research on primary health care

• Systematic Review
• Open access
• Published: 02 June 2023

Successes, weaknesses, and recommendations to strengthen primary health care: a scoping review

• Aklilu Endalamaw   ORCID: orcid.org/0000-0002-9121-6549 1 , 2 ,
• Daniel Erku   ORCID: orcid.org/0000-0002-8878-0317 1 , 3 , 4 ,
• Resham B. Khatri   ORCID: orcid.org/0000-0001-5216-606X 1 , 5 ,
• Frehiwot Nigatu 6 ,
• Eskinder Wolka 6 ,
• Anteneh Zewdie 6 &
• Yibeltal Assefa   ORCID: orcid.org/0000-0003-2393-1492 1  

Archives of Public Health volume  81 , Article number:  100 ( 2023 ) Cite this article

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Primary health care (PHC) is a roadmap for achieving universal health coverage (UHC). There were several fragmented and inconclusive pieces of evidence needed to be synthesized. Hence, we synthesized evidence to fully understand the successes, weaknesses, effective strategies, and barriers of PHC.

We followed the PRISMA extension for scoping reviews checklist. Qualitative, quantitative, or mixed-approach studies were included. The result synthesis is in a realistic approach with identifying which strategies and challenges existed at which country, in what context and why it happens.

A total of 10,556 articles were found. Of these, 134 articles were included for the final synthesis. Most studies (86 articles) were quantitative followed by qualitative (26 articles), and others (16 review and 6 mixed methods). Countries sought varying degrees of success and weakness. Strengths of PHC include less costly community health workers services, increased health care coverage and improved health outcomes. Declined continuity of care, less comprehensive in specialized care settings and ineffective reform were weaknesses in some countries. There were effective strategies: leadership, financial system, ‘Diagonal investment’, adequate health workforce, expanding PHC institutions, after-hour services, telephone appointment, contracting with non-governmental partners, a ‘Scheduling Model’, a strong referral system and measurement tools. On the other hand, high health care cost, client’s bad perception of health care, inadequate health workers, language problem and lack of quality of circle were barriers.

Conclusions

There was heterogeneous progress towards PHC vision. A country with a higher UHC effective service coverage index does not reflect its effectiveness in all aspects of PHC. Continuing monitoring and evaluation of PHC system, subsidies to the poor, and training and recruiting an adequate health workforce will keep PHC progress on track. The results of this review can be used as a guide for future research in selecting exploratory and outcome parameters.

Peer Review reports

A comprehensive primary health care (PHC) allows all members of the population to access essential health services without financial catastrophe [ 1 ] that is given in district hospitals, health centres, clinics and health posts [ 2 , 3 , 4 ]. PHC is a ‘whole system approach’—to deliver health promotion, disease prevention, curative and rehabilitative care—supported by medical supplies, multidisciplinary health teams, health governance and financing [ 5 , 6 , 7 ]. Moreover, it delivers health care services which have gotten attention since 1978 at ‘Alma-Ata’ declaration [ 8 ] and other prioritized services through time, like public health emergencies, common eye-nose-throat and oral health problems and mental health services [ 7 , 9 , 10 ].

PHC in its first inception aimed for ‘Health for All by the Year 2000’. Eventually, PHC is amenable to any global and national health policies, and most recently, it is a roadmap for achieving universal health coverage (UHC) by 2030 [ 11 ]. As a result, the global leaders and country representatives proclaimed a renewed action on PHC towards UHC in an international conference held in Kazakhstan, in October 2018 [ 12 ].

However, the World Health Organisation (WHO) projected that only 39% to 63% of the global population would be covered for essential health services by 2030 [ 13 ]. Hence, to take corrective actions and support government investment in PHC, health policy needs evidence about the challenges and effective strategies. In 2013, a review paper reported the impact of PHC delivery models [ 14 ] that discussed PHC models in improving access, quality and care coordination. However, it did not address PHC success, strategies, weaknesses, or challenges. Capacity building, human resources for health, technology, financing, and empowering individuals and communities complement the health system [ 8 , 12 , 15 , 16 , 17 , 18 ].

