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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">abc</journal-id>
      <journal-id journal-id-type="nlm-ta">Arq Bras Cardiol</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 pub-id-type="publisher-id" specific-use="scielo-v3">wDvtbCvRf6bGrNZfQvZ7cnJ</article-id>
      <article-id pub-id-type="publisher-id" specific-use="scielo-v2">S0066-782X2015002300371</article-id>
      <article-id pub-id-type="publisher-id" specific-use="previous-id">S0066-782X2015005050096</article-id>
      <article-id pub-id-type="doi">10.5935/abc.20150096</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Mortality from Cardiovascular Diseases in the Elderly: Comparative Analysis of Two Five-year Periods</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Piuvezam</surname>
            <given-names>Grasiela</given-names>
          </name>
          <xref ref-type="aff" rid="aff01"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Medeiros</surname>
            <given-names>Wilton Rodrigues</given-names>
          </name>
          <xref ref-type="aff" rid="aff01"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Costa</surname>
            <given-names>Andressa Vellasco</given-names>
          </name>
          <xref ref-type="aff" rid="aff01"/>
          <xref ref-type="corresp" rid="c01"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Emerenciano</surname>
            <given-names>Felipe Fonseca</given-names>
          </name>
          <xref ref-type="aff" rid="aff01"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Santos</surname>
            <given-names>Renata Cristina</given-names>
          </name>
          <xref ref-type="aff" rid="aff01"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Seabra</surname>
            <given-names>Danilo Silveira</given-names>
          </name>
          <xref ref-type="aff" rid="aff01"/>
        </contrib>
      </contrib-group>
      <aff id="aff01">
        <label> </label>
        <institution content-type="orgname">Universidade Federal do Rio Grande do Norte</institution>
        <addr-line>
          <named-content content-type="city">Natal</named-content>
          <named-content content-type="state">RN</named-content>
        </addr-line>
        <country country="BR">Brazil</country>
        <institution content-type="original">Universidade Federal do Rio Grande do Norte, Natal, RN &#8211; Brazil</institution>
        <institution content-type="normalized">Universidade Federal do Rio Grande do Norte</institution>
      </aff>
      <author-notes>
        <corresp id="c01"><bold>Mailing Address: Andressa Vellasco Brito Costa,</bold>Universidade Federal do Rio Grande do Norte. Rua Joana D&#8217;Arc, 1.780, Candel&#225;ria. Postal Code 59065-620, Natal, RN &#8211; Brazil. E-mail:   <email>andressavellasco04@gmail.com</email></corresp>
        <fn fn-type="con">
          <p>
            <bold>Author contributions</bold>
          </p>
          <p>Conception and design of the research and Critical revision of the manuscript for intellectual content: Piuvezam G, Medeiros WR; Acquisition of data, Analysis and interpretation of the data and Statistical analysis: Costa AV, Emerenciano FF, Seabra DS; Obtaining financing: Piuvezam G; Writing of the manuscript: Piuvezam G, Medeiros WR, Costa AV, Emerenciano FF, Seabra DS.</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 pub-type="epub">
        <day>18</day>
        <month>08</month>
        <year>2015</year>
      </pub-date>
      <pub-date pub-type="collection">
        <month>10</month>
        <year>2015</year>
      </pub-date>
      <volume>105</volume>
      <issue>4</issue>
      <fpage>371</fpage>
      <lpage>380</lpage>
      <history>
        <date date-type="received">
          <day>22</day>
          <month>12</month>
          <year>2014</year>
        </date>
        <date date-type="rev-recd">
          <day>04</day>
          <month>05</month>
          <year>2015</year>
        </date>
        <date date-type="accepted">
          <day>06</day>
          <month>05</month>
          <year>2015</year>
        </date>
      </history>
      <permissions>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/">
          <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>
        <sec>
          <title>Background:</title>
          <p>Cardiovascular diseases are the leading cause of death in Brazil. The better understanding of the spatial and temporal distribution of mortality from cardiovascular diseases in the Brazilian elderly population is essential to support more appropriate health actions for each region of the country.</p>
        </sec>
        <sec>
          <title>Objective:</title>
          <p>To describe and to compare geospatially the rates of mortality from cardiovascular disease in elderly individuals living in Brazil by gender in two 5-year periods: 1996 to 2000 and 2006 to 2010.</p>
        </sec>
        <sec>
          <title>Methods:</title>
          <p>This is an ecological study, for which rates of mortality were obtained from DATASUS and the population rates from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estat&#237;stica). An average mortality rate for cardiovascular disease in elderly by gender was calculated for each period. The spatial autocorrelation was evaluated by TerraView 4.2.0 through global Moran index and the formation of clusters by the index of local Moran-LISA.</p>
        </sec>
        <sec>
          <title>Results:</title>
          <p>There was an increase, in the second 5-year period, in the mortality rates in the Northeast and North regions, parallel to a decrease in the South, South-East and Midwest regions. Moreover, there was the formation of clusters with high mortality rates in the second period in Roraima among females, and in Cear&#225;, Pernambuco and Roraima among males.</p>
        </sec>
        <sec>
          <title>Conclusion:</title>
          <p>The increase in mortality rates in the North and Northeast regions is probably related to the changing profile of mortality and improvement in the quality of information, a result of the increase in surveillance and health care measures in these regions.</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>Cardiovascular Diseases / mortality</kwd>
        <kwd>Cardiovascular Diseases / epidemiology</kwd>
        <kwd>Comparative Study</kwd>
        <kwd>Aged</kwd>
      </kwd-group>
      <counts>
        <fig-count count="10"/>
        <table-count count="2"/>
        <equation-count count="0"/>
        <ref-count count="22"/>
        <page-count count="10"/>
      </counts>
    </article-meta>
  </front>
  <body>
    <sec sec-type="intro">
      <title>Introduction</title>
      <p>Noncommunicable Diseases (NCD), according to the worldwide trend of recent decades, currently determine the majority of causes of death in Brazil, changing the profile of diseases that occur in the population, being higher than mortality rates from infectious and parasitic diseases<xref ref-type="bibr" rid="B01">1</xref>.</p>
      <p>In the country, the NCD in 2007 accounted for 72% of causes of death and affected more individuals that belong to vulnerable groups, such as the elderly<xref ref-type="bibr" rid="B02">2</xref>. Over the past decade, cardiovascular diseases (CVD) accounted for 50% of the mortality of all the NCD<xref ref-type="bibr" rid="B03">3</xref>. According to data from the Ministry of Health, NCD corresponded to the first cause of death in Brazil and accounted in 2008 for 40.8% of deaths of individuals aged 60 or older<xref ref-type="bibr" rid="B04">4</xref>.</p>
      <p>Although CVD are the leading cause of death in Brazil, few studies have addressed the spatial and temporal distribution of mortality caused by them, especially regarding the elderly age group. Mortality from CVD is a phenomenon that has different risk factors, from behavioral and social factors to genetic ones and, therefore, one can infer that their distribution can be shown in different ways, as the context in which different population groups are inserted is variable. In this sense, it can be observed that the territory configurations, as well as the process of urbanization, have a direct impact on the way several population groups deal with this group of diseases<xref ref-type="bibr" rid="B05">5</xref>.</p>
      <p>From this perspective, this study aimed to describe the geographical distribution of mortality from CVD in the elderly population in Brazil by gender, in the five-year period of 1996 to 2000 and from 2006 to 2010, and compare them in both periods. The search for a better understanding of the spatial and temporal distribution of these rates is critical for planning evidence-based sustainable public policies. This set of information can contribute to a better control and prevention of CVD, as it supports the achievement of more targeted actions for each country region, aiming thereby to reduce health inequalities.</p>
    </sec>
    <sec sec-type="methods">
      <title>Methods</title>
      <p>This was an ecological study, of which the area analysis units were the Brazilian states, which constitute 27 elements in the total sample. Data considered in the study are covered by the five-year periods of 1996-2000 and 2006-2010.</p>
      <p>The study population was a group of elderly residents in Brazil who died from CVD in the analyzed period. For inclusion in the study, it was considered an elderly any individual aged &#8805; 60 years<xref ref-type="bibr" rid="B06">6</xref>.</p>
      <p>Data were obtained from the Data Processing Department of the Unified Health System (DATASUS), from the mortality information system (SIM). These data are grouped by SIM through the records of its legal instrument collection, the death certificate (DC). This information is available on the Internet for free consultation as data amassed by municipalities, that is, they were not individually and nominally collected. In this sense, there is no possibility of physical or moral damage from the individual's and community&#8217;s perspective, as the principles contained in Resolution 466 of 12 December 2012 were followed. Therefore, this article did not require approval by the Ethics Committee of Universidade Federal do Rio Grande do Norte (CEP-UFRN).</p>
      <p>The outcome variables were the adjusted mortality rate from cardiovascular diseases (MRCD) in female elderly (MRCDf) and male (MRCDm) for each state. MRCD is calculated by the ratio of the number of elderly deaths from CVD by gender in Brazil in the assessed period and the elderly population in Brazil in the same period, by gender and per thousand inhabitants.</p>
      <p>It is appropriate to clarify that the present investigation was based on the rate adjusted by the year 2003 population (corresponding to half of the study period - five-year periods of 1996-2000 and 2006-2010), as well as by age groups detailed by five-year intervals, from 60 years to 80 years and older, according to the standardization established by DATASUS.</p>
      <p>We chose to perform, simultaneously, the comparison of mortality rates from III-defined causes (IDC) in both periods, to better demonstrate the information qualification process during the assessed period. Thus, a similar methodology was used to obtain adjusted rates of mortality from IDC in male and female elderly individuals.</p>
      <p>Therefore, for comparative analysis, data were selected for two five-year periods, the first from 1996 to 2000, and the second from 2006 to 2010. The necessary population data to calculate the MRCD for each municipality were obtained from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estat&#237;stica - IBGE), available in DATASUS site. The TabWin software was used for data tabulation and calculation of mortality rates. The analysis of this coefficient in two different five-year periods allowed this study to assess changes in the epidemiology of mortality from CVD in Brazil, focusing on their geospatial characteristics.</p>
