#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Geographic variation in cardiometabolic risk distribution: A cross-sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia


Autoři: Renin Toms aff001;  Darren J. Mayne aff001;  Xiaoqi Feng aff002;  Andrew Bonney aff001
Působiště autorů: School of Medicine, University of Wollongong, Wollongong, NSW, Australia aff001;  Illawarra Health and Medical Research Institute, Wollongong, NSW, Australia aff002;  Public Health Unit, Illawarra Shoalhaven Local Health District, Warrawong, NSW, Australia aff003;  School of Public Health, The University of Sydney, Sydney, NSW, Australia aff004;  School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, Australia aff005;  Population Wellbeing and Environment Research Lab (PowerLab), School of Health and Society, University of Wollongong, Wollongong, NSW, Australia aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223179

Souhrn

Introduction

Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches.

Method

A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into ‘higher risk’ and ‘lower risk’ values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran’s I test and Local Indicators of Spatial Autocorrelation (LISA).

Results

A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of ‘higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran’s I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area.

Conclusion

The study describes a range of CMRFs with different distributions in the study region. The results allow generation of hypotheses to test in future research concerning location specific population health approaches.

Klíčová slova:

Australia – Cardiovascular diseases – Geographic distribution – Health services research – Medical risk factors – Spatial autocorrelation


Zdroje

1. Danaei G, Lu Y, Singh GM, Carnahan E, Stevens GA, Cowan MJ, et al. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment. Lancet Diabetes Endocrinol. 2014; doi: 10.1016/S2213-8587(14)70102-0 24842598

2. Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, Jafar TH, Heerspink HJL, Mann JF, et al. Chronic kidney disease and cardiovascular risk: Epidemiology, mechanisms, and prevention. The Lancet. 2013. doi: 10.1016/S0140-6736(13)60595-4

3. Wadwa RP, Urbina EM, Daniels SR. Cardiovascular disease risk factors. Epidemiology of Pediatric and Adolescent Diabetes. 2008.

4. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: The Framingham heart study. Circulation. 2008; doi: 10.1161/CIRCULATIONAHA.107.699579 18212285

5. Liu M, Li XC, Lu L, Cao Y, Sun RR, Chen S, et al. Cardiovascular disease and its relationship with chronic kidney disease. European review for medical and pharmacological sciences. 2014.

6. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: A 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983; doi: 10.1161/01.CIR.67.5.968 6219830

7. World Health Organisation. WHO | The top 10 causes of death [Internet]. World Health Organization; 2017. Available: http://www.who.int/mediacentre/factsheets/fs310/en/

8. World Health Organization. WHO | Noncommunicable diseases. In: WHO [Internet]. WHO; 2017 [cited 10 Mar 2018]. Available: http://www.who.int/mediacentre/factsheets/fs355/en/

9. World Health Organization; World Heart Federation and World Stroke Organization. Global Atlas on Cardiovascular disease prevention and control. Glob atlas Cardiovasc Dis Prev Control. 2011; 155. NLM classification: WG 120

10. AIHW-Australian Institute of Health and Welfare. Cardiovascular disease, diabetes and chronic kidney disease: Australian facts: morbidity—hospital care [Internet]. 2017. Available: https://www.aihw.gov.au/reports/heart-stroke-vascular-disease/cardiovascular-diabetes-chronic-kidney-morbidity/contents/table-of-contents

11. AIHW-Australian Institute of Health and Welfare. Cardiovascular disease, diabetes and chronic kidney disease—Australian facts: Morbidity–Hospital care. Cardiovascular, diabetes and chronic kidney disease series no. 3. Canberra; 2014.

12. AIHW-Australian Institute of Health and Welfare. Australia’s Health 2014 [Internet]. Canberra; 2014. Available: http://www.aihw.gov.au/publication-detail/?id=60129547206

13. Merlo J, Mulinari S, Wemrell M, Subramanian S V., Hedblad B. The tyranny of the averages and the indiscriminate use of risk factors in public health: The case of coronary heart disease. SSM—Popul Heal. 2017; doi: 10.1016/j.ssmph.2017.08.005 29349257

14. Ma J, King AC, Wilson SR, Xiao L, Stafford RS. Evaluation of lifestyle interventions to treat elevated cardiometabolic risk in primary care (E-LITE): A randomized controlled trial. BMC Fam Pract. 2009; doi: 10.1186/1471-2296-10-71 19909549

15. Tourlouki E, Matalas AL, Panagiotakos DB. Dietary habits and cardiovascular disease risk in middle-aged and elderly populations: a review of evidence. Clinical interventions in aging. 2009.

