articles on research in healthcare

Research articles

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Page 1 of 519

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Identifying pregnant and postpartum women’s priorities for enhancing nutrition support through social needs programmes in a resource-constrained urban community in South Africa

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Research Article

Assessing the impact of healthcare research: A systematic review of methodological frameworks

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliation Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

Roles Data curation, Formal analysis, Methodology, Validation, Writing – review & editing

Roles Formal analysis, Methodology, Supervision, Validation, Writing – review & editing

• Samantha Cruz Rivera, 
• Derek G. Kyte, 
• Olalekan Lee Aiyegbusi, 
• Thomas J. Keeley, 
• Melanie J. Calvert

• Published: August 9, 2017
• https://doi.org/10.1371/journal.pmed.1002370
• Reader Comments

Increasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix.

Methods and findings

Two independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched.

Conclusions

The measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Author summary

Why was this study done.

• There is a growing interest in demonstrating the impact of research in order to minimise research waste, allocate resources efficiently, and maximise the benefit of research. However, there is no consensus on which is the most appropriate tool to measure the impact of research.
• To our knowledge, this review is the first to synthesise existing methodological frameworks for healthcare research impact, and the associated impact metrics by which various authors have proposed impact should be measured, into a unified matrix.

What did the researchers do and find?

• We conducted a systematic review identifying 24 existing methodological research impact frameworks.
• We scrutinised the sample, identifying and summarising 5 proposed impact categories, 16 impact subcategories, and over 80 metrics into an impact matrix and methodological framework.

What do these findings mean?

• This simplified consolidated methodological framework will help researchers to understand how a research study may give rise to differing forms of impact, as well as in what ways and at which time points these potential impacts might be measured.
• Incorporating these insights into the design of a study could enhance impact, optimizing the use of research resources.

Citation: Cruz Rivera S, Kyte DG, Aiyegbusi OL, Keeley TJ, Calvert MJ (2017) Assessing the impact of healthcare research: A systematic review of methodological frameworks. PLoS Med 14(8): e1002370. https://doi.org/10.1371/journal.pmed.1002370

Academic Editor: Mike Clarke, Queens University Belfast, UNITED KINGDOM

Received: February 28, 2017; Accepted: July 7, 2017; Published: August 9, 2017

Copyright: © 2017 Cruz Rivera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and supporting files.

Funding: Funding was received from Consejo Nacional de Ciencia y Tecnología (CONACYT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript ( http://www.conacyt.mx/ ).

Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: MJC has received consultancy fees from Astellas and Ferring pharma and travel fees from the European Society of Cardiology outside the submitted work. TJK is in full-time paid employment for PAREXEL International.

Abbreviations: AIHS, Alberta Innovates—Health Solutions; CAHS, Canadian Academy of Health Sciences; CIHR, Canadian Institutes of Health Research; CINAHL+, Cumulative Index to Nursing and Allied Health Literature; EMBASE, Excerpta Medica Database; ERA, Excellence in Research for Australia; HEFCE, Higher Education Funding Council for England; HMIC, Health Management Information Consortium; HTA, Health Technology Assessment; IOM, Impact Oriented Monitoring; MDG, Millennium Development Goal; NHS, National Health Service; MEDLINE, Medical Literature Analysis and Retrieval System Online; PHC RIS, Primary Health Care Research & Information Service; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROM, patient-reported outcome measures; QALY, quality-adjusted life year; R&D, research and development; RAE, Research Assessment Exercise; REF, Research Excellence Framework; RIF, Research Impact Framework; RQF, Research Quality Framework; SDG, Sustainable Development Goal; SIAMPI, Social Impact Assessment Methods for research and funding instruments through the study of Productive Interactions between science and society

Introduction

In 2010, approximately US$240 billion was invested in healthcare research worldwide [ 1 ]. Such research is utilised by policy makers, healthcare providers, and clinicians to make important evidence-based decisions aimed at maximising patient benefit, whilst ensuring that limited healthcare resources are used as efficiently as possible to facilitate effective and sustainable service delivery. It is therefore essential that this research is of high quality and that it is impactful—i.e., it delivers demonstrable benefits to society and the wider economy whilst minimising research waste [ 1 , 2 ]. Research impact can be defined as ‘any identifiable ‘benefit to, or positive influence on the economy, society, public policy or services, health, the environment, quality of life or academia’ (p. 26) [ 3 ].

There are many purported benefits associated with the measurement of research impact, including the ability to (1) assess the quality of the research and its subsequent benefits to society; (2) inform and influence optimal policy and funding allocation; (3) demonstrate accountability, the value of research in terms of efficiency and effectiveness to the government, stakeholders, and society; and (4) maximise impact through better understanding the concept and pathways to impact [ 4 – 7 ].

Measuring and monitoring the impact of healthcare research has become increasingly common in the United Kingdom [ 5 ], Australia [ 5 ], and Canada [ 8 ], as governments, organisations, and higher education institutions seek a framework to allocate funds to projects that are more likely to bring the most benefit to society and the economy [ 5 ]. For example, in the UK, the 2014 Research Excellence Framework (REF) has recently been used to assess the quality and impact of research in higher education institutions, through the assessment of impact cases studies and selected qualitative impact metrics [ 9 ]. This is the first initiative to allocate research funding based on the economic, societal, and cultural impact of research, although it should be noted that research impact only drives a proportion of this allocation (approximately 20%) [ 9 ].

In the UK REF, the measurement of research impact is seen as increasingly important. However, the impact element of the REF has been criticised in some quarters [ 10 , 11 ]. Critics deride the fact that REF impact is determined in a relatively simplistic way, utilising researcher-generated case studies, which commonly attempt to link a particular research outcome to an associated policy or health improvement despite the fact that the wider literature highlights great diversity in the way research impact may be demonstrated [ 12 , 13 ]. This led to the current debate about the optimal method of measuring impact in the future REF [ 10 , 14 ]. The Stern review suggested that research impact should not only focus on socioeconomic impact but should also include impact on government policy, public engagement, academic impacts outside the field, and teaching to showcase interdisciplinary collaborative impact [ 10 , 11 ]. The Higher Education Funding Council for England (HEFCE) has recently set out the proposals for the REF 2021 exercise, confirming that the measurement of such impact will continue to form an important part of the process [ 15 ].

With increasing pressure for healthcare research to lead to demonstrable health, economic, and societal impact, there is a need for researchers to understand existing methodological impact frameworks and the means by which impact may be quantified (i.e., impact metrics; see Box 1 , 'Definitions’) to better inform research activities and funding decisions. From a researcher’s perspective, understanding the optimal pathways to impact can help inform study design aimed at maximising the impact of the project. At the same time, funders need to understand which aspects of impact they should focus on when allocating awards so they can make the most of their investment and bring the greatest benefit to patients and society [ 2 , 4 , 5 , 16 , 17 ].

Box 1. Definitions

• Research impact: ‘any identifiable benefit to, or positive influence on, the economy, society, public policy or services, health, the environment, quality of life, or academia’ (p. 26) [ 3 ].
• Methodological framework: ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ].
• Pathway: ‘a way of achieving a specified result; a course of action’ [ 19 ].
• Quantitative metrics: ‘a system or standard of [quantitative] measurement’ [ 20 ].
• Narrative metrics: ‘a spoken or written account of connected events; a story’ [ 21 ].

Whilst previous researchers have summarised existing methodological frameworks and impact case studies [ 4 , 22 – 27 ], they have not summarised the metrics for use by researchers, funders, and policy makers. The aim of this review was therefore to (1) identify the methodological frameworks used to measure healthcare research impact using systematic methods, (2) summarise common impact themes and metrics in an impact matrix, and (3) provide a simplified consolidated resource for use by funders, researchers, and policy makers.

Search strategy and selection criteria

Initially, a search strategy was developed to identify the available literature regarding the different methods to measure research impact. The following keywords: ‘Impact’, ‘Framework’, and ‘Research’, and their synonyms, were used during the search of the Medical Literature Analysis and Retrieval System Online (MEDLINE; Ovid) database, the Excerpta Medica Database (EMBASE), the Health Management Information Consortium (HMIC) database, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL+) database (inception to May 2017; see S1 Appendix for the full search strategy). Additionally, the nonindexed Journal of Research Evaluation was hand searched during the same timeframe using the keyword ‘Impact’. Other relevant articles were identified through 3 Internet search engines (Google, Google Scholar, and Google Images) using the keywords ‘Impact’, ‘Framework’, and ‘Research’, with the first 50 results screened. Google Images was searched because different methodological frameworks are summarised in a single image and can easily be identified through this search engine. Finally, additional publications were sought through communication with experts.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (see S1 PRISMA Checklist ), 2 independent investigators systematically screened for publications describing, evaluating, or utilising a methodological research impact framework within the context of healthcare research [ 28 ]. Papers were eligible if they included full or partial methodological frameworks or pathways to research impact; both primary research and systematic reviews fitting these criteria were included. We included any methodological framework identified (original or modified versions) at the point of first occurrence. In addition, methodological frameworks were included if they were applicable to the healthcare discipline with no need of modification within their structure. We defined ‘methodological framework’ as ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ], whereas we defined ‘pathway’ as ‘a way of achieving a specified result; a course of action’ [ 19 ]. Studies were excluded if they presented an existing (unmodified) methodological framework previously available elsewhere, did not explicitly describe a methodological framework but rather focused on a single metric (e.g., bibliometric analysis), focused on the impact or effectiveness of interventions rather than that of the research, or presented case study data only. There were no language restrictions.

Data screening

Records were downloaded into Endnote (version X7.3.1), and duplicates were removed. Two independent investigators (SCR and OLA) conducted all screening following a pilot aimed at refining the process. The records were screened by title and abstract before full-text articles of potentially eligible publications were retrieved for evaluation. A full-text screening identified the publications included for data extraction. Discrepancies were resolved through discussion, with the involvement of a third reviewer (MJC, DGK, and TJK) when necessary.

Data extraction and analysis

Data extraction occurred after the final selection of included articles. SCR and OLA independently extracted details of impact methodological frameworks, the country of origin, and the year of publication, as well as the source, the framework description, and the methodology used to develop the framework. Information regarding the methodology used to develop each methodological framework was also extracted from framework webpages where available. Investigators also extracted details regarding each framework’s impact categories and subgroups, along with their proposed time to impact (‘short-term’, ‘mid-term’, or ‘long-term’) and the details of any metrics that had been proposed to measure impact, which are depicted in an impact matrix. The structure of the matrix was informed by the work of M. Buxton and S. Hanney [ 2 ], P. Buykx et al. [ 5 ], S. Kuruvila et al. [ 29 ], and A. Weiss [ 30 ], with the intention of mapping metrics presented in previous methodological frameworks in a concise way. A consensus meeting with MJC, DGK, and TJK was held to solve disagreements and finalise the data extraction process.

Included studies

Our original search strategy identified 359 citations from MEDLINE (Ovid), EMBASE, CINAHL+, HMIC, and the Journal of Research Evaluation, and 101 citations were returned using other sources (Google, Google Images, Google Scholar, and expert communication) (see Fig 1 ) [ 28 ]. In total, we retrieved 54 full-text articles for review. At this stage, 39 articles were excluded, as they did not propose new or modified methodological frameworks. An additional 15 articles were included following the backward and forward citation method. A total of 31 relevant articles were included in the final analysis, of which 24 were articles presenting unique frameworks and the remaining 7 were systematic reviews [ 4 , 22 – 27 ]. The search strategy was rerun on 15 May 2017. A further 19 publications were screened, and 2 were taken forward to full-text screening but were ineligible for inclusion.

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https://doi.org/10.1371/journal.pmed.1002370.g001

Methodological framework characteristics

The characteristics of the 24 included methodological frameworks are summarised in Table 1 , 'Methodological framework characteristics’. Fourteen publications proposed academic-orientated frameworks, which focused on measuring academic, societal, economic, and cultural impact using narrative and quantitative metrics [ 2 , 3 , 5 , 8 , 29 , 31 – 39 ]. Five publications focused on assessing the impact of research by focusing on the interaction process between stakeholders and researchers (‘productive interactions’), which is a requirement to achieve research impact. This approach tries to address the issue of attributing research impact to metrics [ 7 , 40 – 43 ]. Two frameworks focused on the importance of partnerships between researchers and policy makers, as a core element to accomplish research impact [ 44 , 45 ]. An additional 2 frameworks focused on evaluating the pathways to impact, i.e., linking processes between research and impact [ 30 , 46 ]. One framework assessed the ability of health technology to influence efficiency of healthcare systems [ 47 ]. Eight frameworks were developed in the UK [ 2 , 3 , 29 , 37 , 39 , 42 , 43 , 45 ], 6 in Canada [ 8 , 33 , 34 , 44 , 46 , 47 ], 4 in Australia [ 5 , 31 , 35 , 38 ], 3 in the Netherlands [ 7 , 40 , 41 ], and 2 in the United States [ 30 , 36 ], with 1 model developed with input from various countries [ 32 ].

https://doi.org/10.1371/journal.pmed.1002370.t001

Methodological framework development

The included methodological frameworks varied in their development process, but there were some common approaches employed. Most included a literature review [ 2 , 5 , 7 , 8 , 31 , 33 , 36 , 37 , 40 – 46 ], although none of them used a recognised systematic method. Most also consulted with various stakeholders [ 3 , 8 , 29 , 31 , 33 , 35 – 38 , 43 , 44 , 46 , 47 ] but used differing methods to incorporate their views, including quantitative surveys [ 32 , 35 , 43 , 46 ], face-to-face interviews [ 7 , 29 , 33 , 35 , 37 , 42 , 43 ], telephone interviews [ 31 , 46 ], consultation [ 3 , 7 , 36 ], and focus groups [ 39 , 43 ]. A range of stakeholder groups were approached across the sample, including principal investigators [ 7 , 29 , 43 ], research end users [ 7 , 42 , 43 ], academics [ 3 , 8 , 39 , 40 , 43 , 46 ], award holders [ 43 ], experts [ 33 , 38 , 39 ], sponsors [ 33 , 39 ], project coordinators [ 32 , 42 ], and chief investigators [ 31 , 35 ]. However, some authors failed to identify the stakeholders involved in the development of their frameworks [ 2 , 5 , 34 , 41 , 45 ], making it difficult to assess their appropriateness. In addition, only 4 of the included papers reported using formal analytic methods to interpret stakeholder responses. These included the Canadian Academy of Health Sciences framework, which used conceptual cluster analysis [ 33 ]. The Research Contribution [ 42 ], Research Impact [ 29 ], and Primary Health Care & Information Service [ 31 ] used a thematic analysis approach. Finally, some authors went on to pilot their framework, which shaped refinements on the methodological frameworks until approval. Methods used to pilot the frameworks included a case study approach [ 2 , 3 , 30 , 32 , 33 , 36 , 40 , 42 , 44 , 45 ], contrasting results against available literature [ 29 ], the use of stakeholders’ feedback [ 7 ], and assessment tools [ 35 , 46 ].

Major impact categories

1. primary research-related impact..

A number of methodological frameworks advocated the evaluation of ‘research-related impact’. This encompassed content related to the generation of new knowledge, knowledge dissemination, capacity building, training, leadership, and the development of research networks. These outcomes were considered the direct or primary impacts of a research project, as these are often the first evidenced returns [ 30 , 62 ].

A number of subgroups were identified within this category, with frameworks supporting the collection of impact data across the following constructs: ‘research and innovation outcomes’; ‘dissemination and knowledge transfer’; ‘capacity building, training, and leadership’; and ‘academic collaborations, research networks, and data sharing’.

1 . 1 . Research and innovation outcomes . Twenty of the 24 frameworks advocated the evaluation of ‘research and innovation outcomes’ [ 2 , 3 , 5 , 7 , 8 , 29 – 39 , 41 , 43 , 44 , 46 ]. This subgroup included the following metrics: number of publications; number of peer-reviewed articles (including journal impact factor); citation rates; requests for reprints, number of reviews, and meta-analysis; and new or changes in existing products (interventions or technology), patents, and research. Additionally, some frameworks also sought to gather information regarding ‘methods/methodological contributions’. These advocated the collection of systematic reviews and appraisals in order to identify gaps in knowledge and determine whether the knowledge generated had been assessed before being put into practice [ 29 ].

1 . 2 . Dissemination and knowledge transfer . Nineteen of the 24 frameworks advocated the assessment of ‘dissemination and knowledge transfer’ [ 2 , 3 , 5 , 7 , 29 – 32 , 34 – 43 , 46 ]. This comprised collection of the following information: number of conferences, seminars, workshops, and presentations; teaching output (i.e., number of lectures given to disseminate the research findings); number of reads for published articles; article download rate and number of journal webpage visits; and citations rates in nonjournal media such as newspapers and mass and social media (i.e., Twitter and blogs). Furthermore, this impact subgroup considered the measurement of research uptake and translatability and the adoption of research findings in technological and clinical applications and by different fields. These can be measured through patents, clinical trials, and partnerships between industry and business, government and nongovernmental organisations, and university research units and researchers [ 29 ].

1 . 3 . Capacity building , training , and leadership . Fourteen of 24 frameworks suggested the evaluation of ‘capacity building, training, and leadership’ [ 2 , 3 , 5 , 8 , 29 , 31 – 35 , 39 – 41 , 43 ]. This involved collecting information regarding the number of doctoral and postdoctoral studentships (including those generated as a result of the research findings and those appointed to conduct the research), as well as the number of researchers and research-related staff involved in the research projects. In addition, authors advocated the collection of ‘leadership’ metrics, including the number of research projects managed and coordinated and the membership of boards and funding bodies, journal editorial boards, and advisory committees [ 29 ]. Additional metrics in this category included public recognition (number of fellowships and awards for significant research achievements), academic career advancement, and subsequent grants received. Lastly, the impact metric ‘research system management’ comprised the collection of information that can lead to preserving the health of the population, such as modifying research priorities, resource allocation strategies, and linking health research to other disciplines to maximise benefits [ 29 ].

1 . 4 . Academic collaborations , research networks , and data sharing . Lastly, 10 of the 24 frameworks advocated the collection of impact data regarding ‘academic collaborations (internal and external collaborations to complete a research project), research networks, and data sharing’ [ 2 , 3 , 5 , 7 , 29 , 34 , 37 , 39 , 41 , 43 ].

