>(-6,6;0,8)</td>
                <td align="center">-0,3<break/>(-0,9;0,3)</td>
              </tr>
              <tr>
                <td align="left">Salvador</td>
                <td align="center">APC<break/>IC 95%</td>
                <td align="center">-0,9<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-1,4;-0,4)</td>
                <td align="center">-3,4<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-4,1;-2,7)</td>
                <td align="center">-1,7<break/>(-4,4;1,1)</td>
                <td align="center">-4,3<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-4,9;-3,6)</td>
                <td align="center">0,9<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(0,3;1,5)</td>
                <td align="center">-1,4<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,1;-0,8)</td>
                <td align="center">0,5<break/>(0;1)</td>
                <td align="center">-2,4<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,9;-1,9)</td>
              </tr>
              <tr>
                <td align="left">Goi&#226;nia</td>
                <td align="center">APC<break/>IC 95%</td>
                <td align="center">0,4<break/>(-0,4;1,2)</td>
                <td align="center">-4,0<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-4,5;-3,4)</td>
                <td align="center">0,7<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(0,1;1,4)</td>
                <td align="center">-4,0<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-4,8;-3,2)</td>
                <td align="center">1,2<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(0,4;2)</td>
                <td align="center">-2,3<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-3;-1,7)</td>
                <td align="center">1,4<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(0,7-2,1)</td>
                <td align="center">-2,5<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-3;-2)</td>
              </tr>
              <tr>
                <td align="left">S&#227;o Paulo</td>
                <td align="center">APCIC 95%</td>
                <td align="center">-1,9<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,1;-1,7)</td>
                <td align="center">-2,6<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-3,1;-2,2)</td>
                <td align="center">-0,9<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-1,1;-0,8)</td>
                <td align="center">-2,9<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-3,5;-2,3)</td>
                <td align="center">-1,5<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-1,7;-1,3)</td>
                <td align="center">-3,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-3,3;-2,8)</td>
                <td align="center">-1,0<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-1,3;-0,8)</td>
                <td align="center">-3,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-3,4;-2,8)</td>
              </tr>
              <tr>
                <td align="left">Curitiba</td>
                <td align="center">APC<break/>IC 95%</td>
                <td align="center">-3,4<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-4,2;-2,7)</td>
                <td align="center">0,3<break/>(-6,8;7,8)</td>
                <td align="center">-2,2<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,8;-1,5)</td>
                <td align="center">-6,5<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-7,1;-5,9)</td>
                <td align="center">-1,9<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,3;-1,4)</td>
                <td align="center">-4,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-4,5;-3,6)</td>
                <td align="center">-1,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-1,9;-0,3)</td>
                <td align="center">-4,4<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-4,8;-3,9)</td>
              </tr>
              <tr>
                <td align="left">Brasil</td>
                <td align="center">APC<break/>IC 95%</td>
                <td align="center">-0,9<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-1,1;-0,7)</td>
                <td align="center">-2,7<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-3,5;-1,9)</td>
                <td align="center">0,1<break/>(-0,1;0,4)</td>
                <td align="center">-2,4<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,6;-2,2)</td>