This study synthesized successes, strategies, weakness, and barriers of PHC dimensions. Therefore, the current study’s findings will be crucial to supplement PHC-related policy design, implementation, and evaluation.

The review was conducted per Levac and colleagues’ [ 19 ] five-step approach, including identifying research questions, identifying and selecting relevant studies, extracting data, and summarizing and reporting results. In addition, we followed the PRISMA extension for scoping reviews checklist to report this review ( Additional file ).

Search strategy

The required data were collected by searching on 4 May 2022 in the PubMed database and hand search by using the Google Scholar search engine. The search was updated on 28 April 2023. The key search terms or phrases used for searching articles fitted to PubMed were ("primary health care"[Title]) OR ("primary healthcare"[Title]) OR ("primary health-care"[Title]) Filters: English.

Inclusion and exclusion criteria

We included all types of articles that evaluated primary health care. These articles are quantitative, qualitative, mixed, or review by using data from clients, communities, document or article reviews, or health institutions. The types of articles were identified during the screening and data-extraction phase. Quantitative articles are estimated and presented the results mathematically, while qualitative articles are perspectives, in-depth interviews, focus-group discussions, and observations in which results are presented in texts. A review was any types of one or more principles of PHC. We considered mixed studies when quantitative and qualitative approaches are integrated into a single study. Since primary care is a subset of primary health care, we focused on the core principles of primary care in this synthesis. When the success and weakness of PHC researched its core principles i.e., accessibility, quality of care, effectiveness, cost-effectiveness, coordination, continuity, comprehensiveness, efficiency, equity and patient-centredness, we included all these as well. There were no time and place restrictions.

Articles with abstract or title only, letters to editors, perspectives, commentaries, conference abstracts and studies that do not have reported relevant findings to the current objectives were excluded. Articles published other than in English were also excluded.

Study selection and data extraction

Title, abstract and full-text screening was conducted by two authors (AE, DE) and the third author was involved whenever disagreement happened (YA). Then, appropriate data was extracted from included articles. These are: first author, publication year, country (study setting), study approach, study population, attributes, and objectives are displayed in the supplementary file (Table S 1 ), and the main findings are presented in the result section.

Statistical analysis and synthesis

UHC effective service coverage index of countries mentioned in the included articles are presented using the Choropleth map. We generated Choropleth map using R-software. Data of UHC effective service coverage index was taken from the Global health observatory [ 20 ]. UHC effective service coverage index is a composite of a single summary indicator estimated from the coverage value of 14 tracer indicators, mainly from infectious diseases (tuberculosis and HIV/AIDS); reproductive, maternal, neonatal and child health services; non-communicable disease treatments (hypertension control); service capacity and access [ 20 ]. As a ‘whole-of-society’ context, leadership, financial system, human resources and other facilitators or barriers were identified. The result synthesis is in a realistic approach i.e., showing which strategies and challenges were identified in which country, in what context and why it happens. Then, the strategies and barriers of PHC dimensions is summerised in figure.

Search results

Using search strategy, 10,323 articles were found in PubMed (9,466 on 04 May 2022 and 857 on 28 April 2023) and 233 from Google Scholar. A total of 569 remain after title screening. Following excluding title only, abstract only and unrelated abstract, 219 were eligible for full-text review. Letters, editorials, commentaries, perspectives, and full-text articles with unrelated findings were screened further. Finally, 134 articles were included for the result synthesis. Most studies (86 articles) were quantitative followed by qualitative (26 articles), and others (16 reviews and 6 mixed methods) (sT1).

Primary health care success, weakness, strategies, and barriers

We can see UHC as an immediate outcome of PHC. The choropleth map shows the UHC effective service coverage index of 45-countries (Fig.  1 ). The average UHC effective service coverage index was 67.6; the minimum was 37 in Albania and Niger, while the highest value was 89 in Canada. The UHC effective service coverage index value for each country is in the supplementary file (Table S 2 ). Additionally, country-specific progress to specific primary care core principles and long-term health system outcomes.