2. Influence on policy making.

Methodological frameworks addressing this major impact category focused on measurable improvements within a given knowledge base and on interactions between academics and policy makers, which may influence policy-making development and implementation. The returns generated in this impact category are generally considered as intermediate or midterm (1 to 3 years). These represent an important interim stage in the process towards the final expected impacts, such as quantifiable health improvements and economic benefits, without which policy change may not occur [ 30 , 62 ]. The following impact subgroups were identified within this category: ‘type and nature of policy impact’, ‘level of policy making’, and ‘policy networks’.

2 . 1 . Type and nature of policy impact . The most common impact subgroup, mentioned in 18 of the 24 frameworks, was ‘type and nature of policy impact’ [ 2 , 7 , 29 – 38 , 41 – 43 , 45 – 47 ]. Methodological frameworks addressing this subgroup stressed the importance of collecting information regarding the influence of research on policy (i.e., changes in practice or terminology). For instance, a project looking at trafficked adolescents and women (2003) influenced the WHO guidelines (2003) on ethics regarding this particular group [ 17 , 21 , 63 ].

2 . 2 . Level of policy impact . Thirteen of 24 frameworks addressed aspects surrounding the need to record the ‘level of policy impact’ (international, national, or local) and the organisations within a level that were influenced (local policy makers, clinical commissioning groups, and health and wellbeing trusts) [ 2 , 5 , 8 , 29 , 31 , 34 , 38 , 41 , 43 – 47 ]. Authors considered it important to measure the ‘level of policy impact’ to provide evidence of collaboration, coordination, and efficiency within health organisations and between researchers and health organisations [ 29 , 31 ].

2 . 3 . Policy networks . Five methodological frameworks highlighted the need to collect information regarding collaborative research with industry and staff movement between academia and industry [ 5 , 7 , 29 , 41 , 43 ]. A policy network emphasises the relationship between policy communities, researchers, and policy makers. This relationship can influence and lead to incremental changes in policy processes [ 62 ].

3. Health and health systems impact.

A number of methodological frameworks advocated the measurement of impacts on health and healthcare systems across the following impact subgroups: ‘quality of care and service delivering’, ‘evidence-based practice’, ‘improved information and health information management’, ‘cost containment and effectiveness’, ‘resource allocation’, and ‘health workforce’.

3 . 1 . Quality of care and service delivery . Twelve of the 24 frameworks highlighted the importance of evaluating ‘quality of care and service delivery’ [ 2 , 5 , 8 , 29 – 31 , 33 – 36 , 41 , 47 ]. There were a number of suggested metrics that could be potentially used for this purpose, including health outcomes such as quality-adjusted life years (QALYs), patient-reported outcome measures (PROMs), patient satisfaction and experience surveys, and qualitative data on waiting times and service accessibility.

3 . 2 . Evidence-based practice . ‘Evidence-based practice’, mentioned in 5 of the 24 frameworks, refers to making changes in clinical diagnosis, clinical practice, treatment decisions, or decision making based on research evidence [ 5 , 8 , 29 , 31 , 33 ]. The suggested metrics to demonstrate evidence-based practice were adoption of health technologies and research outcomes to improve the healthcare systems and inform policies and guidelines [ 29 ].

3 . 3 . Improved information and health information management . This impact subcategory, mentioned in 5 of the 24 frameworks, refers to the influence of research on the provision of health services and management of the health system to prevent additional costs [ 5 , 29 , 33 , 34 , 38 ]. Methodological frameworks advocated the collection of health system financial, nonfinancial (i.e., transport and sociopolitical implications), and insurance information in order to determine constraints within a health system.

3 . 4 . Cost containment and cost-effectiveness . Six of the 24 frameworks advocated the subcategory ‘cost containment and cost-effectiveness’ [ 2 , 5 , 8 , 17 , 33 , 36 ]. ‘Cost containment’ comprised the collection of information regarding how research has influenced the provision and management of health services and its implication in healthcare resource allocation and use [ 29 ]. ‘Cost-effectiveness’ refers to information concerning economic evaluations to assess improvements in effectiveness and health outcomes—for instance, the cost-effectiveness (cost and health outcome benefits) assessment of introducing a new health technology to replace an older one [ 29 , 31 , 64 ].

3 . 5 . Resource allocation . ‘Resource allocation’, mentioned in 6frameworks, can be measured through 2 impact metrics: new funding attributed to the intervention in question and equity while allocating resources, such as improved allocation of resources at an area level; better targeting, accessibility, and utilisation; and coverage of health services [ 2 , 5 , 29 , 31 , 45 , 47 ]. The allocation of resources and targeting can be measured through health services research reports, with the utilisation of health services measured by the probability of providing an intervention when needed, the probability of requiring it again in the future, and the probability of receiving an intervention based on previous experience [ 29 , 31 ].

3 . 6 . Health workforce . Lastly, ‘health workforce’, present in 3 methodological frameworks, refers to the reduction in the days of work lost because of a particular illness [ 2 , 5 , 31 ].

4. Health-related and societal impact.

Three subgroups were included in this category: ‘health literacy’; ‘health knowledge, attitudes, and behaviours’; and ‘improved social equity, inclusion, or cohesion’.

4 . 1 . Health knowledge , attitudes , and behaviours . Eight of the 24 frameworks suggested the assessment of ‘health knowledge, attitudes, behaviours, and outcomes’, which could be measured through the evaluation of levels of public engagement with science and research (e.g., National Health Service (NHS) Choices end-user visit rate) or by using focus groups to analyse changes in knowledge, attitudes, and behaviour among society [ 2 , 5 , 29 , 33 – 35 , 38 , 43 ].

4 . 2 . Improved equity , inclusion , or cohesion and human rights . Other methodological frameworks, 4 of the 24, suggested capturing improvements in equity, inclusion, or cohesion and human rights. Authors suggested these could be using a resource like the United Nations Millennium Development Goals (MDGs) (superseded by Sustainable Development Goals [SDGs] in 2015) and human rights [ 29 , 33 , 34 , 38 ]. For instance, a cluster-randomised controlled trial in Nepal, which had female participants, has demonstrated the reduction of neonatal mortality through the introduction of maternity health care, distribution of delivery kits, and home visits. This illustrates how research can target vulnerable and disadvantaged groups. Additionally, this research has been introduced by the World Health Organisation to achieve the MDG ‘improve maternal health’ [ 16 , 29 , 65 ].

4 . 3 . Health literacy . Some methodological frameworks, 3 of the 24, focused on tracking changes in the ability of patients to make informed healthcare decisions, reduce health risks, and improve quality of life, which were demonstrably linked to a particular programme of research [ 5 , 29 , 43 ]. For example, a systematic review showed that when HIV health literacy/knowledge is spread among people living with the condition, antiretroviral adherence and quality of life improve [ 66 ].

5. Broader economic impacts.

Some methodological frameworks, 9 of 24, included aspects related to the broader economic impacts of health research—for example, the economic benefits emerging from the commercialisation of research outputs [ 2 , 5 , 29 , 31 , 33 , 35 , 36 , 38 , 67 ]. Suggested metrics included the amount of funding for research and development (R&D) that was competitively awarded by the NHS, medical charities, and overseas companies. Additional metrics were income from intellectual property, spillover effects (any secondary benefit gained as a repercussion of investing directly in a primary activity, i.e., the social and economic returns of investing on R&D) [ 33 ], patents granted, licences awarded and brought to the market, the development and sales of spinout companies, research contracts, and income from industry.

The benefits contained within the categories ‘health and health systems impact’, ‘health-related and societal impact’, and ‘broader economic impacts’ are considered the expected and final returns of the resources allocated in healthcare research [ 30 , 62 ]. These benefits commonly arise in the long term, beyond 5 years according to some authors, but there was a recognition that this could differ depending on the project and its associated research area [ 4 ].

Data synthesis

Five major impact categories were identified across the 24 included methodological frameworks: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These major impact categories were further subdivided into 16 impact subgroups. The included publications proposed 80 different metrics to measure research impact. This impact typology synthesis is depicted in ‘the impact matrix’ ( Fig 2 and Fig 3 ).

CIHR, Canadian Institutes of Health Research; HTA, Health Technology Assessment; PHC RIS, Primary Health Care Research & Information Service; RAE, Research Assessment Exercise; RQF, Research Quality Framework.

https://doi.org/10.1371/journal.pmed.1002370.g002

AIHS, Alberta Innovates—Health Solutions; CAHS, Canadian Institutes of Health Research; IOM, Impact Oriented Monitoring; REF, Research Excellence Framework; SIAMPI, Social Impact Assessment Methods for research and funding instruments through the study of Productive Interactions between science and society.

https://doi.org/10.1371/journal.pmed.1002370.g003

Commonality and differences across frameworks

The ‘Research Impact Framework’ and the ‘Health Services Research Impact Framework’ were the models that encompassed the largest number of the metrics extracted. The most dominant methodological framework was the Payback Framework; 7 other methodological framework models used the Payback Framework as a starting point for development [ 8 , 29 , 31 – 35 ]. Additional methodological frameworks that were commonly incorporated into other tools included the CIHR framework, the CAHS model, the AIHS framework, and the Exchange model [ 8 , 33 , 34 , 44 ]. The capture of ‘research-related impact’ was the most widely advocated concept across methodological frameworks, illustrating the importance with which primary short-term impact outcomes were viewed by the included papers. Thus, measurement of impact via number of publications, citations, and peer-reviewed articles was the most common. ‘Influence on policy making’ was the predominant midterm impact category, specifically the subgroup ‘type and nature of policy impact’, in which frameworks advocated the measurement of (i) changes to legislation, regulations, and government policy; (ii) influence and involvement in decision-making processes; and (iii) changes to clinical or healthcare training, practice, or guidelines. Within more long-term impact measurement, the evaluations of changes in the ‘quality of care and service delivery’ were commonly advocated.

In light of the commonalities and differences among the methodological frameworks, the ‘pathways to research impact’ diagram ( Fig 4 ) was developed to provide researchers, funders, and policy makers a more comprehensive and exhaustive way to measure healthcare research impact. The diagram has the advantage of assorting all the impact metrics proposed by previous frameworks and grouping them into different impact subgroups and categories. Prospectively, this global picture will help researchers, funders, and policy makers plan strategies to achieve multiple pathways to impact before carrying the research out. The analysis of the data extraction and construction of the impact matrix led to the development of the ‘pathways to research impact’ diagram ( Fig 4 ). The diagram aims to provide an exhaustive and comprehensive way of tracing research impact by combining all the impact metrics presented by the different 24 frameworks, grouping those metrics into different impact subgroups, and grouping these into broader impact categories.

NHS, National Health Service; PROM, patient-reported outcome measure; QALY, quality-adjusted life year; R&D, research and development.

https://doi.org/10.1371/journal.pmed.1002370.g004

This review has summarised existing methodological impact frameworks together for the first time using systematic methods ( Fig 4 ). It allows researchers and funders to consider pathways to impact at the design stage of a study and to understand the elements and metrics that need to be considered to facilitate prospective assessment of impact. Users do not necessarily need to cover all the aspects of the methodological framework, as every research project can impact on different categories and subgroups. This review provides information that can assist researchers to better demonstrate impact, potentially increasing the likelihood of conducting impactful research and reducing research waste. Existing reviews have not presented a methodological framework that includes different pathways to impact, health impact categories, subgroups, and metrics in a single methodological framework.

Academic-orientated frameworks included in this review advocated the measurement of impact predominantly using so-called ‘quantitative’ metrics—for example, the number of peer-reviewed articles, journal impact factor, and citation rates. This may be because they are well-established measures, relatively easy to capture and objective, and are supported by research funding systems. However, these metrics primarily measure the dissemination of research finding rather than its impact [ 30 , 68 ]. Whilst it is true that wider dissemination, especially when delivered via world-leading international journals, may well lead eventually to changes in healthcare, this is by no means certain. For instance, case studies evaluated by Flinders University of Australia demonstrated that some research projects with non-peer-reviewed publications led to significant changes in health policy, whilst the studies with peer-reviewed publications did not result in any type of impact [ 68 ]. As a result, contemporary literature has tended to advocate the collection of information regarding a variety of different potential forms of impact alongside publication/citations metrics [ 2 , 3 , 5 , 7 , 8 , 29 – 47 ], as outlined in this review.

The 2014 REF exercise adjusted UK university research funding allocation based on evidence of the wider impact of research (through case narrative studies and quantitative metrics), rather than simply according to the quality of research [ 12 ]. The intention was to ensure funds were directed to high-quality research that could demonstrate actual realised benefit. The inclusion of a mixed-method approach to the measurement of impact in the REF (narrative and quantitative metrics) reflects a widespread belief—expressed by the majority of authors of the included methodological frameworks in the review—that individual quantitative impact metrics (e.g., number of citations and publications) do not necessary capture the complexity of the relationships involved in a research project and may exclude measurement of specific aspects of the research pathway [ 10 , 12 ].

Many of the frameworks included in this review advocated the collection of a range of academic, societal, economic, and cultural impact metrics; this is consistent with recent recommendations from the Stern review [ 10 ]. However, a number of these metrics encounter research ‘lag’: i.e., the time between the point at which the research is conducted and when the actual benefits arise [ 69 ]. For instance, some cardiovascular research has taken up to 25 years to generate impact [ 70 ]. Likewise, the impact may not arise exclusively from a single piece of research. Different processes (such as networking interactions and knowledge and research translation) and multiple individuals and organisations are often involved [ 4 , 71 ]. Therefore, attributing the contribution made by each of the different actors involved in the process can be a challenge [ 4 ]. An additional problem associated to attribution is the lack of evidence to link research and impact. The outcomes of research may emerge slowly and be absorbed gradually. Consequently, it is difficult to determine the influence of research in the development of a new policy, practice, or guidelines [ 4 , 23 ].

A further problem is that impact evaluation is conducted ‘ex post’, after the research has concluded. Collecting information retrospectively can be an issue, as the data required might not be available. ‘ex ante’ assessment is vital for funding allocation, as it is necessary to determine the potential forthcoming impact before research is carried out [ 69 ]. Additionally, ex ante evaluation of potential benefit can overcome the issues regarding identifying and capturing evidence, which can be used in the future [ 4 ]. In order to conduct ex ante evaluation of potential benefit, some authors suggest the early involvement of policy makers in a research project coupled with a well-designed strategy of dissemination [ 40 , 69 ].

Providing an alternate view, the authors of methodological frameworks such as the SIAMPI, Contribution Mapping, Research Contribution, and the Exchange model suggest that the problems of attribution are a consequence of assigning the impact of research to a particular impact metric [ 7 , 40 , 42 , 44 ]. To address these issues, these authors propose focusing on the contribution of research through assessing the processes and interactions between stakeholders and researchers, which arguably take into consideration all the processes and actors involved in a research project [ 7 , 40 , 42 , 43 ]. Additionally, contributions highlight the importance of the interactions between stakeholders and researchers from an early stage in the research process, leading to a successful ex ante and ex post evaluation by setting expected impacts and determining how the research outcomes have been utilised, respectively [ 7 , 40 , 42 , 43 ]. However, contribution metrics are generally harder to measure in comparison to academic-orientated indicators [ 72 ].

Currently, there is a debate surrounding the optimal methodological impact framework, and no tool has proven superior to another. The most appropriate methodological framework for a given study will likely depend on stakeholder needs, as each employs different methodologies to assess research impact [ 4 , 37 , 41 ]. This review allows researchers to select individual existing methodological framework components to create a bespoke tool with which to facilitate optimal study design and maximise the potential for impact depending on the characteristic of their study ( Fig 2 and Fig 3 ). For instance, if researchers are interested in assessing how influential their research is on policy making, perhaps considering a suite of the appropriate metrics drawn from multiple methodological frameworks may provide a more comprehensive method than adopting a single methodological framework. In addition, research teams may wish to use a multidimensional approach to methodological framework development, adopting existing narratives and quantitative metrics, as well as elements from contribution frameworks. This approach would arguably present a more comprehensive method of impact assessment; however, further research is warranted to determine its effectiveness [ 4 , 69 , 72 , 73 ].

Finally, it became clear during this review that the included methodological frameworks had been constructed using varied methodological processes. At present, there are no guidelines or consensus around the optimal pathway that should be followed to develop a robust methodological framework. The authors believe this is an area that should be addressed by the research community, to ensure future frameworks are developed using best-practice methodology.

For instance, the Payback Framework drew upon a literature review and was refined through a case study approach. Arguably, this approach could be considered inferior to other methods that involved extensive stakeholder involvement, such as the CIHR framework [ 8 ]. Nonetheless, 7 methodological frameworks were developed based upon the Payback Framework [ 8 , 29 , 31 – 35 ].

Limitations

The present review is the first to summarise systematically existing impact methodological frameworks and metrics. The main limitation is that 50% of the included publications were found through methods other than bibliographic databases searching, indicating poor indexing. Therefore, some relevant articles may not have been included in this review if they failed to indicate the inclusion of a methodological impact framework in their title/abstract. We did, however, make every effort to try to find these potentially hard-to-reach publications, e.g., through forwards/backwards citation searching, hand searching reference lists, and expert communication. Additionally, this review only extracted information regarding the methodology followed to develop each framework from the main publication source or framework webpage. Therefore, further evaluations may not have been included, as they are beyond the scope of the current paper. A further limitation was that although our search strategy did not include language restrictions, we did not specifically search non-English language databases. Thus, we may have failed to identify potentially relevant methodological frameworks that were developed in a non-English language setting.

In conclusion, the measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise benefit, and to help minimise research waste. This review provides a collective summary of existing methodological impact frameworks and metrics, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Supporting information

S1 appendix. search strategy..

https://doi.org/10.1371/journal.pmed.1002370.s001

S1 PRISMA Checklist. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.

https://doi.org/10.1371/journal.pmed.1002370.s002

Acknowledgments

We would also like to thank Mrs Susan Bayliss, Information Specialist, University of Birmingham, and Mrs Karen Biddle, Research Secretary, University of Birmingham.

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Health care is the discipline concerned with the provision and delivery of health services to a patient population by personnel in professional and allied health occupations.

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In the past 2 years, substantial improvements have been made in the management of advanced-stage EGFR -mutant non-small-cell lung cancer. Recent studies have suggested added benefit from the combination of third-generation tyrosine-kinase inhibitors with either chemotherapy or a bispecific antibody targeting EGFR and MET. Herein, we summarize these advances and their implications for clinical practice.

