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

English version

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
prolekare.web.journal.doi_sk: https://doi.org/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 – Public and occupational health – Geography – Urban areas – Urban geography – Health services research

Zdroje

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