                <td align="center">0,1<break/>(-0,1;0,3)</td>
                <td align="center">-2,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,5;-1,6)</td>
                <td align="center">0,2<break/>(-0,1;0,4)</td>
                <td align="center">-2,1<xref ref-type="table-fn" rid="TFN2">*</xref><break/>(-2,6;-1,7)</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn id="TFN2">
              <label>*</label>
              <p>indica associa&#231;&#227;o estatisticamente significativa (p &lt; 0,05). CA: c&#226;ncer; DAC: doen&#231;as do aparelho circulat&#243;rio; APC: mudan&#231;a percentual anual; IC: intervalo de confian&#231;a.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>Diferentemente do observado para DAC, os coeficientes de mortalidade por CA apresentaram comportamentos peculiares nas regi&#245;es, per&#237;odos e g&#234;neros. Em geral, houve tend&#234;ncia de queda das taxas de mortalidade por CA em S&#227;o Paulo e Curitiba em todos os per&#237;odos e g&#234;neros. Por outro lado, houve aumento da taxa de mortalidade por CA em todos os per&#237;odos em Goi&#226;nia. Em Salvador, houve um comportamento misto, caracterizado pela queda na mortalidade precoce em homens e mulheres e incremento na mortalidade tardia em ambos os g&#234;neros.</p>
        <p>Em Manaus o comportamento fugiu completamente aos padr&#245;es. A mortalidade por DAC caiu em todos os per&#237;odos, exceto na mortalidade tardia em homens. Por outro lado, mortalidade precoce por CA ultrapassou aquela por DAC em homens e mulheres.</p>
        <p>Em resumo, observa-se que o determinante da converg&#234;ncia das curvas de mortalidade por DAC e CA &#233; a maior magnitude da queda da mortalidade por DAC.</p>
        <p>Na <xref ref-type="table" rid="t5">Tabela 2</xref>, est&#227;o dispostos os anos de cruzamento ocorridos ou presumidos das curvas de mortalidade por CA e DAC. Quanto &#224; mortalidade precoce, CA j&#225; ocupa o primeiro lugar no sexo masculino em Goi&#226;nia e no feminino em todas as capitais estudadas. J&#225; na mortalidade tardia, o cruzamento das curvas ainda n&#227;o ocorreu em nenhuma das capitais estudadas e s&#243; dever&#225; acontecer, mantidas as tend&#234;ncias, a partir de 2026.</p>
        <table-wrap id="t5">
          <label>Tabela 2</label>
          <caption>
            <title>Estimativas do ano do cruzamento das curvas de mortalidade por doen&#231;as do aparelho circulat&#243;rio e c&#226;ncer para as capitais mais populosas das cinco regi&#245;es brasileiras</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="20%"/>
              <col width="20%"/>
              <col width="20%"/>
              <col width="20%"/>
              <col width="20%"/>
            </colgroup>
            <thead>
              <tr>
                <th rowspan="3" align="left">Localidade</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Faixa et&#225;ria de mortalidade precoce</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Faixa et&#225;ria de mortalidade tardia</th>
              </tr>
              <tr>
                <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Masculino</th>
                <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Feminino</th>
                <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Masculino</th>
                <th align="center" style="border-bottom-width:thin;border-bottom-style:solid">Feminino</th>
              </tr>
              <tr>
                <th align="center" colspan="4">Ano do cruzamento</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left">Manaus</td>
                <td align="center">2009</td>
                <td align="center">1992</td>
                <td align="center">-</td>
                <td align="center">-</td>
              </tr>
              <tr>
                <td align="left">Salvador</td>
                <td align="center">2023</td>
                <td align="center">-</td>
                <td align="center">2031</td>
                <td align="center">2038</td>
              </tr>
              <tr>
                <td align="left">Goi&#226;nia</td>
                <td align="center">2018</td>
                <td align="center">2000</td>
                <td align="center">2026</td>
                <td align="center">2034</td>
              </tr>
              <tr>
                <td align="left">S&#227;o Paulo</td>
