Original article
Geostatistical analysis from the clinical laboratory in cardiovascular prevention for primary care
Análisis geoestadístico desde el laboratorio clínico en prevención cardiovascular para atención primaria
Salomón Martín Péreza,
, Teresa Arrobas Velillaa, Juan Fabiani de la Iglesiab, Ignacio Vázquez Ricoc, Gema Varo Sánchezd, Antonio León-Justele
Corresponding author
a Laboratorio de Nutrición y Riesgo Cardiovascular, Unidad de Bioquímica Clínica, Hospital Universitario Virgen Macarena, Sevilla, Spain
b Medicina de Familia, Hospital Infanta Elena, Huelva, Spain
c Laboratorio de Análisis Clínicos, Unidad de Lípidos, Hospital Juan Ramón Jiménez, Huelva, Spain
d Laboratorio de Análisis Clínicos, Hospital comarcal Riotinto, Huelva, Spain
e Unidad de Bioquímica Clínica, Hospital Universitario Virgen Macarena, Sevilla, Spain
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"detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Clustering clusters for triglyceride values above 150<span class="elsevierStyleHsp" style=""></span>mg/dl by postcode in the province of Huelva.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Cardiovascular diseases (CVD) are major health issues worldwide due to their increasing prevalence and mortality and disability, imposing a heavy economic burden.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> In most developed countries, including Spain, CVD is the leading cause of death and causes significant health loss.<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> The mortality rate attributed to CVD in 2018 was 258 per 100,000 inhabitants. This figure rises to 270 in Andalusia.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> CVD are also the leading cause of death worldwide and account for an estimated 17.9 million lives lost each year. More than 4 out of every 5 deaths from CVD are due to coronary heart disease and stroke, and one third of these deaths occur prematurely in people under 70 years of age.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The results of the DRECA study by the Andalusian Health Service in 1998 conducted to determine the risk factors for CVD in a comprehensive and representative way in the Andalusian population, showed a highly worrying picture from a public health perspective, prompting health promotion and education measures, intersectoral action, changes in lifestyles and nutrition in a large number of citizens.<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">According to the DARIOS study, the prevalence of hypertension, dyslipidaemia, obesity, smoking, and diabetes mellitus is high, with relatively low variability in the population aged 35–74 years between the autonomous communities. The Canary Islands, Extremadura, and Andalusia show a higher prevalence of cardiovascular risk factors than the average of the 11 component studies.<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Prospective population-based studies have shown that plasma Lp(a) concentration is positively associated with the development of coronary heart disease and stroke independently of the main cardiovascular risk factors.<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">CVD prevention is an essential task of primary care.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Improving cardiovascular prevention requires an alliance between policy makers, administrations, scientific and professional associations, health foundations, consumer associations, patients and their families, to drive population and individual strategy by using the full spectrum of available scientific evidence, from clinical trials in patients to observational studies and mathematical models for the evaluation of population-based interventions, including cost-effectiveness analyses.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">The SARS-CoV-2 pandemic has led to the creation and development of applications and websites to fight COVID-19, both by official institutions and corporate or private initiatives.<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">Many projects have emerged that use technologies such as geolocation, geopositioning, geofencing, tracking, and contact registration via Bluetooth to generate a huge amount of data. Big data analysis techniques, geographic information systems (GIS) and artificial intelligence have produced information for health institutions and for society itself, which has helped deal with the health crisis more efficiently.<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">For many years, governmental, community, administrative, and political organisations have been trying to understand the relationships between geography and health.<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> As a result, GIS systems have been widely used in epidemiology. Initiatives using this technology in heart disease have emerged because of the increased concern regarding CVD over the last decade.<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13–15</span></a></p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Objectives</span><p id="par0045" class="elsevierStylePara elsevierViewall">The aim was to quantify the prevalence of patients with dyslipidaemia and their geolocation in the health areas selected for implementing cardiovascular prevention strategies in primary care and to study whether the distribution of the areas found is statistically significant using cluster analysis.</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Material and methods</span><p id="par0050" class="elsevierStylePara elsevierViewall">We conducted a retrospective cohort study to examine the prevalence of dyslipidaemia in our area. To that end, we consulted the computer systems of the laboratories of Hospital Infanta Elena and Hospital Juan Ramón Jiménez, which contain information on all the tests of interest performed in the population in 2019 and 2020. The Juan Ramón Jiménez Hospital provides healthcare coverage for 264,300 inhabitants at the basic level of care, and the Infanta Elena Hospital provides coverage for an estimated population of 175,000 inhabitants.</p><p id="par0055" class="elsevierStylePara elsevierViewall">Based on the recommendations of the clinical guidelines according to risk, cut-off points were established for each of the parameters, in particular, according to the recommendations of the latest clinical guidelines of the European Atherosclerosis Society.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> The cut-off points chosen were triglycerides (TG)<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl, LDL cholesterol (LDLC)<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl (cut-off point established by clinical guidelines as a possible suspicion of familial hypercholesterolaemia) and lipoprotein (a) (Lp[a])<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>50<span class="elsevierStyleHsp" style=""></span>mg/dl.