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Accurate blood test for Alzheimer’s disease

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Improving Perinatal Maternal Mental Health Starts With Addressing Structural Inequities

• 1 Columbia University Irving Medical Center, Department of Psychiatry, New York, New York
• 2 Columbia University Medical Center, Division of Developmental Neuroscience, New York State Psychiatric Institute, New York
• 3 Department of Child & Adolescent Psychiatry, New York University School of Medicine, New York
• 4 Department of Population Health, New York University School of Medicine, New York
• 5 Department of Applied Psychology, New York University, New York

Psychological distress during the perinatal period has increased during the COVID-19 pandemic. A systematic review of 81 studies (N = 132 917 pregnant or postpartum women; research published prior to January 31, 2021) reported prevalence of depression and anxiety ranging from 20% to 64% during the ongoing pandemic. 1 Although there is still a need to examine more representative samples, a large cross-sectional study 1 of mostly US women corroborated these findings, reporting clinical levels of depression in 36% of perinatal women compared with an estimated prepandemic prevalence of 11% to 17%. Changes in prenatal care and childbirth experiences, including lack of support during delivery and recovery, may also contribute to heightened feelings of distress. Women who gave birth during the pandemic reported an increased acute stress response to childbirth (odds ratio, 1.38; 95% CI, 1.01-1.89), correlating with increased posttraumatic stress disorder symptoms and maternal bonding or breastfeeding problems. In this Viewpoint, we focus on the potential consequences of perinatal maternal mental health on child development and highlight structural inequities that need to be addressed to mitigate maternal psychological distress and improve both maternal and child health and well-being.

Potential Effects of Prenatal Maternal Psychological Distress on Child Development

A major question that arises in the context of population-level changes in perinatal maternal mental health is how the added stress of this period may directly influence the developing child. A robust literature within the Developmental Origins of Health and Disease framework has linked prenatal maternal depression, anxiety, and stress to neuropsychiatric sequelae in offspring, likely mediated through both inflammatory and endocrine pathways. Observational studies of prior viral outbreaks, such as the 1918 H1N1 influenza pandemic, primarily focused on the associations between in utero viral exposure and increased risk for neuropsychiatric disorders in offspring. However, it is likely that increased prenatal-maternal psychological distress is an additional mechanism underlying these associations. Although the postnatal environment can potentiate or attenuate neuropsychiatric outcomes, prenatal-maternal psychological distress is associated with alterations in fetal neural networks, including protracted development of connectivity between superior frontal and motor regions, and between posterior insula and temporoparietal brain regions. 2 A recent study 3 posted as a preprint that has not been peer reviewed found that interactions between prenatal-maternal depressive symptoms and in utero SARS-CoV-2 exposure were associated with individual differences in infant attentional processing at 6 months of age, with these differences in attention associated with infant socioemotional skills at 12 months of age. Taken together, this evidence suggests that children born during the current pandemic may be at increased risk of neuropsychiatric sequelae owing to increases in perinatal maternal mental health problems.

Removing Obstacles to Perinatal Mental Health Treatment

The US Preventive Services Task Force recommends mental health screening for all pregnant women through at least 1 year post partum, yet even prior to pandemic-related health care disruptions, there were infrastructure barriers in diagnosis and treatment. Many mental health care professionals do not accept Medicaid, resulting in added obstacles for women with low income and racial and ethnic minority women, who report higher rates of postpartum depression and are less likely to receive treatment. The COVID-19 pandemic has exacerbated these existing obstacles, as women of racial and ethnic minority groups are more likely to report that the pandemic has affected their mental health care access compared with non-Hispanic White women (odds ratio, 3.25; 95% CI, 1.23-8.59), 4 resulting in an amplified need for equitable access to evidence-based maternal mental health services. There are several policies that could significantly improve perinatal psychological distress if implemented, including guaranteed Medicaid coverage through 1 year post partum, enforcement of mental health parity, and expanded coverage of telehealth services. 4 However, it is important to note that although virtual services have gained popularity during the pandemic, telehealth may not adequately meet the needs of under-resourced communities. 5 In addition to federal- and state-level policies to improve access, the expansion of perinatal quality collaboratives and population-based intervention programs that emphasize perinatal mental health screening and treatment could effect equitable change at the systems level. Interventions that target individual or community-level factors are limited in their efficacy unless structural determinants of perinatal maternal mental health are first resolved.

Increasing Perinatal Support Through Paid Leave and Affordable Childcare

Even if access to mental health treatment is improved, mothers need the time and resources to seek treatment during the perinatal period. The US is the only high-income country in the world without a federal policy that mandates paid leave to working women who give birth. Furthermore, in the US, 25% of mothers return to work within 2 weeks after childbirth. 7 Currently only 15% of workers have access to paid maternity leave, and rates are skewed toward higher socioeconomic status women. 7 Black and Hispanic mothers are less likely to have access to time off or be financially able to take time off after childbirth compared with White mothers. Mandating paid leave to all working mothers could potentially lead to reductions in sociodemographic disparities in maternal and child health, as paid leave is associated with lower likelihood of postpartum depression and maternal stress, as well as increases in breastfeeding, more secure attachment between mother and child, and fewer socioemotional and behavioral problems during toddlerhood. 6 Emerging evidence from a recent study 7 posted as a preprint that has not been peer reviewed, also reported that compared with unpaid leave, experiences of paid leave were related to distinct infant neurophysiological profiles, possibly reflecting more mature patterns of brain activity.

Pandemic-related shifts in increased domestic tasks, including childcare and homeschooling, coupled with financial insecurities have exacerbated prepandemic structural inequities. Parental job loss during the pandemic has been linked with parents’ depressive symptoms and stress levels, as well as higher maternal and child cortisol concentration levels, with greater maternal cortisol levels associated with increased internalizing symptoms in children. 8 According to data from the Kaiser Family Foundation, more than half of women who reported COVID-19–related job losses (beginning early in the pandemic and among mothers who quit their jobs) reported school or daycare closures as their rationale. It is important to note that COVID-19–related stressors are not uniformly experienced by all mothers. For women of racial and ethnic minority groups and women from low-income households, prepandemic disparities have been magnified; women who self identify as either Black or Hispanic or with low income were more likely to report quitting or taking unpaid leave compared with higher income or White women. Recent research suggests that mothers without childcare or with informal childcare arrangements have higher depressive symptoms than mothers whose children were enrolled in a free center-based program. 9 In contrast, several studies have found that access to stable and high-quality center-based early childcare moderates associations between maternal depression and child internalizing problems. 10 In the midst of an unprecedented nationwide childcare shortage in the US, access to affordable childcare for infants and universal free preschool for 3- and 4-year-old children would reduce unnecessary burden on mothers, with the potential to improve rates of maternal psychological distress and downstream effects on child neurobehavior.

Conclusions

It is imperative to address COVID-19–related population-level changes in maternal mental health and the resulting effects on early childhood development. Supporting mothers through specific policy changes that are associated with increasing access to mental health treatment, paid leave, and affordable childcare can improve mental health and well-being for mothers, their children, and generations yet to come.

Corresponding Author : Lauren C. Shuffrey, PhD, Columbia University Medical Center, Department of Psychiatry, Division of Developmental Neuroscience, New York State Psychiatric Institute, 1051 Riverside Dr, Unit 40, New York, NY 10032 ( [email protected] ).

Published Online: March 9, 2022. doi:10.1001/jamapsychiatry.2022.0097

Conflict of Interest Disclosures: Dr Shuffrey reported grants from The Eunice Kennedy Shriver National Institute of Child Health and Human Development (K99HD103910) during the submitted work. No other disclosures were reported.

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Shuffrey LC , Thomason ME , Brito NH. Improving Perinatal Maternal Mental Health Starts With Addressing Structural Inequities. JAMA Psychiatry. 2022;79(5):387–388. doi:10.1001/jamapsychiatry.2022.0097

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• Systematic Review
• Open access
• Published: 14 August 2024

Experiences of informal caregivers supporting individuals with upper gastrointestinal cancers: a systematic review

• Melinda Furtado   ORCID: orcid.org/0000-0001-5472-4707 1 ,
• Dawn Davis 1 ,
• Jenny M. Groarke 1 , 2 &
• Lisa Graham-Wisener 1  

BMC Health Services Research volume  24 , Article number:  932 ( 2024 ) Cite this article

118 Accesses

Metrics details

Upper gastrointestinal cancers (UGICs) are increasingly prevalent. With a poor prognosis and significant longer-term effects, UGICs present significant adjustment challenges for individuals with cancer and their informal caregivers. However, the supportive care needs of these informal caregivers are largely unknown. This systematic review of qualitative studies synthesises and critically evaluates the current evidence base on the experience of informal caregivers of individuals with UGIC.

A Joanna Briggs Institute systematic review was conducted. Searches were performed in four databases (MEDLINE, PsycINFO, Embase, CINAHL) from database inception to February 2021. Included studies explored experiences of informal caregivers of individuals diagnosed with primary cancer of the oesophagus, stomach, pancreas, bile duct, gallbladder, or liver. Studies were independently screened for eligibility and included studies were appraised for quality by two reviewers. Data were extracted and synthesised using meta-aggregation.

19 papers were included in this review, and 328 findings were extracted. These were aggregated into 16 categories across three findings: (1) UGIC caregiver burden; UGIC caregivers undertake extensive responsibilities, especially around patient diet as digestion is severely impacted by UGICs. (2) Mediators of caregiver burden; The nature of UGICs, characterised by disruptive life changes for caregivers, was identified as a mediator for caregiver burden. (3) Consequences of caregiver burden: UGIC caregivers’ experiences were shaped by unmet needs, a lack of information and a general decline in social interaction.

Conclusions

The findings of this review suggest the need for a cultural shift within health services. Caregiving for UGIC patients is suggested to adversely affect caregivers’ quality of life, similarly to other cancer caregiving populations and therefore they should be better incorporated as co-clients in care-planning and execution by including them in discussions about the patient’s diagnosis, treatment options, and potential side effects.

Peer Review reports

The National Institute for Clinical Excellence (NICE) [ 1 ] define upper gastrointestinal cancers (UGICs) as cancers of the oesophagus, stomach, pancreas, bile duct/gallbladder, or liver. Of all new cancer diagnoses in 2020 globally, 16.6% were UGICs [ 2 ]. Incidence of UGICs is increasing in countries under economic transition, and in Western countries due to heightened exposure to certain risk factors [ 3 ]. Overall prevalence of UGICs is also expected to rise annually with growing life expectancy and improved diagnostics [ 4 ]. Despite this, UGICs still have a uniquely poor prognosis in comparison to other cancer populations [ 5 ]. UGICs do not typically benefit from screening programmes and individuals are more likely to present at diagnosis with advanced disease [ 6 ]. This is compounded by a high rate of recurrence for individuals able to receive curative treatment [ 7 , 8 , 9 ]. As a result, UGICs persistently account for a significant proportion of global cancer deaths; 27.1% in 2020 [ 2 ]. Poor prognosis contributes significantly to the heightened disease burden of UGIC, alongside increased utilisation of health services due to the complexity of the treatment trajectory and symptom management [ 10 , 11 ]. In comparison to other cancer populations, having UGIC is associated with late consultation with palliative care services [ 12 ] meaning patients and their families have delayed access, if any, to supportive interventions such as counselling, psycho-education, financial advice and structured family meetings [ 13 ].

The supportive care needs of the sizeable population of individuals with UGIC are considerable, with sustained late and longer-term effects. In addition to the common sequalae from cancer diagnosis and treatment, disruption to the digestive system presents problems with swallowing, nausea and keeping food down, a modified diet, extreme changes in weight, chronic pain and living with a stoma [ 14 , 15 ]. The poor prognosis and longer-term effects present a challenge in adjustment both for the individual with UGIC and their informal caregiver, defined as “close persons” who may be related to the diagnosed individual (siblings, relatives, or spouses) or not (friends, neighbours). A caregiver is anyone identified as such by the patient to provide unpaid ongoing care and support [ 16 ]. Examples of challenges for caregivers include learning new practical skills such as managing negative responses to foods, providing a new diet, monitoring weight changes, chronic pain management and stoma management [ 17 , 18 ]. With biomedical advances leading to a reduction in hospital stay length [ 19 ], there is increasing emphasis placed on the role of the UGIC caregiver to provide support to the individual with cancer in the community.

This unique caregiver population face distinct challenges which contribute to caregiver burden which reflects the need for further research into their experiences. For example, due to changes in the diet of the individual with UGIC, the social aspect of dining for both is compromised and can lead to feelings of loneliness, anxiety, and shame [ 20 , 21 ]. Evidence of caregiver burden is suggested by high levels of anxiety and depression. In caregivers of post-treatment oesophageal cancer patients, 30% of caregivers reported moderate-high levels of anxiety and 10% reported moderate-high levels of depression, alongside a significant fear of recurrence [ 22 ]. Research suggests that UGIC caregivers may experience higher levels of psychological distress than the individual with UGIC, and that clinical levels of anxiety and depression may be sustained in the longer-term [ 22 , 23 ]. However it is worth noting that a lot of the effects of UGIC caregiving acknowledged in the literature are consistent with the general experience of informally providing care and as such there is scope to apply the beneficial practices from other settings (both extra-GI cancer and non-cancer).

It is crucial that we recognise the role of caregivers as co-clients and understand the experiences of this significant caregiver population. Caregivers’ personal experiences are inherently subjective, and due to this subjective nature, a qualitative research approach is optimal [ 24 ]. A synthesis of existing qualitative studies will help to establish a knowledge base on the experience of informal caregivers of individuals with UGIC and will help to inform the provision for supportive care. An initial search of PROSPERO, MEDLINE, the Cochrane Database of Systematic Reviews and the Joanna Briggs Institute (JBI) Database of Systematic Reviews and Implementation Reports was conducted and no current or underway systematic reviews on the topic were identified.

This qualitative systematic review aims to synthesise the best available evidence on the experiences of informal caregivers supporting individuals diagnosed with UGIC.

This systematic review was conducted following the JBI approach to qualitative systematic reviews [ 25 ]. A protocol was pre-registered in PROSPERO (registration number CRD42021235354). The systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement [ 26 ].

Search strategy

An initial limited search of MEDLINE (Ovid) and PsycINFO (Ovid) was undertaken using the following keywords: Oesophageal cancer OR Stomach cancer OR Gastrointestinal cancer OR pancreas cancer OR gallbladder cancer OR liver cancer AND caregiver AND Qualitative. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles were used to develop a full search strategy for MEDLINE and adapted for the other databases.

The final search strategy (Additional information 1 ) was then employed against four databases: MEDLINE (Ovid), PsycINFO (Ovid), Embase (Elsevier) and CINAHL (EBSCOhost). Each database was searched on 12th February 2021.

Study selection

Following the formal searches, all identified citations were collated and uploaded into Endnote [ 27 ] to identify and remove duplicates. Rayyan reference management software [ 28 ] was then used by independent two reviewers (DD, MF) to screen titles and abstracts against the eligibility criteria. Potentially relevant articles were retrieved in full and screened against the eligibility criteria by two independent reviewers (DD, MF). Reasons for exclusion of papers at full text review were recorded. Any disagreements that arose between the reviewers at each stage of the selection process was resolved through discussion (DD, MF), or with an additional reviewer (LGW). The reference list and citation list of all eligible articles was searched for additional studies.

Inclusion and exclusion criteria

This review included studies exploring experiences of adults (≥ 18 years of age) who are informal caregivers of individuals diagnosed with UGIC at any stage within the disease process. This included those diagnosed with cancer of the oesophagus, stomach, pancreas, bile duct, gallbladder, or liver [ 1 ]. This diagnosis must be the primary cancer site. Studies involving informal caregivers of individuals who had secondary gastrointestinal system metastases were not included.

A caregiver is anyone identified as such by the patient to provide unpaid ongoing care and support [ 16 ]. Paid professional caregivers were not included. The caregivers included provided various services, such as practical (providing transport, overseeing meals) or emotional support roles in caring for the patient. Caregivers with any gender or ethnicity were considered for inclusion. Both active and bereaved caregivers were eligible, if discussing their pre-bereavement experience.

Studies which reviewed experiences of multiple groups (e.g., patients, caregivers, healthcare professionals) or multiple cancers beyond the remit of UGIC were included, provided the data pertaining to informal caregivers and UGICs was clearly delineated and could be extracted separately. Where data was hard to distinguish regarding participant-type or cancer-type, the study was only included if at least 50% of the sample size was drawn from the target population.

Phenomena of interest

The review included qualitative studies that looked at caregivers’ experiences of caring for an individual with UGIC.

Studies for inclusion were based in any geographic location or setting. All care contexts were considered relevant (e.g., primary care, secondary, tertiary, community, or home settings).

Types of studies

Research studies considered for inclusion were focused on qualitative data including, but not limited to; designs such as phenomenology, grounded theory, ethnography, action research and feminist research. Mixed method studies were considered relevant if data from the qualitative component could be clearly extracted. Only English language studies were included.

Only empirical studies published in peer-reviewed journals were included. There was no restriction on publication year. Systematic reviews were not included, however relevant studies were harvested from them, when relevant. Editorials, opinion papers, case studies and any articles without relevant, original data were excluded, alongside grey literature.

Quality Appraisal

Subsequently, two independent reviewers (DD, MF) critically appraised the included studies to evaluate the strength of the evidence for methodological quality using the JBI Critical Appraisal Checklist for Qualitative Research [ 29 ]. All studies, regardless of the results of their methodological quality, underwent data extraction and synthesis. One of the included studies employed use of free-test questionnaires [ 30 ], the robustness of which has been called into question by qualitative researchers as the data generated from these responses is rarely rich enough to provide the necessary strong insights [ 31 ]. However, the reviewers felt the robustness of this study was upheld by the fact that the researchers conducted a comprehensive search on existing literature prior to data collection, thus allowing questionnaire findings to be scaffolded onto existing conceptual frameworks.

Data extraction

Data were extracted using standardized JBI data extraction tool [ 32 ] by two independent reviewers (DD, MF). Each undertook data extraction for half of the articles and then checked the other reviewer’s data extraction. The extracted data included specific details about the population, context, study methods and the phenomena of interest relevant to the review objective. Disagreements between the reviewers were resolved through discussion. Four authors of papers were contacted to request missing or additional data for clarification mainly regarding breakdown of participant populations by cancer type of which no new information arose.

A finding is defined by the JBI as “a verbatim extract of the author’s analytic interpretation accompanied by either a participant voice, or fieldwork observations or other data.” [ 33 , p40]. Findings were identified through repeated reading of the text, and extraction of findings included any distinct analytic observation reported by authors with an accompanying illustration (Additional information 2 ).

