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      How many people will need palliative care in Scotland by 2040? A mixed-method study of projected palliative care need and recommendations for service delivery

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          Abstract

          Objective

          To estimate future palliative care need and complexity of need in Scotland, and to identify priorities for future service delivery.

          Design

          We estimated the prevalence of palliative care need by analysing the proportion of deaths from defined chronic progressive illnesses. We described linear projections up to 2040 using national death registry data and official mortality forecasts. An expert consultation and subsequent online consensus survey generated recommendations on meeting future palliative care need.

          Setting

          Scotland, population of 5.4 million.

          Participants

          All decedents in Scotland over 11 years (2007 to 2017). The consultation had 34 participants; 24 completed the consensus survey.

          Primary and secondary outcomes

          Estimates of past and future palliative care need in Scotland from 2007 up to 2040. Multimorbidity was operationalised as two or more registered causes of death from different disease groups (cancer, organ failure, dementia, other). Consultation and survey data were analysed descriptively.

          Results

          We project that by 2040, the number of people requiring palliative care will increase by at least 14%; and by 20% if we factor in multimorbidity. The number of people dying from multiple diseases associated with different disease groups is projected to increase from 27% of all deaths in 2017 to 43% by 2040. To address increased need and complexity, experts prioritised sustained investment in a national digital platform, roll-out of integrated electronic health and social care records; and approaches that remain person-centred.

          Conclusions

          By 2040 more people in Scotland are projected to die with palliative care needs, and the complexity of need will increase markedly. Service delivery models must adapt to serve growing demand and complexity associated with dying from multiple diseases from different disease groups. We need sustained investment in secure, accessible, integrated and person-centred health and social care digital systems, to improve care coordination and optimise palliative care for people across care settings.

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          Most cited references31

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          Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.

          Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. In a cross-sectional study we extracted data on 40 morbidities from a database of 1,751,841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. 42·2% (95% CI 42·1-42·3) of all patients had one or more morbidities, and 23·2% (23·08-23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210,500 vs 194,996). Onset of multimorbidity occurred 10-15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9-11·2% in most deprived area vs 5·9%, 5·8%-6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59-6·90 for five or more disorders vs 1·95, 1·93-1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21-2·32 vs 1·08, 1·05-1·11). Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Scottish Government Chief Scientist Office. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories

            Summary Background Understanding potential trajectories in health and drivers of health is crucial to guiding long-term investments and policy implementation. Past work on forecasting has provided an incomplete landscape of future health scenarios, highlighting a need for a more robust modelling platform from which policy options and potential health trajectories can be assessed. This study provides a novel approach to modelling life expectancy, all-cause mortality and cause of death forecasts —and alternative future scenarios—for 250 causes of death from 2016 to 2040 in 195 countries and territories. Methods We modelled 250 causes and cause groups organised by the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) hierarchical cause structure, using GBD 2016 estimates from 1990–2016, to generate predictions for 2017–40. Our modelling framework used data from the GBD 2016 study to systematically account for the relationships between risk factors and health outcomes for 79 independent drivers of health. We developed a three-component model of cause-specific mortality: a component due to changes in risk factors and select interventions; the underlying mortality rate for each cause that is a function of income per capita, educational attainment, and total fertility rate under 25 years and time; and an autoregressive integrated moving average model for unexplained changes correlated with time. We assessed the performance by fitting models with data from 1990–2006 and using these to forecast for 2007–16. Our final model used for generating forecasts and alternative scenarios was fitted to data from 1990–2016. We used this model for 195 countries and territories to generate a reference scenario or forecast through 2040 for each measure by location. Additionally, we generated better health and worse health scenarios based on the 85th and 15th percentiles, respectively, of annualised rates of change across location-years for all the GBD risk factors, income per person, educational attainment, select intervention coverage, and total fertility rate under 25 years in the past. We used the model to generate all-cause age-sex specific mortality, life expectancy, and years of life lost (YLLs) for 250 causes. Scenarios for fertility were also generated and used in a cohort component model to generate population scenarios. For each reference forecast, better health, and worse health scenarios, we generated estimates of mortality and YLLs attributable to each risk factor in the future. Findings Globally, most independent drivers of health were forecast to improve by 2040, but 36 were forecast to worsen. As shown by the better health scenarios, greater progress might be possible, yet for some drivers such as high body-mass index (BMI), their toll will rise in the absence of intervention. We forecasted global life expectancy to increase by 4·4 years (95% UI 2·2 to 6·4) for men and 4·4 years (2·1 to 6·4) for women by 2040, but based on better and worse health scenarios, trajectories could range from a gain of 7·8 years (5·9 to 9·8) to a non-significant loss of 0·4 years (–2·8 to 2·2) for men, and an increase of 7·2 years (5·3 to 9·1) to essentially no change (0·1 years [–2·7 to 2·5]) for women. In 2040, Japan, Singapore, Spain, and Switzerland had a forecasted life expectancy exceeding 85 years for both sexes, and 59 countries including China were projected to surpass a life expectancy of 80 years by 2040. At the same time, Central African Republic, Lesotho, Somalia, and Zimbabwe had projected life expectancies below 65 years in 2040, indicating global disparities in survival are likely to persist if current trends hold. Forecasted YLLs showed a rising toll from several non-communicable diseases (NCDs), partly driven by population growth and ageing. Differences between the reference forecast and alternative scenarios were most striking for HIV/AIDS, for which a potential increase of 120·2% (95% UI 67·2–190·3) in YLLs (nearly 118 million) was projected globally from 2016–40 under the worse health scenario. Compared with 2016, NCDs were forecast to account for a greater proportion of YLLs in all GBD regions by 2040 (67·3% of YLLs [95% UI 61·9–72·3] globally); nonetheless, in many lower-income countries, communicable, maternal, neonatal, and nutritional (CMNN) diseases still accounted for a large share of YLLs in 2040 (eg, 53·5% of YLLs [95% UI 48·3–58·5] in Sub-Saharan Africa). There were large gaps for many health risks between the reference forecast and better health scenario for attributable YLLs. In most countries, metabolic risks amenable to health care (eg, high blood pressure and high plasma fasting glucose) and risks best targeted by population-level or intersectoral interventions (eg, tobacco, high BMI, and ambient particulate matter pollution) had some of the largest differences between reference and better health scenarios. The main exception was sub-Saharan Africa, where many risks associated with poverty and lower levels of development (eg, unsafe water and sanitation, household air pollution, and child malnutrition) were projected to still account for substantive disparities between reference and better health scenarios in 2040. Interpretation With the present study, we provide a robust, flexible forecasting platform from which reference forecasts and alternative health scenarios can be explored in relation to a wide range of independent drivers of health. Our reference forecast points to overall improvements through 2040 in most countries, yet the range found across better and worse health scenarios renders a precarious vision of the future—a world with accelerating progress from technical innovation but with the potential for worsening health outcomes in the absence of deliberate policy action. For some causes of YLLs, large differences between the reference forecast and alternative scenarios reflect the opportunity to accelerate gains if countries move their trajectories toward better health scenarios—or alarming challenges if countries fall behind their reference forecasts. Generally, decision makers should plan for the likely continued shift toward NCDs and target resources toward the modifiable risks that drive substantial premature mortality. If such modifiable risks are prioritised today, there is opportunity to reduce avoidable mortality in the future. However, CMNN causes and related risks will remain the predominant health priority among lower-income countries. Based on our 2040 worse health scenario, there is a real risk of HIV mortality rebounding if countries lose momentum against the HIV epidemic, jeopardising decades of progress against the disease. Continued technical innovation and increased health spending, including development assistance for health targeted to the world's poorest people, are likely to remain vital components to charting a future where all populations can live full, healthy lives. Funding Bill & Melinda Gates Foundation.
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              Consensus methods for medical and health services research.

