Exploring the geography of serious mental illness and type 2 diabetes comorbidity in Illawarra—Shoalhaven, Australia (2010 -2017)


Autoři: Ramya Walsan aff001;  Darren J. Mayne aff001;  Nagesh Pai aff001;  Xiaoqi Feng aff002;  Andrew Bonney aff001
Působiště autorů: School of Medicine, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, Australia aff001;  Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia aff002;  Public Health Unit, Illawarra Shoalhaven Local Health District, Warrawong, Australia aff003;  The University of Sydney, School of Public Health, Sydney, Australia aff004;  Mental Health Services, Illawarra Shoalhaven Local Health District, Wollongong Hospital, Wollongong, Australia aff005;  Population Wellbeing and Environment Research Lab (PowerLab), School of Health and Society, Faculty of Social Sciences, University of Wollongong, Wollongong, Australia aff006;  School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia aff007
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225992

Souhrn

Objectives

The primary aim of this study was to describe the geography of serious mental illness (SMI)–type 2 diabetes comorbidity (T2D) in the Illawarra-Shoalhaven region of NSW, Australia. The Secondary objective was to determine the geographic concordance if any, between the comorbidity and the single diagnosis of SMI and diabetes.

Methods

Spatial analytical techniques were applied to clinical data to explore the above objectives. The geographic variation in comorbidity was determined by Moran’s I at the global level and the local clusters of significance were determined by Local Moran’s I and spatial scan statistic. Choropleth hotspot maps and spatial scan statistics were generated to assess the geographic convergence of SMI, diabetes and their comorbidity. Additionally, we used bivariate LISA (Local Indicators of Spatial Association) and multivariate spatial scan to identify coincident areas with higher rates of both SMI and T2D.

Results

The study identified significant geographic variation in the distribution of SMI–T2D comorbidity in Illawarra Shoalhaven. Consistently higher burden of comorbidity was observed in some urban suburbs surrounding the major metropolitan city. Comparison of comorbidity hotspots with the hotspots of single diagnosis SMI and T2D further revealed a geographic concordance of high-risk areas again in the urban areas outside the major metropolitan city.

Conclusion

The identified comorbidity hotspots in our study may serve as a basis for future prioritisation and targeted interventions. Further investigation is required to determine whether contextual environmental factors, such as neighbourhood socioeconomic disadvantage, may be explanatory.

Implications for public health

Ours is the first study to explore the geographic variations in the distribution of SMI and T2D comorbidity. Findings highlight the importance of considering the role of neighbourhood environments in influencing the T2D risk in people with SMI.

Klíčová slova:

Geographic distribution – Geography – Health services research – Public and occupational health – Urban areas – Urban geography


Zdroje

1. Ward M, Druss B. The epidemiology of diabetes in psychotic disorders. The Lancet Psychiatry. 2015;2(5):431–51. doi: 10.1016/S2215-0366(15)00007-3 26360287

2. David L, Kirsten JH, Stephen K. The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ: British Medical Journal. 2013(7909):13.

3. Holt RI, Mitchell AJ. Diabetes mellitus and severe mental illness: mechanisms and clinical implications. Nat Rev Endocrinol. 2015;11(2):79–89. doi: 10.1038/nrendo.2014.203 25445848

4. Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001;24(6):1069–78. doi: 10.2337/diacare.24.6.1069 11375373

5. Wandell P, Ljunggren G, Wahlstrom L, Carlsson AC. Diabetes and psychiatric illness in the total population of Stockholm. Journal of psychosomatic research. 2014;77(3):169–73. doi: 10.1016/j.jpsychores.2014.06.012 25149026

6. Tirupati S, Chua LE. Obesity and metabolic syndrome in a psychiatric rehabilitation service. The Australian and New Zealand journal of psychiatry. 2007;41(7):606–10. doi: 10.1080/00048670701392841 17558623

7. Ribe AR, Laursen TM, Sandbaek A, Charles M, Nordentoft M, Vestergaard M. Long-term mortality of persons with severe mental illness and diabetes: a population-based cohort study in Denmark. Psychological medicine. 2014;44(14):3097–107. doi: 10.1017/S0033291714000634 25065292

8. Šprah L, Dernovšek MZ, Wahlbeck K, Haaramo P. Psychiatric readmissions and their association with physical comorbidity: a systematic literature review. BMC psychiatry. 2017;17.

9. Kurdyak P, Vigod S, Duchen R, Jacob B, Stukel T, Kiran T. Diabetes quality of care and outcomes: Comparison of individuals with and without schizophrenia. General hospital psychiatry. 2017;46:7–13. doi: 10.1016/j.genhosppsych.2017.02.001 28622820

10. Dauncey K, Giggs J, Baker K, Harrison G. Schizophrenia in Nottingham: lifelong residential mobility of a cohort. The British journal of psychiatry: the journal of mental science. 1993;163:613–9.