Data synthesis

Each finding was identified by an alphanumeric code (e.g., A1, A2, B1, etc.). Each letter corresponded to a study and each number to a unique finding. The progressive numbers indicate the order of the findings within the original article. Each finding was rated with one of three levels of credibility as per the ConQual system [ 34 ]:

Unequivocal - findings accompanied by an illustration that is beyond reasonable doubt and therefore not open to challenge.

Credible - findings accompanied by an illustration lacking clear association with it and therefore open to challenge.

Not Supported - findings are not supported by the data.

Qualitative research findings were pooled with the meta-aggregation approach and captured in a Microsoft Excel spreadsheet [ 33 ]. Findings were aggregated by assembling the findings and categorizing these findings based on similarity in meaning, then labelling the categories accordingly. Categories were then synthesised to produce a comprehensive set of synthesized findings. Two reviewers (DD, MF) repeatedly read the findings and developed a set of categories. To assess the quality and confidence of each qualitative finding synthesised within this review, authors utilised the ConQual system (Additional information 3 ), a tool used to assign ratings of confidence in synthesised qualitative research findings [ 34 ]. Only unequivocal and credible findings were included in the synthesis.

The combined database searches yielded 5465 records. After removing duplicates and screening studies against eligibility criteria (Fig.  1 ), the review included 19 studies [ 18 , 30 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Additional information 4 displays the characteristics of the 19 included studies.

PRISMA flowchart of study selection process

Description of included studies

All included studies were published between 2004 and 2021. Most commonly, studies focused on caregivers of individuals with oesophageal cancer ( N  = 7), or pancreatic ( N  = 7), including one study of pancreatic and bile duct cancer. Other studies included caregivers of individuals with liver cancer ( N  = 2), gastric cancer ( N  = 1) and the gastrointestinal tract generally ( N  = 2). Geographically, studies were conducted in eight regions. The largest group ( N  = 6) were conducted in the US [ 35 , 37 , 38 , 39 , 40 , 41 ], followed by Denmark ( N  = 3) [ 42 , 43 , 44 ]. Most samples included a variety of within-family caregivers ( N  = 13), generally spouses/partners, children, and siblings. Others ( N  = 3) looked specifically at spouses and three did not specify the caregiver-patient relationship. Most studies included a semi-structured interview format ( N  = 12), others used focus groups ( N  = 4), secondary analysis of existing data ( N  = 2) or questionnaires ( N  = 1).

Quality of included studies

The JBI Critical Appraisal Checklist [ 25 ] was used to establish the quality of the research. The included studies were generally of good quality, with all 19 papers achieving at least 60% across the ten JBI quality assessment criteria (Additional information 5 ). Within the JBI checklist there are five questions assessing study dependability, where the studies performed at a lower satisfactory level. Of the included papers, two achieved a 5/5 score on dependability questions, seven achieved 4/5, nine scored 3/5 and one scored 2/5. Only 26% of studies could adequately locate the researcher(s) culturally or theoretically and only 37% of papers addressed the influence of the researcher on the research and vice-versa. Conversely, nearly all papers adequately addressed the research methodology’s congruity on objectives, data collection, data representation and analysis.

Meta-aggregation findings

Across the 19 studies, 328 supported findings were extracted, of which 239 were unequivocal and 89 were credible. Findings could be aligned into 23 categories with unique core meanings, which were then synthesised into three findings: (1) UGIC caregiver burden; (2) Mediators of caregiver burden; (3) Consequences of caregiver burden (Additional information 6 ). Figure  2 outlines how the categories relate to the overarching synthesised findings. To remain grounded in the data, the actual participants’ words are used throughout the narrative and double quotation marks illustrate a direct caregiver quote. References given after a quotation links the quote to the study as outlined in Additional information 2 .

Structural arrangement of categories and synthesised findings

Synthesised finding 1: UGIC caregiver burden

As caregivers began supporting those with UGIC, they faced numerous challenges to adjustment. This largely stemmed from efforts to integrate a broad and complex caregiving role within their existing routine. Difficulties such as disruption to daily routines and meals impacted caregivers’ psychological wellbeing. Caregivers were often unprepared for this life disruption, leading them to seek out information from which to learn and distribute to others.

1. Breadth of the caregiver role

The extent of responsibilities on UGIC caregivers was perceived as broad and complex, with an ‘all encompassing’ focus on patient outcomes. UGIC caregivers ‘assume different roles’ [ 42 ].

“The food thing is omnipresent. We have been told that he is not allowed to have further weight loss (K23).

Specific responsibilities included working around reduced appetite and oral intake; monitoring physical signs e.g., patient weight; perioperative management such as care of surgical wounds and organising medical appointments and treatments.

“We’d have to keep. . .going with all the medical appointments and surgery and treatment” (B11).

2. Challenges around patients’ meals

Treatment for and progression of UGIC severely impacts the patient’s relationship with food; with diet quantity and content at times significantly altered. Adaption for the caregiver involved learning about dietary modifications and management of digestive symptoms such as dysphagia. Several studies found that the new dietary restrictions were a source of worry for caregivers regarding the patients’ weight [ 44 , 45 , 46 , 47 ]. The social importance of food was a common theme throughout the included studies, with interruption to established social norms perceived as distressing. Mealtimes are considered a ‘unifying family ritual’ [ 49 ], but when mealtimes constantly serve to remind caregivers of their responsibility of monitoring, they became a potential source of distress.

“I can’t get Bernard out of the small meals. . I have to ring him every day from work to tell him to eat” (A7).

3. Life disruption

UGIC was experienced as coming unexpectedly into caregivers’ lives, intruding on their existing routines, for instance, as working professionals or parents. Caregivers described their responsibilities as time and energy-consuming. This conflict caused caregivers to feel a loss of control [ 44 ]. Caregiving responsibilities for UGICs demanded commitment over a long-time frame, impacting caregivers’ employability and their ‘own social life’ [ 35 ]:

“It’s changed my daily routine. It totally disrupted my life. I have to rearrange a lot of things such as my kids , my work , and getting help for my house cleaning” (J4).

4. Unpreparedness

Caregivers expressed being ill-equipped and unqualified to manage the needs of the UGIC patient. Caregivers reported feeling out of their depth, partially attributed to the lack of available support, relating to patients’ medical requirements:

“I went , ‘You’re not supposed to call 911? What am I supposed to do? What if he just dies right here?’ I mean , it seems they should have somebody say , ’OK , if he’s with you , then here’s the procedure…[The nurse] gave me really no support about what to do” (R21).

Caregivers sometimes felt misled about the extent of their new responsibilities, as while the patient was cared for in hospital by medical staff, they could not gauge what caregiving at home would involve.

“I wish they would have talked to me about it as well… it was a bit of a shock. …but the next morning it all dawned on me that I had just replaced a whole team” (E10).

5. Information manager

Caregivers perceived a key responsibility was to make executive decisions in the dissemination of information, for instance symptomatology, treatment plans and prognosis. Caregivers felt they were the ‘conduit’ [ 18 ] through which medical details were communicated to members of the extended social circle, a time-consuming role where they spent “hours on the phone telling everyone what is happening” (I32).

The caregivers also viewed their role as giving healthcare providers (HCPs) valuable insight into how the patient was coping outside of the medical setting:

“[describing a discussion during a clinical consultation , contradicting the patient] It is not correct that you almost eat as usual. You are eating food of more liquid substance than you usually do and your drinks are high-protein” (C1).

Synthesised finding 2: mediators of caregiver burden

While supporting patients with UGIC, caregivers are exposed to mediators which could increase or reduce caregiver burden, including their use of coping strategies, financial and social resources, and their caregiving context. For instance, higher levels of social support helped alleviate some caregiver burden. Similarly, how excluded a caregiver felt in the medical setting influenced the burden experienced.

1. Degree of inclusion in medical settings

Many studies reported that caregivers perceived they are often kept at a distance in medical settings, increasing caregiver burden. Although some caregivers felt this was fitting and chose to take a ‘subordinate position’ [ 44 ], others struggled with a sense of exclusion, which commonly left unresolved questions:

“…my husband could ask questions , but I didn’t have the space to ask questions , not unless my husband allowed it” (K39).

In such cases, caregivers relied on HCPs’ judgement. Caregivers described only being ‘seen’ if they actively called attention to themselves [ 41 ]. Caregivers experienced being left out of important decisions.

Caregivers expressed wanting to ask questions without the patient present but felt they had no opportunity to directly communicate with HCPs. This pervasive, default invisibility was perceived as disempowering:

“No health professionals involved me in this decision” (K38).

2. Social resources

The degree and quality of support received by caregivers varied and shaped their overall caring experience. The support network is especially beneficial for normalisation of caregivers’ experiences, providing hope and reducing feelings of isolation.

“it was only when I came here that I started talking to people … it was just like a breath of fresh air. . this dumping syndrome , he [the patient] wasn’t the only one” (A10).

Support could be from spiritual groups ( “I have a lot of people that stand behind me…” (B19), empathetic HCPs ( “It’s easier to talk with a nurse when it concerns important questions. You may receive quite good and reassuring answers” (H22) or peers who have undergone a similar caregiving experience, and therefore could reliably address and empathise with caregivers’ challenges.

3. Financial resources

Caregivers reported financial pressure as they had to consider the dyad’s financial situation while one or both members may not be able to work. Providing full-time care was a drain on caregivers’ resources, time, and money. Caregivers struggled with financial planning for the future in the face of prognostic ambiguity.

“We talked about if we should stay on at the house or sell it” (K6).

There were additional pressures on dyads living in countries where utilisation of private health services is the norm.

“Now my grandmother is sick and I can understand how high is the cost of the disease” (D5).

4. Patient-caregiver relationship

The caregiving experience was shaped by the inter-dyad relationship. Some caregivers reported having an emotionally distant relationship with the patient before the diagnosis which led to poor attachment during the cancer trajectory. Others reported a decline in the relationship quality due to cancer-related pressures.

“When I got upset , I would say to my husband , ‘You got cancer because you didn’t listen to me! You deserve it!” (F35).

Others noted a shift within the relationship, transitioning from ‘caregiver’ to ‘curer’ or from a spousal role to a parental one [ 45 ] especially where the caregiver was actively involved in delivering treatment:

“Sometimes I felt like a mother talking to a child: ‘Remember to do this and that’ ” (K29).

Caregivers experienced reciprocal suffering when seeing the patient suffer, especially if an established close relationship existed:

“up when the patient is up and down when the patient is down” (I21).

5. Emotion-focused coping

The cancer experience was perceived to result in significant distress for caregivers. To address this challenge, caregivers engaged in positive emotion-focused coping strategies to directly regulate distress. Many caregivers reported trying to maintain positive thinking. One participant recalled using humour:

“Sometimes you can’t believe what happens and the only thing you can do is laugh” (I41).

Maintaining a positive outlook was perceived to involve “looking for the good in every situation” and by being selective about what news caregivers received through ‘denial’ and “choosing what to hear” (I44). Conversely, another study described positivity as an open-minded reflection on the conflict between current suffering and spiritual beliefs [ 38 ]. Caregivers described how formally addressing their feelings through therapy was also helpful.

Individuals were limited in their opportunity for emotional expression. Caregivers described hiding their own negative thoughts from the patient and took practical measures to divert the patient’s attention by doing “normal things like [going] for a drive and [having] visits from our children and grandchildren” (C15).

6. Information seeking

Caregivers perceived challenges around a lack of information from HCPs regarding UGIC’s pathology and related management options. The experiences of caregivers included difficulties in accessing information.

“We have little information in these areas. When we go to the physician’s office for treatment , the doctor is too busy to give us information in this regard and he merely visits the patients. When we see that nobody could survive from such diseases , we get worried more” (D9).

Caregivers addressed the information challenge by persistently seeking information relating to the disease itself, namely cancer-related symptomatology, prognosis, and treatment options (including alternative therapies). Caregivers referred to sources like medically knowledgeable peers, the internet and print (e.g., encyclopaedias). HCPs were trusted for honest information, with their word choices and body language carefully analysed:

“When my husband and I visit the doctor together , you see when he opens the door that there is no good news today” (H6).

Caregivers were especially empowered when they could differentiate between symptoms due to disease progression and treatment-related adverse effects.

Synthesised finding 3: consequences of caregiver burden

There were consequences of caregiver burden such as feelings of helplessness, distress, anger, guilt and a strong fear of losing the patient. Conversely, there was potential for positive outcomes as caregivers experienced growth and feelings of hope.

1. Distress and helplessness

When recounting the most involved phase of providing care, active treatment, many caregivers reported experiencing heightened distress. One caregiver perceived gastric cancer a ‘death sentence’ [ 49 ], and seeing the patient struggle with the effects of disease and treatments an unbearably ‘ challenging experience’ [ 40 ]. This distress also affected children with one spouse noting their child’s “grades dropped disastrously during his first term” (H14).

Helplessness originates from a lack of control over the disease progression. A particular source of distress were the delays along the cancer trajectory, especially at diagnosis due to the ambiguous presentation of UGICs and lack of control over symptom management.

“It is distressing seeing him in pain all the time” (E6).

2. Anger and guilt

Caregivers experienced a sense of guilt and anger because they perceived stigma from society towards certain cancers. Others may assume that the diagnosis was caused by the patient’s behaviours and therefore indirectly the caregiver may also have been involved. A few studies described this judgement from society towards the patient, with caregivers fearing that others would see the diagnosis as a justified fate:

“You know , when you say cirrhosis of the liver , they think , ‘Oh , you drank yourself’” (R7).

Caregivers also harboured anger at being forced to take on caregiving responsibilities, describing they had “been dealt a bad hand” (I39); however, they felt guilty for feeling this way.

3. Fear of cancer progression and recurrence

Due to the unpredictability of UGICs, caregivers described living in constant dread of the patient’s health declining, and the potential for disease progression or recurrence:

“I am not sure I am going to like the answers I get. Maybe it is better not to know so very much but to do like the ostrich , to bury your head in the sand and hope for the best and keep your fingers crossed” (H41).

Caregivers were fearful of any new physical or psychological symptoms in patients, especially weight-loss, as caregivers saw this as a marker of recurrence. Further, caregivers feared the cancer would progress to a terminal stage which meant they were afraid of the means through which the bereavement would occur and their own subsequent reaction.

“the fear of not being sure of how it’s going to happen and how I’m going to react…I’m afraid of losing him” (L1).

The high mortality associated with most UGICs caused several caregivers to experience acceptance, with the realisation of the long-term impact of their loved one’s cancer and possibility of bereavement.

“The possibility is there for one of us dying quickly” (K5).

4. Isolation and loneliness

Caregivers commonly reported experiencing isolation within their unique role, feeling unable to share their anxieties. As patients were burdened already, caregivers did not want to unload their own worries on to the patient.

“And I had nobody to talk to…There was just nobody. I couldn’t let myself down , my guard down and I found the isolation terrible” (A3).

Loneliness was not only an ongoing concern, but a future threat as spousal caregivers relayed their fear of life post-bereavement.

5. Personal growth

Caregivers reflected that they saw the experience of caregiving as a catalyst for personal change, resulting in positive outcomes such as personal growth and appreciation for life, individually and within the relationship. Caregivers recounted that this unexpected, immense challenge had given them ‘ new perspectives about life’ [ 35 ]. Couples got to spend time together that they would not have had otherwise which led to an improved quality of relationship.

“We’ll talk three or four times a month. Where 10 years ago it might be 6 months or 10 months you know between phone calls” (B14).

The current study presents the first comprehensive synthesis of qualitative research on the experiences of caregivers of individuals with UGICs. This review is the first to systematically identify and synthesise the current evidence base on the experiences of informal caregivers of individuals with UGIC. Given the emergence of this prominent caregiver population, this review contributes to advancing cancer caregiver literature as a whole, an important area of study recognised by individuals with cancer, their family and healthcare professionals [ 52 ]. The review included 19 studies, presented synthesised findings, and identified aspects of caregiving experiences that UGIC caregivers have in common with other cancer caregivers, and aspects more distinct to UGICs. UGIC caregivers experience significant challenges contributing to high levels of burden which are mediated by social, psychological, and practical resources, as well as aspects of health service delivery. The consequences of caregiver burden are primarily negative, including distress, anger, fear, and loneliness.

Caregivers of UGIC patients experienced burden due to the breath and complexity of their role for which they felt unprepared. Caring involved incorporating novel skills into existing responsibilities, causing significant life disruption. Caregivers perceive burden in providing multifaceted care with demands that shift along the illness trajectory. For example, in the beginning caregivers felt it necessary to partake in provision of care, and due to UGIC treatment and disease progression, many responsibilities evolved to monitor and maximise physical health, such as diligent weight monitoring and meal preparation [ 45 , 46 , 47 ]. These findings align to the general cancer caregiver literature [ 53 ], with caregivers recognised in having steep initial learning curves to rapidly acquire skills to provide care. Only one of the 19 studies evaluated data over an extended period [ 45 ]. An extended review is needed to map supportive care resources available across the disease path and longitudinal studies tracking UGIC caregiver support needs across the illness trajectory is warranted.

One of the most reported findings in this review was informal caregivers’ continuous search for information related to their role. Many struggle to satisfy their informational needs at different stages of the disease trajectory contributing to caregiver burden. This corresponds with systematic review findings of Wang et al. [ 54 ] that informational needs were the most common unmet need of informal caregivers. To begin addressing this need, caregivers could be signposted to existing sources of general caregiver support information and interventions, such as Cancer Caring Coping [ 55 , 56 ]. These supports could be used to develop informational resources tailored for UGIC caregivers. A core information set has been developed to aid HCPs at consultation with UGIC patients, to ensure key information is being delivered [ 57 , 58 ] and now the focus of improving patient-carer education should be raising awareness of this key information toolkit to HCPs who commonly interact with this population. A similar approach could be utilised by identifying informational needs of UGIC caregivers at consultations and developing standardised information points delivered by HCPs to caregivers within those consultations. There is also potential to expand the pool of reliable sources of information to individuals outside of the HCP cohort, such as peer networks or psychologists in providing longitudinal support without necessarily adding to the cost burden required for the development of additional personnel and resources.