</p><p id="par0060" class="elsevierStylePara elsevierViewall">The statistical package IBM SPSS Statistics for Windows, version 25.0 (Armonk, IBM Corp, USA) was used for the statistical analysis of the data.</p><p id="par0065" class="elsevierStylePara elsevierViewall">The number of cases with values above the cut-off points established for each postcode was quantified and patients with lower analytical values were established as a control group. The percentage of cases with the selected analytical criteria in the area was calculated for each postcode. Only those areas whose population was higher than .5% of the total population were included in the study to avoid over-representation of outliers in the maps.</p><p id="par0070" class="elsevierStylePara elsevierViewall">The number of Lp[a] measurements was not sufficient to be of interest in the choropleth representation, nor to conduct stratification in postcodes.</p><p id="par0075" class="elsevierStylePara elsevierViewall">The choice of the colorimetric scale for the choropleth maps was adjusted according to the maximum and minimum percentages for each of the parameters studied.</p><p id="par0080" class="elsevierStylePara elsevierViewall">A free and open-source geographic information system (QGIS Geographic Information System 3.16.3-Hannover, QGIS Association) was used to represent the data obtained. GIS is software that allows users to visualise, analyse, and interpret geographic data to help them understand and solve problems related to relationships and patterns. It identifies high-risk areas that require attention and action.<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a></p><p id="par0085" class="elsevierStylePara elsevierViewall">The maps are delimited by postcodes and were obtained from Andalusia’s Institute of Statistics and Cartography. These maps are licensed under Creative Commons Attribution 4.0 (CC BY 4.0) (Instituto de Estadística y Cartografía de Andalucía, Creative Commons Reconocimiento 4.0, CC BY 4.0).</p><p id="par0090" class="elsevierStylePara elsevierViewall">The spatial analysis was performed with Kulldorff M. and Information Management Services (Inc. SaTScanTM v. 8.0, Software for the spatial and space-time scan statistics)<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> to test for the presence of clusters of increased cardiovascular risk and identify their approximate location, so that, for each cluster, the likelihood ratio was calculated assuming an independent Bernoulli distribution of cases, allowing more focused cluster mapping and assessment, as it uses location data directly.<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a></p><p id="par0095" class="elsevierStylePara elsevierViewall">The Research Ethics Committee of the Centro Hospital Universitario Virgen Macarena de Sevilla approved the study.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Results</span><p id="par0100" class="elsevierStylePara elsevierViewall">The number of analytical data included in the study was 289,594 patients for LDL-C, 365,384 patients for TG, and 502 patients for Lp(a). The distribution of patients is not homogeneous between the different postcodes, as shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>, and therefore centralisation measures were used for the calculations; to avoid over-representation of areas with too few cases, a minimum cut-off point of .5% of the total cases was set for the inclusion of postcodes in the study.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Choropleth maps</span><p id="par0105" class="elsevierStylePara elsevierViewall">The results for the different cut-off points (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>) were a total of 405 (.11%) for TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>880<span class="elsevierStyleHsp" style=""></span>mg/dl; 85. 376 (23.37%) with TG between 150−880<span class="elsevierStyleHsp" style=""></span>mg/dl; for LDLC<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl there were 5907 (2.04%) cases, and for Lp(a)<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>50<span class="elsevierStyleHsp" style=""></span>mg/dl only 150 (29.88%) were found.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0110" class="elsevierStylePara elsevierViewall">The areas with the highest and lowest percentage of cases are shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> shows the percentages of the population with TG above 150<span class="elsevierStyleHsp" style=""></span>mg/dl. The towns with the highest percentages are Santa Bárbara de Casa (31.64%), Villanueva de las Cruces (30.58%), and Las Herrerías (30.46%); while the towns whose postcodes represented the lowest proportion of cases were Isla Cristina (19.57%), Bonares (18.83%), and Palos de la Frontera (18.22%).</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0115" class="elsevierStylePara elsevierViewall">With the cut-off point of TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>880<span class="elsevierStyleHsp" style=""></span>mg/dl, the number of total cases detected in the whole region was 405, which represents .11% of the study sample and includes the towns of Paymogo, Almonte, and Cartaya with .59; .56, and .40% of the cases, respectively. Due to the low number of cases, we considered that it was not representative to perform choropleth mapping at this cut-off point.</p><p id="par0120" class="elsevierStylePara elsevierViewall">The cut-off point established for LDLC was 190<span class="elsevierStyleHsp" style=""></span>mg/dl. <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> shows the percentages of cases by postcode: those with the highest percentage of cases are located in Nerva (4.30%), Lepe (3.15%), and Aljaraque (3.09%). The postcodes with the lowest incidence are the towns of Isla Cristina (1.01%), Paymogo (.59%), and Aroche (.51%).</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0125" class="elsevierStylePara elsevierViewall">The number of Lp(a) measurements made during the study period was 524, insufficient for mapping.