              Health providers face the problem of trying to make decisions in situations where there is insufficient information and also where there is an overload of (often contradictory) information. Statistical methods such as meta-analysis have been developed to summarise and to resolve inconsistencies in study findings--where information is available in an appropriate form. Consensus methods provide another means of synthesising information, but are liable to use a wider range of information than is common in statistical methods, and where published information is inadequate or non-existent these methods provide a means of harnessing the insights of appropriate experts to enable decisions to be made. Two consensus methods commonly adopted in medical, nursing, and health services research--the Delphi process and the nominal group technique (also known as the expert panel)--are described, together with the most appropriate situations for using them; an outline of the process involved in undertaking a study using each method is supplemented by illustrations of the authors' work. Key methodological issues in using the methods are discussed, along with the distinct contribution of consensus methods as aids to decision making, both in clinical practice and in health service development.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                3 February 2021
                : 11
                : 2
                : e041317
                Affiliations
                [1 ]departmentResearch , Marie Curie Hospice Edinburgh , Edinburgh, UK
                [2 ]departmentPrimary Palliative Care Research Group, Usher Institute , University of Edinburgh , Edinburgh, UK
                [3 ]departmentCicely Saunders Institute of Palliative Care, Policy and Rehabilitation , King’s College London , London, UK
                [4 ]Public Health Scotland , Glasgow, UK
                [5 ]departmentPolicy and Public Affairs , Marie Curie , Edinburgh, UK
                [6 ]departmentHealth Education , Université Paris , Paris, UK
                Author notes
                [Correspondence to ] Dr Anne M Finucane; anne.finucane@ 123456mariecurie.org.uk
                Author information
                http://orcid.org/0000-0002-3056-059X
                http://orcid.org/0000-0002-6649-9428
                Article
                bmjopen-2020-041317
                10.1136/bmjopen-2020-041317
                7868264
                33536318
                fa3165ea-1ca3-4581-8c60-b0da42516c4e
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 04 June 2020
                : 08 December 2020
                : 05 January 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000654, Marie Curie;
                Award ID: Award letter dated 28/2/19 (£14,277).
                Categories
                Palliative Care
                1506
                1720
                Original research
                Custom metadata
                unlocked

                Medicine
                adult palliative care,public health,health policy,geriatric medicine,dementia
                Medicine
                adult palliative care, public health, health policy, geriatric medicine, dementia

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