      <p>This rate was calculated for each year of the two assessed five-year periods. Then, for each five-year period, using arithmetic mean, we obtained the mean mortality rate from cardiovascular diseases (MMRCD) per thousand individuals for each state of Brazil. This MMRCD was then distributed spatially to carry out exploratory and geostatistical analysis.</p>
      <p>The IBGE cartographic shape was used in the study, which was obtained from its site. Initially, thematic maps were built for the two five-year periods, a phase that consisted in the exploratory analysis of spatial data. Their production was carried out using the SIG TerraView 4.2.0. program, in which the distribution amount was divided into five ranges for the legend, through the "equal step" division for the second five-year period, which was the basis for the first period distribution. At this point, the gray scale was chosen for visual comparison. At the time when the legend was created, which was carried out using a color gradient, the darker color represented the group of municipalities with the worse situations.</p>
      <p>The spatial autocorrelation was calculated using the free Software TerraView 4.2.0 through the global Moran index for the MMRCD distribution in both analyzed periods. The value of the global Moran index ranges from -1 to 1. Values close to zero indicate lack of spatial autocorrelation; positive values indicate positive spatial autocorrelation; and negative values indicate negative autocorrelation. Subsequently, the standard analysis of the spatial distribution and the possible cluster formation was performed. For this, we used the local index of Moran-LISA, in order to map the intensity of clusters, considering a p value &lt; 0.05 as statistically significant. The representative map of this situation is the Moran Map.</p>
    </sec>
    <sec sec-type="results">
      <title>Results</title>
      <p>The overall population of elderly individuals in Brazil varied by 17.24% in the first analyzed five-year period, totaling 14,536,029 in 2000. Regarding the period between 2006 and 2010, there was a 30.57% increase in the elderly population, i.e., in the end of the last year of the second five-year period, there were 20,590,599 Brazilians aged 60 and older in absolute numbers. In relative terms, 7.86% of the population of the country was elderly in 1996 and, after 15 years, this number increased to 10.79%. Regarding the elderly population divided by gender, there was an increase between 1996 and 2010 of 38.22% for males and 41.03% for females.</p>
      <p>In the period of 1996-2000, there were 4,629,638 deaths in Brazil, of which 53.8% occurred in individuals aged 60 and older. In the years 2006-2010, 5,396,557 records of deaths were released by SIM, with 60.5% being related to elderly individuals. Converting this analysis to the causes related to Chapter IX - Circulatory Disorders of the International Classification of Diseases and Health-Related Problems (ICD-10)<xref ref-type="bibr" rid="B07">7</xref>, it is possible to see that such diseases accounted for 27.51% of deaths in the general population and 37.42% among the elderly, in the first five-year period. For the second period, these numbers were respectively 29.19% and 37.17%.</p>
      <p>Regarding the eight leading causes of death, <xref ref-type="fig" rid="f01">Graph 1</xref> shows the proportional mortality of the elderly, by gender, in both studied periods. In both genders, in the first five-year period, the three main causes of death corresponded to diseases of the circulatory system in the first place, IDC in the second, and neoplasms in the third; in the second five-year period, the top three were: diseases of the circulatory system, cancer and respiratory diseases.</p>
      <fig id="f01" fig-type="graphic">
        <label>Graph 1</label>
        <caption>
          <title>(A) Proportional mortality among female elderly in Brazil, divided by cause of death in five-year periods, from 1996 to 2000 and 2006 to 2010. (B) Proportional mortality among male elderly in Brazil, divided by cause of death in five-year periods, from 1996 to 2000 and 2006 2010.</title>
          <p>Chapter I: infectious and parasitic diseases; Chapter II: neoplasms; Chapter IV: endocrine, nutritional and metabolic diseases; Chapter IX: circulatory system diseases; Chapter X: respiratory diseases; Chapter XI: digestive diseases; Chapter XVIII: poorly-defined causes; Chapter XX: External causes. Source: Department of Informatics of the Unified Health System (DATASUS) / Ministry of Health.</p>
        </caption>
        <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf01.jpg"/></fig>
      <p>Regarding the categories of most prevalent cause of CVD in Brazil, it can be observed that in the first five years, more than 50% were caused by the following conditions: acute myocardial infarction (21.18%), CVA (19.50 %), heart failure (13.43%) and chronic ischemic heart disease (6.09%). In the second five-year period, it was observed that the most prevalent were: acute myocardial infarction, with 22.05%; hemorrhagic or ischemic CVA, with 15.86%; heart failure, with 9.64% and primary hypertension, with 6.64%.</p>
      <p><xref ref-type="table" rid="t01">Table 1</xref> shows the distribution of MMRCD in the elderly, per thousand inhabitants, by gender, in the Brazilian states in the period from 1996 to 2000 and from 2006 to 2010. The highest rates in the first five year period was concentrated in the South and Southeast states. The lowest value found in Brazil was observed in the state of Maranh&#227;o (4.24% for females and 5.32% for males), in the Northeast, and the highest in the state of Paran&#225; (19.78% for females and 23.07% for males), in the South.</p>
      <table-wrap id="t01">
        <label>Table 1</label>
        <caption>
          <title>Distribution by gender of the mean mortality rate from cardiovascular disease (MMRCD) in the elderly, per thousand inhabitants, in the Brazilian states in the periods 1996-2000 and 2006-2010</title>
        </caption>
        <table frame="hsides" rules="groups">
          <thead>
            <tr>
              <th rowspan="2" align="left">
                <bold>Federation Unit</bold>
              </th>
              <th colspan="2" align="center" style="border-bottom-style:solid; border-bottom-width:thin">
                <bold>First 5-year period (A)</bold>
              </th>
              <th colspan="2" align="center" style="border-bottom-style:solid; border-bottom-width:thin">
                <bold>Second 5-year period (B)</bold>
              </th>
              <th colspan="2" align="center" style="border-bottom-style:solid; border-bottom-width:thin">
                <bold>Delta (B-A)</bold>
              </th>
            </tr>
            <tr>
              <th align="center">
                <bold>Female</bold>
              </th>
              <th align="center">
                <bold>Male</bold>
              </th>
              <th align="center">
                <bold>Female</bold>
              </th>
              <th align="center">
                <bold>Male</bold>
              </th>
              <th align="center">
                <bold>Female</bold>
              </th>
              <th align="center">
                <bold>Male</bold>
              </th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Rond&#244;nia</td>
              <td align="center">13.56</td>
              <td align="center">14.15</td>
              <td align="center">11.75</td>
              <td align="center">13.12</td>
              <td align="center">-1.81</td>
              <td align="center">-1.03</td>
            </tr>
            <tr>
              <td>Acre</td>
              <td align="center">8.31</td>
              <td align="center">9.32</td>
              <td align="center">9.58</td>
              <td align="center">11.96</td>
              <td align="center">1.27</td>
              <td align="center">2.64</td>
            </tr>
            <tr>
              <td>Amazonas</td>
              <td align="center">8.02</td>
              <td align="center">8.94</td>
              <td align="center">7.90</td>
              <td align="center">9.86</td>
              <td align="center">-0.12</td>
              <td align="center">0.92</td>
            </tr>
            <tr>
              <td>Roraima</td>
              <td align="center">11.03</td>
              <td align="center">15.87</td>
              <td align="center">9.49</td>
              <td align="center">12.72</td>
              <td align="center">-1.54</td>
              <td align="center">-3.15</td>
            </tr>
            <tr>
              <td>Par&#225;</td>
              <td align="center">8.69</td>
              <td align="center">9.46</td>
              <td align="center">8.74</td>
              <td align="center">10.72</td>
              <td align="center">0.04</td>
              <td align="center">1.26</td>
            </tr>
            <tr>
              <td>Amap&#225;</td>
              <td align="center">9.76</td>
              <td align="center">12.97</td>
              <td align="center">6.02</td>
              <td align="center">8.72</td>
              <td align="center">-3.73</td>
              <td align="center">-4.25</td>
            </tr>
            <tr>
              <td>Tocantins</td>
              <td align="center">8.33</td>
              <td align="center">9.56</td>
              <td align="center">13.04</td>
              <td align="center">14.54</td>
              <td align="center">4.71</td>
              <td align="center">4.98</td>
            </tr>
            <tr>
              <td>Maranh&#227;o</td>
              <td align="center">4.24</td>
              <td align="center">5.32</td>
              <td align="center">9.84</td>
              <td align="center">13.54</td>
              <td align="center">5.60</td>
              <td align="center">8.22</td>
            </tr>
            <tr>
              <td>Piau&#237;</td>
              <td align="center">6.59</td>
              <td align="center">8.43</td>
              <td align="center">14.68</td>
              <td align="center">17.83</td>
              <td align="center">8.09</td>
              <td align="center">9.39</td>
            </tr>
            <tr>
              <td>Cear&#225;</td>
              <td align="center">8.19</td>
              <td align="center">9.31</td>
              <td align="center">11.04</td>
              <td align="center">13.61</td>
              <td align="center">2.85</td>
              <td align="center">4.30</td>
            </tr>
            <tr>
              <td>Rio Grande do Norte</td>
              <td align="center">7.44</td>
              <td align="center">8.69</td>
              <td align="center">9.99</td>
              <td align="center">12.67</td>
              <td align="center">2.55</td>
              <td align="center">3.98</td>
            </tr>
            <tr>
              <td>Para&#237;ba</td>
              <td align="center">5.43</td>
              <td align="center">6.00</td>
              <td align="center">11.99</td>
              <td align="center">14.00</td>
              <td align="center">6.56</td>
              <td align="center">8.00</td>
            </tr>
            <tr>
              <td>Pernambuco</td>
              <td align="center">12.67</td>
              <td align="center">14.42</td>
              <td align="center">13.51</td>
              <td align="center">15.35</td>
              <td align="center">0.84</td>
              <td align="center">0.93</td>
            </tr>
            <tr>
              <td>Alagoas</td>
              <td align="center">9.04</td>
              <td align="center">10.47</td>
              <td align="center">12.71</td>
              <td align="center">15.32</td>
              <td align="center">3.68</td>
              <td align="center">4.85</td>
            </tr>
            <tr>
              <td>Sergipe</td>
              <td align="center">7.96</td>
              <td align="center">8.58</td>
              <td align="center">11.96</td>
              <td align="center">13.32</td>
              <td align="center">4.01</td>
              <td align="center">4.74</td>
            </tr>
            <tr>
              <td>Bahia</td>
              <td align="center">8.10</td>
              <td align="center">9.01</td>