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Spatial cluster analysis</span><p id="par0130" class="elsevierStylePara elsevierViewall">In the analysis of LDLC above 190<span class="elsevierStyleHsp" style=""></span>mg/dl, 2 clusters were obtained (<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>) with statistical significance (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.001). The first included a population of 66,922 measurements with a total of 1749 detected cases; the expected number of cases in the area was 1,381.99, which implied a relative risk (RR) for being included within the area of 1.38. The second cluster was a population of 26,162 measurements, 638 of which exceeded the cut-off point: this gives a RR of 1.2. The area radius for cluster 1 was 27.58<span class="elsevierStyleHsp" style=""></span>km and 12.41<span class="elsevierStyleHsp" style=""></span>km for cluster 2, shown in <a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>.</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0135" class="elsevierStylePara elsevierViewall">For TG values<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl, a total of 6 clusters were obtained (<span class="elsevierStyleItalic">P</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>.001) (<a class="elsevierStyleCrossRef" href="#fig0025">Fig. 5</a>). Cluster 1 has a population of 18,113, with a total number of cases of 4917, with 4,260.4 expected cases. The RR was 1.16, with a radius of 11.81<span class="elsevierStyleHsp" style=""></span>km. Cluster 2 had a population of 45,360, 11,364 cases, 10,669.23 expected cases, and an RR of 1.08. Cluster 3 population was 5055, with 1437 cases, 1189 expected and an RR of 1.21 with a radius of 5.46<span class="elsevierStyleHsp" style=""></span>km. The radius of cluster 4 was the smallest of those included, with .1<span class="elsevierStyleHsp" style=""></span>km, and an RR of 1.09 in this area. The last 2 were the largest: cluster 5 had a radius of 19.06<span class="elsevierStyleHsp" style=""></span>km with an RR of 1.16 while cluster 6 had a radius of 23.34 and an RR of 1.13.</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Discussion</span><p id="par0140" class="elsevierStylePara elsevierViewall">We found areas with very different prevalence for the selected cut-off points for each variable and, thanks to the representation in choropleth maps (<a class="elsevierStyleCrossRefs" href="#fig0010">Figs. 2 and 3</a>), we achieved better visualisation of the data to obtain greater impact of the findings. In addition, several clusters were detected with a higher number of cases than expected in their area (<a class="elsevierStyleCrossRefs" href="#fig0020">Figs. 4 and 5</a>). These findings may be very useful to identify geographical areas in which to initiate cardiovascular prevention strategies.<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">Cardiovascular prevention remains one of the great challenges facing our society, as this group of diseases has a high morbidity and mortality rate.<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">The cardiovascular epidemic is a serious public health problem in Spain, which can only be tackled by implementing appropriate preventive measures.<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a></p><p id="par0155" class="elsevierStylePara elsevierViewall">These tools can be of great help in terms of prevention; however, despite their potential, geographically specific intelligence on public health interventions is still rarely used in disease mapping.<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a></p><p id="par0160" class="elsevierStylePara elsevierViewall">Cardiovascular risk remains a concern for health organisations, and in the Spanish population it is high. Population-based monitoring of cardiovascular risk is essential in the planning of preventive and care measures.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a></p><p id="par0165" class="elsevierStylePara elsevierViewall">Hypertriglyceridaemia (HTG) is a risk factor that not only increases the incidence of pancreatitis, but also contributes significantly to an increased residual cardiovascular risk and is important because of its high prevalence and clinical relevance as it is associated with an increased risk of arteriosclerotic CVD.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a></p><p id="par0170" class="elsevierStylePara elsevierViewall">The high prevalence of HTG has serious socioeconomic and health consequences because it increases cardiovascular morbidity and mortality. Assessment of the prevalence of HTG is very important to better plan cardiovascular prevention intervention policies, optimise available health resources, and improve patient care and quality of life.<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a></p><p id="par0175" class="elsevierStylePara elsevierViewall">In a multicentre study conducted in hospitals in Andalusia and Ceuta,<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a> a geographical triangle of higher prevalence of hypercholesterolaemia was observed between the provinces of Huelva, Seville, and Cadiz. It was detected that .14% of the study population had LDLC values<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>250<span class="elsevierStyleHsp" style=""></span>mg/dl. In our study, 2.04% had LDLC values<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl. These data reflect a far from an ideal situation.</p><p id="par0180" class="elsevierStylePara elsevierViewall">One third of the patients whose Lp(a) levels were measured (29.38%) had values above 50<span class="elsevierStyleHsp" style=""></span>mg/dl. This is confirmed by the Spanish SAFEHEART study,<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a> which was designed to analyse the situation and improve knowledge of heterozygous familial hypercholesterolaemia in Spain and found that 30% of patients with this condition had Lp(a) values above 50<span class="elsevierStyleHsp" style=""></span>mg/dl. Patients with values above this cut-off point had familial hypercholesterolaemia.</p><p id="par0185" class="elsevierStylePara elsevierViewall">Elevated Lp(a) in hospitals in southern Spain is under-diagnosed, and there is no uniformity of protocols for its application or in the analytical methodology used.<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a></p><p id="par0190" class="elsevierStylePara elsevierViewall">Information technologies, such as GIS, provide cost-effective tools to evaluate interventions and policies that may affect health outcomes.<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">28</span></a> The use of GIS and the spatial representation of various health issues enable professionals to reach conclusions faster and better in both public health and decision-making.<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">29</span></a></p><p id="par0195" class="elsevierStylePara elsevierViewall">The results of our study could be complemented in the future with the joint representation of geolocated cardiological events to study whether there is a spatial correlation between biochemical parameters and events.