16. Dehghani A, Kumar Bhasin S, Dwivedi S, Kumar Malhotra R. Influence of Comprehensive Life Style Intervention in Patients of CHD. Glob J Health Sci. 2015; doi: 10.5539/gjhs.v7n7p6 26153198

17. Ard JD, Gower B, Hunter G, Ritchie CS, Roth DL, Goss A, et al. Effects of Calorie Restriction in Obese Older Adults: The CROSSROADS Randomized Controlled Trial. J Gerontol A Biol Sci Med Sci. 2017; doi: 10.1093/gerona/glw237 28003374

18. Weiss EP, Fontana L. Caloric restriction: Powerful protection for the aging heart and vasculature. American Journal of Physiology—Heart and Circulatory Physiology. 2011. doi: 10.1152/ajpheart.00685.2011 21841020

19. Nissinen A, Berrios X, Puska P. Community-based noncommunicable disease interventions: Lessons from developed countries for developing ones. Bulletin of the World Health Organization. 2001.

20. O’Connor Duffany K, Finegood DT, Matthews D, McKee M, Venkat Narayan KM, Puska P, et al. Community Interventions for Health (CIH): A novel approach to tackling the worldwide epidemic of chronic diseases. CVD Prev Control. 2011; doi: 10.1016/j.cvdpc.2011.02.005

21. Parker DR, Assaf AR. Community interventions for cardiovascular disease. Primary Care—Clinics in Office Practice. 2005. doi: 10.1016/j.pop.2005.09.012 16326217

22. Fradelos EC, Papathanasiou I V, Mitsi D, Tsaras K, Kleisiaris CF, Kourkouta L. Health Based Geographic Information Systems (GIS) and their Applications. Acta Inform Med. 2014;22: 402–5. doi: 10.5455/aim.2014.22.402-405 25684850

23. Auchincloss AH, Gebreab SY, Mair C, Diez Roux A V. A review of spatial methods in epidemiology, 2000–2010. Annu Rev Public Health. 2012;33: 107–22. doi: 10.1146/annurev-publhealth-031811-124655 22429160

24. Lawson A ( Andrew B. Statistical methods in spatial epidemiology [Internet]. Wiley; 2006. Available: https://www.wiley.com/en-us/Statistical+Methods+in+Spatial+Epidemiology%2C+2nd+Edition-p-9780470014844

25. Craglia M, Maheswaran R, Maheswaran R. GIS in Public Health Practice [Internet]. Craglia M, Maheswaran R, editors. CRC Press; 2016. doi: 10.1201/9780203720349

26. Jarrahi AM, Zare M, Sadeghi A. Geographic Information Systems (GIS), an Informative Start for Challenging Process of Etiologic Investigation of Diseases and Public Health Policy Making. Asian Pacific J Cancer Care. 2017;2: 1–1. doi: 10.31557/APJCC.2017.2.1.1

27. Nykiforuk CIJ, Flaman LM. Geographic Information Systems (GIS) for Health Promotion and Public Health: A Review. Health Promot Pract. 2011;12: 63–73. doi: 10.1177/1524839909334624 19546198

28. Cromley EK. Using GIS to Address Epidemiologic Research Questions. Curr Epidemiol Reports. 2019;6: 162–173. doi: 10.1007/s40471-019-00193-6

29. Shafran-Nathan R, Levy I, Levin N, Broday DM. Ecological bias in environmental health studies: the problem of aggregation of multiple data sources. Air Qual Atmos Heal. 2017;10: 411–420. doi: 10.1007/s11869-016-0436-x

30. Portnov BA, Dubnov J, Barchana M. On ecological fallacy, assessment errors stemming from misguided variable selection, and the effect of aggregation on the outcome of epidemiological study. J Expo Sci Environ Epidemiol. 2007;17: 106–121. doi: 10.1038/sj.jes.7500533 17033679

31. Rezaeian M, Dunn G, St Leger S, Appleby L. Geographical epidemiology, spatial analysis and geographical information systems: a multidisciplinary glossary. J Epidemiol Community Health. 2007;61: 98–102. doi: 10.1136/jech.2005.043117 17234866

32. Lawson A ( Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition.

33. Lawson A ( Andrew B., Browne WJ( William J, Vidal Rodeiro CL. Disease mapping with WinBUGS and MLwiN [Internet]. J. Wiley; 2003. Available: https://www.wiley.com/en-us/Disease+Mapping+with+WinBUGS+and+MLwiN-p-9780470856048

34. Lawson A ( Andrew B., Kleinman K. Spatial and syndromic surveillance for public health [Internet]. John Wiley; 2005. Available: https://www.wiley.com/en-us/Spatial+and+Syndromic+Surveillance+for+Public+Health-p-9780470092484