                <td align="center">2071</td>
                <td align="center">2009</td>
                <td align="center">2047</td>
                <td align="center">2051</td>
              </tr>
              <tr>
                <td align="left">Curitiba</td>
                <td align="center">-</td>
                <td align="center">2004</td>
                <td align="center">2032</td>
                <td align="center">2033</td>
              </tr>
              <tr>
                <td align="left">Brasil</td>
                <td align="center">2035</td>
                <td align="center">2015</td>
                <td align="center">2045</td>
                <td align="center">2057</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <attrib>Fonte: DATASUS, conforme cap&#237;tulos II e IX do CID 10. Mortalidade precoce: entre 30 e 69 anos de idade; Mortalidade tardia: &#8805; 70 anos de idade.</attrib>
          </table-wrap-foot>
        </table-wrap>
        <p>Na <xref ref-type="table" rid="t6">Tabela 3</xref>, encontram-se dispostas as tr&#234;s principais causas espec&#237;ficas de mortalidade, estratificadas em precoce e tardia e por g&#234;nero, nas cinco capitais mais populosas das regi&#245;es brasileiras. H&#225; diferen&#231;as entre as cidades, as faixas de idade e g&#234;neros.</p>
        <table-wrap id="t6">
          <label>Tabela 3</label>
          <caption>
            <title>Tr&#234;s maiores causas de mortes espec&#237;ficas dos cap&#237;tulos II e IX da CID 10 para as capitais mais populosas das cinco regi&#245;es brasileiras, 2015</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
              <col width="9%"/>
            </colgroup>
            <thead>
              <tr>
                <th align="left" rowspan="2">Sexo/Faixa et&#225;ria</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Manaus</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Salvador</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Goi&#226;nia</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">S&#227;o Paulo</th>
                <th align="center" colspan="2" style="border-bottom-width:thin;border-bottom-style:solid">Curitiba</th>
              </tr>
              <tr>
                <th align="center">C&#226;ncer</th>
                <th align="center">DAC</th>
                <th align="center">C&#226;ncer</th>
                <th align="center">DAC</th>
                <th align="center">C&#226;ncer</th>
                <th align="center">DAC</th>
                <th align="center">C&#226;ncer</th>
                <th align="center">DAC</th>
                <th align="center">C&#226;ncer</th>
                <th align="center">DAC</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left" rowspan="3">Feminino 30-69 anos</td>
                <td align="center">Colo de &#250;tero</td>
                <td align="center">DCV</td>
                <td align="center">Mama</td>
                <td align="center">DCV</td>
                <td align="center">Mama</td>
                <td align="center">DIC</td>
                <td align="center">Mama</td>
                <td align="center">DIC</td>
                <td align="center">Mama</td>
                <td align="center">DIC</td>
              </tr>
              <tr>
                <td align="center">Mama</td>
                <td align="center">DIC</td>
                <td align="center">C&#243;lon</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DCV</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DCV</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DCV</td>
              </tr>
              <tr>
                <td align="center">Pulm&#245;es</td>
                <td align="center">IAM</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">IAM</td>
                <td align="center">Colo de &#250;tero</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">IAM</td>
              </tr>
              <tr>
                <td align="left" rowspan="3">Feminino &#8805;70 anos</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DCV</td>
                <td align="center">Mama</td>
                <td align="center">DCV</td>
                <td align="center">Mama</td>
                <td align="center">DCV</td>
                <td align="center">Mama</td>
                <td align="center">DIC</td>
                <td align="center">Mama</td>
                <td align="center">DCV</td>