</p><p id="par0200" class="elsevierStylePara elsevierViewall">As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, during the COVID-19 pandemic, geographic information systems and methods have proven indispensable for timely and effective surveillance and response to the epidemic.<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">30</span></a> It should be noted because of the ease and speed with which maps were produced during the pandemic, many map makers seemed to forget the fundamental principles of good, easy-to-read choropleth maps, which require knowledge of geospatial data for correct representation.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">31</span></a></p><p id="par0205" class="elsevierStylePara elsevierViewall">Advances in health informatics can be made when GIS is applied through research. However, improvements in the quantity and quality of data input into these systems are needed to ensure that better geographic health maps are used so that appropriate conclusions can be drawn between public health and environmental factors.<a class="elsevierStyleCrossRef" href="#bib0160"><span class="elsevierStyleSup">32</span></a></p><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Study limitations</span><p id="par0210" class="elsevierStylePara elsevierViewall">The limitations of our study include the absence of some characteristics, such as the standard of living of the geographical area analysed, or the level of education of the population.</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Conclusions</span><p id="par0215" class="elsevierStylePara elsevierViewall">Geolocation techniques are a valuable tool and have been widely used in the epidemiology of diseases with a high capacity to spread; however, their use in prevention has not been as extensive.</p><p id="par0220" class="elsevierStylePara elsevierViewall">CVD is a priority in healthcare systems and the development of efficient prevention remains a major goal. We need, therefore, to make use of new technologies that offer different perspectives to improve prevention.</p><p id="par0225" class="elsevierStylePara elsevierViewall">Cluster detection, and the representation of choropleth maps, can be of great help in detecting geographical areas that require more attention to intervene and improve cardiovascular risk.</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Funding</span><p id="par0230" class="elsevierStylePara elsevierViewall">This study was conducted with funding from a research grant awarded in 2019 by the <span class="elsevierStyleGrantSponsor" id="gs0005">Spanish Atherosclerosis Society</span>.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Conflict of interests</span><p id="par0235" class="elsevierStylePara elsevierViewall">The authors have no conflict of interests to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:14 [ 0 => array:3 [ "identificador" => "xres2082382" "titulo" => "Abstract" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Results" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1776507" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres2082383" "titulo" => "Resumen" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0020" "titulo" => "Introducción y objetivos" ] 1 => array:2 [ "identificador" => "abst0025" "titulo" => "Resultados" ] 2 => array:2 [ "identificador" => "abst0030" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1776508" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Objectives" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Material and methods" ] 7 => array:3 [ "identificador" => "sec0020" "titulo" => "Results" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0025" "titulo" => "Choropleth maps" ] 1 => array:2 [ "identificador" => "sec0030" "titulo" => "Spatial cluster analysis" ] ] ] 8 => array:3 [ "identificador" => "sec0035" "titulo" => "Discussion" "secciones" => array:1 [ 0 => array:2 [ "identificador" => "sec0040" "titulo" => "Study limitations" ] ] ] 9 => array:2 [ "identificador" => "sec0045" "titulo" => "Conclusions" ] 10 => array:2 [ "identificador" => "sec0050" "titulo" => "Funding" ] 11 => array:2 [ "identificador" => "sec0055" "titulo" => "Conflict of interests" ] 12 => array:2 [ "identificador" => "xack725511" "titulo" => "Acknowledgements" ] 13 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2022-05-04" "fechaAceptado" => "2022-08-08" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1776507" "palabras" => array:6 [ 0 => "Cardiovascular diseases" 1 => "Maps" 2 => "Clusters" 3 => "LDL cholesterol" 4 => "Triglycerides" 5 => "Lipoprotein (a)" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1776508" "palabras" => array:6 [ 0 => "Enfermedades cardiovasculares" 1 => "Mapas" 2 => "Clústeres" 3 => "Colesterol LDL" 4 => "Triglicéridos" 5 => "Lipoproteína (a)" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction and objectives</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Cardiovascular diseases continue to lead the ranking of mortality in Spain. The implementation of geostatistical analysis techniques in the clinical laboratory are innovative tools that allow the design of new strategies in primary prevention of cardiovascular disease. The aim of this study was to study the prevalence and geolocation of severe dyslipidemia in the health areas under study in order to implement prevention strategies in primary care. A retrospective cohort study of low-density protein-bound cholesterol, triglyceride and lipoprotein (a) levels in the years 2019 and 2020 were carried out. In addition, a geostatistical analysis was performed including representation in choropleth maps and the detection of clustering clusters, using geographic information in zip code format included in the demographic data of each analytic.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Results</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">The analytical data included in the study were triglycerides (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>365,384), low density protein-bound cholesterol (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>289,594) and lipoprotein to lipoprotein (a) (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>502). Areas with the highest and lowest percentage of cases were identified for the established cut-off points of LDL-C<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl and TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl. Two clustering clusters with statistical significance were detected for cLDL<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl and a total of 6 clusters for TG values<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Conclusions</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">The detection of clusters, as well as the representation of choropleth maps, can be of great help in detecting geographic areas that require greater attention to intervene and improve cardiovascular risk.