              <td align="center">8.73</td>
              <td align="center">10.03</td>
              <td align="center">0.63</td>
              <td align="center">1.02</td>
            </tr>
            <tr>
              <td>Minas Gerais</td>
              <td align="center">13.76</td>
              <td align="center">16.25</td>
              <td align="center">9.84</td>
              <td align="center">12.03</td>
              <td align="center">-3.93</td>
              <td align="center">-4.21</td>
            </tr>
            <tr>
              <td>Esp&#237;rito Santo</td>
              <td align="center">13.63</td>
              <td align="center">17.46</td>
              <td align="center">12.50</td>
              <td align="center">15.89</td>
              <td align="center">-1.13</td>
              <td align="center">-1.58</td>
            </tr>
            <tr>
              <td>Rio de Janeiro</td>
              <td align="center">17.14</td>
              <td align="center">22.27</td>
              <td align="center">11.36</td>
              <td align="center">15.77</td>
              <td align="center">-5.78</td>
              <td align="center">-6.50</td>
            </tr>
            <tr>
              <td>S&#227;o Paulo</td>
              <td align="center">18.00</td>
              <td align="center">22.00</td>
              <td align="center">11.51</td>
              <td align="center">14.96</td>
              <td align="center">-6.49</td>
              <td align="center">-7.04</td>
            </tr>
            <tr>
              <td>Paran&#225;</td>
              <td align="center">19.78</td>
              <td align="center">23.07</td>
              <td align="center">12.46</td>
              <td align="center">15.45</td>
              <td align="center">-7.32</td>
              <td align="center">-7.61</td>
            </tr>
            <tr>
              <td>Santa Catarina</td>
              <td align="center">16.36</td>
              <td align="center">19.41</td>
              <td align="center">21.92</td>
              <td align="center">13.50</td>
              <td align="center">5.57</td>
              <td align="center">-5.91</td>
            </tr>
            <tr>
              <td>Rio Grande do Sul</td>
              <td align="center">17.56</td>
              <td align="center">19.45</td>
              <td align="center">11.88</td>
              <td align="center">14.64</td>
              <td align="center">-5.69</td>
              <td align="center">-4.81</td>
            </tr>
            <tr>
              <td>Mato Grosso do Sul</td>
              <td align="center">16.19</td>
              <td align="center">19.26</td>
              <td align="center">12.67</td>
              <td align="center">16.47</td>
              <td align="center">-3.52</td>
              <td align="center">-2.79</td>
            </tr>
            <tr>
              <td>Mato Grosso</td>
              <td align="center">14.80</td>
              <td align="center">16.44</td>
              <td align="center">11.66</td>
              <td align="center">14.30</td>
              <td align="center">-3.14</td>
              <td align="center">-2.14</td>
            </tr>
            <tr>
              <td>Goi&#225;s</td>
              <td align="center">14.41</td>
              <td align="center">15.54</td>
              <td align="center">11.31</td>
              <td align="center">12.99</td>
              <td align="center">-3.10</td>
              <td align="center">-2.55</td>
            </tr>
            <tr>
              <td>Distrito Federal</td>
              <td align="center">17.25</td>
              <td align="center">21.35</td>
              <td align="center">9.63</td>
              <td align="center">14.29</td>
              <td align="center">-7.62</td>
              <td align="center">-7.05</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="TFN01">
            <p>Source: Mortality Information System (SIM). Department of Informatics of the Brazilian Unified Health System (DATASUS)/Ministry of Health, 2015.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>In the period 2006-2010, the lowest rate was found in the state of Amap&#225; (6.02% for females and 8.72% for men) and the highest in the state of Santa Catarina, for elderly women (21,92%) and Mato Grosso do Sul, for males (16.47%). A significant decrease was observed regarding results found in the South, Southeast and Midwest regions. In contrast, the North and Northeast regions showed significant increase in their rates, with emphasis on the states of Piau&#237;, Para&#237;ba, Maranh&#227;o and Tocantins, for both genders.</p>
      <p>The analysis concerning the distribution of MMRCDf and MMRCDm in the Brazilian states is shown in <xref ref-type="fig" rid="f02">Figure 1</xref>. The results were obtained from the mean rate of deaths from cardiovascular diseases in the elderly, in the periods 1996-2000 and 2006-2010, and the significance test for the global Moran's index, under the null hypothesis of absence of spatial autocorrelation.</p>
      <fig id="f02">
        <label>Figure 1</label>
        <caption>
          <title>Thematic maps of the mean mortality rate from cardiovascular diseases (MMRCD) per thousand inhabitants in each Brazilian state and gender in the periods from 1996 to 2000 and from 2006 to 2010. Natal (RN), Brazil, 2015. Source: Department of Informatics the Unified Health System (DATASUS)/ Ministry of Health, 2015.</title>
        </caption>
        <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf02.jpg"/></fig>
      <p>It was observed that the global Moran&#8217;s index for the first five years was 0.225048 for females and 0.209145 for males, with p = 0.09 and 0.16, respectively. In the second five-year period, the value was 0.0887927, with p = 0.21 for elderly women and 0.0842536 and p = 0.21 for men older than 60 years.</p>
      <p>In order to support the results obtained by the aforementioned analysis, the characterization of deaths in the elderly was performed using an analogous methodology, by gender and Brazilian Federation unit, classified as ill-defined causes (IDC) (Chapter XVIII of ICD 10). The global Moran&#8217;s index values found for elderly women and men, in the first five-year period were, respectively: 0.388822 (p = 0.01) and 0.335994 (p = 0.04). For the second five-year period, the values were 0.06128 (p = 0.38) and -0.00415266 (p = 44). Distribution of deaths from IDC can be seen in <xref ref-type="fig" rid="f03">Figure 2</xref>.</p>
      <fig id="f03">
        <label>Figure 2</label>
        <caption>
          <title>Thematic maps of the mean rate of mortality from ill-defined causes (IDC) per thousand inhabitants for each Brazilian state and by gender in the periods from 1996 to 2000 and from 2006 to 2010. Natal (RN), Brazil, 2015. Source: Department of Informatics of the Unified Health System (DATASUS) / Ministry of Health, 2015.</title>
        </caption>
        <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf03.jpg"/></fig>
      <p>To create the maps depicted in <xref ref-type="fig" rid="f04">Figure 3</xref>, we used the interpretation capabilities of the Moran Map, which allows the visualization of the statistically significant spatial autocorrelation areas<xref ref-type="bibr" rid="B08">8</xref> and identify the location of homogeneous regions consisting of states with spatial association, regarding MMRCDf and MMRCDm. Thus, the Federation units were classified according to their location in relation to the Moran scatter plot quadrants: the quadrants 1 (high-high) and 2 (low-low) indicate areas with positive spatial association, i.e., the values were similar to those shown for neighboring states; quadrant 3 (high-low) and 4 (low-high) showed that the results did not follow the global trend and therefore had a negative spatial association, as there were neighbors that had discordant values<xref ref-type="bibr" rid="B08">8</xref>.</p>
      <fig id="f04">
        <label>Figure 3</label>
        <caption>
          <title>Moran Map (significant) of spatial autocorrelation of the variable mean mortality rate from cardiovascular disease by gender, per thousand individuals for each state in Brazil, in the periods 1996-2000, and 2006 to 2010. Natal (RN), Brazil, 2015. CVD: cardiovascular disease; Source: Department of Informatics of the Unified Health System (DATASUS) / Ministry of Health, 2015.</title>
        </caption>
        <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf04.jpg"/></fig>
      <p>Therefore, in the first five-year period, in relation to the outcome variables, clusters were formed in the states of Cear&#225; and Pernambuco for the female gender and in Rio Grande do Norte, Cear&#225; and Pernambuco for the male gender. In the second five-year period, the autocorrelation was observed in the states of Roraima, for the group of elderly women, and Roraima, Cear&#225; and Pernambuco, in relation to males.</p>
      <p>It is noteworthy the fact that for the spatial autocorrelation analysis performed for deaths classified as IDC, as seen in <xref ref-type="fig" rid="f05">Figure 4</xref>, the methods described for the MMRCDf and MMRCDm variables were used. Therefore, clusters were formed, in the first five-year period, in Cear&#225; and Pernambuco for both genders. In the second five-year period, the autocorrelation was observed in the states of Roraima, Acre, Rondonia, Goias and Minas Gerais for the elderly females, and Roraima, Acre, Goias and Minas Gerais for males.</p>
      <fig id="f05">
        <label>Figure 4</label>
        <caption>
          <title>Moran Map (significant) of spatial autocorrelation of the mean mortality rate from ill-defined causes (IDC) by gender, per thousand individuals for each state in Brazil, in the periods 1996-2000, and 2006 to 2010. Natal (RN), Brazil, 2015. Source: Department of Informatics of the Unified Health System (DATASUS) / Ministry of Health, 2015.</title>
        </caption>
        <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf05.jpg"/></fig>
    </sec>
    <sec sec-type="discussion">
      <title>Discussion</title>
      <p>Based on the results, it was observed that in the first five-year period, the highest MRCD were mainly concentrated in the South and Southeast regions. In the second five-year period, there was a considerable increase in the rates observed in the Northeast, as well as a decrease in the rates in the South and Southeast regions. There was also a slight increase in mortality rates in the North region.</p>
      <p>The emergence of a cluster in the states of Rio Grande do Norte, Cear&#225; and Pernambuco, as seen in the Moran Map for both genders, in the period 1996-2000, allowed the identification of a positive spatial association of MMRCD, indicating the similarity of this rate between those states and their neighbors. Thus, a homogeneous area in the Northeast was observed, characterized by high levels of MMRCD that appeared in the second five-year period.</p>
      <p>The literature shows that mortality statistics, as shown in this study, are the most often used to obtain health status information of a population and to plan necessary actions for health promotion. However, it is of utmost importance to also discuss the proportion of deaths attributed to IDC, as it constitutes one of the indicators used to assess the quality of that information and the correct trend of the mortality analysis<xref ref-type="bibr" rid="B09">9</xref>.</p>
      <p>At the stratification of deaths in the elderly population in Brazil for the period of 1996-2000, the causes identified as ill-defined encompassed the second overall position, with a total of 17.64% of notifications. For the second five-year period, there was substantial improvement, relocating mortality from undetermined causes to the fourth position (8.10%). Therefore, the lowest proportion of notifications from Chapter XVIII of ICD-10 (CMD) indicates more accurate statistics on mortality<xref ref-type="bibr" rid="B10">10</xref>.</p>