              </tr>
              <tr>
                <td align="center">Colo de &#250;tero</td>
                <td align="center">DIC</td>
                <td align="center">C&#243;lon</td>
                <td align="center">DIC</td>
                <td align="center">C&#243;lon</td>
                <td align="center">DIC</td>
                <td align="center">C&#243;lon</td>
                <td align="center">DCV</td>
                <td align="center">C&#243;lon</td>
                <td align="center">DIC</td>
              </tr>
              <tr>
                <td align="center">Mama</td>
                <td align="center">IAM</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">IAM</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">HIP</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">IAM</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">IAM</td>
              </tr>
              <tr>
                <td align="left" rowspan="3">Masculino 30-69 anos</td>
                <td align="center">Est&#244;mago</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DIC</td>
              </tr>
              <tr>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DCV</td>
                <td align="center">Pr&#243;stata</td>
                <td align="center">DCV</td>
                <td align="center">C&#243;lon</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">IAM</td>
              </tr>
              <tr>
                <td align="center">Laringe</td>
                <td align="center">IAM</td>
                <td align="center">Faringe</td>
                <td align="center">IAM</td>
                <td align="center">Faringe</td>
                <td align="center">DCV</td>
                <td align="center">Est&#244;mago</td>
                <td align="center">DCV</td>
                <td align="center">Est&#244;mago</td>
                <td align="center">DCV</td>
              </tr>
              <tr>
                <td align="left" rowspan="3">Masculino &#8805;70 anos</td>
                <td align="center">Pr&#243;stata</td>
                <td align="center">DCV</td>
                <td align="center">Pr&#243;stata</td>
                <td align="center">DCV</td>
                <td align="center">Pr&#243;stata</td>
                <td align="center">DCV</td>
                <td align="center">Pr&#243;stata</td>
                <td align="center">DIC</td>
                <td align="center">Pr&#243;stata</td>
                <td align="center">DCV</td>
              </tr>
              <tr>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">DIC</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">DIC</td>
              </tr>
              <tr>
                <td align="center">Est&#244;mago</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">IAM</td>
                <td align="center">C&#243;lon</td>
                <td align="center">DCV</td>
                <td align="center">Pulm&#245;es</td>
                <td align="center">IAM</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <attrib>Fonte: DATASUS. DAC: doen&#231;as do aparelho circulat&#243;rio; DCV: doen&#231;as cerebrovasculares; DIC: doen&#231;as isqu&#234;micas do cora&#231;&#227;o; IAM: infarto agudo do mioc&#225;rdio; HIP: doen&#231;as hipertensivas. Para fins desta an&#225;lise, IAM foi considerado causa separada das DIC, e o C&#243;digo 069, do cap&#237;tulo das DAC, e o C&#243;digo 052, no cap&#237;tulo de C&#226;ncer da Classifica&#231;&#227;o Internacional de Doen&#231;as-10, n&#227;o foram considerados para efeito de ranking das doen&#231;as.</attrib>
          </table-wrap-foot>
        </table-wrap>
      </sec>
      <sec sec-type="discussion">
        <title>Discuss&#227;o</title>
        <sec>
          <title>Mortalidade por DAC e CA no Brasil em contraste ao contexto mundial</title>