</p></span>" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Results" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "<span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Introducción y objetivos</span><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Las enfermedades cardiovasculares continúan encabezando la mortalidad en España. Las técnicas de análisis geoestadístico en el laboratorio clínico son herramientas innovadoras que permiten el diseño de nuevas estrategias en prevención primaria de enfermedad cardiovascular. El objetivo fue estudiar la prevalencia y geolocalización de dislipidemias en las áreas sanitarias de estudio para implementar estrategias de prevención en atención primaria. Se llevó a cabo un estudio de cohorte retrospectivo de los niveles de colesterol unido a proteínas de baja densidad, triglicéridos y lipoproteína (a) en los años 2019 y 2020. Además, se realizó un análisis geoestadístico que incluyó la representación en mapas coropléticos y la detección de clústeres de agrupación; para ello, se utilizó la información geográfica en formato de código postal incluida en los datos demográficos de cada analítica.</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Resultados</span><p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Los datos analíticos incluidos en el estudio fueron triglicéridos (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>365.384), colesterol unido a proteínas de baja densidad (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>289.594) y lipoproteína (a) (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>502). Se identificaron las áreas con mayor y menor porcentaje de casos para los puntos de corte establecidos de cLDL<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl y TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl. Se detectaron 2 clústeres de agrupación con significación estadística para cLDL<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl y un total de 6 clústeres para los valores de TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl.</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Conclusiones</span><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">La detección de clústeres, así como la representación de mapas coropléticos, pueden ser de gran ayuda en la detección de áreas geográficas que requieran de mayor atención para intervenir en el riesgo cardiovascular.</p></span>" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0020" "titulo" => "Introducción y objetivos" ] 1 => array:2 [ "identificador" => "abst0025" "titulo" => "Resultados" ] 2 => array:2 [ "identificador" => "abst0030" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Article sent in reference to the oral communication awarded with the prize: Atención Primaria - Epidemiología del XXXIII Congreso de la Sociedad Española de Arteriosclerosis (Primary Care - Epidemiology of the XXXIII Congress of the Spanish Society of Arteriosclerosis) with the title “Aplicación de big data y análisis geoestadístico desde el laboratorio clínico en prevención cardiovascular para Atención Primaria” (Application of big data and geostatistical analysis from the clinical laboratory in cardiovascular prevention for Primary Care). According to the rules of the call, the prize will be awarded if the journal's committee receives and agrees to publish an original paper referring to the winning publication.</p>" ] ] "multimedia" => array:8 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1821 "Ancho" => 2925 "Tamanyo" => 387469 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0025" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Percentage of population with triglyceride values above 150<span class="elsevierStyleHsp" style=""></span>mg/dl by postcode in the province of Huelva.</p>" ] ] 1 => array:8 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1785 "Ancho" => 2508 "Tamanyo" => 560829 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0030" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Clustering clusters for triglyceride values above 150<span class="elsevierStyleHsp" style=""></span>mg/dl by postcode in the province of Huelva.</p>" ] ] 2 => array:8 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1742 "Ancho" => 2925 "Tamanyo" => 359599 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0035" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Percentage of population with low-density lipoprotein cholesterol (LDLC) values above 190<span class="elsevierStyleHsp" style=""></span>mg/dl by postcode in the province of Huelva.</p>" ] ] 3 => array:8 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1792 "Ancho" => 2508 "Tamanyo" => 535767 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0040" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Clustering clusters for low-density lipoprotein cholesterol (LDLC) values above 190<span class="elsevierStyleHsp" style=""></span>mg/dl by postcode in the province of Huelva.</p>" ] ] 4 => array:8 [ "identificador" => "fig0025" "etiqueta" => "Figure 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 1997 "Ancho" => 2925 "Tamanyo" => 317907 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0045" "detalle" => "Figure " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Number of patients clustered by sex from the main postcodes of the province of Huelva and mean age per postcode.</p>" ] ] 5 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0050" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">LDLC: Low-density lipoprotein cholesterol; Lp(a): Lipoprotein (a); TG: Triglycerides.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Study parameter mg/dl \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Number of analytical measurements (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>880 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">405 (.11) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TG 150−880 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">85,376 (23.37) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LDLC<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5907 (2.04) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Lp(a)<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>50 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">150 (29.88) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3448831.