11. Kirkbride JB, Boydell J, Ploubidis GB, Morgan C, Dazzan P, McKenzie K, et al. Testing the association between the incidence of schizophrenia and social capital in an urban area. Psychological medicine. 2008;38(8):1083–94. doi: 10.1017/S0033291707002085 17988420

12. Kirkbride JB, Jones PB, Ullrich S, Coid JW. Social deprivation, inequality, and the neighborhood-level incidence of psychotic syndromes in East London. Schizophrenia bulletin. 2014;40(1):169–80.

13. Astell-Burt T, Feng X, Kolt GS, McLean M, Maberly G. Understanding geographical inequities in diabetes: multilevel evidence from 114,755 adults in Sydney, Australia. Diabetes Res Clin Pract. 2014;106(3):e68–73. doi: 10.1016/j.diabres.2014.09.033 25451908

14. Cross R, Bonney A, Mayne DJ, Weston KM. Cross-sectional study of area-level disadvantage and glycaemic-related risk in community health service users in the Southern.IML Research (SIMLR) cohort. Australian health review: a publication of the Australian Hospital Association. 2017.

15. Geraghty EM, Balsbaugh T, Nuovo J, Tandon S. Using Geographic Information Systems (GIS) to Assess Outcome Disparities in Patients with Type 2 Diabetes and Hyperlipidemia. The Journal of the American Board of Family Medicine. 2010;23(1):88. doi: 10.3122/jabfm.2010.01.090149 20051547

16. Moreno B, García-Alonso CR, Negrín Hernández MA, Torres-González F, Salvador-Carulla L. Spatial analysis to identify hotspots of prevalence of schizophrenia. Social Psychiatry & Psychiatric Epidemiology. 2008;43(10):782–91.

17. Almog M, Curtis S, Copeland A, Congdon P. Geographical variation in acute psychiatric admissions within New York City 1990–2000: growing inequalities in service use? Social Science & Medicine. 2004;59:361–76.

18. Green C, Hoppa RD, Young TK, Blanchard JF. Geographic analysis of diabetes prevalence in an urban area. Social Science & Medicine. 2003;57(3):551–60.

19. Walsan R, Bonney A, Mayne DJ, Pai N, Feng X, Toms R. Serious Mental Illness, Neighborhood Disadvantage, and Type 2 Diabetes Risk: A Systematic Review of the Literature. Journal of Primary Care & Community Health. 2018;9:2150132718802025.

20. Baigent M. Managing patients with dual diagnosis in psychiatric practice. Current opinion in psychiatry. 2012;25(3):201–5. doi: 10.1097/YCO.0b013e3283523d3d 22449766

21. Population by age, sex, regions of australia [Internet]. 2011. http://www.abs.gov.au/ausstats/abs@.nsf/0/151AA7593B394934CA2573210018DA4A?Opendocument.

22. ABS. Diabetes Biomarkers [Internet]. Australian Health Survey: Users’ Guide, 2011–13. Commonwealth of Australia: Australian Bureau of Statistics; 2013.

23. Anselin L LL, Koschinsky J. Rate transformations and smoothing. In: Spatial Analysis Laboratory Department of Geography. 2006 UoI, editor. Urbana-Champaign2006.

24. Waller LA, G C. Applied spatial statistics for public health data: Wiley-Interscience; 2004.

25. Abbas T, Younus M, Muhammad SA. Spatial cluster analysis of human cases of Crimean Congo hemorrhagic fever reported in Pakistan. Infectious Diseases of Poverty. 2015;4(1):9.

26. Anselin L. Local Indicators of Spatial Association—LISA. Geographical Analysis. 1995;27(2):93–115.

27. Anselin L, Syabri I, Kho Y. GeoDa: An Introduction to Spatial Data Analysis. Geographical Analysis. 2006;38(1):5–22.

28. Kulldorff M. SaTScanTM User Guide. In: https://www.satscan.org/, editor. 2018.

29. Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Statistics in medicine. 1995;14(8):799–810. doi: 10.1002/sim.4780140809 7644860

30. Mitchell A. The ESRI guide to GIS analysis / Andy Mitchell2018.

31. Anselin L, Kho Y, Syabri I. GeoDa: An introduction to spatial data analysis. Geographical Analysis. 2006;38(1):5–22.

32. team Rc. R: A language and environment for statisticalcomputing. In: Computing RFfS, editor. Vienna, Austria2103.

33. ESRI. ArcGIS Desktop: Release 10. In: Institute. ESR, editor. Redlands, CA2011.

34. Astell-Burt T, Feng X, Kolt G. Identification of the impact of crime on physical activity depends upon neighbourhood scale: multilevel evidence from 203,883 Australians: Research Online; 2015.

35. Jacka FN, Cherbuin N, Anstey KJ, Butterworth P. Dietary Patterns and Depressive Symptoms over Time: Examining the Relationships with Socioeconomic Position, Health Behaviours and Cardiovascular Risk. 2014.

36. Morgan VA, Waterreus A, Jablensky A, Mackinnon A, McGrath JJ, Carr V, et al. People living with psychotic illness in 2010: The second Australian national survey of psychosis. Australian & New Zealand Journal of Psychiatry. 2012;46(8):735–52.


Článek vyšel v časopise

PLOS One


2019 Číslo 12