          <p>No Brasil, a discuss&#227;o do aumento da mortalidade por CA &#233; mais recente que em pa&#237;ses europeus e nos EUA, onde a transi&#231;&#227;o epidemiol&#243;gica ocorreu antes que no Brasil. Em 2005, no Brasil, 32% dos &#243;bitos bem-definidos foram causados por DAC, seguidos de 15% pelo CA. &#192; &#233;poca, Rosa et al.<xref ref-type="bibr" rid="B19">19</xref> chamaram a aten&#231;&#227;o para o prov&#225;vel cruzamento das curvas de mortalidade entre DAC e CA. No Reino Unido, em 2011, as DAC passaram de primeira causa de mortalidade para a segunda posi&#231;&#227;o pela primeira vez desde a metade do s&#233;culo XX;<xref ref-type="bibr" rid="B20">20</xref> 29% das mortes foram causadas por CA, enquanto que 28% delas por DAC.<xref ref-type="bibr" rid="B21">21</xref> A queda da mortalidade por DAC no Reino Unido se deveu &#224; queda da letalidade por infarto do mioc&#225;rdio, aumento do tratamento farmacol&#243;gico e cir&#250;rgico e a diminui&#231;&#227;o de fatores de risco como o tabagismo.<xref ref-type="bibr" rid="B21">21</xref><sup>-</sup><xref ref-type="bibr" rid="B23">23</xref> Outra situa&#231;&#227;o semelhante &#224; brasileira &#233; a observada nos EUA onde a mortalidade por DAC declinou mais acentuadamente que a por CA. Caso essa tend&#234;ncia continue, CA ser&#225; o l&#237;der como causa de mortes em 2020.<xref ref-type="bibr" rid="B24">24</xref></p>
          <p>Os diferentes est&#225;gios do crescimento e desenvolvimento das regi&#245;es brasileiras fizeram com que desenh&#225;ssemos uma an&#225;lise particularizada, posto ser dif&#237;cil tra&#231;ar um retrato fidedigno do Brasil como um todo. A escolha das capitais mais populosas partiu do pressuposto do maior grau de urbaniza&#231;&#227;o e interfer&#234;ncia dessa na sa&#250;de de seus habitantes. De modo geral, no mundo ocidental, o cruzamento das curvas de mortalidade se d&#225; pela queda expressiva da mortalidade por DAC, especialmente nos pa&#237;ses de maior desenvolvimento socioecon&#244;mico.</p>
        </sec>
        <sec>
          <title>Tend&#234;ncias gerais das curvas de mortalidade por DAC e CA no Brasil</title>
          <p>A an&#225;lise da tend&#234;ncia hist&#243;rica das curvas de mortalidade por DAC e CA revelaram queda expressiva e sustentada dos &#243;bitos por DAC nas capitais mais populosas de cada uma das cinco regi&#245;es brasileiras, com exce&#231;&#227;o de Manaus. Nessa cidade, a mortalidade tardia por DAC aumentou em homens. Os dados consolidados do Brasil mostram que as DAC continuam como a principal causa de mortalidade, no entanto, analisando particularmente os munic&#237;pios, o CA j&#225; superou as DAC em quase 10% das cidades brasileiras.<xref ref-type="bibr" rid="B25">25</xref></p>
          <p>Os resultados do presente trabalho sugerem dois padr&#245;es de tend&#234;ncias que levaram ao agrupamento das cinco capitais em dois subgrupos: no primeiro, S&#227;o Paulo e Curitiba com padr&#227;o mais pr&#243;ximo &#224; realidade dos pa&#237;ses desenvolvidos, onde h&#225; queda expressiva da mortalidade por DAC e manuten&#231;&#227;o ou queda discreta da mortalidade por CA. Neste padr&#227;o, a converg&#234;ncia das curvas se d&#225; pela magnitude da queda das DAC. No segundo padr&#227;o, apresentam-se Goi&#226;nia, Salvador e Manaus, onde tamb&#233;m houve queda da mortalidade por DAC, por&#233;m menos expressiva, contraposta pelo aumento discreto da mortalidade por CA. Neste segundo grupo, a converg&#234;ncia das curvas demora mais a se concretizar. No munic&#237;pio de Manaus, observou-se comportamento singular com o aumento expressivo da mortalidade tardia por DAC no subgrupo masculino.</p>
          <p>Nas capitais brasileiras aqui estudadas, segundo os dados de 2015, a doen&#231;a isqu&#234;mica do cora&#231;&#227;o e a doen&#231;a cerebrovascular s&#227;o as principais causas de DAC. Enquanto na faixa et&#225;ria precoce morre-se mais por doen&#231;a isqu&#234;mica do cora&#231;&#227;o, na faixa et&#225;ria tardia h&#225; maior mortalidade por doen&#231;a cerebrovascular. Entre 1996 e 2011, foi observado no Brasil um decl&#237;nio consistente na taxa de mortalidade por doen&#231;a cerebrovascular em ambos os g&#234;neros, com diferen&#231;as na magnitude do decl&#237;nio entre as regi&#245;es.<xref ref-type="bibr" rid="B26">26</xref> Al&#233;m do desenvolvimento socioecon&#244;mico, o controle dos fatores de risco cardiovasculares, assim como um aumento consider&#225;vel (450%) no acesso aos servi&#231;os de aten&#231;&#227;o prim&#225;ria, podem ter interferido na queda.<xref ref-type="bibr" rid="B27">27</xref> Como observado em pa&#237;ses desenvolvidos, esfor&#231;os para o diagn&#243;stico e tratamento dos fatores de risco e comorbidades, muito provavelmente contribu&#237;ram para o decl&#237;nio da mortalidade por acidente vascular cerebral<xref ref-type="bibr" rid="B28">28</xref> e, secundariamente, para a queda da mortalidade no cap&#237;tulo das DAC.</p>
        </sec>