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Number and percentage of patients with levels above those of the cut-off points.</p>" ] ] 6 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0055" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">LDLC: Low-density lipoprotein cholesterol; Lp(a): Lipoprotein (a); PC: Postcode; TG: Triglycerides.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col">Parameter \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_colgroup " colspan="6" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Highest incidence</th><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_colgroup " colspan="6" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Lowest incidence</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">PC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Total \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cases \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Percentage \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Mean age \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Municipality \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">PC \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Total \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cases \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Percentage \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Mean age \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Municipality \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="3" align="left" valign="middle">TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl</td><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21570 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">923 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">292 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31.64 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Santa Bárbara de Casa \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21430 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">889 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">174 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19.57 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.55 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Isla Cristina \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21592 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">412 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">126 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">30.58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">65.65 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Villanueva de las Cruces \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21830 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4200 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">791 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Bonares \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21559 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">174 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">30.46 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.47 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Puebla de Guzmán \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21819 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">214 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">39 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.22 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59.56 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">La Rábida \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="3" align="left" valign="middle">TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>880<span class="elsevierStyleHsp" style=""></span>mg/dl</td><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21560 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1019 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.59 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Paymogo \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21120 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">569 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.84 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Aljaraque \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21750 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1079 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.56 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.83 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">El Rocío \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21850 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">791 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.13 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Villarrasa \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21420 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1000 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.40 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55.79 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pozo del Camino \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21710 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2574 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.66 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Bollullos Par del Condado \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead rowgroup " rowspan="3" align="left" valign="middle">LDL<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl</td><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21670 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">186 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.74 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Nerva \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21430 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">889 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.55 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Isla Cristina \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21449 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">952 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Lepe \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21560 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1019 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.59 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.48 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Paymogo \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21122 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1519 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">47 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.95 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Aljaraque \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21580 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">982 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.51 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59.71 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Aroche \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3448832.