      <p>In the country, the highest number deaths from ill-defined causes concentrated in the age group older than 60 years; that is, regarding the data for 2005, 67.2% of deaths from IDC corresponded to this population group. One explanation for the high proportion of deaths from IDC is the difficulty in establishing the underlying cause of death in the elderly. This is probably due to the presence of multiple diseases in the elderly and the influence of age on the clinical expression of signs and symptoms for the correct diagnosis of the underlying cause of death<xref ref-type="bibr" rid="B11">11</xref>. In this sense, the data obtained in the first five years for the analysis carried out on deaths from IDC in the elderly corroborate the above statements, as they show statistically significant results (p &lt; 0.05).</p>
      <p>One question to be assessed is the increased prevalence of CVD in the North and Northeast states during the study period. This fact was possibly related to the information production qualification, both regarding the collection and sending of data to the health management central level. Studies have shown that over the past three decades, advances in health information systems used in Brazil were supported by the development of computer technology and the training of Health Secretariat employees<xref ref-type="bibr" rid="B12">12</xref><sup>,</sup><xref ref-type="bibr" rid="B13">13</xref>.  </p>
      <p>The mortality information is compiled from SIM of the Ministry of Health, which was designed in 1975 and initially covered only some Brazilian states, which already held the collection of this information<xref ref-type="bibr" rid="B12">12</xref>. Another milestone related to the development of the mentioned information system was the creation of the current design of the DO, along the development of a new computerized application, which was first used in 1999<xref ref-type="bibr" rid="B14">14</xref>.</p>
      <p>The information on mortality is obtained from the SIM of the Ministry of Health, designed in 1975 and that initially covered only a few Brazilian states, which already performed this information collection<xref ref-type="bibr" rid="B12">12</xref>. Another milestone related to the development of the aforementioned information system was the creation of the current design of the DC, along with the development of a new computer application, which was first used in 1999<xref ref-type="bibr" rid="B14">14</xref>.</p>
      <p>In 2004, the Health Surveillance Secretariat of the Ministry of Health included the program Percentage Reduction of Death from Ill-defined Causes in the Multi-Year Plan 2004-2008 and the "Investigation of the cause of death&#8221; form was standardized. This program included the data provided by medical or health professionals, or those obtained from medical records and the results of additional tests, to also ensure a more accurate recording of information on the causes of death<xref ref-type="bibr" rid="B15">15</xref>.</p>
      <p>In addition to this investigation, in March 2008, the Ministry of Health launched a project to implement the verbal autopsy in the country as a method to investigate deaths from IDC, so that its analysis would allow the physician to identify the sequence of events that led to the death. Several international studies using verbal autopsy methodology also observed changes in the structure of causes of death, decrease in IDC and identification of external causes, with the most frequent diseases being allocated in the chapter of circulatory diseases and external causes - results that are similar to those observed in this study<xref ref-type="bibr" rid="B16">16</xref>.</p>
      <p>This phenomenon is called the "paradox of information", which is characterized by the relocation of deaths from IDC to other chapters of ICD-10. That is, the variation in the proportion of death notifications from IDC can modify the temporal series of mortality rates for certain groups of causes<xref ref-type="bibr" rid="B17">17</xref>. In this regard, the absolute increase in the number of deaths from diseases of the circulatory system is closely associated to the decrease in notifications originating from Chapter XVIII.</p>
      <p>Another factor to be considered is the significant increase in mortality from CVD among the elderly in the North and Northeast states of the country, a trend less intense in the South, Southeast and Midwest regions, where the increases were discreet. Such regional variation is influenced by the fact that primary and secondary prevention would be more appropriate in more developed regions, with better control of risk factors for CVD, such as smoking, dyslipidemia, diabetes and systemic hypertension<xref ref-type="bibr" rid="B09">9</xref>.</p>
      <p>In a study carried out between 2000 and 2009 in Brazil, it was clear that the coverage provided by the family health strategy was associated with a reduction in MRCD (acute myocardial infarction and CVA). However, it is noteworthy that this study evaluated 1,662 of the 5,507 Brazilian municipalities and used as an exclusion criterion the municipalities with high mortality rates from IDC. Therefore, their results are valid for municipalities where the quality of information is better<xref ref-type="bibr" rid="B18">18</xref><sup>,</sup><xref ref-type="bibr" rid="B19">19</xref>.  </p>
      <p>Another relevant aspect is related to the distribution of medical professionals registered in the Regional Councils of Medicine in their physician per thousand inhabitant ratio. In this regard, the North and Northeast regions have the lowest proportions, 0.98% and 1.19%, respectively, below the national average, which is 1.95%<xref ref-type="bibr" rid="B20">20</xref>. That is, the North and Northeast regions have higher limitation regarding health services provided to the population, a fact that also corroborates the increase in MRCD in the study.  </p>
      <p>Regarding the aspects related to the epidemiological transition, it is possible to estimate that, in general, it occurs together with socioeconomic transformations and, therefore, it shows major demographic differences<xref ref-type="bibr" rid="B21">21</xref>. Hence, the majority of published studies provide evidence of the association between social inequalities and morbimortality. Brazil is the tenth most unequal country in the world in terms of income distribution; even though changes in the economy have resulted in improvements in this regard, they do not seem to have been able to reduce mortality inequalities. Hence, important differences persist in the distribution of morbidity and mortality, both between different Brazilian states and within the same state<xref ref-type="bibr" rid="B22">22</xref>.</p>
      <p>It is worth mentioning that this study has as limiting factor the use of data collected by a Federation state, which can conceal the heterogeneous distribution of deaths and, therefore, mask relevant intrastate differences. Furthermore, the use of secondary data record is subject to several data recording errors and underreporting.</p>
      <p>Another limitation is due to the "ecological fallacy" in which, due to the effects of data aggregation and scale, the results found for a population cannot be repeated at the individual level. In this study, the mortality analysis was based only on the cause of death and there was no analysis of multiple causes; thus, there may be an underestimation of cardiovascular mortality, especially among the elderly, which may have several comorbidities.</p>
    </sec>
    <sec sec-type="conclusions">
      <title>Conclusion</title>
      <p>Based on the analyses, it was observed that the proportion of mortality from circulatory system diseases in the elderly, in the 2006-2010 period, decreased significantly in the South, Southeast and Midwest states, and showed a considerable increase in the North and Northeast regions. These results are consistent with geographical clusters obtained in the aforementioned period, during which spatial autocorrelation was observed between the states of Rio Grande do Norte, Cear&#225;, Pernambuco and Roraima.</p>
      <p>These findings can be explained by the information qualification, reallocation of deaths from ill-defined causes and improved health care. Added to these, other issues were also evaluated, such as changes in the socioeconomic situation of the country, mainly regarding the phase of epidemiological transition in which Brazil currently is, with decreased morbidity and mortality from infectious and parasitic diseases, and increase in the number of deaths due to chronic non-communicable diseases and external injuries.</p>
      <p>Effective planning of health promotion actions originates from the knowledge of a population&#8217;s health status, based on mortality statistics. The quality of information on the causes of death is, therefore, essential. The search for a better understanding of the spatial and temporal distribution of these rates is critical for the planning of evidence-based sustainable public policies. Therefore, the study may contribute to better control and prevention of cardiovascular disease, as it supports the achievement of more targeted actions for the different regions of the country, thus aiming to reduce health inequalities.</p>
      <p>The study was carried out with the authors&#8217; own resources and also received a scientific initiation research grant (PIBIC) from Pr&#243;-Reitoria de Pesquisa da Universidade Federal do Rio Grande do Norte (PROPESQ-UFRN).</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 Bolsa de pesquisa de Inicia&#231;&#227;o Cient&#237;fica (PIBIC) da Pr&#243;-reitoria de Pesquisa da Universidade Federal do Rio Grande do Norte (PROPESQ-UFRN).</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-group>
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  </back>
  <sub-article article-type="translation" id="SA1" xml:lang="pt">
    <front-stub>
      <title-group>
        <article-title>Mortalidade em Idosos por Doen&#231;as Cardiovasculares: An&#225;lise Comparativa de Dois Quinqu&#234;nios</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Piuvezam</surname>
            <given-names>Grasiela</given-names>
          </name>
          <xref ref-type="aff" rid="aff02"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Medeiros</surname>
            <given-names>Wilton Rodrigues</given-names>
          </name>
          <xref ref-type="aff" rid="aff02"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Costa</surname>
            <given-names>Andressa Vellasco</given-names>
          </name>
          <xref ref-type="aff" rid="aff02"/>
          <xref ref-type="corresp" rid="c02"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Emerenciano</surname>
            <given-names>Felipe Fonseca</given-names>
          </name>
          <xref ref-type="aff" rid="aff02"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Santos</surname>
            <given-names>Renata Cristina</given-names>
          </name>
          <xref ref-type="aff" rid="aff02"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Seabra</surname>
            <given-names>Danilo Silveira</given-names>
          </name>
          <xref ref-type="aff" rid="aff02"/>
        </contrib>
      </contrib-group>
      <aff id="aff02">
        <institution content-type="orgname">Universidade Federal do Rio Grande do Norte</institution>
        <addr-line>
          <named-content content-type="city">Natal</named-content>
          <named-content content-type="state">RN</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Universidade Federal do Rio Grande do Norte, Natal, RN &#8211; Brasil</institution>
        <institution content-type="original">Universidade Federal do Rio Grande do Norte</institution>
      </aff>
      <author-notes>
        <corresp id="c02"><bold>Correspond&#234;ncia: Andressa Vellasco Brito Costa,</bold>Universidade Federal do Rio Grande do Norte. Rua Joana D&#8217;Arc, 1.780, Candel&#225;ria. CEP 59065-620, Natal, RN &#8211; Brasil. E-mail:   <email>andressavellasco04@gmail.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 e Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante: Piuvezam G, Medeiros WR; Obten&#231;&#227;o de dados, An&#225;lise e interpreta&#231;&#227;o dos dados e An&#225;lise estat&#237;stica: Costa AV, Emerenciano FF, Seabra DS; Obten&#231;&#227;o de financiamento: Piuvezam G; Reda&#231;&#227;o do manuscrito: Piuvezam G, Medeiros WR, Costa AV, Emerenciano FF, Seabra DS.</p>