        <sec>
          <title>Tend&#234;ncias regionais das curvas de mortalidade por DAC e CA</title>
          <p>S&#227;o Paulo e Curitiba mostraram uma diminui&#231;&#227;o na mortalidade precoce e tardia para CA em ambos os g&#234;neros. Isso poderia ser explicado, em parte, pelo acesso mais amplo aos servi&#231;os de diagn&#243;stico e tratamento oncol&#243;gico. H&#225; concentra&#231;&#227;o de oferta dos servi&#231;os de quimioterapia e radioterapia nas regi&#245;es sul e sudeste do Brasil.<xref ref-type="bibr" rid="B29">29</xref></p>
          <p>Em Salvador, observou-se decr&#233;scimo na mortalidade precoce por CA no sexo masculino. O CA de pulm&#227;o, de elevada letalidade, &#233; o principal tumor a afetar essa popula&#231;&#227;o. Atualmente, o CA de pulm&#227;o &#233; a principal causa de morte entre os homens na Am&#233;rica do Norte e Europa e sua mortalidade vem aumentando significativamente na &#193;sia, Am&#233;rica Latina e &#193;frica.<xref ref-type="bibr" rid="B30">30</xref> No Brasil, o adenocarcinoma &#233; a principal causa de mortalidade precoce em homens e relaciona-se &#224; maior preval&#234;ncia de tabagismo no sexo masculino.<xref ref-type="bibr" rid="B15">15</xref><sup>,</sup><xref ref-type="bibr" rid="B31">31</xref> A queda da mortalidade por CA em Salvador pode ser atribu&#237;da &#224;s pol&#237;ticas p&#250;blicas de preven&#231;&#227;o ao tabagismo ao longo das &#250;ltimas d&#233;cadas, sendo que em 2004, Salvador apresentava a menor taxa de tabagismo do Brasil.<xref ref-type="bibr" rid="B32">32</xref> Por outro lado, a mortalidade tardia por CA em Salvador apresentou aumento para homens e mulheres. Uma hip&#243;tese levantada para esse aumento no sexo feminino s&#227;o as altas taxas de mortalidade por CA de mama, que representa a maior causa de mortalidade tardia feminina.<xref ref-type="bibr" rid="B15">15</xref> As taxas de mortalidade por CA de mama na popula&#231;&#227;o brasileira t&#234;m apresentado varia&#231;&#245;es geogr&#225;ficas, com tend&#234;ncia &#224; estabiliza&#231;&#227;o na regi&#227;o sudeste, de decl&#237;nio na regi&#227;o sul e de aumento nas regi&#245;es norte, nordeste e centro-oeste. Na regi&#227;o nordeste, no per&#237;odo entre 2000 a 2010, observou-se aumento de 100% na mortalidade por CA em mulheres brancas, com aumento populacional desse estrato em apenas 10%. Por outro lado, houve aumento na mortalidade em mulheres negras de 183%, com respectivo aumento demogr&#225;fico de 58%.<xref ref-type="bibr" rid="B33">33</xref> Uma hip&#243;tese para explicar o aumento na taxa de mortalidade tardia por CA nos homens em Salvador &#233; a grande porcentagem de popula&#231;&#227;o afrodescendente naquela capital. O CA de pr&#243;stata &#233; a principal causa de mortalidade tardia em homens no Brasil<xref ref-type="bibr" rid="B15">15</xref> e um homem negro tem 1,6 mais chance de ser diagnosticado e 2,4 mais chances de morrer por CA de pr&#243;stata que um homem branco.<xref ref-type="bibr" rid="B34">34</xref></p>
          <p>Manaus apresenta as maiores singularidades em rela&#231;&#227;o &#224;s demais capitais no tocante &#224;s curvas de mortalidade por DAC e CA. A mortalidade precoce por CA em mulheres apresentou aumento significativo. O principal CA &#233; o de colo de &#250;tero,<xref ref-type="bibr" rid="B15">15</xref> cujas taxas de mortalidade aumentaram nas regi&#245;es norte e nordeste.<xref ref-type="bibr" rid="B35">35</xref><sup>-</sup><xref ref-type="bibr" rid="B37">37</xref> Para os homens de Manaus, as taxas de mortalidade precoce diminu&#237;ram, enquanto as de mortalidade tardia aumentaram. Os principais tipos de CA como causa de mortalidade precoce foi CA de est&#244;mago seguido de CA de pulm&#227;o. No Brasil, as taxas de mortalidade por CA de est&#244;mago cresceram consideravelmente ap&#243;s a idade de 59 anos. Na regi&#227;o norte, aumentos nas taxas de mortalidade foram identificados, para homens e mulheres, a partir de 75 anos.<xref ref-type="bibr" rid="B37">37</xref> A mortalidade por CA de pr&#243;stata ainda cresce no Brasil, com uma vasta quantidade de casos subnotificados ou diagnosticados tardiamente.</p>