35. Gabert R, Thomson B, Gakidou E, Roth G. Identifying High-Risk Neighborhoods Using Electronic Medical Records: A Population-Based Approach for Targeting Diabetes Prevention and Treatment Interventions. PLoS ONE [Electronic Resour. 2016;11: e0159227. doi: 10.1371/journal.pone.0159227 27463641

36. Barber S, Diez Roux A V, Cardoso L, Santos S, Toste V, James S, et al. At the intersection of place, race, and health in Brazil: Residential segregation and cardio-metabolic risk factors in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Soc Sci Med. 2017/08/20. 2018;199: 67–76. doi: 10.1016/j.socscimed.2017.05.047 28821371

37. Toms R, Bonney A, Mayne DJ, Feng X, Walsan R. Geographic and area-level socioeconomic variation in cardiometabolic risk factor distribution: a systematic review of the literature. Int J Health Geogr. 2019;18: 1. doi: 10.1186/s12942-018-0165-5 30621786

38. Astell-Burt T, Feng X. Geographic inequity in healthy food environment and type 2 diabetes: can we please turn off the tap? Med J Aust. 2015;203: 246–248.

39. Zhou M, Astell-Burt T, Bi Y, Feng X, Jiang Y, Li Y, et al. Geographical variation in diabetes prevalence and detection in china: multilevel spatial analysis of 98,058 adults. Diabetes Care. 2015;38: 72–81. doi: 10.2337/dc14-1100 25352654

40. Alkerwi A, Bahi IE, Stranges S, Beissel J, Delagardelle C, Noppe S, et al. Geographic Variations in Cardiometabolic Risk Factors in Luxembourg. Int J Environ Res Public Heal [Electronic Resour. 2017;14: 16. https://dx.doi.org/10.3390/ijerph14060648

41. Lawlor DA, Bedford C, Taylor M, Ebrahim S. Geographical variation in cardiovascular disease, risk factors, and their control in older women: British Women’s Heart and Health Study. J Epidemiol Community Heal. 2003;57: 134–140. Available: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=med4&AN=12540690

42. Barker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the U.S.: a diabetes belt. Am J Prev Med. 2011;40: 434–439. doi: 10.1016/j.amepre.2010.12.019 21406277

43. Valdes S, Garcia-Torres F, Maldonado-Araque C, Goday A, Calle-Pascual A, Soriguer F, et al. Prevalence of obesity, diabetes and other cardiovascular risk factors in Andalusia (southern Spain). Comparison with national prevalence data. The Di@bet.es study. Rev Esp Cardiol. 2014;67: 442–448. http://dx.doi.org/10.1016/j.rec.2013.09.029

44. Paquet C, Chaix B, Howard NJ, Coffee NT, Adams RJ, Taylor AW. Geographic clustering of cardiometabolic risk factors in metropolitan centres in France and Australia. Int J Env Res Public Heal. 2016;13. doi: 10.3390/ijerph13050519 27213423

45. Ocana-Riola R. Common errors in disease mapping. Geospat Health. 2010;4: 139–154. Available: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=med5&NEWS=N&AN=20503184 doi: 10.4081/gh.2010.196 20503184

46. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Public Health. 2009; doi: 10.1186/1471-2458-9-88 19320986

47. Australian Bureau of Statistics. 2011 Census data [Internet]. Commonwealth of Australia; Available: https://www.abs.gov.au/websitedbs/censushome.nsf/home/historicaldata2011?opendocument&navpos=280

48. Bonney A, Mayne DJ, Jones BD, Bott L, Andersen SE, Caputi P. Area-level socioeconomic gradients in overweight and obesity in a community-derived cohort of health service users—a cross-sectional study. PLoS ONE. 2015;10. doi: 10.1371/journal.pone.0137261 26317861

49. Australian Bureau of Statistics. Australian Statistical Geography Standard (ASGS): Volume 1—Main Structure and Greater Capital City Statistical Areas:STATISTICAL AREA LEVEL 1 (SA1) [Internet]. 2016 [cited 21 Oct 2018]. Available: http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/1270.0.55.001~July 2016~Main Features~Statistical Area Level 1 (SA1)~10013

50. The Royal Australian College of General Practitioners, Diabetes Australia. General Practice Management of Type 2 Diabetes 2016–2018 [Internet]. The Royal Australian College of General Practitioners. 2016. doi: 10.1007/s00125-010-2011-6

51. Australian Bureau of Statistics. Australian Health Survey: Biomedical Results for Chronic Diseases, 2011–12 [Internet]. Commonwealth of Australia; ou = Australian Bureau of Statistics; Available: http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/4364.0.55.005main+features12011-12

