Mortality, predictors and causes among people with intellectual disabilities: A systematic narrative review supplemented by machine learning

ABSTRACT

Background

There is a need to systematically compare and contrast mortality predictors and disparities in people with intellectual disabilities (ID) for global prevention strategy development.

Method

Bibliographic databases and grey literature were searched using systematic review methodology and the machine learning tool “Abstrackr.”

Results

Fifty-four relevant articles and reports published from 2010 to 2019 were identified. Nearly all (n = 53) were from high-income countries. Mortality disparities were apparent and consistent across countries and publication years, with no evidence of a decrease over time. People with ID can still expect to live 12–23 years less than the general population and are particularly vulnerable to deaths from respiratory infections and epilepsy.

Conclusions

Both population and individual-level approaches to prevention are indicated to tackle the continuing mortality disparities in people with ID, including consideration of reasonable adjustments in general population efforts to reduce health inequalities.

KEYWORDS:

• Intellectual disability
• machine learning
• mortality
• comorbidities
• life expectancy

Acknowledgments

This study was funded from a Baily Thomas Doctoral Fellowship award (TRUST/VC/AC/SG/5366-8393). The funding body has imposed no restrictions on free access to or publication of the research data. We gratefully acknowledge Professor Gyles Glover for his excellent support, advice and comments on several versions of this manuscript. We would also like to thank Professor Panos Vostanis for his comments and suggestions on the first draft of this manuscript. Finally, we acknowledge the carers and service users who have been involved in this programme of work by discussing their own health-related experiences, in particular: the Charnwood Action Group (Leicestershire); the Talk & Listen Group (Leicester city); Gill Huddleston; Kate Dolan; Sarah Stanyer and Amy Stanway.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Baily Thomas Charitable Fund [grant number TRUST/VC/AC/SG/5366-8393].