        </fn>
        <fn fn-type="conflict">
          <p>
            <bold>Potencial conflito de interesse</bold>
          </p>
          <p>Declaro n&#227;o haver conflito de interesses pertinentes.</p>
        </fn>
      </author-notes>
      <abstract>
        <sec>
          <title>Fundamento:</title>
          <p>As doen&#231;as cardiovasculares constituem a primeira causa de morte no Brasil. A melhor compreens&#227;o da distribui&#231;&#227;o espacial e temporal das taxas de mortalidade por doen&#231;as cardiovasculares na popula&#231;&#227;o idosa brasileira &#233; fundamental para embasar a&#231;&#245;es em sa&#250;de mais direcionadas para cada regi&#227;o do pa&#237;s.</p>
        </sec>
        <sec>
          <title>Objetivo:</title>
          <p>Descrever e comparar geoespacialmente as taxas de mortalidade por doen&#231;as cardiovasculares nos idosos residentes no Brasil, por sexo, nos quinqu&#234;nios de 1996 a 2000 e de 2006 a 2010.</p>
        </sec>
        <sec>
          <title>M&#233;todos:</title>
          <p>Trata-se de um estudo ecol&#243;gico, para o qual os dados sobre mortalidade foram obtidos do DATASUS e os populacionais, do Instituto Brasileiro de Geografia e Estat&#237;stica. A taxa m&#233;dia de mortalidade para doen&#231;as cardiovasculares nos idosos, para cada sexo, foi calculada em cada per&#237;odo. A autocorrela&#231;&#227;o espacial dessa vari&#225;vel foi avaliada pelo <italic>TerraView</italic> 4.2.0 por meio do &#237;ndice global de Moran e a forma&#231;&#227;o de aglomerados (<italic>clusters</italic>), pelo &#237;ndice local de Moran-LISA.</p>
        </sec>
        <sec>
          <title>Resultados:</title>
          <p>Observou-se, no segundo quinqu&#234;nio, um aumento da taxa de mortalidade por doen&#231;as cardiovasculares nas Regi&#245;es Norte e Nordeste, concomitantemente &#224; sua diminui&#231;&#227;o no Sul, Sudeste e Centro-Oeste. Tamb&#233;m houve forma&#231;&#227;o de aglomerados com altas taxas de mortalidade no segundo quinqu&#234;nio em Roraima, para o sexo feminino, e no Cear&#225;, Pernambuco e Roraima, para o sexo masculino.</p>
        </sec>
        <sec>
          <title>Conclus&#227;o:</title>
          <p>A eleva&#231;&#227;o das taxas de mortalidade para o Norte e Nordeste possivelmente esteve relacionada &#224; mudan&#231;a do perfil de mortalidade e melhoria na qualidade da informa&#231;&#227;o, resultado do incremento das a&#231;&#245;es de vigil&#226;ncia e assist&#234;ncia &#224; sa&#250;de nessas regi&#245;es.</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="pt">
        <kwd>Doen&#231;as Cardiovasculares / mortalidade</kwd>
        <kwd>Doen&#231;as Cardiovasculares /epidemiologia</kwd>
        <kwd>Estudo Comparativo</kwd>
        <kwd>Idoso</kwd>
      </kwd-group>
    </front-stub>
    <body>
      <sec sec-type="intro">
        <title>Introdu&#231;&#227;o</title>
        <p>Doen&#231;as e agravos n&#227;o transmiss&#237;veis (DANT), acompanhando a tend&#234;ncia mundial das &#250;ltimas d&#233;cadas, passaram a determinar a maioria das causas de &#243;bito no Brasil, modificando o perfil das doen&#231;as que ocorrem na popula&#231;&#227;o, e ultrapassando as taxas de mortalidade por doen&#231;as infecciosas e parasit&#225;rias<xref ref-type="bibr" rid="B01">1</xref>.</p>
        <p>No pa&#237;s, as DANT, em 2007, foram respons&#225;veis por 72% das causas de morte e atingiram de forma mais intensa aqueles pertencentes a grupos vulner&#225;veis, como os idosos<xref ref-type="bibr" rid="B02">2</xref>. Ao longo da &#250;ltima d&#233;cada, as doen&#231;as cardiovasculares (DCV) representaram at&#233; 50% da mortalidade do conjunto das DANT<xref ref-type="bibr" rid="B03">3</xref>. De acordo com dados do Minist&#233;rio da Sa&#250;de, as DANT corresponderam &#224; primeira causa de morte no Brasil, e foram respons&#225;veis, em 2008, por 40,8% dos &#243;bitos de indiv&#237;duos com 60 anos ou mais<xref ref-type="bibr" rid="B04">4</xref>.</p>
        <p>Apesar de as DCV serem a primeira causa de morte no Brasil, poucos estudos abordam a distribui&#231;&#227;o espacial e temporal da mortalidade por elas, principalmente no que se refere &#224; faixa et&#225;ria dos idosos. A mortalidade por DCV &#233; um fen&#244;meno que possui diferentes fatores de risco associados, desde fatores comportamentais e sociais a gen&#233;ticos e, por isso, &#233; poss&#237;vel inferir que sua distribui&#231;&#227;o pode se apresentar de forma diferenciada, uma vez que o contexto no qual diferentes grupos populacionais est&#227;o inseridos &#233; vari&#225;vel. Nesse sentido, pode-se observar que as configura&#231;&#245;es territoriais, bem como o processo de urbaniza&#231;&#227;o, t&#234;m repercuss&#227;o direta na forma como os mais diversos grupos populacionais enfrentam esse conjunto de doen&#231;as<xref ref-type="bibr" rid="B05">5</xref>.</p>
        <p>Nesta perspectiva, esse estudo teve o objetivo de descrever a distribui&#231;&#227;o geogr&#225;fica das taxas de mortalidade por DCV na popula&#231;&#227;o idosa do Brasil por sexo nos quinqu&#234;nios de 1996 a 2000, e 2006 a 2010, al&#233;m de compar&#225;-las em ambos os per&#237;odos. A busca por uma melhor compreens&#227;o da distribui&#231;&#227;o espacial e temporal dessas taxas &#233; fundamental para o planejamento de pol&#237;ticas p&#250;blicas sustent&#225;veis baseadas em evid&#234;ncias. Esse conjunto de informa&#231;&#245;es pode contribuir para um melhor controle e preven&#231;&#227;o das DCV, na medida em que embasa a realiza&#231;&#227;o de a&#231;&#245;es mais direcionadas para cada regi&#227;o do pa&#237;s, visando, assim, &#224; redu&#231;&#227;o das desigualdades em sa&#250;de.</p>
      </sec>
      <sec sec-type="methods">
        <title>M&#233;todos</title>
        <p>Tratou-se de um estudo ecol&#243;gico, cujas unidades de an&#225;lise de &#225;rea foram os Estados brasileiros, que perfazem 27 elementos na amostra total. Os dados considerados no estudo est&#227;o compreendidos nos quinqu&#234;nios de 1996 a 2000 e de 2006 a 2010.</p>
        <p>A popula&#231;&#227;o estudada foi o grupo dos idosos residentes no Brasil que foi a &#243;bito por DCV no per&#237;odo analisado. Para inclus&#227;o no estudo, considerou-se idoso qualquer indiv&#237;duo com idade &#8804; 60 anos<xref ref-type="bibr" rid="B06">6</xref>.</p>
        <p>Os dados foram obtidos do Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS), provenientes do sistema de informa&#231;&#227;o sobre mortalidade (SIM). Esses dados s&#227;o agrupados pelo SIM por meio dos registros de seu instrumento legal de coleta, a declara&#231;&#227;o de &#243;bito (DO). Essas informa&#231;&#245;es est&#227;o dispon&#237;veis na   <italic>internet</italic> para consulta livre na forma de dados agregados por munic&#237;pios, ou seja, as mesmas n&#227;o foram coletadas de maneira individualizada e nominal. Nesse sentido, n&#227;o h&#225; qualquer possibilidade de dano de ordem f&#237;sica ou moral na perspectiva do indiv&#237;duo e das coletividades, por terem sidos respeitados os princ&#237;pios contidos na resolu&#231;&#227;o 466, de 12 de dezembro de 2012. Desse modo, o presente artigo n&#227;o demandou submiss&#227;o ao Comit&#234; de &#201;tica em Pesquisa da Universidade Federal do Rio Grande do Norte (CEP-UFRN).</p>
        <p>As vari&#225;veis de desfecho consideradas foram as taxas ajustadas de mortalidade por doen&#231;as cardiovasculares (TMDC) em idosos do sexo feminino (TMDCf) e masculino (TMDCm) para cada estado. A TMDC &#233; calculada pela raz&#227;o entre o n&#250;mero de &#243;bitos por DCV em idosos por sexo no Brasil no per&#237;odo determinado e a popula&#231;&#227;o idosa no Brasil no mesmo per&#237;odo, por sexo e por mil habitantes.</p>
        <p>Torna-se oportuno explicitar que a presente investiga&#231;&#227;o baseou-se na taxa ajustada pela popula&#231;&#227;o do ano de 2003 (correspondente &#224; metade do per&#237;odo avaliado &#8722; quinqu&#234;nios de 1996 a 2000, e de 2006 a 2010), assim como pelas faixas et&#225;rias detalhadas por intervalos de 5 anos a partir de 60 anos de idade at&#233; 80 anos e mais, conforme a padroniza&#231;&#227;o estabelecida pelo DATASUS.</p>
        <p>Optou-se por realizar, simultaneamente, a compara&#231;&#227;o das taxas de mortalidade por causas mal definidas (CMD) nos dois per&#237;odos, a fim de melhor evidenciar o processo de qualifica&#231;&#227;o da informa&#231;&#227;o no per&#237;odo avaliado. Dessa forma, utilizou-se metodologia an&#225;loga para obter as taxas ajustadas de mortalidade por CMD em idosos do sexo feminino e masculino.</p>
        <p>Assim, foram selecionados, para a an&#225;lise comparativa, dados relativos a dois quinqu&#234;nios, sendo o primeiro de 1996 a 2000, e o segundo de 2006 a 2010. Os dados populacionais necess&#225;rios ao c&#225;lculo da TMDC para cada munic&#237;pio foram obtidos do Instituto Brasileiro de Geografia e Estat&#237;stica (IBGE), disponibilizados no pr&#243;prio   <italic>site</italic> do DATASUS. Para a tabula&#231;&#227;o dos dados e c&#225;lculo das taxas de mortalidade, foi usado o <italic>software</italic> TabWin. A an&#225;lise desse coeficiente em dois quinqu&#234;nios diferentes permitiu a este estudo avaliar as mudan&#231;as do perfil epidemiol&#243;gico da mortalidade por DCV no Brasil, com enfoque a suas caracter&#237;sticas geoespaciais.</p>
        <p>Essa taxa foi calculada para cada ano dos dois quinqu&#234;nios estudados. Em seguida, para cada quinqu&#234;nio, por m&#233;dia aritm&#233;tica, obteve-se a taxa m&#233;dia da mortalidade por doen&#231;as cardiovasculares (TMMDC) por mil habitantes para cada Estado do Brasil. Essa TMMDC foi, ent&#227;o, distribu&#237;da espacialmente para a realiza&#231;&#227;o das an&#225;lises explorat&#243;ria e geoestat&#237;stica.</p>
        <p>A base cartogr&#225;fica (<italic>shape</italic>) utilizada foi a do IBGE, obtida em seu pr&#243;prio <italic>site.  </italic>Inicialmente foram constru&#237;dos mapas tem&#225;ticos dos dois quinqu&#234;nios, fase que se constituiu na an&#225;lise explorat&#243;ria dos dados espaciais. Sua produ&#231;&#227;o se deu utilizando o SIG <italic>TerraView 4.2.0.,  </italic>no qual, para a legenda, o quantitativo da distribui&#231;&#227;o foi dividido em cinco faixas, por meio da op&#231;&#227;o de divis&#227;o &#8220;passos iguais&#8221; para o segundo quinqu&#234;nio, que serviu de base para a distribui&#231;&#227;o do primeiro per&#237;odo. Nesse momento, foi escolhida a escala de cinza para a compara&#231;&#227;o visual. Assim, para a confec&#231;&#227;o da composi&#231;&#227;o da legenda, que foi em   <italic>d&#233;grad&#233;</italic>, o tom mais escuro representou o conjunto de munic&#237;pios com a pior situa&#231;&#227;o apresentada.</p>
        <p>A autocorrela&#231;&#227;o espacial foi calculada no <italic>software</italic> livre   <italic>TerraView 4.2.0</italic> por meio do &#237;ndice global de Moran para a distribui&#231;&#227;o da TMMDC nos dois per&#237;odos analisados. O valor do &#237;ndice global de Moran varia de -1 a 1. Valores pr&#243;ximos de zero indicam a inexist&#234;ncia de autocorrela&#231;&#227;o espacial; valores positivos indicam autocorrela&#231;&#227;o espacial positiva; e valores negativos indicam autocorrela&#231;&#227;o negativa. Posteriormente, foi realizada a an&#225;lise do padr&#227;o da distribui&#231;&#227;o espacial e a poss&#237;vel forma&#231;&#227;o de aglomerado (<italic>cluster</italic>). Para isso, foi utilizado o &#237;ndice local de Moran-LISA, com o intuito de mapear a intensidade dos aglomerados, tendo sido considerado o valor de p &lt; 0,05 como de signific&#226;ncia estat&#237;stica. O mapa representativo dessa situa&#231;&#227;o &#233; o <italic>Moran Map.</italic></p>
      </sec>
      <sec sec-type="results">
        <title>Resultados</title>
