Aims: Cardiovascular disease (CVD) is one of the leading causes of mortality and morbidity worldwide, including in Sweden. The main aim of this study was to explore the temporal trends and spatial patterns of CVD in Sweden using spatial autocorrelation analyses. Methods: The CVD admission rates between 2000 and 2010 throughout Sweden were entered as the input disease data for the analytic processes performed for the Swedish capital, Stockholm, and also for the whole of Sweden. Age-adjusted admission rates were calculated using a direct standardisation approach for men and women, and temporal trends analysis were performed on the standardised rates. Global Moran’s I was used to explore the structure of patterns and Anselin’s local Moran’s I, together with Kulldorff’s scan statistic were applied to explore the geographical patterns of admission rates. Results: The rates followed a spatially clustered pattern in Sweden with differences occurring between sexes. Accordingly, hot spots were identified in northern Sweden, with higher intensity identified for men, together with clusters in central Sweden. Cold spots were identified in the adjacency of the three major Swedish cities of Stockholm, Gothenburg and Malmö. Conclusions: The findings of this study can serve as a basis for distribution of health-care resources, preventive measures and exploration of aetiological factors.
ZöllerBLiXSundquistJet al. Risk of subsequent coronary heart disease in patients hospitalized for immune-mediated diseases: a nationwide follow-up study from Sweden. PLoS One2012;7:e33442.
2.
GiangKWBjörckLNovakMet al. Stroke and coronary heart disease: predictive power of standard risk factors into old age – long-term cumulative risk study among men in Gothenburg, Sweden. Eur Heart J2013;34:1068–74.
3.
MorensDMFolkersGKFauciAS.The challenge of emerging and re-emerging infectious diseases. Nature2004;430:242–9.
4.
BergJBjörckLLappasGet al. Continuing decrease in coronary heart disease mortality in Sweden. BMC Cardiovasc Disord2014;14:9.
5.
UnalBCritchleyJACapewellS.Explaining the decline in coronary heart disease mortality in England and Wales between 1981 and 2000. Circulation2004;109:1101–7.
6.
KuulasmaaKTunstall-PedoeHDobsonAet al. Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. Lancet2000;355:675–87.
7.
BjörckLRosengrenABennettKet al. Modelling the decreasing coronary heart disease mortality in Sweden between 1986 and 2002. Eur Heart J2009;30:1036–56.
8.
RosvallMChaixBLynchJet al. Contribution of main causes of death to social inequalities in mortality in the whole population of Scania, Sweden. BMC Public Health2006;6:1.
9.
RinaldiLMusellaVBiggeriAet al. New insights into the application of geographical information systems and remote sensing in veterinary parasitology. Geospat Health2006;1:33–47.
10.
OstfeldRSGlassGEKeesingF.Spatial epidemiology: an emerging (or re-emerging) discipline. Trends Ecol Evol2005;20:328–36.
11.
Diez-RouxAVNietoFJMuntanerCet al. Neighborhood environments and coronary heart disease: a multilevel analysis. Am J Epidemiol1997;146:48–63.
12.
ChaixB.Geographic life environments and coronary heart disease: a literature review, theoretical contributions, methodological updates, and a research agenda. Annu Rev Public Health2009;30:81–105.
13.
ChowCKLockKTeoKet al. Environmental and societal influences acting on cardiovascular risk factors and disease at a population level: a review. Int J Epidemiol2009;38:1580–94.
14.
HoffmannBMoebusSStangAet al. Residence close to high traffic and prevalence of coronary heart disease. Eur Heart J2006;27:2696–702.
15.
XiaCBergquistRLynnHet al. Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China. Parasit Vectors2017;10:136.
16.
LoughnanMENichollsNTapperNJ.Demographic, seasonal, and spatial differences in acute myocardial infarction admissions to hospital in Melbourne Australia. Int J Health Geogr2008;7:42.
17.
SoljakMSamarasunderaEIndulkarTet al. Variations in cardiovascular disease under-diagnosis in England: national cross-sectional spatial analysis. BMC Cardiovasc Disord2011;11:12.
18.