This review found caregivers experienced exclusion in the medical setting, suggesting enhanced communication between HCPs and caregivers could improve caregivers’ experience. Indeed, a qualitative study by Reblin et al. [ 59 ] identified communication within health services as a key driver for improving cancer caregiver support. One potential avenue to bridge the gap between HCPs and patient-caregiver dyad is incorporating better the clinical nurse specialist (CNS) [ 60 ] as these professionals can be a key contact for bi-directional communication between HCPs and caregivers. That is, caregivers support and help the clinical team to understand the patient’s progress and through this process HCPs acknowledge and include caregivers in the patient’s care. However, the current issue of under resourcing in cancer nursing would need to be addressed as it presently limits the amount of CNS time available to support caregivers [ 61 ].

One review finding specific to UGIC caregiver burden was the challenge around preparing meals. Taleghani and colleagues [ 62 ] mirror this, highlighting gastric caregivers experienced inadequate education in managing patient’s dietary requirements appropriately, resulting in feeling inefficient, uncomfortable, and fearful. Dietician-led interventions are typically patient focused [ 63 , 64 , 65 ]. However, this review highlights an opportunity for HCPs to include caregivers in dietician-led interventions as many caregivers assume responsibility over meal preparation and grocery shopping. The challenge around meals also has social consequences as meals are important social settings. Changes in eating behaviours can lead to both dyad members feeling isolated and lonely [ 18 , 66 ]. Loneliness is prevalent among people living with cancer and is influenced by cancer-specific and non-cancer specific risk factors, such as lack of social support [ 67 ]. There is less of an understanding of loneliness among UGIC caregivers compared to general cancer caregivers [ 68 ]. This is of concern as negative physical and mental health impacts of loneliness are well-established [ 69 , 70 ]. Peer support is the most used intervention to reduce caregivers’ loneliness, with strategies of psychoeducation and emotional support featuring prominently [ 71 ]. Research is needed to identify risk and protective factors for loneliness among UGIC caregivers.

In addition to loneliness, distress and negative affect were identified as consequences of UGIC caregiver burden. There is evidence of heightened distress and reduced physical and mental health among UGIC caregivers relative to UGIC patients [ 72 , 73 ]. This review also found that caregivers engage in emotion-focused strategies to cope with their caregiving role. A review by Teixeira et al. [ 74 ] found that among cancer caregivers, emotion-focused coping was related to higher distress, whereas problem-focused coping was related to better adjustment and reduced burden. There is a need to develop targeted theory-based psychosocial interventions for this caregiver group. The Transactional Theory of Stress and Coping (TTSC) framework could be utilised to understand how mediating processes specific to coping strategies influence distress and negative affect among UGIC caregivers [ 75 , 76 , 77 ], similar to how Bowan et al. [ 78 ] used a Baltes and Baltes [ 79 ] coping framework to develop interventions for cancer patients’ families. Candidate interventions could involve problem-solving and coping skills training [ 80 , 81 ], which could in turn ameliorate the negative consequences of caregiver burden. If effective with UGIC caregivers, such interventions could be extended to all caregivers as part of a standard care pathway. This review recommends further research to develop an understanding of adjustment in UGIC caregivers.

In contrast to the many negative consequences described by informal caregivers, there were a small group of findings which indicated some positive outcomes. These findings align with a review of the positive aspects of caregiving, which reported improved relationship quality, reward, fulfilment, and personal growth [ 82 ]. The review concluded that positive aspects of caregiving are interconnected and suggested, in addition to interventions reducing negative burden, that interventions could be developed to enhance positive outcomes, such as personal growth. Tedeschi and Calhoun’s Transformational Model (TM) [ 83 ] proposes that potentially traumatic stressors, such as caring for an individual diagnosed with cancer, cause a disruption in one’s worldview triggering attempts to make meaning in response to the stressor. Cognitive disruptions also lead to distress, which in turn can act as a catalyst for post-traumatic growth (PTG). Studies have found that caregivers of people with advanced cancer and early-stage breast cancer experience PTG in relation to their caregiving role [ 84 , 85 ], and that PTG was positively associated with greater social support and perceived hope [ 86 ]. Additional research is needed to understand how the challenging UGIC caregiver role may facilitate growth and help the caregiver adjust to their role.

Study limitations

The current systematic review has several strengths. Firstly, it followed an internationally recognised methodology (JBI) for the conduct of qualitative systematic reviews. This helped ensure methodological approach rigour and subsequently, confidence in findings should they be used to inform policy and practice. There are however several limitations. Although studies in the review are generally of good quality, only 19 studies were identified. Indeed, the UK Less Survivable Cancers Taskforce [ 87 ] advocates for more research focused on cancers with low life expectancy, two-thirds of which are UGICs. This lack of research into UGICs extends to the evidence on caregivers. Synthesised findings are therefore based on a small number of studies, largely conducted in the US and Denmark. Within the studies, caregivers of individuals with oesophageal and pancreatic cancer were well represented. However, there were a dearth of studies focused on caregivers’ experiences with gallbladder, or stomach cancer, alongside multiple studies exploring caregivers’ experiences related to dysphagia and malabsorption but fewer exploring jaundice. Therefore, more primary qualitative research is necessary to understand experiences of all UGIC caregiver populations.

Clinical implications

Of relevance for clinical practice was the finding that caregivers often felt excluded in medical settings, increasing caregiver burden. Caregivers should be seen as co-clients along with patients in the medical setting. This is very much in line with the priorities of care within palliative healthcare settings. Since the palliative care approach seeks to addresses the physical, psychological, cultural, social, and spiritual needs [ 88 ] of both individuals with life-limiting and chronic illnesses like cancer and their support networks, early referral to palliative care services could be particularly beneficial for caregivers as their needs are formally and expertly acknowledged and thus help alleviate the burden identified for informal caregivers in this study.

HCPs have an opportunity to give caregivers reliable, specific, and up-to-date information, pitched at the right level to reassure but not overwhelm. Morris and Thomas [ 89 ] mirror this suggestion and highlight its importance, as there is potential for tension in information exchange due to HCP’s lack of formal acknowledgement of caregivers. Clinical guidance and policy could be updated to include recognition of caregivers as co-clients, and with caregiver training, could formally be part of the patient support team. This could help meet the caregivers’ needs, especially post-diagnosis. On an institutional level, caregivers may be more recognised within their role if acknowledged formally, for example in NICE [ 1 ] guidelines for UGICs. In understanding the considerable role caregivers undertake supporting the care of UGIC patients outside of the healthcare system, policymakers and HCPs need to improve support for caregivers which will in turn reduce the burden on health services.

The aim of this qualitative systematic review was to synthesize evidence about the experiences of UGIC caregivers and has found that caregivers face significant challenges leading to caregiver burden which negatively impacts adjustment. Due to the nature of UGICs, caregivers experienced unique challenges such as how best to manage disruptions to mealtimes and how to monitor surrogate markers of patient health, such as weight. UGICs are a medically complex and evolving chronic condition and caregivers struggle to gain information. This review found that caregiver burden was impacted by feeling excluded in medical settings which could be improved with better communication between HCPs, patients, and their caregivers. There is a lack of data relating to the experiences of certain UGIC caregivers (e.g., gallbladder, stomach) in comparison to others (e.g., oesophageal), as well as a lack of understanding on how to manage the impact of caregiving for these types of cancer, thus providing directions for future research.

Data availability

No datasets were generated or analysed during the current study.

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Amid medicaid's 'unwinding,' many states work to expand health care access.

Phil Galewitz

North Carolina Department of Health and Human Services secretary Kody Kinsley discusses the impact of Medicaid expansion on prescriptions during a news conference at the North Carolina Executive Mansion in Raleigh, N.C., on Friday, July 12, 2024. When the state expanded access to Medicaid in December, more than 500,000 residents gained access to health coverage. Makiya Seminera/AP hide caption

Trisha Byers left behind one crucial item when she moved to North Carolina last year to be closer to her family after suffering a brain injury: health insurance.

In Massachusetts, Byers, 39, was enrolled in Medicaid, the government health program that covers low-income people. But she was ineligible in North Carolina, which had not yet expanded Medicaid coverage under the Affordable Care Act. She said she racked up thousands of dollars in unpaid emergency room bills while uninsured for several months after her move.

Then in December, North Carolina joined 39 other states and Washington, D.C., in widening Medicaid eligibility to include adults with incomes up to 138% of the federal poverty level, or $20,783 for an individual.

“I could finally get all the doctor appointments I needed,” said Byers, one of more than 500,000 North Carolinians who gained coverage.

The North Carolina expansion came amid the biggest upheaval in Medicaid’s nearly six-decade history. Since April 2023 — when protections that had blocked states from disenrolling Medicaid beneficiaries during the pandemic expired — states have disenrolled more than 24 million people whom they said no longer qualified or had failed to renew coverage.

Florida kicked their son off Medicaid in the 'unwinding' but not their daughter

This Medicaid “unwinding” led to fears that the number of people without insurance would spike. But it also coincided with moves in more than a dozen states to expand health coverage for lower-income people, including children, pregnant women, and the incarcerated.

These expansions will mitigate the effects of the unwinding to some degree, though it’s still unclear how much. Five states have not finished culling their rolls, and the effect on the uninsured rate won’t be clear until the U.S. Census Bureau releases official figures in September of next year.

“The pandemic was destructive and concerning and clearly demonstrated that Medicaid is so crucially important for our national safety net,” said Jennifer Babcock, senior vice president for Medicaid policy at the Association for Community Affiliated Plans, a trade group representing nonprofit health insurers that cover people on Medicaid. “These expansions are incredibly meaningful.”

Unwinding-era expansions include:

• South Dakota, like North Carolina, expanded Medicaid coverage under the Affordable Care Act last year. About 22,000 people enrolled in the first eight months.
• In July, Oregon launched a Medicaid-like coverage option for those who earn too much to qualify for Medicaid under federal limits. The plan is available to all adults with incomes between 138% and 200% — up to $30,120 for an individual — of the federal poverty level. More than 50,000 people have enrolled so far, Oregon officials say.
• In January, a new federal law required states to allow children to stay covered under Medicaid for at least a year after signing up. Several states are going beyond that: Oregon, New Mexico, and Washington, for example, allow children to stay covered up to age 6. California passed legislation to expand continuous eligibility for children up to age 4 but has not yet implemented the policy.
• Three states widened income eligibility for children to qualify for Medicaid: Arizona, Maine, and North Dakota.
• This year, Utah began offering a Medicaid-like coverage option for children regardless of immigration status , though the program is capped at about 2,000 children.
• Several states expanded coverage for pregnant women. Nevada, North Dakota, and Tennessee widened income eligibility to make it easier for pregnant women to qualify for Medicaid. Alabama and Maryland expanded eligibility to cover those who are pregnant regardless of immigration status. And Maine, Oregon, and Vermont extended postpartum coverage to 12 months, up from two. With those changes, 47 states now offer one year of postpartum coverage.
• In June, five states — Illinois, Kentucky, Oregon, Utah, and Vermont — received approval from the Biden administration to extend Medicaid coverage to incarcerated people up to 90 days before their release. Those states will join several states, including California, Massachusetts, Montana, and Washington, in offering that coverage.

States, which split funding of Medicaid with the federal government, typically expand Medicaid eligibility during times of economic growth when they have more revenue. But several other factors have contributed to the expansion trend. These include heightened awareness over rising maternal mortality rates and new restrictions on abortion, which have reinforced the need for expansions for pregnant women, said Allison Orris, a senior fellow with the left-leaning Center on Budget and Policy Priorities.

In particular, the pandemic showed how important health coverage is to ensure people’s health and communities’ safety from infectious diseases, Orris said. “It is not surprising to see states look at their Medicaid programs and find ways to strengthen in the midst of the unwinding,” she said.

For example, while federal Medicaid funding cannot be used for people living in the country unlawfully, a small but growing number of states have used their money to expand coverage to residents lacking legal status.

During the pandemic, as a requirement to gain extra federal funding, states were prohibited from cutting off Medicaid coverage even for those no longer eligible. The experience showed states the benefits of keeping people enrolled, rather than churning them in and out as their income fluctuates, Orris said. It also brought the nation’s uninsured rate to a record-low 7.7%.

Some advocates fear the unwinding of that pandemic-era policy will reverse key gains. A KFF survey published in April found 23% of adults reported being uninsured after they were disenrolled from Medicaid in 2023. A Centers for Disease Control and Prevention report released Aug. 6 found the uninsured rate rose to 8.2% in the first quarter of 2024, from 7.7% in the same quarter in 2023.

Enrollment increased by about 23 million people during the pandemic. As of Aug. 1, with about 85% of the unwinding completed, roughly 14.8 million people have been removed from Medicaid rolls. As a result, it’s unlikely the uninsured rate will rise as sharply as some advocates feared a year ago, said Jennifer Tolbert, deputy director of the Program on Medicaid and the Uninsured at KFF, a health information nonprofit that includes KFF Health News.

“We have seen some amazing coverage expansion in places like Oregon and California,” said Ben Anderson, deputy senior director of health policy at Families USA, a consumer advocacy group. “But if you live in Texas, Florida, and Georgia, since the pandemic your health coverage has been disrupted in ways that were preventable by state leaders.” Those three states are among the 10 that have chosen not to expand Medicaid under the ACA.

Still, Anderson said, the effect of the expansions, even in a limited number of states, will ensure some people can better afford health care and avoid medical debt.

The unwinding process has been rife with fumbling, particularly in states that didn’t steer enough resources to connect people with coverage. A study by the federal Government Accountability Office released in July revealed a Centers for Medicare & Medicaid Services’ finding that almost all states made mistakes that led to eligible individuals losing Medicaid coverage.

The recent Medicaid expansions provide examples of how some states prioritize health coverage, particularly for certain vulnerable groups.

Tricia Brooks, a Medicaid expert at Georgetown University, noted that some states are “targeting little pockets of coverage and doing it for a variety of reasons.”

Getting and keeping children insured means they are more likely to have a regular health provider and be ready to learn in school, she said. “There is no doubt there is a return on investment,” she said.

Medicaid advocates wonder, though, whether a second Trump administration would curtail coverage expansions. Republicans have signaled they do not want to extend the federal subsidies that reduce what lower-income people pay for ACA marketplace plans and that are scheduled to expire in 2025.

“We are bracing for that potential impact,” said Erin Delaney, director of health care policy at the Progressive Policy Institute.

KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF — an independent source for health policy research, polling, and journalism.

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Global Issues Are Taking a Major Toll on Young People’s Mental Health

Economic, climate and technology woes are weighing on young adults, a report finds. It recommends overhauling how we approach mental health care.

By Christina Caron

Chloé Johnson, 22, has been feeling hopeless lately.

She’s struggling to focus on classes at her local community college in Dallas while also working full-time, making $18 an hour as a receptionist.

Her car broke down, so the $500 that she had managed to save will now go toward a down payment for a used vehicle.

And she was recently passed over for a promotion.

“Right now it just feels, like, very suffocating to be in this position,” said Ms. Johnson, who was diagnosed last year with bipolar II disorder, depression and A.D.H.D. “I’m not getting anywhere or making any progress.”

It’s an endless loop: Ms. Johnson’s mental health has worsened because of her financial difficulties and her financial problems have grown, partly because of the cost of mental health treatment but also because her disorders have made it more difficult to earn a college degree that could lead to a more lucrative job.

“I’ve failed several classes,” she said. “I burn out really easily, so I just give up.”

The mental health of adolescents and young adults has been on the decline and it’s partly because of “harmful megatrends” like financial inequality, according to a new report published on Tuesday in the scientific journal The Lancet Psychiatry. The global trends affecting younger generations also include wage theft , unregulated social media , job insecurity and climate change , all of which are creating “a bleak present and future for young people in many countries,” according to the authors.

Why focus on global trends?

The report was produced over the course of five years by a commission of more than 50 people, including mental health and economic policy experts from several continents and young people who have experienced mental illness.

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NIH launches program to advance research led by Native American communities on substance use and pain

Effort aims to elevate Indigenous Knowledge and culture in research, to respond to the overdose crisis and address related health disparities

The National Institutes of Health (NIH) has launched a program that will support Native American communities to lead public health research to address overdose, substance use, and pain, including related factors such as mental health and wellness. Despite the inherent strengths in Tribal communities, and driven in part by social determinants of health, Native American communities face unique health disparities related to the opioid crisis. For instance, in recent years, overdose death rates have been highest among American Indian and Alaska Native people . Research prioritized by Native communities is essential for enhancing effective, culturally grounded public health interventions and promoting positive health outcomes.

“Elevating the knowledge, expertise, and inherent strengths of Native people in research is crucial for creating sustainable solutions that can effectively promote public health and health equity,” said Nora D. Volkow, M.D., director of NIH’s National Institute on Drug Abuse (NIDA). “As we look for ways to best respond to the overdose crisis across the country, it is crucial to recognize that Native American communities have the best perspective for developing prevention and therapeutic interventions consistent with their traditions and needs. This program will facilitate research that is led by Native American communities, for Native American communities.”

Totaling approximately $268 million over seven years, pending the availability of funds, the Native Collective Research Effort to Enhance Wellness (N CREW) Program will support research projects that are led directly by Tribes and organizations that serve Native American communities, and was established in direct response to priorities identified by Tribes and Native American communities.

Many Tribal Nations have developed and continue to develop innovative approaches and systems of care for community members with substance use and pain disorders. During NIH Tribal Consultations in 2018 and 2022 , Tribal leaders categorized the opioid overdose crisis as one of their highest priority issues and called for research and support to respond. They shared that Native communities must lead the science and highlighted the need for research capacity building, useful real-time data, and approaches that rely on Indigenous Knowledge and community strengths to meet the needs of Native people.

The N CREW Program focuses on:

• Supporting research prioritized by Native communities, including research elevating and integrating Indigenous Knowledge and culture
• Enhancing capacity for research led by Tribes and Native American Serving Organizations by developing and providing novel, accessible, and culturally grounded technical assistance and training, resources, and tools
• Improving access to, and quality of, data on substance use, pain, and related factors to maximize the potential for use of these data in local decision-making.

“Native American communities have been treating pain in their communities for centuries, and this program will uplift that knowledge to support research that is built around cultural strengths and priorities,” said Walter Koroshetz, M.D., director of NIH’s National Institute of Neurological Disorders and Stroke (NINDS). “These projects will further our collective understanding of key programs and initiatives that can effectively improve chronic pain management for Native American and other communities.”

The first phase of the program will support projects to plan, develop, and pilot community-driven research and/or data improvement projects to address substance use and pain. In this phase, NIH will also support the development of a Native Research Resource Network to provide comprehensive training, resources, and real-time support to N CREW participants.