        <p>A popula&#231;&#227;o total de idosos no Brasil variou em 17,24% no primeiro quinqu&#234;nio analisado, chegando a 14.536.029, no ano 2000. No que tange ao per&#237;odo compreendido entre 2006 e 2010, observou-se aumento percentual de 30,57 na popula&#231;&#227;o idosa, ou seja, no final do &#250;ltimo ano do segundo quinqu&#234;nio, os brasileiros com 60 anos e mais somaram, em n&#250;mero absoluto, 20.590.599. Em termos relativos, 7,86% dos habitantes do pa&#237;s eram idosos em 1996 e, ap&#243;s 15 anos, esse montante subiu para 10,79%. Em rela&#231;&#227;o &#224; popula&#231;&#227;o idosa dividida por sexo, observou-se um aumento, entre o ano de 1996 e 2010, de 38,22% para o sexo masculino e de 41,03% para o feminino.</p>
        <p>No per&#237;odo de 1996 a 2000, foram registrados 4.629.638 &#243;bitos no Brasil, dos quais 53,8% ocorreram em pessoas com 60 anos e mais. J&#225; nos anos de 2006 a 2010, foram lan&#231;ados no SIM 5.396.557 registros de &#243;bitos, sendo 60,5% eram relativos a pessoas idosas. Convertendo essa an&#225;lise para as causas relacionadas ao Cap&#237;tulo IX &#8211; Doen&#231;as do Aparelho Circulat&#243;rio da Classifica&#231;&#227;o Internacional de Doen&#231;as e Problemas Relacionados &#224; Sa&#250;de (CID-10)<xref ref-type="bibr" rid="B07">7</xref>, &#233; poss&#237;vel observar que tais agravos foram respons&#225;veis por 27,51% dos &#243;bitos na popula&#231;&#227;o geral e 37,42% nos idosos, no primeiro quinqu&#234;nio. Para o segundo per&#237;odo, esses valores foram, respectivamente, 29,19% e 37,17%.</p>
        <p>Em rela&#231;&#227;o &#224;s oito principais causas de &#243;bito, observou-se, no <xref ref-type="fig" rid="f06">gr&#225;fico 1</xref>, a mortalidade proporcional de idosos, por sexo, nos dois per&#237;odos avaliados no estudo. Em ambos os sexos, no primeiro quinqu&#234;nio, as tr&#234;s principais causas de &#243;bito corresponderam &#224;s doen&#231;as do sistema circulat&#243;rio, em primeiro lugar, CMD, em segundo, e neoplasias, em terceiro; no segundo quinqu&#234;nio, os tr&#234;s primeiros lugares foram: doen&#231;as do sistema circulat&#243;rio, neoplasias e doen&#231;as do aparelho respirat&#243;rio.</p>
        <fig id="f06" fig-type="graphic">
          <label>Gr&#225;fico 1</label>
          <caption>
            <title>(A) Mortalidade proporcional em idosos do sexo feminino no Brasil, dividida por causa, nos quinqu&#234;nios 1996 a 2000, e 2006 a 2010. (B) Mortalidade proporcional de idosos do sexo masculino no Brasil, dividida por causa, nos quinqu&#234;nios 1996 a 2000, e 2006 a 2010.</title>
            <p>Cap&#237;tulo I: doen&#231;as infecciosas e parasit&#225;rias; Cap&#237;tulo II: neoplasias; Cap&#237;tulo IV: doen&#231;as end&#243;crinas, nutricionais e metab&#243;licas; Cap&#237;tulo IX: doen&#231;as do aparelho circulat&#243;rio; Cap&#237;tulo X: doen&#231;as do aparelho respirat&#243;rio; Cap&#237;tulo XI: doen&#231;as do aparelho digestivo; Cap&#237;tulo XVIII: causas mal definidas; Cap&#237;tulo XX: causas externas. Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS).</p>
          </caption>
          <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf01-pt.jpg"/></fig>
        <p>Em rela&#231;&#227;o &#224;s categorias de causa mais prevalentes para DCV no Brasil, p&#244;de-se destacar que, no primeiro quinqu&#234;nio, mais da metade foi causada pelas seguintes patologias: infarto agudo do mioc&#225;rdio (21,18%), acidente vascular encef&#225;lico (19,50%), insufici&#234;ncia card&#237;aca (13,43%) e doen&#231;a isqu&#234;mica cr&#244;nica do cora&#231;&#227;o (6,09%). No segundo quinqu&#234;nio, observou-se que as mais prevalentes foram infarto agudo do mioc&#225;rdio, com 22,05%, acidente vascular encef&#225;lico hemorr&#225;gico ou isqu&#234;mico, com 15,86%, insufici&#234;ncia card&#237;aca, com 9,64% e hipertens&#227;o essencial, com 6,64%.</p>
        <p>A <xref ref-type="table" rid="t02">Tabela 1</xref> demonstra a distribui&#231;&#227;o da TMMDC em idosos, por mil habitantes, por sexo, nos Estados brasileiros, no per&#237;odo de 1996 a 2000 e de 2006 a 2010. As maiores taxas, no primeiro quinqu&#234;nio, concentraram-se nos Estados das Regi&#245;es Sul e Sudeste. O menor valor encontrado no Brasil foi referente ao Estado do Maranh&#227;o (4,24% para o sexo feminino e 5,32% para o masculino), na Regi&#227;o Nordeste, e o mais elevado ao Paran&#225; (19,78% para o sexo feminino e 23,07% para o masculino), na Regi&#227;o Sul.</p>
        <table-wrap id="t02">
          <label>Tabela 1</label>
          <caption>
            <title>Distribui&#231;&#227;o por sexo da taxa m&#233;dia de mortalidade por doen&#231;as cardiovasculares (TMMDC) em idosos, por mil habitantes, nos estados brasileiros nos per&#237;odos de 1996 a 2000 e 2006 a 2010</title>
          </caption>
          <table frame="hsides" rules="groups">
            <thead>
              <tr>
                <th rowspan="2" align="left">
                  <bold>Unidade da Federa&#231;&#227;o</bold>
                </th>
                <th colspan="2" align="center" style="border-bottom-style:solid; border-bottom-width:thin">
                  <bold>Primeiro quinqu&#234;nio (A)</bold>
                </th>
                <th colspan="2" align="center" style="border-bottom-style:solid; border-bottom-width:thin">
                  <bold>Segundo quinqu&#234;nio (B)</bold>
                </th>
                <th colspan="2" align="center" style="border-bottom-style:solid; border-bottom-width:thin">
                  <bold>Delta (B-A)</bold>
                </th>
              </tr>
              <tr>
                <th align="center">
                  <bold>Feminino</bold>
                </th>
                <th align="center">
                  <bold>Masculino</bold>
                </th>
                <th align="center">
                  <bold>Feminino</bold>
                </th>
                <th align="center">
                  <bold>Masculino</bold>
                </th>
                <th align="center">
                  <bold>Feminino</bold>
                </th>
                <th align="center">
                  <bold>Masculino</bold>
                </th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td>Rond&#244;nia</td>
                <td align="center">13,56</td>
                <td align="center">14,15</td>
                <td align="center">11,75</td>
                <td align="center">13,12</td>
                <td align="center">-1,81</td>
                <td align="center">-1,03</td>
              </tr>
              <tr>
                <td>Acre</td>
                <td align="center">8,31</td>
                <td align="center">9,32</td>
                <td align="center">9,58</td>
                <td align="center">11,96</td>
                <td align="center">1,27</td>
                <td align="center">2,64</td>
              </tr>
              <tr>
                <td>Amazonas</td>
                <td align="center">8,02</td>
                <td align="center">8,94</td>
                <td align="center">7,90</td>
                <td align="center">9,86</td>
                <td align="center">-0,12</td>
                <td align="center">0,92</td>
              </tr>
              <tr>
                <td>Roraima</td>
                <td align="center">11,03</td>
                <td align="center">15,87</td>
                <td align="center">9,49</td>
                <td align="center">12,72</td>
                <td align="center">-1,54</td>
                <td align="center">-3,15</td>
              </tr>
              <tr>
                <td>Par&#225;</td>
                <td align="center">8,69</td>
                <td align="center">9,46</td>
                <td align="center">8,74</td>
                <td align="center">10,72</td>
                <td align="center">0,04</td>
                <td align="center">1,26</td>
              </tr>
              <tr>
                <td>Amap&#225;</td>
                <td align="center">9,76</td>
                <td align="center">12,97</td>
                <td align="center">6,02</td>
                <td align="center">8,72</td>
                <td align="center">-3,73</td>
                <td align="center">-4,25</td>
              </tr>
              <tr>
                <td>Tocantins</td>
                <td align="center">8,33</td>
                <td align="center">9,56</td>
                <td align="center">13,04</td>
                <td align="center">14,54</td>
                <td align="center">4,71</td>
                <td align="center">4,98</td>
              </tr>
              <tr>
                <td>Maranh&#227;o</td>
                <td align="center">4,24</td>
                <td align="center">5,32</td>
                <td align="center">9,84</td>
                <td align="center">13,54</td>
                <td align="center">5,60</td>
                <td align="center">8,22</td>
              </tr>
              <tr>
                <td>Piau&#237;</td>
                <td align="center">6,59</td>
                <td align="center">8,43</td>
                <td align="center">14,68</td>
                <td align="center">17,83</td>
                <td align="center">8,09</td>
                <td align="center">9,39</td>
              </tr>
              <tr>
                <td>Cear&#225;</td>
                <td align="center">8,19</td>
                <td align="center">9,31</td>
                <td align="center">11,04</td>
                <td align="center">13,61</td>
                <td align="center">2,85</td>
                <td align="center">4,30</td>
              </tr>
              <tr>
                <td>Rio Grande do Norte</td>
                <td align="center">7,44</td>
                <td align="center">8,69</td>
                <td align="center">9,99</td>
                <td align="center">12,67</td>
                <td align="center">2,55</td>
                <td align="center">3,98</td>
              </tr>
              <tr>
                <td>Para&#237;ba</td>
                <td align="center">5,43</td>
                <td align="center">6,00</td>
                <td align="center">11,99</td>
                <td align="center">14,00</td>
                <td align="center">6,56</td>
                <td align="center">8,00</td>
              </tr>
              <tr>
                <td>Pernambuco</td>
                <td align="center">12,67</td>
                <td align="center">14,42</td>
                <td align="center">13,51</td>
                <td align="center">15,35</td>
                <td align="center">0,84</td>
                <td align="center">0,93</td>
              </tr>
              <tr>
                <td>Alagoas</td>
                <td align="center">9,04</td>
                <td align="center">10,47</td>
                <td align="center">12,71</td>
                <td align="center">15,32</td>
                <td align="center">3,68</td>
                <td align="center">4,85</td>
              </tr>
              <tr>
                <td>Sergipe</td>
                <td align="center">7,96</td>
                <td align="center">8,58</td>
                <td align="center">11,96</td>
                <td align="center">13,32</td>
                <td align="center">4,01</td>
                <td align="center">4,74</td>
              </tr>
              <tr>
                <td>Bahia</td>
                <td align="center">8,10</td>
                <td align="center">9,01</td>
                <td align="center">8,73</td>
                <td align="center">10,03</td>
                <td align="center">0,63</td>
                <td align="center">1,02</td>
              </tr>
              <tr>
                <td>Minas Gerais</td>
                <td align="center">13,76</td>
                <td align="center">16,25</td>
                <td align="center">9,84</td>
                <td align="center">12,03</td>
                <td align="center">-3,93</td>
                <td align="center">-4,21</td>
              </tr>
              <tr>
                <td>Esp&#237;rito Santo</td>
                <td align="center">13,63</td>
                <td align="center">17,46</td>
                <td align="center">12,50</td>
                <td align="center">15,89</td>
                <td align="center">-1,13</td>
                <td align="center">-1,58</td>
              </tr>
              <tr>
                <td>Rio de Janeiro</td>