Choropleth map for UHC effective service coverage index in 2019

Success and weakness

PHC from an accessibility and quality of care point of view scored positive progress per countries contexts. Accessibility matters of how services are available, waiting time to receive care (timeliness), travel time or distance to reach PHC health institutions (geographic accessibility) and the affordability access. Reduced length of hospital stay in the Netherlands [ 21 ] and high continuity of care in India [ 22 ] was taken as exemplary lessons. Once increased accessibility, a more equitable distribution of health resources was achieved in Kazakhstan [ 23 ]. PHC Specialized reference clinics decreased health problem burdens by reducing waiting time and health care cost, and increased client satisfaction in Saudi Arabia [ 24 ].

There were an increased number of PHC facilities in Argentina [ 25 ]. Australia improved health care services accessibility for prisoners during their release [ 26 ]. Primary care was also evaluated for the provision of quality of care. Quality of care was assessed with client satisfaction, services outcome or in a logic-system process. There were diverse achievements of high quality health care for children in Brazil [ 27 ] and older people in Poland [ 28 ], for immunization, maternal health and epidemic disease control in Saudi Arabia [ 29 ], high patient satisfaction in Albania [ 30 ] and high patients perceived quality-care in privately owned institutions in Sweden [ 31 ].

From cost-effectiveness perspective, an evaluation of the cost-effectiveness of PHC projects in the USA showed that the non-physician service providers ratio were cost-effective [ 32 ]. The reason for this difference was not explicitly explained to confirm whether the variation was due to productivity or salary differences. A cost-efficiency measure of PHC in Indonesia showed that community health worker services were less costly than clinic-based care [ 33 ] because community services focus on preventive health care. A tool is important to monitor and evaluate the released fund or to generate a new fund. A new health service-related cost monitoring and evaluation tool was developed for fund raising purpose in Bangladesh [ 9 ]. High level of coordination, continuity of care and comprehensiveness of PHC in Brazil [ 34 , 35 , 36 ], high level of understanding of patient-centredness care in Uganda [ 37 , 38 ] and presence of better patient-centred care in private clinics in Thailand [ 39 ] were successes. India scaled-up comprehensive PHC using ‘Ayushman Bharat’ program in India [ 10 ].

There were diverse progress towards narrowing disparity in PHC such as reduced disparities in immigrant populations’ health [ 40 ], the presence of inclusive interventions for diverse populations with adequate government budgets in different countries [ 41 ] and promotion of health equity (e.g., include equity statement in all health policy) in Australia [ 42 , 43 ], Canada [ 44 ] and in China [ 45 ]. Furthermore, policy inclusiveness implemented in some countries through including community engagement in the policy strategy (e.g. Mexico [ 46 ], Italy [ 47 ] and Kenya [ 48 ], engagement of donor agencies and high female representation (e.g., in Nigeria [ 49 , 50 ] and the UK [ 51 ]. Additionally, community oriented and poor-based services in Asia [ 52 ] and migrant health volunteer participation in Thailand [ 53 ] indicate successful initiation to narrow the gaps. In addition, the higher service readiness has resulted in better effectiveness in Mozambique [ 54 ].

There were observed gaps as weaknesses in various countries. For instance, weak continuity care, low accessibility score of comprehensiveness of PHC and community participation in Brazil [ 34 , 55 ] and a declined continuity of care from 2012 to 2017 in England (due to the unsatisfactory appointment system for patients) [ 56 ] wear weakness. Clinics in metropolitan areas and capital cities were less comprehensive as these facilities provided more specialized care and treat medical problems referred from lower health care settings in South Korea [ 57 ]. Ineffective PHC reform due to a lack of prior or timely monitoring and evaluation procedures for PHC activities [ 58 ] and technical inefficiency in Greek [ 59 ], inefficient management in China [ 60 , 61 ], and lower level of technical efficiency in Spain [ 62 ] were weaknesses. PHC services and facility disparities based on geography, education and income status, race, ethnicity and citizenship in Sweden [ 63 , 64 ], Ghana [ 65 ], Nigeria [ 66 ], the UK [ 67 , 68 ] and the UAE [ 69 ], South Africa [ 70 ], Poland [ 71 ] and Brazil [ 7 , 27 , 34 , 72 , 73 ]. To mention, high population density area in China [ 74 ] and people live in far distance did not have access to PHC in Ghana [ 75 ]. There was lower service coverage in certified facilities compared to non-certified institutions in Philippines [ 76 ].