The second phase of the program, anticipated to begin in fall 2026, will build on the work conducted in the initial phase of the program to further capacity building efforts and implement community-driven research and/or data improvements projects. Additional activities that support the overarching goals of the N CREW Program may also be identified as the program develops.

The N CREW Program is led by the NIH’s NIDA, NINDS, and National Center for Advancing Translational Sciences (NCATS), with participation from numerous other NIH Institutes, Centers, and Offices. The N CREW Program is funded through the NIH Helping to End Addiction Long-term Initiative (or NIH HEAL Initiative) , which is jointly managed by NIDA and NINDS. For the purposes of the N CREW Program, Native Americans include American Indians, Alaska Natives, and Native Hawaiians. Projects will be awarded on a rolling basis and publicly listed .

This new program is part of work to advance the President’s Unity Agenda and the HHS Overdose Prevention Strategy .

If you or someone you know is struggling or in crisis, help is available. Call or text 988  or chat at 988lifeline.org . To learn how to get support for mental health, drug or alcohol conditions, visit  FindSupport.gov . If you are ready to locate a treatment facility or provider, you can go directly to  FindTreatment.gov or call  800-662-HELP (4357) .

Helping to End Addiction Long-term® and NIH HEAL Initiative® are registered service marks of the Department of Health and Human Services.

About the National Institute on Drug Abuse (NIDA): NIDA is a component of the National Institutes of Health, U.S. Department of Health and Human Services. NIDA supports most of the world’s research on the health aspects of drug use and addiction. The Institute carries out a large variety of programs to inform policy, improve practice, and advance addiction science. For more information about NIDA and its programs, visit www.nida.nih.gov .

About the National Institute of Neurological Disorders and Stroke (NINDS): NINDS is the nation’s leading funder of research on the brain and nervous system. The mission of NINDS is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease. For more information about NINDS and its programs, visit www.ninds.nih.gov .

About the National Institutes of Health (NIH) : NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit https://www.nih.gov .

About substance use disorders: Substance use disorders are chronic, treatable conditions from which people can recover. In 2023, nearly 49 million people in the United States had at least one substance use disorder. Substance use disorders are defined in part by continued use of substances despite negative consequences. They are also relapsing conditions, in which periods of abstinence (not using substances) can be followed by a return to use. Stigma can make individuals with substance use disorders less likely to seek treatment. Using preferred language can help accurately report on substance use and addiction. View NIDA’s online guide.

About chronic pain: Chronic pain affects more than 50 million adults in the U.S. It may last for months, years, or a lifetime after its onset from trauma or another chronic health disorder. Multidisciplinary approaches and access to safe, effective, and quality care are essential for reducing pain and improving quality of life.

NIH…Turning Discovery Into Health ®

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New brain-computer interface allows man with ALS to ‘speak’ again

Technology developed by uc davis health restores interpersonal communication.

A new brain-computer interface (BCI) developed at UC Davis Health translates brain signals into speech with up to 97% accuracy — the most accurate system of its kind.

The researchers implanted sensors in the brain of a man with severely impaired speech due to amyotrophic lateral sclerosis (ALS) . The man was able to communicate his intended speech within minutes of activating the system.

A study about this work was published today in the New England Journal of Medicine.

ALS, also known as Lou Gehrig's disease, affects the nerve cells that control movement throughout the body. The disease leads to a gradual loss of the ability to stand, walk and use one’s hands. It can also cause a person to lose control of the muscles used to speak, leading to a loss of understandable speech.

The new technology is being developed to restore communication for people who can’t speak due to paralysis or neurological conditions like ALS. It can interpret brain signals when the user tries to speak and turns them into text that is ‘spoken’ aloud by the computer.

“Our BCI technology helped a man with paralysis to communicate with friends, families and caregivers,” said UC Davis neurosurgeon David Brandman . “Our paper demonstrates the most accurate speech neuroprosthesis (device) ever reported.”

Brandman is the co-principal investigator and co-senior author of this study. He is an assistant professor in the UC Davis Department of Neurological Surgery and co-director of the UC Davis Neuroprosthetics Lab .

The new BCI breaks the communication barrier

When someone tries to speak, the new BCI device transforms their brain activity into text on a computer screen. The computer can then read the text out loud.

To develop the system, the team enrolled Casey Harrell, a 45-year-old man with ALS, in the BrainGate clinical trial . At the time of his enrollment, Harrell had weakness in his arms and legs ( tetraparesis ). His speech was very hard to understand ( dysarthria ) and required others to help interpret for him.

In July 2023, Brandman implanted the investigational BCI device. He placed four microelectrode arrays into the left precentral gyrus, a brain region responsible for coordinating speech. The arrays are designed to record the brain activity from 256 cortical electrodes.

“We’re really detecting their attempt to move their muscles and talk,” explained neuroscientist Sergey Stavisky . Stavisky is an assistant professor in the Department of Neurological Surgery . He is the co-director of the UC Davis Neuroprosthetics Lab and co-principal investigator of the study. “We are recording from the part of the brain that’s trying to send these commands to the muscles. And we are basically listening into that, and we’re translating those patterns of brain activity into a phoneme — like a syllable or the unit of speech — and then the words they’re trying to say.”

Faster training, better results

Despite recent advances in BCI technology, efforts to enable communication have been slow and prone to errors. This is because the machine-learning programs that interpreted brain signals required a large amount of time and data to perform.

“Previous speech BCI systems had frequent word errors. This made it difficult for the user to be understood consistently and was a barrier to communication,” Brandman explained. “Our objective was to develop a system that empowered someone to be understood whenever they wanted to speak.”

Harrell used the system in both prompted and spontaneous conversational settings. In both cases, speech decoding happened in real time, with continuous system updates to keep it working accurately.

The decoded words were shown on a screen. Amazingly, they were read aloud in a voice that sounded like Harrell’s before he had ALS. The voice was composed using software trained with existing audio samples of his pre-ALS voice.

New Brain-Computer Interface Allows Man with ALS to 'Speak’ Again

At the first speech data training session, the system took 30 minutes to achieve 99.6% word accuracy with a 50-word vocabulary.

“The first time we tried the system, he cried with joy as the words he was trying to say correctly appeared on-screen. We all did,” Stavisky said.

In the second session, the size of the potential vocabulary increased to 125,000 words. With just an additional 1.4 hours of training data, the BCI achieved a 90.2% word accuracy with this greatly expanded vocabulary. After continued data collection, the BCI has maintained 97.5% accuracy.

The first time we tried the system, he cried with joy as the words he was trying to say correctly appeared on-screen. We all did.” — neuroscientist Sergey Stavisky

“At this point, we can decode what Casey is trying to say correctly about 97% of the time, which is better than many commercially available smartphone applications that try to interpret a person’s voice,” Brandman said. “This technology is transformative because it provides hope for people who want to speak but can’t. I hope that technology like this speech BCI will help future patients speak with their family and friends.”

The study reports on 84 data collection sessions over 32 weeks. In total, Harrell used the speech BCI in self-paced conversations for over 248 hours to communicate in person and over video chat.

This technology is transformative because it provides hope for people who want to speak but can’t.” — neurosurgeon David Brandman

“Not being able to communicate is so frustrating and demoralizing. It is like you are trapped,” Harrell said. “Something like this technology will help people back into life and society.”

“It has been immensely rewarding to see Casey regain his ability to speak with his family and friends through this technology,” said the study’s lead author, Nicholas Card . Card is a postdoctoral scholar in the UC Davis Department of Neurological Surgery.

Not being able to communicate is so frustrating and demoralizing. It is like you are trapped. Something like this technology will help people back into life and society.” — Casey Harrell, patient with ALS and a participant in the BrainGate2 BCI trial

“Casey and our other BrainGate participants are truly extraordinary. They deserve tremendous credit for joining these early clinical trials. They do this not because they’re hoping to gain any personal benefit, but to help us develop a system that will restore communication and mobility for other people with paralysis,” said co-author and BrainGate trial sponsor-investigator Leigh Hochberg . Hochberg is a neurologist and neuroscientist at Massachusetts General Hospital, Brown University and the VA Providence Healthcare System.

Brandman is the site-responsible principal investigator of the BrainGate2 clinical trial. The trial is enrolling participants. To learn more about the study, visit braingate.org or contact [email protected] .

A complete list of coauthors and funders is available in the article .

Caution: Investigational device. Limited by Federal law to investigational use.

Additional readings

• Read the study
• Neurological surgery researcher Sergey Stavisky and team awarded $3.5 million in grants
• Clinical Trial Aims to Develop New Methods to Restore Speech with Brain-Computer Interface

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Nadine A Yehya

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Phone: 916-734-9040

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The role of data science in healthcare advancements: applications, benefits, and future prospects

Sri venkat gunturi subrahmanya.

1 Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka India

Dasharathraj K. Shetty

2 Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka India

Vathsala Patil

3 Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal Karnataka, India

B. M. Zeeshan Hameed

4 Department of Urology, Father Muller Medical College, Mangalore, Karnataka India

5 Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA USA

Komal Smriti

Nithesh naik.

6 Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka India

Bhaskar K. Somani

7 Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK

Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical examinations, insurance, etc. The data collected from the Internet of Things (IoT) devices attract the attention of data scientists. Data science provides aid to process, manage, analyze, and assimilate the large quantities of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to acquire factual results. The process of data cleansing, data mining, data preparation, and data analysis used in healthcare applications is reviewed and discussed in the article. The article provides an insight into the status and prospects of big data analytics in healthcare, highlights the advantages, describes the frameworks and techniques used, briefs about the challenges faced currently, and discusses viable solutions. Data science and big data analytics can provide practical insights and aid in the decision-making of strategic decisions concerning the health system. It helps build a comprehensive view of patients, consumers, and clinicians. Data-driven decision-making opens up new possibilities to boost healthcare quality.

Introduction

The evolution in the digital era has led to the confluence of healthcare and technology resulting in the emergence of newer data-related applications [ 1 ]. Due to the voluminous amounts of clinical data generated from the health care sector like the Electronic Health Records (EHR) of patients, prescriptions, clinical reports, information about the purchase of medicines, medical insurance-related data, investigations, and laboratory reports, there lies an immense opportunity to analyze and study these using recent technologies [ 2 ]. The huge volume of data can be pooled together and analyzed effectively using machine-learning algorithms. Analyzing the details and understanding the patterns in the data can help in better decision-making resulting in a better quality of patient care. It can aid to understand the trends to improvise the outcome of medical care, life expectancy, early detection, and identification of disease at an initial stage and required treatment at an affordable cost [ 3 ]. Health Information Exchange (HIE) can be implemented which will help in extracting clinical information across various distinct repositories and merge it into a single person’s health record allowing all care providers to access it securely. Hence, the organizations associated with healthcare must attempt to procure all the available tools and infrastructure to make use of the big data, which can augment the revenue and profits and can establish better healthcare networks, and stand apart to reap significant benefits [ 4 , 5 ]. Data mining techniques can create a shift from conventional medical databases to a knowledge-rich, evidence-based healthcare environment in the coming decade.

Big data and its utility in healthcare and medical sciences have become more critical with the dawn of the social media era (platforms such as Facebook and Twitter) and smartphone apps that can monitor personal health parameters using sensors and analyzers [ 6 , 7 ]. The role of data mining is to improvise the stored user information to provide superior treatment and care. This review article provides an insight into the advantages and methodologies of big data usage in health care systems. It highlights the voluminous data generated in these systems, their qualities, possible security-related problems, data handling, and how this analytics support gaining significant insight into these data set.

Search strategy

A non-systematic review of all data science, big data in healthcare-related English language literature published in the last decade (2010–2020) was conducted in November 2020 using MEDLINE, Scopus, EMBASE, and Google Scholar. Our search strategy involved creating a search string based on a combination of keywords. They were: “Big Data,” “Big Data Analytics,” “Healthcare,” “Artificial Intelligence,” “AI,” “Machine learning,” “ML,” “ANN,” “Convolutional Networks,” “Electronic Health Records,” “EHR,” “EMR,” “Bioinformatics,” and “Data Science.” We included original articles published in English.

Inclusion criteria

• Articles on big data analytics, data science, and AI.
• Full-text original articles on all aspects of application of data science in medical sciences.

Exclusion criteria

• Commentaries, reviews, and articles with no full-text context and book chapters.
• Animal, laboratory, or cadaveric studies.

The literature review was performed as per the above-mentioned strategy. The evaluation of titles and abstracts, screening, and the full article text was conducted for the chosen articles that satisfied the inclusion criteria. Furthermore, the authors manually reviewed the selected article’s references list to screen for any additional work of interest. The authors resolved the disagreements about eligibility for a consensus decision after discussion.

Knowing more about “big data”

Big data consists of vast volumes of data, which cannot be managed using conventional technologies. Although there are many ways to define big data, we can consider the one defined by Douglas Laney [ 8 ] that represents three dimensions, namely, volume, velocity, and variety (3 Vs). The “big” in big data implies its large volume. Velocity demonstrates the speed or rate at which data is processed. Variety focuses on the various forms of structured and raw data obtained by any method or device, such as transaction-level data, videos, audios, texts, emails, and logs. The 3 Vs became the default description of big data, while many other Vs are added to the definition [ 9 ]. “Veracity” remains the most agreed 4th “V.” Data veracity focuses on the accuracy and reliability of a dataset. It helps to filter through what is important and what is not. The data with high veracity has many records that are valuable to analyze and that contribute in a meaningful way to the overall results. This aspect poses the biggest challenge when it comes to big data. With so much data available, ensuring that it is relevant and of high quality is important. Over recent years, big data has become increasingly popular across all parts of the globe.

Big data needs technologically sophisticated applications that use high-end computing resources and Artificial Intelligence (AI)-based algorithms to understand such huge volumes of data. Machine learning (ML) approaches for automatic decision-making by applying fuzzy logic and neural networks will be added advantage. Innovative and efficient strategies for dealing with data, smart cloud-based applications, effective storage, and user-friendly visualization are required for big data to gain practical insights [ 10 ].

Medical care as a repository for big data

Healthcare is a multilayered system developed specifically for preventing, diagnosing, and treating diseases. The key elements of medical care are health practitioners (physicians and nurses), healthcare facilities (which include clinics, drug delivery centers, and other testing or treatment technologies), and a funding agency that funds the former. Health care practitioners belong to different fields of health such as dentistry, pharmacy, medicine, nursing, psychology, allied health sciences, and many more. Depending on the severity of the cases, health care is provided at many levels. In all these stages, health practitioners need different forms of information such as the medical history of the patient (data related to medication and prescriptions), clinical data (such as data from laboratory assessments), and other personal or private medical data. The usual practice for a clinic, hospital, or patient to retain these medical documents would be maintaining either written notes or in the form of printed reports [ 11 ].

The clinical case records preserve the incidence and outcome of disease in a person’s body as a tale in the family, and the doctor plays an integral role in this tale [ 12 ]. With the emergence of electronic systems and their capacity, digitizing medical exams, health records, and investigations is a common procedure today. In 2003, the Institute of Medicine, a division in the National Academies of Sciences and Engineering coined the term “Electronic Health Records” for representing an electronic portal that saves the records of the patients. Electronic health records (EHRs) are automated medical records of patients related to an individual’s physical/mental health or significant reports that are saved in an electronic system and used to record, send, receive, store, retrieve, and connect the medical personnel and patient with medical services [ 13 ].

Open-source big data platforms

It is an inefficient idea to work with big data or vast volumes of data into storage considering even the most powerful computers. Hence, the only logical approach to process large quantities of big data available in a complex form is by spreading and processing it on several parallel connected nodes. Nevertheless, the volume of the data is typically so high that a large number of computing machines are needed in a reasonable period to distribute and finish processing. Working with thousands of nodes involves coping with issues related to paralleling the computation, spreading of data, and manage failures. Table ​ Table1 1 shows the few open sources of big data platforms and their utilities for data scientists.

source big data platforms and their utilities

Data mining

Data types can be classified based on their nature, source, and data collection methods [ 14 ]. Data mining techniques include data grouping, data clustering, data correlation, and mining of sequential patterns, regression, and data storage. There are several sources to obtain healthcare-related data (Fig.  1 ). The most commonly used type (77%) is the data generated by humans (HG data) which includes Electronic Medical Records (EMR), Electronic Health Records (EHR), and Electronic Patient Records (EPR). Online data through Web Service (WS) is considered as the second largest form of data (11%) due to the increase in the number of people using social media day by day and current digital development in the medical sector [ 15 ]. Recent advances in the Natural Language Processing (NLP)-based methodologies are also making WS simpler to use [ 16 ]. The other data forms such as Sensor Data (SD), Big Transactional Data (BTD), and Biometric Data (BM) make around 12% of overall data use, but wearable personal health monitoring devices’ prominence and market growth [ 17 ] may need SD and BM data.

Sources of big data in healthcare

Applications of analytics in healthcare

There are six areas of applications of analytics in healthcare (Fig.  2 ) including disease surveillance, health care management and administration, privacy protection and fraud detection, mental health, public health, and pharmacovigilance. Researchers have implemented data extraction for data deposition and cloud-based computing, optimizing quality, lowering costs, leveraging resources, handling patients, and other fields.

Various applications of data science in healthcare

Disease surveillance

It involves the perception of the disease, understanding its condition, etiology (the manner of causation of a disease), and prevention (Fig.  3 ).

The disease analysis system

Information obtained with the help of EHRs, and the Internet has a huge prospect for disease analysis. The various surveillance methods would aid the planning of services, evaluation of treatments, priority setting, and the development of health policy and practice.

Image processing of healthcare data from the big data point of view

Image processing on healthcare data offers valuable knowledge about anatomy and organ functioning and identifies the disease and patient health conditions. The technique currently has been used for organ delineation, identification of lung tumors, diagnosis of spinal deformity, detection of arterial stenosis, detection of an aneurysm, etc. [ 18 ]. The wavelets technique is commonly used for image processing techniques such as segmentation, enhancement, and noise reduction. The use of artificial intelligence in image processing will enhance aspects of health care including screening, diagnosis, and prognosis, and integrating medical images with other types of data and genomic data will increase accuracy and facilitate early diagnosis of diseases [ 18 , 19 ]. The exponential increase in the count of medical facilities and patients has led to better use of clinical settings of computer-based healthcare diagnostics and decision-making systems.