                <td align="center">17,14</td>
                <td align="center">22,27</td>
                <td align="center">11,36</td>
                <td align="center">15,77</td>
                <td align="center">-5,78</td>
                <td align="center">-6,50</td>
              </tr>
              <tr>
                <td>S&#227;o Paulo</td>
                <td align="center">18,00</td>
                <td align="center">22,00</td>
                <td align="center">11,51</td>
                <td align="center">14,96</td>
                <td align="center">-6,49</td>
                <td align="center">-7,04</td>
              </tr>
              <tr>
                <td>Paran&#225;</td>
                <td align="center">19,78</td>
                <td align="center">23,07</td>
                <td align="center">12,46</td>
                <td align="center">15,45</td>
                <td align="center">-7,32</td>
                <td align="center">-7,61</td>
              </tr>
              <tr>
                <td>Santa Catarina</td>
                <td align="center">16,36</td>
                <td align="center">19,41</td>
                <td align="center">21,92</td>
                <td align="center">13,50</td>
                <td align="center">5,57</td>
                <td align="center">-5,91</td>
              </tr>
              <tr>
                <td>Rio Grande do Sul</td>
                <td align="center">17,56</td>
                <td align="center">19,45</td>
                <td align="center">11,88</td>
                <td align="center">14,64</td>
                <td align="center">-5,69</td>
                <td align="center">-4,81</td>
              </tr>
              <tr>
                <td>Mato Grosso do Sul</td>
                <td align="center">16,19</td>
                <td align="center">19,26</td>
                <td align="center">12,67</td>
                <td align="center">16,47</td>
                <td align="center">-3,52</td>
                <td align="center">-2,79</td>
              </tr>
              <tr>
                <td>Mato Grosso</td>
                <td align="center">14,80</td>
                <td align="center">16,44</td>
                <td align="center">11,66</td>
                <td align="center">14,30</td>
                <td align="center">-3,14</td>
                <td align="center">-2,14</td>
              </tr>
              <tr>
                <td>Goi&#225;s</td>
                <td align="center">14,41</td>
                <td align="center">15,54</td>
                <td align="center">11,31</td>
                <td align="center">12,99</td>
                <td align="center">-3,10</td>
                <td align="center">-2,55</td>
              </tr>
              <tr>
                <td>Distrito Federal</td>
                <td align="center">17,25</td>
                <td align="center">21,35</td>
                <td align="center">9,63</td>
                <td align="center">14,29</td>
                <td align="center">-7,62</td>
                <td align="center">-7,05</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn id="TFN18">
              <p>Fonte: Sistema de Informa&#231;&#245;es sobre Mortalidade (SIM). Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS)/Minist&#233;rio da Sa&#250;de, 2015.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>No per&#237;odo de 2006 a 2010, a menor taxa esteve no Estado do Amap&#225; (6,02% para o sexo feminino e 8,72% para o masculino) e a maior no Estado de Santa Catarina, em rela&#231;&#227;o &#224;s mulheres idosas (21,92%), e de Mato Grosso do Sul, para o sexo masculino (16,47%). Constatou-se que houve sens&#237;vel diminui&#231;&#227;o nos resultados encontrados nas Regi&#245;es Sul, Sudeste e Centro-Oeste. Em contrapartida, as Regi&#245;es Norte e Nordeste apresentaram aumento consider&#225;vel em suas taxas, com &#234;nfase para os Estados do Piau&#237;, Para&#237;ba, Maranh&#227;o e Tocantins, para ambos os sexos.</p>
        <p>A an&#225;lise referente &#224; distribui&#231;&#227;o da TMMDCf e TMMDCm nos Estados brasileiros encontra-se exposta na <xref ref-type="fig" rid="f07">Figura 1</xref>. Os resultados foram obtidos da taxa m&#233;dia de &#243;bitos por doen&#231;as do aparelho circulat&#243;rio, em idosos, nos per&#237;odos de 1996 a 2000, e 2006 a 2010, e do teste de signific&#226;ncia para o &#237;ndice global de Moran, sob a hip&#243;tese nula de aus&#234;ncia de autocorrela&#231;&#227;o espacial.</p>
        <fig id="f07">
          <label>Figura 1</label>
          <caption>
            <title>Mapas tem&#225;ticos da taxa m&#233;dia de mortalidade por doen&#231;as cardiovasculares (TMMDC) por mil habitantes para cada estado brasileiro e por sexo nos per&#237;odos entre 1996 a 2000, e de 2006 a 2010. Natal (RN), Brasil, 2015. Fonte: Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS)/Minist&#233;rio da Sa&#250;de, 2015.</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf02-pt.jpg"/></fig>
        <p>Observou-se que o &#237;ndice global de Moran, para o primeiro quinqu&#234;nio, foi de 0,225048 para o sexo feminino e de 0,209145 para o masculino, com valor de p = 0,09 e 0,16, respectivamente. Em rela&#231;&#227;o ao segundo quinqu&#234;nio, o valor apresentado foi de 0,0887927, com valor de p = 0,21, para as mulheres idosas, e de 0,0842536 e p = 0,21, para os homens maiores de 60 anos.</p>
        <p>A fim de embasar os resultados obtidos pela an&#225;lise mencionada, realizou-se, ainda, por meio da metodologia an&#225;loga, a caracteriza&#231;&#227;o dos &#243;bitos em idosos, por sexo e unidades da Federa&#231;&#227;o, classificados como CMD (Cap&#237;tulo XVIII da CID 10). Os valores globais de Moran encontrados, para mulheres e homens idosos, no primeiro quinqu&#234;nio, foram, respectivamente: 0,388822 (p = 0,01) e 0,335994 (p = 0,04). Para o segundo per&#237;odo avaliado, os valores foram 0,06128 (p = 0,38) e -0,00415266 (p = 0,44). A distribui&#231;&#227;o dos &#243;bitos por CMD pode ser observada na <xref ref-type="fig" rid="f08">Figura 2</xref>.</p>
        <fig id="f08">
          <label>Figura 2</label>
          <caption>
            <title>Mapas tem&#225;ticos da taxa m&#233;dia de mortalidade por causas mal definidas (CMD) por mil habitantes para cada Estado brasileiro e por sexo nos per&#237;odos entre 1996 a 2000, e de 2006 a 2010. Natal (RN), Brasil, 2015. Fonte: Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS)/Minist&#233;rio da Sa&#250;de, 2015.</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf03-pt.jpg"/></fig>
        <p>Para a confec&#231;&#227;o dos mapas contidos na <xref ref-type="fig" rid="f09">Figura 3</xref>, utilizaram-se os recursos de interpreta&#231;&#227;o do <italic>Moran Map</italic>, o qual permite visualizar as &#225;reas de autocorrela&#231;&#227;o espacial estatisticamente significativa<xref ref-type="bibr" rid="B08">8</xref> e identificar a localiza&#231;&#227;o de regi&#245;es homog&#234;neas constitu&#237;das por Estados com associa&#231;&#227;o espacial, no que diz respeito &#224; TMMDCf e &#224; TMMDCm. Assim, classificaram-se as unidades da Federa&#231;&#227;o de acordo com sua localiza&#231;&#227;o em rela&#231;&#227;o aos quadrantes do diagrama de espalhamento de Moran: os quadrantes 1 (alto-alto) e 2 (baixo-baixo) indicaram &#225;reas com associa&#231;&#227;o espacial positiva, ou seja, os valores eram an&#225;logos aos apresentados pelos Estados vizinhos; os quadrantes 3 (alto-baixo) e 4 (baixo-alto) demonstraram que os resultados n&#227;o acompanharam a tend&#234;ncia global e, por conseguinte, apresentaram associa&#231;&#227;o espacial negativa, pois existiam vizinhos que possu&#237;am valores destoantes<xref ref-type="bibr" rid="B08">8</xref>.</p>
        <fig id="f09">
          <label>Figura 3</label>
          <caption>
            <title>Moran Map (significativo) de autocorrela&#231;&#227;o espacial da vari&#225;vel taxa m&#233;dia de mortalidade por doen&#231;as cardiovasculares, por sexo, por mil habitantes para cada Estado do Brasil, nos per&#237;odos de 1996 a 2000, e 2006 a 2010. Natal (RN), Brasil, 2015. DCV: doen&#231;as cardiovasculares. Fonte: Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS)/Minist&#233;rio da Sa&#250;de, 2015.</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf04-pt.jpg"/></fig>
        <p>Dessa maneira, no primeiro quinqu&#234;nio, em rela&#231;&#227;o &#224;s vari&#225;veis desfecho, formaram-se aglomerados nos Estados do Cear&#225; e Pernambuco para o sexo feminino e Rio Grande do Norte, Cear&#225; e Pernambuco para o masculino. J&#225; no segundo quinqu&#234;nio, a autocorrela&#231;&#227;o foi observada nos Estados de Roraima, para o conjunto de mulheres idosas, e Roraima, Cear&#225; e Pernambuco, em rela&#231;&#227;o aos indiv&#237;duos do sexo masculino.</p>
        <p>Importante salientar que, para a an&#225;lise de autocorrela&#231;&#227;o espacial realizada para os &#243;bitos classificados como CMD, conforme observado na <xref ref-type="fig" rid="f10">Figura 4</xref>, foram utilizados os m&#233;todos descritos para as vari&#225;veis TMMDCf e TMMDCm. Com isso, formaram-se aglomerados, no primeiro quinqu&#234;nio, no Cear&#225; e em Pernambuco para ambos os sexos. No segundo quinqu&#234;nio, observou-se a autocorrela&#231;&#227;o nos Estados de Roraima, Acre, Rond&#244;nia, Goi&#225;s e Minas Gerais para a popula&#231;&#227;o idosa do sexo feminino, e Roraima, Acre, Goi&#225;s e Minas Gerais para o sexo masculino.</p>
        <fig id="f10">
          <label>Figura 4</label>
          <caption>
            <title>Moran Map (significativo) de autocorrela&#231;&#227;o espacial da taxa m&#233;dia da mortalidade por causas mal definidas (CMD), por sexo, por mil habitantes para cada Estado do Brasil, nos per&#237;odos de 1996 a 2000, e 2006 a 2010. Natal (RN), Brasil, 2015. Fonte: Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS)/ Minist&#233;rio da Sa&#250;de 2015.</title>
          </caption>
          <graphic xlink:href="1678-4170-abc-105-04-0371-50096-gf05-pt.jpg"/></fig>
      </sec>
      <sec sec-type="discussion">
        <title>Discuss&#227;o</title>
        <p>A partir dos resultados obtidos, foi constatado que, no primeiro quinqu&#234;nio, as maiores TMMDC estavam concentradas principalmente nas Regi&#245;es Sul e Sudeste. No segundo per&#237;odo analisado, existiu consider&#225;vel incremento nas taxas dos dados referentes ao Nordeste do pa&#237;s, al&#233;m de diminui&#231;&#227;o das taxas nas Regi&#245;es Sul e Sudeste. Observou-se, ainda, discreta eleva&#231;&#227;o na taxa de mortalidade na Regi&#227;o Norte.</p>
        <p>O surgimento de um <italic>cluster  </italic>nos Estados do Rio Grande do Norte, Cear&#225; e Pernambuco, visualizado no <italic>Moran Map</italic>, para ambos os sexos, no per&#237;odo de 1996 a 2000, permitiu identificar uma associa&#231;&#227;o espacial positiva da TMMDC, indicando similaridade dessa taxa entre esses Estados e seus vizinhos. Desse modo, houve forma&#231;&#227;o de uma &#225;rea homog&#234;nea no Nordeste, marcada por elevados valores da TMMDC, surgidos no segundo quinqu&#234;nio.</p>
        <p>A literatura mostra que as estat&#237;sticas de mortalidade, como a apresentada no presente estudo, constituem a forma mais utilizada para conhecer o estado de sa&#250;de da popula&#231;&#227;o e para o planejamento de a&#231;&#245;es necess&#225;rias para a promo&#231;&#227;o de sa&#250;de. Todavia, &#233; de fundamental import&#226;ncia que se discuta tamb&#233;m a propor&#231;&#227;o de mortes atribu&#237;das a CMD, j&#225; que ela constitue um dos indicadores utilizados para a avalia&#231;&#227;o da qualidade dessas informa&#231;&#245;es e para a orienta&#231;&#227;o correta da an&#225;lise da mortalidade<xref ref-type="bibr" rid="B09">9</xref>.</p>
        <p>Na estratifica&#231;&#227;o dos &#243;bitos, na popula&#231;&#227;o idosa no Brasil, para o per&#237;odo de 1996 a 2000, as causas indicadas como mal definidas abarcavam a segunda coloca&#231;&#227;o geral, perfazendo um total de 17,64% das notifica&#231;&#245;es. Para o segundo quinqu&#234;nio, observou-se substancial melhoria, realocando a mortalidade por causas indeterminadas para a quarta posi&#231;&#227;o (8,10%). Dessa forma, a menor propor&#231;&#227;o de notifica&#231;&#245;es a partir do Cap&#237;tulo XVIII da CID-10 (CMD) indica maior exatid&#227;o das estat&#237;sticas sobre mortalidade<xref ref-type="bibr" rid="B10">10</xref>.</p>