          <p>Em Goi&#226;nia, houve aumento na mortalidade por CA em ambos os g&#234;neros, nas faixas precoce e tardia. Nas mulheres, o principal CA na faixa de idade considerada precoce foi CA de mama. Isso pode ser explicado pela dificuldade do acesso ao diagn&#243;stico adequado, uma vez que apenas 18% dos mam&#243;grafos do estado de Goi&#225;s pertencem ao Sistema &#218;nico de Sa&#250;de (SUS) em contraponto a 80% daquela popula&#231;&#227;o que &#233; dependente exclusivamente do SUS.<xref ref-type="bibr" rid="B38">38</xref><sup>,</sup><xref ref-type="bibr" rid="B39">39</xref> Outro fator a influenciar esse efeito &#233; a elevada m&#233;dia de idade da realiza&#231;&#227;o da primeira mamografia em Goi&#226;nia, 49 anos.<xref ref-type="bibr" rid="B40">40</xref></p>
        </sec>
        <sec>
          <title>Diferen&#231;as de g&#234;nero nas tend&#234;ncias das curvas de mortalidade por DAC e CA</title>
          <p>As taxas de mortalidade por DAC entre as mulheres foram menores que entre os homens em todas as capitais estudadas e em ambas as faixas et&#225;rias, precoce e tardia, e apresentaram decl&#237;nio mais expressivo dessa taxa ao longo dos anos nas mulheres. Uma hip&#243;tese para esses achados &#233; o fato de que as mulheres t&#234;m maior ades&#227;o aos programas de rastreamento e preven&#231;&#227;o na aten&#231;&#227;o prim&#225;ria.</p>
          <p>No tocante ao CA, h&#225; diferen&#231;as regionais na incid&#234;ncia dos diferentes tumores, com taxas de letalidade distintas. Por exemplo, os tumores do col&#243;n j&#225; ocupam o segundo lugar em incid&#234;ncia em S&#227;o Paulo, onde o CA de colo de &#250;tero caiu para a quarta posi&#231;&#227;o. Em Manaus, o CA de colo de &#250;tero &#233; o mais incidente.<xref ref-type="bibr" rid="B37">37</xref></p>
          <p>O CA mais prevalente em mulheres no Brasil e na maioria das capitais estudadas &#233; o CA de mama. O tratamento desse tumor se d&#225; pela cirurgia, quimioterapia, radioterapia, assim como o uso de bloqueadores hormonais. Apesar de in&#250;meros avan&#231;os no tratamento, como o uso de exames imunohistoqu&#237;micos e agentes anti-HER<sub>2</sub>, o acesso a esses tratamentos pelo sistema p&#250;blico foi um pouco mais tardio, e muito provavelmente n&#227;o impactou nos desfechos das pacientes analisadas neste trabalho.</p>
          <p>Algumas limita&#231;&#245;es precisam ser consideradas na an&#225;lise dos resultados ora apresentados. Destaca-se o fato de os dados serem oriundos das declara&#231;&#245;es de &#243;bito, cuja imprecis&#227;o &#233; not&#243;ria. O diagn&#243;stico do CA &#233; confirmado por exames de imagem e/ou anatomopatol&#243;gicos, que conferem maior fidedignidade. J&#225; as DAC t&#234;m diagn&#243;stico baseado em avalia&#231;&#227;o predominantemente cl&#237;nica. Deve-se considerar que os dados apresentados referem-se &#224; popula&#231;&#227;o urbana de grandes centros, e extrapola&#231;&#245;es para cidades de m&#233;dio e pequeno porte podem n&#227;o ser adequadas, e os dados n&#227;o necessariamente reprodut&#237;veis. Ainda, os determinantes de mortalidade podem sofrer interfer&#234;ncia das pol&#237;ticas p&#250;blicas e as estimativas de tend&#234;ncias podem ser modificadas por tais a&#231;&#245;es.</p>
        </sec>
      </sec>
      <sec sec-type="conclusions">
        <title>Conclus&#245;es</title>
        <p>De modo geral e ressalvadas exce&#231;&#245;es regionais espec&#237;ficas, houve queda progressiva e expressiva da taxa de mortalidade por DAC nas capitais brasileiras estudadas em oposi&#231;&#227;o &#224; manuten&#231;&#227;o ou discreta eleva&#231;&#227;o da mortalidade por c&#226;ncer entre 2000 e 2015. Tais fen&#244;menos concorrem para o cruzamento das curvas de mortalidade com perspectivas de predom&#237;nio de mortes por CA j&#225; ocorrido ou casos novos.</p>
      </sec>
    </body>
    <back>
      <fn-group>
        <fn fn-type="supported-by">
          <p>
            <bold>Fontes de financiamento</bold>
          </p>
          <p>O presente estudo foi parcialmente financiado pela FAPERJ.</p>
        </fn>
        <fn fn-type="other">
          <p>
            <bold>Vincula&#231;&#227;o acad&#234;mica</bold>
          </p>
          <p>N&#227;o h&#225; vincula&#231;&#227;o deste estudo a programas de p&#243;s-gradua&#231;&#227;o.</p>
        </fn>
        <fn fn-type="other">
          <p>
            <bold>Aprova&#231;&#227;o &#233;tica e consentimento informado</bold>
          </p>
          <p>Este artigo n&#227;o cont&#233;m estudos com humanos ou animais realizados por nenhum dos autores.</p>
        </fn>
      </fn-group>
    </back>
  </sub-article>
</article>