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Postcodes with highest and lowest incidence according to cut-off points and average population age.</p>" ] ] 7 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0060" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">LDLC: Low-density lipoprotein cholesterol; TG: Triglycerides.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_colgroup " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LDL<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>190<span class="elsevierStyleHsp" style=""></span>mg/dl</th><th class="td-with-role" title="\n \t\t\t\t\ttable-head\n \t\t\t\t ; entry_with_role_colgroup " colspan="6" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">TG<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>150<span class="elsevierStyleHsp" style=""></span>mg/dl</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cluster \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">1 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">2 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">1 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">2 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">3 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">4 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">5 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">6 \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Population \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">66,922 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26,162 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18,113 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">45,360 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5055 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20,161 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6349 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2876 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Number of cases \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1749 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">638 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4917 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11,364 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1437 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5150 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1721 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">763 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Expected cases \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1381.99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">540.27 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4260.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10,669.23 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1189 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4742.11 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1493.36 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">676.47 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Observed/expected \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.27 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.07 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.21 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.15 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.13 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Relative risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.38 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.08 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.21 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.09 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.16 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.13 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Percentage of cases in area \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.4 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Number of locations \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Radius (km) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27.58 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12.41 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11.81 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.91 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.46 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19.06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23.34 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleItalic">P-value</span> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.0018 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><.001 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">.031 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3448833.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Description of clusters found.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:32 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Trend analysis of cardiovascular disease mortality, incidence, and mortality-to-incidence ratio: results from global burden of disease study 2017" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "M. Amini" 1 => "F. Zayeri" 2 => "M. Salehi" ] ] ] ] ] "host" => array:2 [ 0 => array:2 [ "doi" => "10.1186/s12889-021-10429-0" "Revista" => array:6 [ "tituloSerie" => "BMC Public Health [Internet]." "fecha" => "2021" "volumen" => "21" "numero" => "Dec 25" "paginaInicial" => "401" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/33632204" "web" => "Medline" ] ] ] ] 1 => array:2 [ "doi" => "10.1186/s12889-021-10429-0" "WWW" => array:1 [ "link" => "https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-10429-0" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0010" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:1 [ "titulo" => "Manuscript A, Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 count" ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/S0140-6736(15)61340-X" "Revista" => array:7 [ "tituloSerie" => "Lancet." 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| Year/Month | Html | Total | |
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| 2024 November | 5 | 1 | 6 |
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| 2024 September | 26 | 9 | 35 |
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