        <p>No pa&#237;s, a maior quantidade de &#243;bitos mal definidos concentra-se na faixa de idade acima de 60 anos; isto &#233;, em dados referentes a 2005, 67,2% das mortes por CMD correspondiam a esse grupo populacional. Uma das explica&#231;&#245;es para a elevada propor&#231;&#227;o de &#243;bitos por CMD &#233; a dificuldade em se estabelecer a causa b&#225;sica do &#243;bito nos idosos. Esta, provavelmente, &#233; consequ&#234;ncia da presen&#231;a de m&#250;ltiplas doen&#231;as no idoso e da influ&#234;ncia da idade na express&#227;o cl&#237;nica de sinais e sintomas para o diagn&#243;stico correto da causa b&#225;sica do &#243;bito<xref ref-type="bibr" rid="B11">11</xref>. Nesse sentido, os dados obtidos no primeiro quinqu&#234;nio para a an&#225;lise realizada a partir das CMD de &#243;bitos em idosos corroboram as afirmativas acima, por apresentarem resultados estatisticamente significativos (p &lt; 0,05).</p>
        <p>Uma quest&#227;o a ser analisada &#233; o aumento da preval&#234;ncia de DCV nos Estados do Norte e Nordeste do pa&#237;s durante o per&#237;odo estudado. Tal fato possivelmente esteve relacionado com a qualifica&#231;&#227;o na produ&#231;&#227;o da informa&#231;&#227;o, tanto na coleta quanto no envio para o n&#237;vel central da gest&#227;o de sa&#250;de. Estudos demonstram que, nas &#250;ltimas tr&#234;s d&#233;cadas, o avan&#231;o nos sistemas de informa&#231;&#245;es em sa&#250;de, utilizados no Brasil, foi embasado no progresso da tecnologia em inform&#225;tica e em a&#231;&#245;es de treinamento de funcion&#225;rios das Secretarias de Sa&#250;de<xref ref-type="bibr" rid="B12">12</xref><sup>,</sup><xref ref-type="bibr" rid="B13">13</xref>.</p>
        <p>As informa&#231;&#245;es sobre mortalidade s&#227;o compiladas a partir do SIM, do Minist&#233;rio da Sa&#250;de, o qual foi idealizado no ano de 1975 e, inicialmente, abrangia apenas alguns Estados brasileiros, que j&#225; realizavam a coleta desse tipo de informa&#231;&#227;o<xref ref-type="bibr" rid="B12">12</xref>. Outro marco relacionado com o desenvolvimento do sistema de informa&#231;&#227;o mencionado foi a cria&#231;&#227;o do atual desenho da DO, juntamente do desenvolvimento de um novo aplicativo informatizado, que come&#231;ou a ser utilizado em 1999<xref ref-type="bibr" rid="B14">14</xref>. Em 2004, a Secretaria de Vigil&#226;ncia em Sa&#250;de do Minist&#233;rio da Sa&#250;de inseriu o Programa Redu&#231;&#227;o do Percentual de &#211;bitos com Causa Mal Definida no Plano Plurianual 2004-2008, sendo padronizado o formul&#225;rio &#8220;Investiga&#231;&#227;o da causa do &#243;bito&#8221;. Esse programa contemplou os dados fornecidos por profissionais m&#233;dicos ou de sa&#250;de, ou os obtidos nos prontu&#225;rios m&#233;dicos e de resultados de exames complementares, de modo a tamb&#233;m garantir um registro mais acurado das informa&#231;&#245;es sobre as causas de morte<xref ref-type="bibr" rid="B15">15</xref>.</p>
        <p>Para complementar essa investiga&#231;&#227;o, em mar&#231;o de 2008, o Minist&#233;rio da Sa&#250;de lan&#231;ou um projeto para implanta&#231;&#227;o da aut&#243;psia verbal no pa&#237;s como um m&#233;todo para investiga&#231;&#227;o de &#243;bitos por CMD, de modo que sua an&#225;lise permitiria ao m&#233;dico identificar a sequ&#234;ncia de eventos que levou ao &#243;bito. V&#225;rios estudos internacionais que utilizaram a metodologia da aut&#243;psia verbal tamb&#233;m observaram mudan&#231;a na estrutura das causas de morte, redu&#231;&#227;o das CMD, identifica&#231;&#227;o de causas externas, sendo as doen&#231;as mais frequentes alocadas no cap&#237;tulo das doen&#231;as do aparelho circulat&#243;rio e das causas externas &#8722; resultados estes semelhantes aos encontrados neste estudo<xref ref-type="bibr" rid="B16">16</xref>.  </p>
        <p>Tal fen&#244;meno &#233; denominado &#8220;paradoxo de informa&#231;&#227;o&#8221;, o qual se caracteriza pela realoca&#231;&#227;o dos &#243;bitos por CMD para outros cap&#237;tulos da CID-10. Isso &#233;, a varia&#231;&#227;o na propor&#231;&#227;o de notifica&#231;&#245;es de &#243;bitos por CMD pode modificar as s&#233;ries temporais das taxas de mortalidade para determinados grupos de causas<xref ref-type="bibr" rid="B17">17</xref>. Nesse sentido, o aumento absoluto no n&#250;mero de &#243;bitos por doen&#231;as do sistema circulat&#243;rio est&#225; intimamente arraigado &#224; diminui&#231;&#227;o de notifica&#231;&#245;es oriundas do Cap&#237;tulo XVIII.</p>
        <p>Outro fator a ser colocado &#233; o expressivo aumento na mortalidade por DCV em idosos nas Regi&#245;es Norte e Nordeste do pa&#237;s, tend&#234;ncia essa menos intensa no Sul, Sudeste e Centro-Oeste, regi&#245;es nas quais os aumentos foram discretos. Tal varia&#231;&#227;o regional &#233; influenciada pelo fato de que as preven&#231;&#245;es prim&#225;rias e as secund&#225;rias seriam mais adequadas nas regi&#245;es mais desenvolvidas, com o melhor controle de fatores de risco para DCV, como tabagismo, dislipidemias, diabetes e hipertens&#227;o arterial sist&#234;mica<xref ref-type="bibr" rid="B09">9</xref>.</p>
        <p>Em estudo realizado entre 2000 e 2009, no Brasil, foi evidenciado que a cobertura pela estrat&#233;gia de sa&#250;de da fam&#237;lia estava associada com a redu&#231;&#227;o das TMDC (infarto agudo do mioc&#225;rdio e acidente vascular encef&#225;lico). No entanto, &#233; fundamental destacar que esse estudo analisou 1.662 dos 5.507 munic&#237;pios brasileiros e utilizou como crit&#233;rio de exclus&#227;o os munic&#237;pios que apresentavam altas taxas de mortalidade por CMD. Portanto, seus resultados s&#227;o v&#225;lidos para munic&#237;pios onde a qualidade da informa&#231;&#227;o &#233; melhor<xref ref-type="bibr" rid="B18">18</xref><sup>,</sup><xref ref-type="bibr" rid="B19">19</xref>.</p>
        <p>Outro aspecto relevante referiu-se &#224; distribui&#231;&#227;o dos profissionais m&#233;dicos registrados nos Conselhos Regionais de Medicina em sua rela&#231;&#227;o m&#233;dico por mil habitantes. Nesse quesito, as Regi&#245;es Norte e Nordeste apresentam as menores propor&#231;&#245;es, 0,98% e 1,19%, respectivamente, abaixo da m&#233;dia nacional, que &#233; de 1,95%<xref ref-type="bibr" rid="B20">20</xref>. Isso &#233;, as Regi&#245;es Norte e Nordeste possuem uma maior limita&#231;&#227;o no que se refere aos servi&#231;os de sa&#250;de prestados &#224; popula&#231;&#227;o, fato que tamb&#233;m corrobora o aumento das TMDC no estudo.</p>
        <p>No que tange aos aspectos relacionados &#224; transi&#231;&#227;o epidemiol&#243;gica, &#233; poss&#237;vel avaliar que, em geral, ela ocorre de maneira conjunta com transforma&#231;&#245;es socioecon&#244;micas e, dessa forma, apresenta grandes diferen&#231;as demogr&#225;ficas<xref ref-type="bibr" rid="B21">21</xref>. Dessa maneira, a maioria dos estudos publicados fornece evid&#234;ncia das rela&#231;&#245;es entre as desigualdades sociais e morbimortalidade. O Brasil &#233; o d&#233;cimo pa&#237;s mais desigual do mundo em termos de distribui&#231;&#227;o de renda; apesar das altera&#231;&#245;es na economia do pa&#237;s terem resultado em melhorias nesse quesito, elas n&#227;o parecem ter sido capazes de reduzir as desigualdades na mortalidade. Persistem, ent&#227;o, importantes diferen&#231;as na distribui&#231;&#227;o da morbidade e mortalidade, tanto entre os Estados brasileiros quanto dentro de um mesmo Estado<xref ref-type="bibr" rid="B22">22</xref>.</p>
        <p>&#201; v&#225;lido salientar que o presente estudo apresenta como fator limitante o uso de dados agregados por Estado da Federa&#231;&#227;o, o que pode ocultar a distribui&#231;&#227;o heterog&#234;nea dos &#243;bitos e, portanto, mascarar diferen&#231;as intraestaduais relevantes. Al&#233;m disso, o uso de dados secund&#225;rios est&#225; sujeito a diversos erros de registro e subnotifica&#231;&#245;es.</p>
        <p>Outra limita&#231;&#227;o deve-se &#224; &#8220;fal&#225;cia ecol&#243;gica&#8221;, na qual, em virtude dos efeitos de agrega&#231;&#227;o dos dados e de escala, os resultados encontrados para uma popula&#231;&#227;o podem n&#227;o se repetir no n&#237;vel do indiv&#237;duo. Nesse estudo, a avalia&#231;&#227;o da mortalidade foi baseada apenas na causa b&#225;sica de &#243;bito e n&#227;o houve an&#225;lise de causas m&#250;ltiplas; com isso, pode haver subestima&#231;&#227;o da mortalidade cardiovascular, especialmente na popula&#231;&#227;o idosa, rica em comorbidades.</p>
      </sec>
      <sec sec-type="conclusions">
        <title>Conclus&#227;o</title>
        <p>A partir das an&#225;lises realizadas, observou-se que a propor&#231;&#227;o da mortalidade em idosos, no per&#237;odo de 2006 a 2010, por doen&#231;as do sistema circulat&#243;rio, diminuiu sensivelmente nos Estados das Regi&#245;es Sul, Sudeste e Centro-Oeste, e apresentou um aumento consider&#225;vel nas Regi&#245;es Norte e Nordeste. Tais resultados est&#227;o de acordo com os aglomerados geogr&#225;ficos obtidos no per&#237;odo acima mencionado, durante o qual se observou autocorrela&#231;&#227;o espacial entre os Estados do Rio Grande do Norte, Cear&#225;, Pernambuco e Roraima. Esses achados podem ser explicados pela qualifica&#231;&#227;o da informa&#231;&#227;o, realoca&#231;&#227;o dos &#243;bitos por causas mal definidas e melhoria da assist&#234;ncia &#224; sa&#250;de. Adicionados a estes, outros quesitos tamb&#233;m foram avaliados, como altera&#231;&#245;es na situa&#231;&#227;o socioecon&#244;mica do pa&#237;s, principalmente no que se refere &#224; fase da transi&#231;&#227;o epidemiol&#243;gica em que se encontra o Brasil, havendo diminui&#231;&#227;o da morbimortalidade por doen&#231;as infecciosas e parasit&#225;rias, e eleva&#231;&#227;o de &#243;bitos por patologias cr&#244;nicas n&#227;o transmiss&#237;veis e agravos externos.</p>
        <p>O planejamento eficaz de a&#231;&#245;es para a promo&#231;&#227;o de sa&#250;de decorre do conhecimento do estado de sa&#250;de da popula&#231;&#227;o, a partir das estat&#237;sticas de mortalidade. A qualidade das informa&#231;&#245;es sobre as causas de morte, assim, &#233; imprescind&#237;vel. A busca por uma melhor compreens&#227;o da distribui&#231;&#227;o espacial e temporal desses &#237;ndices &#233; fundamental para o planejamento de pol&#237;ticas p&#250;blicas sustent&#225;veis baseadas em evid&#234;ncias. Portanto, o estudo pode contribuir para um melhor controle e preven&#231;&#227;o das doen&#231;as cardiovasculares, na medida em que embasa a realiza&#231;&#227;o de a&#231;&#245;es mais direcionadas para cada regi&#227;o do pa&#237;s, visando, assim, &#224; redu&#231;&#227;o das desigualdades em sa&#250;de.</p>
        <p>O estudo foi realizado com recursos pr&#243;prios e tamb&#233;m recebeu uma bolsa de pesquisa de Inicia&#231;&#227;o Cient&#237;fica (PIBIC) da Pr&#243;-Reitoria de Pesquisa da Universidade Federal do Rio Grande do Norte (PROPESQ-UFRN).</p>
        <p>Concep&#231;&#227;o e desenho da pesquisa e Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante: Piuvezam G, Medeiros WR; Obten&#231;&#227;o de dados, An&#225;lise e interpreta&#231;&#227;o dos dados e An&#225;lise estat&#237;stica: Costa AV, Emerenciano FF, Seabra DS; Obten&#231;&#227;o de financiamento: Piuvezam G; Reda&#231;&#227;o do manuscrito: Piuvezam G, Medeiros WR, Costa AV, Emerenciano FF, Seabra DS.</p>
        <p>Declaro n&#227;o haver conflito de interesses pertinentes.</p>
        <p>O presente estudo foi parcialmente financiado pela Bolsa de pesquisa de Inicia&#231;&#227;o Cient&#237;fica (PIBIC) da Pr&#243;-reitoria de Pesquisa da Universidade Federal do Rio Grande do Norte (PROPESQ-UFRN).</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 Bolsa de pesquisa de Inicia&#231;&#227;o Cient&#237;fica (PIBIC) da Pr&#243;-reitoria de Pesquisa da Universidade Federal do Rio Grande do Norte (PROPESQ-UFRN).</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-group>
    </back>
  </sub-article>
</article>