Data from wearable technology

Multi-National Companies like Apple and Google are working on health-based apps and wearable technology as part of a broader range of electronic sensors, the so-called IoT, and toolkits for healthcare-related apps. The possibility of collecting accurate medical data on real-time (e.g., mood, diet followed, exercise, and sleep cycles patterns), linked to physiological indicators (e.g., heart rate, calories burned, level of blood glucose, cortisol levels), is perhaps discrete and omnipresent at minimum cost, unrelated to traditional health care. “True Colors” is a wearable designed to collect continuous patient-centric data with the accessibility and acceptability needed to allow for accurate longitudinal follow-up. More importantly, this system is presently being piloted as a daily health-monitoring substitute.

Medical signal analytics

Telemetry and the devices for the monitoring of physiological parameters generate large amounts of data. The data generated generally are retained for a shorter duration, and thus, extensive research into produced data is neglected. However, advancements in data science in the field of healthcare attempt to ensure better management of data and provide enhanced patient care [ 20 – 23 ].

The use of continuous waveform in health records containing information generated through the application of statistical disciplines (e.g., statistical, quantitative, contextual, cognitive, predictive, etc.) can drive comprehensive care decision-making. Data acquisition apart from an ingestion-streaming platform is needed that can control a set of waveforms at various fidelity rates. The integration of this waveform data with the EHR’s static data results in an important component for giving analytics engine situational as well as contextual awareness. Enhancing the data collected by analytics will not just make the method more reliable, but will also help in balancing predictive analytics’ sensitivity and specificity. The signal processing species must mainly rely on the kind of disease population under observation.

Various signal-processing techniques can be used to derive a large number of target properties that are later consumed to provide actionable insight by a pre-trained machine-learning model. Such observations may be analytical, prescriptive, or predictive. Such insights can be furthermore built to activate other techniques such as alarms and physician notifications. Maintaining these continuous waveforms–based data along with specific data obtained from the remaining sources in perfect harmony to find the appropriate patient information to improve diagnosis and treatments of the next generation can be a daunting task [ 24 ]. Several technological criteria and specifications at the framework, analytical, and clinical levels need to be planned and implemented for the bedside implementation of these systems into medical setups.

Healthcare administration

Knowledge obtained from big data analysis gives healthcare providers insights not available otherwise (Fig.  4 ). Researchers have implemented data mining techniques to data warehousing as well as cloud computing, increasing quality, minimizing costs, handling patients, and several other fields of healthcare.

Role of big data in accelerating the treatment process

Data storage and cloud computing

Data warehousing and cloud storage are primarily used for storing the increasing amount of electronic patient-centric data [ 25 , 26 ] safely and cost-effectively to enhance medical outcomes. Besides medical purposes, data storage is utilized for purposes of research, training, education, and quality control. Users can also extract files from a repository containing the radiology results by using keywords following the predefined patient privacy policy.

Cost and quality of healthcare and utilization of resources

The migration of imaging reports to electronic medical recording systems offers tremendous potential for advancing research and practice on radiology through the continuous updating, incorporation, and exchange of a large volume of data. However, the heterogeneity in how these data can be formatted still poses major challenges. The overall objective of NLP is that the natural human language is translated into structured with a standardized set of value choices that are easily manipulated into subsections or searches for the presence or absence of a finding through software, among other things [ 27 ].

Greaves et al. [ 28 ] analyzed sentiment (computationally dividing them into categories such as optimistic, pessimistic, and neutral) based on the online response of patients stating their overall experience to predict healthcare quality. They found an agreement above 80% between online platform sentiment analysis and conventional paper-based quality prediction surveys (e.g., cleanliness, positive conduct, recommendation). The newer solution can be a cost-effective alternative to conventional healthcare surveys and studies. The physician’s overuse of screening and testing often leads to surplus data and excess costs [ 29 ]. The present practice in pathology is restricted by the emphasis on illness. Zhuang et al. [ 29 ] compared the disease-based approach in conjunction with database reasoning and used the data mining technique to build a decision support system based on evidence to minimize the unnecessary testing to reduce the total expense of patient care.

Patient data management

Patient data management involves effective scheduling and the delivery of patient care during the period of a patient’s stay in a hospital. The framework of patient-centric healthcare is shown in Fig.  5 . Daggy et al. [ 30 ] conducted a study on “no shows” or missing appointments that lead to the clinical capability that has been underused. A logistical regression model is developed using electronic medical records to estimate the probabilities of patients to no-show and show the use of estimates for creating clinical schedules that optimize clinical capacity use while retaining limited waiting times and clinical extra-time. The 400-day clinical call-in process was simulated, and two timetables were developed per day: the conventional method, which assigns one patient per appointment slot, and the proposed method, which schedules patients to balance patient waiting time, additional time, and income according to no-show likelihood.

Elemental structure of patient-centric healthcare and ecosystem

If patient no-show models are mixed with advanced programming approaches, more patients can be seen a day thus enhancing clinical performance. The advantages of implementation of planning software, including certain methodologies, should be considered by clinics as regards no-show costs [ 30 ].

A study conducted by Cubillas et al. [ 31 ] pointed out that it takes less time for patients who came for administrative purposes than for patients for health reasons. They also developed a statistical design for estimating the number of administrative visits. With a time saving of 21.73% (660,538 min), their model enhanced the scheduling system. Unlike administrative data/target finding patients, a few come very regularly for their medical treatment and cover a significant amount of medical workload. Koskela et al. [ 32 ] used both supervised and unsupervised learning strategies to identify and cluster records; the supervised strategy performed well in one cluster with 86% accuracy in distinguishing fare documents from the incorrect ones, whereas the unsupervised technique failed. This approach can be applied to the semi-automate EMR entry system [ 32 ].

Privacy of medical data and fraudulency detection

The anonymization of patient data, maintaining the privacy of the medical data and fraudulency detection in healthcare, is crucial. This demands efforts from data scientists to protect the big data from hackers. Mohammed et al. [ 33 ] introduced a unique anonymization algorithm that works for both distributed and centralized anonymization and discussed the problems of privacy security. For maintaining data usefulness without the loss of any data privacy, the researchers further proposed a model that performed far better than the traditional K-anonymization model. In addition to this, their algorithm could also deal with voluminous, multi-dimensional datasets.

A mobile-based cloud-computing framework [ 34 ] of big data has been introduced to overcome the shortcomings of today’s medical records systems. EHR data systems are constrained due to a lack of interoperability, size of data, and privacy. This unique cloud-based system proposed to store EHR data from multiple healthcare providers within the facility of an internet provider to provide authorized restricted access to healthcare providers and patients. They used algorithms for encryption, One Time Password (OTP), or a 2-factor authentication to ensure data security.

The analytics of the big data can be performed using Google’s efficient tools such as big query tools and MapReduce. This approach will reduce costs, improve efficiency, and provide data protection compared to conventional techniques that are used for anonymization. The conventional approach generally leaves data open to re-identification. Li et al. in a case study showed that hacking can make a connection between tiny chunks of information as well as recognize patients [ 35 ]. Fraud detection and abuse (i.e., suspicious care behavior, deliberate act of falsely representing facts, and unwanted repeated visits) make excellent use of big data analytics [ 36 ].

By using data from gynecology-based reports, Yang et al. framed a system that manually distinguishes characteristics of suspicious specimens from a set of medical care plans that any doctor would mostly adopt [ 37 ]. The technique was implemented on the data from Taiwan’s Bureau of National Health Insurance (BNHI), where the proposed technique managed to detect 69% of the total cases as fraudulent, enhancing the current model, which detected only 63% of fraudulent cases. To sum up, the protection of patient data and the detection of fraud are of significant concern due to the growing usage of social media technology and the propensity of people to place personal information on these platforms. The already existing strategies for anonymizing the data may become less successful if they are not implemented because a significant section of the personal details of everyone is now accessible through these platforms.

Mental health

According to National Survey conducted on Drug Use and Health (NSDUH), 52.2% of the total population in the United States (U.S.) was affected by either mental problems or drug addiction/abuse [ 38 ]. In addition, approximately 30 million suffer from panic attacks and anxiety disorders [ 39 ].

Panagiotakopoulos et al. [ 40 ] developed a data analysis–focused treatment technique to help doctors in managing patients with anxiety disorders. The authors used static information that includes personal information such as the age of the individual, sex, body and skin types, and family details and dynamic information like the context of stress, climate, and symptoms to construct static and dynamic information based on user models. For the first three services, relationships between different complex parameters were established, and the remaining one was mainly used to predict stress rates under various scenarios. This model was verified with the help of data collected from twenty-seven volunteers who are selected via the anxiety assessment survey. The applications of data analytics in the disease diagnosis, examination, or treatment of patients with mental wellbeing are very different from using analytics to anticipate cancer or diabetes. In this case, the data context (static, dynamic, or non-observable environment) seems to be more important compared to data volume [ 39 ].

The leading cause of perinatal morbidity and death is premature birth, but an exact mechanism is still unclear. The research carried by Chen et al. [ 41 ] intended to investigate the risk factors of preterm use of neural networks and decision tree C5.0 data mining. The original medical data was obtained by a specialist study group at the National University of Taiwan from a prospective pregnancy cohort. A total of 910 mother–child dyads from 14,551 in the original data have been recruited using the nest case–control design. In this data, thousands of variables are studied, including basic features, medical background, the climate and parents’ occupational factors, and the variables related to children. The findings suggest that the main risk factors for pre-born birth are multiple births, blood pressure during pregnancy, age, disease, prior preterm history, body weight and height of pregnant women, and paternal life risks associated with drinking and smoking. The results of the study are therefore helpful in the attempt to diagnose high-risk pregnant women and to provide intervention early to minimize and avoid early births in parents, healthcare workers, and public health workers [ 41 , 42 ].

Public health

Data analytics have also been applied to the detection of disease during outbreaks. Kostkova et al. [ 43 ] analyzed online records based on behavior patterns and media reporting the factors that affect the public as well as professional patterns of search-related disease outbreaks. They found distinct factors affecting the public health agencies’ skilled and layperson search patterns with indications for targeted communications during emergencies and outbreaks. Rathore et al. [ 44 ] have suggested an emergency tackling response unit using IoT-based wireless network of wearable devices called body area networks (BANs). The device consists of “intelligent construction,” a model that helps in processing and decision making from the data obtained from the sensors. The system was able to process millions of users’ wireless BAN data to provide an emergency response in real-time.

Consultation online is becoming increasingly common and a possible solution to the scarcity of healthcare resources and inefficient delivery of resources. Numerous online consultation sites do however struggle to attract customers who are prepared to pay and maintain them, and health care providers on the site have the additional challenge to stand out from a large number of doctors who can provide similar services [ 45 ]. In this research, Jiang et al. [ 45 ] used ML approaches to mine massive service data, in order (1) to define the important characteristics related to patient payment rather than free trial appointments, (2) explore the relative importance of those features, and (3) understand how these attributes work concerning payment, whether linearly or not. The dataset refers to the largest online medical consultation platform in China, covering 1,582,564 consultation documents among patient pairs between 2009 and 2018. The results showed that compared with features relating to reputation as a physician, service-related features such as quality of service (e.g., intensity of consultation dialogue and response rate), the source of patients (e.g., online vs offline patients), and the involvement of patients (e.g., social returns and previous treatments revealed). To facilitate payment, it is important to promote multiple timely responses in patient-provider interactions.

Pharmacovigilance

Pharmacovigilance requires tracking and identification of adverse drug reactions (ADRs) after launch, to guarantee patient safety. ADR events’ approximate social cost per year reaches a billion dollars, showing it as a significant aspect of the medical care system [ 46 ]. Data mining findings from adverse event reports (AERs) revealed that mild to lethal reactions might be caused in paclitaxel among which docetaxel is linked with the lethal reaction while the remaining 4 drugs were not associated with hypersensitivity [ 47 ] while testing ADR’s “hypersensitivity” to six anticancer agents [ 47 ]. Harpaz et al. [ 46 ] disagreed with the theory that adverse events might be caused not just due to a single medication but also due to a mixture of synthetic drugs. It is found that there is a correlation between a minimum of one drug and two AEs or two drugs and one AE in 84% of AERs studies. Harpaz R et al. [ 47 ] improved precision in the identification of ADRs by jointly considering several data sources. When using EHRs that are available publicly in conjunction with the AER studies of the FDA, they achieved a 31% (on average) increase in detection [ 45 ]. The authors identified dose-dependent ADRs with the help of models built from structured as well as unstructured EHR data [ 48 ]. Of the top 5 ADR-related drugs, 4 were observed to be dose-related [ 49 ]. The use of text data that is unstructured in EHRs [ 50 ]; pharmacovigilance operation was also given priority.

ADRs are uncommon in conventional pharmacovigilance, though it is possible to get false signals while finding a connection between a drug and any potential ADRs. These false alarms can be avoided because there is already a list of potential ADRs that can be of great help in potential pharmacovigilance activities [ 18 ].

Overcoming the language barrier

Having electronic health records shared worldwide can be beneficial in analyzing and comparing disease incidence and treatments in different countries. However, every country would use their language for data recording. This language barrier can be dealt with the help of multilingual language models, which would allow diversified opportunities for Data Science proliferation and to develop a model for personalization of services. These models will be able to understand the semantics — the grammatical structure and rules of the language along with the context — the general understanding of words in different contexts.

For example: “I’ll meet you at the river bank.”

“I have to deposit some money in my bank account.”

The word bank means different things in the two contexts, and a well-trained language model should be able to differentiate between these two. Cross-lingual language model trains on multiple languages simultaneously. Some of the cross lingual language models include:

mBERT — the multilingual BERT which was developed by Google Research team.

XLM — cross lingual model developed by Facebook AI, which is an improvisation over mBERT.

Multifit — a QRNN-based model developed by Fast.Ai that addresses challenges faced by low resource language models.

Millions of data points are accessible for EHR-based phenotyping involving a large number of clinical elements inside the EHRs. Like sequence data, handling and controlling the complete data of millions of individuals would also become a major challenge [ 51 ]. The key challenges faced include:

• The data collected was mostly either unorganized or inaccurate, thus posing a problem to gain insights into it.
• The correct balance between preserving patient-centric information and ensuring the quality and accessibility of this data is difficult to decide.
• Data standardization, maintaining privacy, efficient storage, and transfers require a lot of manpower to constantly monitor and make sure that the needs are met.
• Integrating genomic data into medical studies is critical due to the absence of standards for producing next-generation sequencing (NGS) data, handling bioinformatics, data deposition, and supporting medical decision-making [ 52 ].
• Language barrier when dealing data

Future directions

Healthcare services are constantly on the lookout for better options for improving the quality of treatment. It has embraced technological innovations intending to develop for a better future. Big data is a revolution in the world of health care. The attitude of patients, doctors, and healthcare providers to care delivery has only just begun to transform. The discussed use of big data is just the iceberg edge. With the proliferation of data science and the advent of various data-driven applications, the health sector remains a leading provider of data-driven solutions to a better life and tailored services to its customers. Data scientists can gain meaningful insights into improving the productivity of pharmaceutical and medical services through their broad range of data on the healthcare sector including financial, clinical, R&D, administration, and operational details.

Larger patient datasets can be obtained from medical care organizations that include data from surveillance, laboratory, genomics, imaging, and electronic healthcare records. This data requires proper management and analysis to derive meaningful information. Long-term visions for self-management, improved patient care, and treatment can be realized by utilizing big data. Data Science can bring in instant predictive analytics that can be used to obtain insights into a variety of disease processes and deliver patient-centric treatment. It will help to improvise the ability of researchers in the field of science, epidemiological studies, personalized medicine, etc. Predictive accuracy, however, is highly dependent on efficient data integration obtained from different sources to enable it to be generalized. Modern health organizations can revolutionize medical therapy and personalized medicine by integrating biomedical and health data. Data science can effectively handle, evaluate, and interpret big data by creating new paths in comprehensive medical care.

OOpen access funding provided by Manipal Academy of Higher Education, Manipal.

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The authors declare no competing interests.

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Volume 30, Number 10—October 2024

One Health Investigation into Mpox and Pets, United States

Suggested citation for this article

Monkeypox virus (MPXV) is zoonotic and capable of infecting many mammal species. However, whether common companion animals are susceptible to MPXV infection is unclear. During July 2022–March 2023, we collected animal and environmental swab samples within homes of confirmed human mpox case-patients and tested for MPXV and human DNA by PCR. We also used ELISA for orthopoxvirus antibody detection. Overall, 12% (22/191) of animal and 25% (14/56) of environmental swab samples from 4 households, including samples from 4 dogs and 1 cat, were positive for MPXV DNA, but we did not detect viable MPXV or orthopoxvirus antibodies. Among MPXV PCR-positive swab samples, 82% from animals and 93% the environment amplified human DNA with a statistically significant correlation in observed cycle threshold values. Our findings demonstrate likely DNA contamination from the human mpox cases. Despite the high likelihood for exposure, however, we found no indications that companion animals were infected with MPXV.

Before 2022, the primary mode for monkeypox virus (MPXV) transmission was known to be zoonotic, and only limited human-to-human transmission was documented ( 1 , 2 ). Human MPXV infections resulting in mpox disease were hypothesized to be the result of direct or potentially indirect contact with infected wild mammals in Central and Western Africa ( 3 , 4 ). Our understanding of the potential for human-to-human spread of MPXV considerably broadened in the spring of 2022 ( 5 , 6 ). During that time, variant of clade II MPXV (clade IIb) was found in to be transmitted via direct contact among human populations and spreading primarily through sexual networks outside of mpox endemic regions ( 5 , 6 ).

Given the zoonotic origin and reported broad host-range of MPXV, efforts to understand and limit potential human-to-animal transmission are ongoing ( 4 , 7 ). The Centers for Disease Control and Prevention (CDC) provides guidance that persons with mpox stop or avoid contact with animals and that animals should be kept away from potentially infectious lesion material, objects, or surfaces ( 8 ). Mpox patients are generally urged by public health agencies to isolate at home unless hospitalization is clinically necessary ( 9 , 10 ). A person with mpox is considered infectious throughout their illness and until lesions have fully healed with new skin underneath; therefore, public health officials recommend that mpox patients isolating at home take proper infection control measures to prevent spread of infectious particles throughout the home ( 11 – 13 ). Unless infected persons take measures to completely isolate or reduce transmission potential, companion animals in close contact with mpox patients and their environments could be at higher risk for MPXV exposure than other mammal species, warranting special concern and investigation.

