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Abstract
Context Investigators in palliative care rely heavily on routinely collected data, which carry risk of unobserved confounding and selection bias. ‘Natural experiments’ offer opportunities to generate credible causal treatment effect estimates from observational data.
Objectives We aimed first to review studies that employed ‘natural experiments’ to evaluate palliative care, and second to consider implications for expanding use of these methods.
Methods We searched systematically seven databases to identify studies using ‘natural experiments’ to evaluate palliative care’s effect on outcomes and costs. We searched three grey literature repositories, and hand-searched journals and prior systematic reviews. We assessed reporting using the Strengthening the Reporting of Observational Studies in Epidemiology checklist and a bespoke methodological quality tool, using two reviewers at each stage. We combined results in a narrative synthesis.
Results We included 17 studies, which evaluated a wide range of interventions and populations. Seven studies employed a difference-in-differences design; five each used instrumental variables and interrupted time series analysis. Outcomes of interest related mostly to healthcare use. Reporting quality was variable. Most studies reported lower costs and improved outcomes associated with palliative care, but a third of utilisation and place of death evaluations found no effect.
Conclusion Among the large number of observational studies in palliative care, a small minority have employed causal mechanisms. High-volume routine data collection, the expansion of palliative care services worldwide and recent methodological advances offer potential for increased use of ‘natural experiments’. Such studies would improve the quality of the evidence base.
- Service evaluation
- Terminal care
- Cancer
- Chronic conditions
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information. n/a.
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Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information. n/a.
Footnotes
JJ and NK are joint first authors.
Twitter @GarridoMelissa
Contributors JJ and NK reviewed return studies for eligibility at title and abstract; read full texts for eligibility; performed the quality assessment and data extraction on included studies; conducted the grey literature review and led writing of the paper. MMG co-designed the original review protocol, oversaw all phases of the review process, led the development of methods-specific quality assessment tools, and drafted and revised the paper. MJ co-designed the original review protocol, oversaw all phases of the review process, and drafted and revised the paper. DM was the information specialist, co-designing and running the database searchers, and revising the paper. PM co-designed the original review protocol, oversaw all phases of the review process, and drafted and revised the paper. PM is guarantor.
Funding Dr PM's time was supported by the Health Research Board in Ireland (ARPP/2018/A/005). Dr MMG’s time was supported by NIH/NIA R56AG068178; the views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. Dr MJ and NK acknowledge support from AHRQ 1R01HS026488-01A1a. Dr MJ also acknowledges support from the Gordon and Betty Moore Foundation.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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