AsariaPFortunatoLFechtDet al. Trends and inequalities in cardiovascular disease mortality across 7932 English electoral wards, 1982–2006: Bayesian spatial analysis. Int J Epidemiol2012;41:1737–49.
19.
MaL-GZhaoJRenZ-Pet al. Spatial patterns of the congenital heart disease prevalence among 0-to 14-year-old children in Sichuan Basin, P. R China, from 2004 to 2009. BMC Public Health2014;14:595.
20.
ChengC-LChenY-CLiuT-Met al. Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug-prescribing pattern in Taiwan. BMC Public Health2011;11:380.
21.
MoranPA.The interpretation of statistical maps. J R Stat Soc Series B Stat Methodol1948;10:243–51.
22.
KulldorffMNagarwallaN.Spatial disease clusters: detection and inference. Stat Med1995;14:799–810.
23.
KulldorffM.A spatial scan statistic. Commun Stat Theory Methods1997;26:1481–96.
24.
StatisticsSweden. Statistical database, www.ssd.scb.se (accessed 20 March 2017).
25.
SundquistKMalmströmMJohanssonS.Neighbourhood deprivation and incidence of coronary heart disease: a multilevel study of 2.6 million women and men in Sweden. J Epidemiol Commun Health2004;58:71–7.
26.
CurtinLRKleinRJ.Direct standardization (age-adjusted death rates): US Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Health Statistics, 1995.
27.
AnselinLLozanoNKoschinskyJ.Rate transformations and smoothing. Urbana2006;51:61801.
28.
ClaytonDKaldorJ.Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics1987;43:671–81.
29.
KimH-JFayMPFeuerEJet al. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med2000;19:335–51.
30.
Joinpoint. Joinpoint regression program. 4.4.0.0. ed. Calverton, MD: Statistical Research and Applications Branch, National Cancer Institute, 2017.
31.
KimHJFayMPYuBet al. Comparability of segmented line regression models. Biometrics2004;60:1005–14.
32.
KnoxEG.Detection of clusters. In: ElliotP (ed) Methodology of enquiries into disease clustering. London: Small Area Health Statistics Unit, 1989, pp.17–20.
33.
LawsonAB.Statistical methods in spatial epidemiology. Chichester, UK: John Wiley, 2013.
34.
AnselinL.Local indicators of spatial association – LISA. Geogr Anal1995;27:93–115.
35.
SongCKulldorffM.Power evaluation of disease clustering tests. Int J Health Geogr2003;2:9.
36.
SuloENygårdOVollsetSEet al. Coronary angiography and myocardial revascularization following the first acute myocardial infarction in Norway during 2001–2009: analyzing time trends and educational inequalities using data from the CVDNOR project. Int J Cardiol2016;212:122–8.
37.
O’FlahertyMFordEAllenderSet al. Coronary heart disease trends in England and Wales from 1984 to 2004: concealed levelling of mortality rates among young adults. Heart2008;94:178–81.
38.
FordESCapewellS.Coronary heart disease mortality among young adults in the US from 1980 through 2002. J Am Coll Cardiol2007;50:2128–32.
39.
WennerholmCGripBJohanssonAet al. Cardiovascular disease occurrence in two close but different social environments. Int J Health Geogr2011;10:1.
40.
ZhangCLuoLXuWet al. Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Total Environ2008;398:212–21.
41.
GelmanAPricePN.All maps of parameter estimates are misleading. Stat Med1999;18:3221–34.
42.
TangoTTakahashiK.A flexibly shaped spatial scan statistic for detecting clusters. Int J Health Geogr2005;4:11.
43.
TangoT.A spatial scan statistic scanning only the regions with elevated risk. Adv Dis Surveill2007;4:117.
44.
DeChelloLMSheehanTJ.Spatial analysis of colorectal cancer incidence and proportion of late-stage in Massachusetts residents: 1995–1998. Int J Health Geogr2007;6:20.
45.
SugumaranRLarsonSRDeGrooteJP.Spatio-temporal cluster analysis of county-based human West Nile virus incidence in the continental United States. Int J Health Geogr2009;8:43.