As of July 2024, no cases of MPXV infection or mpox disease had been confirmed in common domestic animals, such as dogs and cats, during the current global outbreak or any past outbreaks. One study in July 2022 described a 4-year-old dog in France that had been living and co-sleeping with 2 mpox case-patients ( 14 ). In that study, MPXV DNA was identified in swab samples from the dog’s skin and surface of mucosal lesions and in anal and oral swab samples ( 14 ). However, follow-up investigations suggested that the animal was not infected with MPXV ( 15 ). A similar case was documented in Brazil in August 2022, when a 5-month-old dog had lesions that were MPXV-positive by quantitative PCR ( 16 ). Whether viral DNA detection was a result of MPXV infection in those animals or the result of environmental contamination due to close contact with infected humans is unclear. We conducted a One Health investigation in the United States to assess the susceptibility of companion animals to mpox and the risk for reverse-zoonotic transmission within households.

Methods and Materials

Study population.

The CDC Muti-National Mpox Response’s One Health Team worked in collaboration with state and local jurisdictions to investigate the susceptibility of companion animals to MPXV infection. As part of that effort, CDC and state public health investigators collected blood samples from companion animals and swab specimens from companion animals and animal-associated objects. CDC tested swab and serum specimens via real-time PCR, orthopoxvirus (OPXV) serology, and viral culture. All animals tested were companion animals in a residence of a person with probable or confirmed mpox while the person was infectious. Animal sampling occurred within 21 days of any direct contact with the ill person before the person recovered ( Table 1 ).

During July 2022–March 2023, we conducted sample collection in the District of Columbia, Virginia, Minnesota, and Tennessee, USA. After the initial sampling timepoint, we attempted follow-up sampling from all households 3–4 months later to collect animal serum samples and assess postexposure or postinfection immune responses.

Questionnaire and Consent

State and local public health personnel from the District of Columbia, Virginia, Minnesota, and Tennessee assisted with the study by interviewing mpox cases in their jurisdictions and requesting their voluntary participation in the study. After a person gave verbal consent to participate, they were provided with a survey questionnaire and consent forms. The questionnaire ascertained details and a timeline of the human case, the animal’s health condition, general household information, types of contact between the person with mpox and the animal or animals in the household, and information about wild or domestic animals in and around the household. This project was reviewed by CDC clearance, cleared for human subjects, and determined to be nonresearch public health surveillance that did not require submission to the CDC institutional review board (project no. 0900f3eb81f79d72).

Swab Sample Collection

We performed all animal handling and sampling procedures in accordance with the approved CDC Institutional Animal Care Use Committee protocol (no. DOTMULX3183), in collaboration with state public health agencies, and with written consent of the animal’s owner. We collected a standardized set of polyester swab (Puritan, https://www.puritanmedproducts.com ) samples from the animal’s dorsum fur, ventral abdomen, oral cavity, and anorectal area under supervision of the owner. We sampled animal lesions, if present. We also collected animal-associated environmental (AAE) specimens from objects and surfaces often used by the animal.

Sample Processing and PCR

We processed swab samples by using the swab extraction tube system (SETS; Roche, https://www.roche.com ) with 400 μL of phosphate-buffered saline; after DNA extraction, we tested all samples for MPXV DNA by real-time PCR using an MPXV clade II–specific assay ( 17 ). In addition, we tested samples for human DNA by using the RNase-P PCR assay, which is used as an endogenous control when testing human specimens ( 18 ). We calculated Pearson correlation coefficients to assess the relationship between cycle threshold (Ct) values of MPXV clade II PCR–positive (Ct values < 37) and RNase-P reactive (Ct values <40) samples.

Viral Culture

We tested all PCR-positive swab samples for viable virus via cell culture by adding an aliquot of swab eluate to BSC-40 cell monolayers in T-25 flasks. We used an inoculation volume of 50 μL + 25 μL, depending on available eluate volume. We incubated flasks at 35.5°C in an atmosphere of 6% CO 2 in Roswell Park Memorial Institute medium ( 19 ). We incubated and observed flasks < 14 days or until ≈100% of monolayer showed cytopathic effect, following methods and media supplements described previously ( 11 ). To control the overgrowth of bacteria or fungi in T-25 flasks, we added penicillin/streptomycin, amphotericin B, and gentamicin to the cell culture medium. If we detected any bacterial or fungal contamination, we performed 4 cycles of medium replacement to wash the monolayers and repeated this process as needed to prevent overgrowth.

Blood Collection and Serologic Testing

We attempted blood collection from all cooperative animals for which the owner provided consent. We collected < 3 mL of blood from 20/34 animals during initial sampling and 21/25 animals during follow-up sampling. We cleaned the external venipuncture site with 90% ethanol and used a syringe or vacutainer needle for blood collection. For dogs and 1 rabbit, we collected blood via the cephalic or lateral saphenous veins. For cats, we collected blood via the jugular or medial saphenous veins. We stored and transported blood tubes at 4°C–20°C before centrifugation, after which we transferred serum into 2-mL cryotubes and stored at temperatures of at least –20°C until laboratory testing. We conducted a modified ELISA on all serum samples to determine presence of OPXV IgG antibodies, as previously described ( 20 , 21 ). We tested serum samples at a dilution of 1:100 by using microtiter plates coated with purified vaccinia virus (Dryvax strain) and using the A/G protein as the secondary antibody at a 1:10,000 concentration and developed plates for 25 minutes.

Data Analysis

When referring to animal swab samples, we defined prevalence as the proportion of total swabs collected from each animal from which we detected either MPXV DNA or RNase-P (RNP) by PCR. When referring to AAE samples, we defined prevalence as the proportion of total swabs collected from the AAE samples within that animal’s household that were MPXV-positive or RNP-positive. We also referred to detection of RNase-P via PCR as presence of human DNA.

For each animal, we calculated the duration of exposure, defined as cumulative number of days before sampling that an infectious owner had direct contact with the animal, including durations where direct contact was not reported but the animal was still sharing a common space with a person with mpox. Duration of exposure represented the total period that infectious lesion material (crusts or exudates) or other infectious particles were potentially shed or transferred within the home, to which the animal potentially had contact, either directly or via fomites.

We investigated factors reported in questionnaires that could affect animal MPXV exposure ( Table 1 ). Those factors included whether the owner was symptomatic during time of sampling (coded SXDS); the degree of animal outdoor activity (coded AOA), which we stratified by none (no outdoor activity), walks (periodic or frequent supervised walks outside), and yard (allowed in yard or outside unsupervised frequently or for prolonged periods); co-sleeping with the animal while the owner was infectious (coded CSI); and a score comprised of the sum of all reported interaction types between animals and humans that involved direct contact (coded DIS), which included cuddling, hugging, petting, kissing, co-sleeping, sharing food, and grooming ( Table 1 ).

We compared bivariate correlation coefficients among variables compiled from questionnaire data or diagnostic testing. We used SPSS Statistics 27 (IBM, https://www.ibm.com ) to compute Pearson correlation coefficients. We performed 2-tailed tests of significance and considered p values of < 0.05 or < 0.01 statistically significant, as applicable.

Overall, we sampled 34 individual companion animals from 21 households: 24 domestic dogs, 9 domestic cats, and 1 domestic rabbit ( Table 2 ). The age of the animals ranged from 4 months to 16 years; 22 were male and 12 were female. All but 1 household had a single human mpox case; the other household had 2 cases. We collected a total of 191 swab specimens from animals and 56 AAE specimens. If excess blood was available, we opportunistically tested select blood specimens via PCR, including 10 whole blood specimens preserved in EDTA and 1 blood clot. At examination, we observed skin lesions in 6 dogs and 1 cat, and lesion features and locations varied.

PCR for Animal Samples

Samples collected from 5 individual animals (4 dogs, 1 cat) from 4 households were MPXV-positive; 2 of the dogs shared a household. Total animal swab MPXV positivity was 12% (22/191); 21 MPXV-positive swabs were from dogs, and 1 was from a cat ( Table 3 ). All MPXV-positive animals also had > 1 sample with an RNP-positive test result. Ct values of MPXV-positive samples were 25.2–36.7 (mean 34.5). Results of specific sample types collected were 29% (4/14) for skin lesions, 16% (6/37) for ventral skin or fur, 12% (4/33) for dorsal fur, 11% (4/35) for periocular area, 8% (3/36) for anorectal area, and 3% (1/36) for oral.

Among animal MPXV-positive specimens, 82% were RNP-positive, whereas 25% of the MPXV DNA–negative specimens were RNP-positive ( Table 3 ). Ct values of MPXV-positive specimens that were RNP-positive positively correlated (p<0.01). In animal specimens, 18% (4/22) were MPXV-positive and RNP-negative, and positive Ct values (range 35.3–36.1) were near the upper limit of detection (Ct 37) for the assay. We did not detect MPXV DNA in any of the blood specimens tested via MPXV PCR. In addition, MPXV DNA prevalence in animal samples alone and when combined with AAE specimens significantly correlated with RNP prevalence in those same samples (p<0.05).

We collected AAE specimens from 20/21 households, predominately from animal beds or bedding, toys, and food and water dishes. Among households, 29% (6/21) were positive for MPXV DNA, as were 25% (14/56) of collected specimens, 93% (13/14) of which were positive for MPXV and RNP ( Table 3 ). In those same samples, AAE MPXV DNA prevalence positively correlated with human DNA prevalence (p<0.05). Of the 4 households with MPXV-positive animal swab specimens, all had MPXV-positive AAE swabs with Ct values of 29.9–35.9 (mean 32.8). For AAE specimens that were MPXV- and RNP-positive, the MPXV and RNase-P Ct values were significantly correlated (p<0.01). Of all AAE specimens, 66% (37/56) were RNP-positive, of which 82% (9/11) of specimens with Ct values <37 were in the 4 households with MPXV-positive AAE and animal swab samples.

Viral Culture and Serology

We attempted viral culture from all specimens with Ct values < 36 (n = 31), and all were negative with no signs of cytopathic effect. Three specimens from 2 dogs had bacterial contamination causing destruction of monolayer by day 6 or 7 postinfection, despite mitigating steps or retesting, and the harvested culture media tested negative by MPXV-specific PCR. In addition, all initial (n = 20) and follow-up (n = 22) serum specimens collected were ELISA-negative, and we detected no OPXV IgG. For 1 dog that had samples with the lowest MPXV Ct values, we collected 2 follow-up samples 2 months apart. Of the 5 animals that had MPXV-positive swab specimens, 3 did not have blood sampled at the initial timepoint due to noncompliance or aggression, and 3 were not available at the postexposure sampling timepoint.

Questionnaire Analysis

In total, 32% (11/34) of animals had preexisting health issues and 5 animals had preexisting skin lesions. In addition to the 5 animals with skin lesions that developed before owner symptom onset (all sampled), 2 additional animals had lesions that developed after owner symptom onset. We observed and sampled those lesions during the initial sampling visit, and 1 animal had skin and fur, periocular, and anorectal specimens that were PCR-positive for MPXV DNA, but we did not detect MPXV DNA from the lesion specimen, and serology results also were negative.

In total, 33% (7/21) of households reported no contact change with their animals. Reported types of changes in animal interactions included reducing frequency of interactions (9/21), stopping interactions (8/21), use of PPE during interactions (6/21), and relocating or isolating the animal (4/21); 1 household reported relegating animal care to uninfected persons outside the household. However, all but 1 household reported > 1 type of direct contact activity with each animal after the MPXV-positive human in the household had symptoms develop ( Table 1 ).

Households comprised apartments (n = 11) or single-family homes (n = 10), and approximate size range was 500–3,500 ft 2 ( Table 1 ). We observed a significant negative correlation between household size and prevalence of either MPXV (p<0.05) or human DNA (p<0.01) in animal samples and human DNA prevalence in environmental samples (p<0.01). Apart from human DNA prevalence, household size, and environmental MPXV prevalence, we observed no other statistically significant relationships for other variables potentially influencing prevalence of MPXV DNA in animal samples.

CDC advises that persons with mpox should avoid contact with animals, including pets, until lesions have fully healed to prevent potential virus spillback. That recommendation is because of uncertainty regarding susceptibility of companion animals to MPXV ( 9 ). If MPXV-infected persons cannot avoid contact with pets within the household, practicing appropriate infection control will prevent further exposure potential. In most households we visited, recommended quarantine and infection control procedures were not consistently followed.

Despite MPXV-positive swab specimens detected on the skin or fur of dogs and cats and in associated environmental samples, no dogs or cats with live virus or antibodies detected have been reported globally. In 2 cases outside of the United States in which MPXV DNA was detected in dogs ( 14 , 16 ), apart from apparent skin lesions, no other signs of infection were reported in the animals, including virus cultured from samples or OPXV antibodies detected by serology after additional investigation ( 15 ).

In our household study, skin lesions in 7 animals were the only observable clinical features that were potentially consistent with mpox disease. However, 5 animals exhibited lesions before owner symptom onset, and the 2 animals with skin lesions that were observed after owner symptom onset were negative for MPXV by PCR. Only 1 animal had MPXV-positive lesions sampled, a dog with lesion swab samples collected from a grouping of 3 large lesions on its rear leg, and the average Ct value of samples was 25.2. After further testing to consider potential DNA contamination from the owner, that sample also had the lowest average RNase-P Ct value (29.3) of all samples tested. In addition, that dog’s lesions were reported to have formed before symptom onset in the owner, culture attempts from that and all other samples were negative, and OPXV antibodies were not detected during any timepoint tested. Therefore, after reviewing all the data, we did not consider this animal a confirmed mpox case.

All animals with MPXV-positive samples in this study also had RNP-positive specimens collected, indicating the presence of human DNA. The statistically significant correlation of MPXV- and RNP-positive samples, MPXV PCR results showing high Ct values indicating low viral DNA loads, and the lack of viable virus or antibodies in the collected samples strongly suggest that observed lesions or scabs in these animals were not the result of MPXV infection. In addition, from our knowledge of MPXV pathology, an MPXV lesion would most likely produce high viral loads and at levels higher than for other sample types ( 22 ).

As reported in other household environmental sampling studies, MPXV DNA can be widely detected in indoor or household settings ( 11 , 12 , 23 – 25 ). In this study, we found that households with smaller shared spaces were significantly correlated with both MPXV and human DNA prevalence, suggesting that the risk for MPXV exposure could be higher in smaller living quarters. Given the capability of MPXV DNA to disseminate within the household of a person with mpox, and after consideration of the PCR results detailed here, persons with mpox, not the companion animals, likely were the source of the MPXV DNA we detected in the household.

The potential for contamination from either direct contact with a person with mpox or indirect exposure to materials containing MPXV DNA should be considered when interpreting results of PCR testing from companion animals. In addition, case definitions should consider potential extraneous contamination and require more than a PCR-positive result from an animal to be considered a confirmed animal mpox case ( 26 ). Contamination should also be considered as a reason for a positive PCR result and false positive results in humans with nonspecific lesions who have potentially had contact with an mpox case-patient.

MPXV infection in companion animals, if they are suitable hosts, is uncharacterized; clinical signs, viral shedding, and duration of infectious period are unknown. Thus, although unlikely, given the limits of our sampling design, it is possible that an infected animal escaped detection in our study. However, the overall PCR and serologic evidence best fits the hypothesis that the MPXV DNA detected in animal samples submitted for PCR testing is a result of DNA contamination from the infected human within the household.

More work is needed to determine the susceptibility of companion animals to clade-IIb MPXV. Thus, CDC still recommends that companion animal owners with mpox limit their interactions with their pets while infectious, particularly if they are sharing smaller living spaces. That precautionary measure is recommended until more information is available about the susceptibility of common mammalian companion animal species to mpox.

In conclusion, no strong evidence yet exists to suggest that common companion animals, such as dogs or cats, are susceptible to infection with clade IIb MPXV. Given high likelihood for exposure among most of these animals, the paucity of evidence indicating infection might indicate resistance to infection. Nonetheless, to prevent further viral spread and potential evolution and establishment of new endemic areas, during public health emergencies caused by emerging zoonotic diseases, responders should apply a One Health approach to investigate potential spillback of human infections to animals, including pets.

Mr. Morgan is a biologist in the Poxvirus and Rabies Branch, Division of High Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention. His research interests include the virus-host interactions of orthopoxviruses and lyssaviruses in the environment.

Acknowledgments

The authors thank the Epidemiology, Laboratory and Testing, and STLT (State, Tribal, Local, or Territorial) Task Forces of the CDC 2022 Multinational Mpox Response and the CDC Poxvirus and Rabies Branch, Division of High Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases. We also acknowledge additional persons and entities who assisted with this study, including the Minnesota Department of Health, including Patrice Vandelinde, Victoria Lappi, and Anna Strain; the Virginia Department of Health, including Kenneth Gordon, Christina Chommanard, Luisa Angel Cortes, Clarissa Bonnefond, Lisa Engle, and Cynthia Rieken; the Tennessee Department of Health and Agriculture, including Jane Yackley, Dilani Goonewardene, and Whitnie Smartt; and DC Department of Health, including Sarah Gillani, Will Still, and Karla Miletti. In addition, we acknowledge, Casey Barton-Behravesh, Yoshinori Nakazawa, Modupe Osinubi, Ashutosh Wadhwa, and Ariel Caudle for their assistance.

All funding for this study was provided by the Centers for Disease Control and Prevention’s 2022 Multinational Mpox Response (CDC Mpox Response). The CDC Mpox Response provided technical review and oversight of this manuscript before publication. This study and report were also supported in part by an appointment to the Applied Epidemiology Fellowship Program, administered by the Council of State and Territorial Epidemiologists (CSTE) and funded by the Centers for Disease Control and Prevention (CDC) (cooperative agreement no. 1NU38OT000297-03-00).

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• Table 1 . Summary of variables coded from household and questionnaire data used in a One Health investigation into mpox and pets, United States
• Table 2 . Animal and environment sampling and diagnostic testing data from a One Health investigation into mpox and pets, United States
• Table 3 . PCR results of for monkeypox virus clade II and RNase-P DNA assays from swab samples of companion animals and animal-associated objects and surfaces during a One Health investigation into mpox...

Suggested citation for this article : Morgan CN, Wendling NM, Baird N, Kling C, Lopez L, Navarra T, et al. One Health investigation into mpox and pets, United States. Emerg Infect Dis. 2024 Oct [ date cited ]. https://doi.org/10.3201/eid3010.240632

DOI: 10.3201/eid3010.240632

Original Publication Date: August 14, 2024

Table of Contents – Volume 30, Number 10—October 2024

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Jeffrey B. Doty, Centers for Disease Control and Prevention, 4055 Tudor Center Dr, Anchorage, Alaska 99508, USA

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