52. Cheng FW, Gao X, Mitchell DC, Wood C, Still CD, Rolston D, et al. Body mass index and all-cause mortality among older adults. Obesity. 2016;24: 2232–2239. doi: 10.1002/oby.21612 27570944

53. Littman A, Boyko E, … MM-P chronic, 2012 undefined. Evaluation of a weight management program for veterans. ncbi.nlm.nih.gov. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3437789/

54. Li W, Kelsey J, Zhang Z, … SL-… journal of public, 2009 undefined. Small-area estimation and prioritizing communities for obesity control in Massachusetts. ajph.aphapublications.org. Available: https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2008.137364

55. Australian Bureau of Statistics. Australian Health Survey: Biomedical Results for Chronic Diseases, 2011–12. Commonwealth of Australia. 2013. 4364.0.55.005

56. National heart foundation of Australia. Lipid management profile for health professionals [Internet]. Available: https://www.heartfoundation.org.au/for-professionals/clinical-information/lipid-management

57. National Kidney foundation(USA). Albumin creatinine Ratio (ACR) [Internet]. 2018. Available: https://www.kidney.org/kidneydisease/siemens_hcp_acr

58. Kidney Health Australia. Fact sheet: Estimated Glomerular Filtration Rate (eGFR) [Internet]. Available: www.kidney.org.au

59. WHO. Obesity: Preventing and managing the global epidemic. World Health Organization: Technical Report Series. WHO Technical Report Series, no. 894. 2000. ISBN 92 4 120894 5

60. Li H, Calder CA, Cressie N. Beyond Moran’s I: Testing for Spatial Dependence Based on the Spatial Autoregressive Model. Geogr Anal. 2007;39: 357–375. doi: 10.1111/j.1538-4632.2007.00708.x

61. Moran PAP. Notes on Continuous Stochastic Phenomena. Biometrika. 1950;37: 17. doi: 10.2307/2332142 15420245

62. Anselin L. Local Indicators of Spatial Association-LISA. Geogr Anal. 2010;27: 93–115. doi: 10.1111/j.1538-4632.1995.tb00338.x

63. R Core Team (2018). R: A language and environment for statistical computing.R Foundation for Statistical Computing, Vienna, Austria. Available online at https://www.R-project.org/.

64. Environmental Systems Research Institute (ESRI). ArcGIS 10.4.1, ESRI Inc. Redlands, CA, USA. (https://www.esri.com/).

65. Merlo J, Wagner P, Ghith N, Leckie G. An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health. Moerbeek M, editor. PLoS One. 2016;11: e0153778. doi: 10.1371/journal.pone.0153778 27120054

66. Stocks NP, McElroy H, Ryan P, Allan J. Statin prescribing in Australia: socioeconomic and sex differences. Med J Aust. 2004;180: 229–231. doi: 10.5694/J.1326-5377.2004.TB05891.X 14984343

67. Stocks N, Ryan P, Allan J, Williams S, Willson K. Gender, socioeconomic status, need or access? Differences in statin prescribing across urban, rural and remote Australia. Aust J Rural Health. 2009;17: 92–96. doi: 10.1111/j.1440-1584.2009.01043.x 19335599

68. Merlo J, Viciana-Fernández FJ, Ramiro-Fariñas D, Research Group of Longitudinal Database of Andalusian Population (LDAP). Bringing the individual back to small-area variation studies: A multilevel analysis of all-cause mortality in Andalusia, Spain. Soc Sci Med. 2012;75: 1477–1487. doi: 10.1016/j.socscimed.2012.06.004 22795359

69. Merlo J, Wagner P, Leckie G. A simple multilevel approach for analysing geographical inequalities in public health reports: The case of municipality differences in obesity. Health Place. 2019;58: 102145. doi: 10.1016/j.healthplace.2019.102145 31195211

70. Merlo J, Asplund K, Lynch J, Rastam L, Dobson A, World Health Organization MONICA Project. Population Effects on Individual Systolic Blood Pressure: A Multilevel Analysis of the World Health Organization MONICA Project. Am J Epidemiol. 2004;159: 1168–1179. doi: 10.1093/aje/kwh160 15191934


Článek vyšel v časopise

PLOS One


2019 Číslo 10
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

plice
INSIGHTS from European Respiratory Congress
nový kurz

Současné pohledy na riziko v parodontologii
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií
Autoři: prof. MUDr. Petr Němec, Ph.D.

Střevní příprava před kolonoskopií
Autoři: MUDr. Klára Kmochová, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#