Strategies to improve primary health care

There are several leaderships, health workforce, technology, health financing, service delivery and contextual-related strategies and barriers. Transactional and transformational leadership styles [ 77 ] facilitated the success of PHC management system. In addition, struggling to shift from a hierarchical to a more relational style in South Africa [ 78 ] improved PHC. More comprehensive primary-care improved quality of care and efficiency in the USA [ 79 ]. Iceland approached telephone services where no telephone service difference in private and community-owned clinics [ 80 ] (Table 1 ).

Barriers of primary health care

Principles of PHC affected one another. For example, problem in ‘access’ and ‘non-comprehensiveness services’ [ 27 , 106 , 113 ], uncoordinated care in Brazil [ 113 ] and China [ 114 ] and continuity of care in China [ 114 ] impaired quality of care. Additionally, accessibility problems (unavailability and timeliness [ 115 ], financial inaccessibility) in Burkina Faso [ 116 ] affects the quality of care. Similarly, a high proportion of walk-in care and high patient volume in Canada [ 95 ], problems in accessibility and community orientation in the UK [ 117 ] interrupted continuity of care (Table  2 ). Figure  2 shows the conceptual frameworks to practice, policy, and researchers on the comprehensive PHC based on the main strategies and barriers.

Strategies and barriers of Primary Health Care. Supporting information: additional file, characteristic of studies (Table S 1 ) and UHC effective service coverage index (Table S 2 )

There was heterogeneous progress towards PHC vision. This review identified effective leadership, financial system, diagonal investment, health workforce development, expanding PHC institutions, after-hour services, telephone appointments, contracting with NGOs, a ‘Scheduling Model and a strong referral system and tools effective strategies to PHC achievement. High health care costs, client’s bad perception to health care, health workers inadequacy, language barrier and lack of quality of circle that barred PHC progress.

The leadership/governance functions greatly impacted PHC. One of its functions is working with NGOs. Working with NGOs improved PHC system because it strengthen the health system [ 142 ]. Effective leadership constructing appropriate health care infrastructure expanded municipality areas certainly improves PHC [ 143 ] because it would be inclusive to all individuals (e.g., disabled) and up-to-date technologies for health [ 144 , 145 ]. Effective leadership also allows a bidirectional management system to improve accountability, community participation and support participatory decision-making process in PHC. When people become more responsible, accountability is more likely to be kept in human mind [ 146 ]. Effective leaders are also proactive in reviewing health system policy, and monitoring and following health policy inclusiveness [ 42 , 47 ]. Countries should be curious about their health system reform because ineffective health system reform dismantled the existing PHC system [ 58 , 60 ]. Health policy reforms depends on how, when and by whom the reform is implemented, and requires public understanding and support, continuous monitoring and evaluation before, during and after implementation[ 147 ].

Expanding the municipality or institution of PHC was another effective strategy. The presence of primary health care institutions near to the community can be a prior strategy to PHC performance. It is important in reducing direct, indirect and intangible costs. Walking short distance to health institution reduce transport cost, food cost and productivity loss because clients and care giver (client supporters) can receive service shortly and return to their job. Traveling short distance to health institutions can also prevent/reduce intangible cost, which could happen if clients may not return to work for long time due to long travel. It is supported by providing low-cost services, offering outreach services, providing free transportation to the poor [ 14 , 84 ] and reaching poor geographical areas improved the accessibility of PHC [ 89 ].

A strong Health financing system supported the PHC system. Provision of free transportation to and from PHC institutions to clients (the poor) and availing low-cost services improved PHC [ 14 ]. This requires an adequate health budget and sustainable financing [ 41 , 45 ]. The diagonal investment was a successful strategy for filling the gap due to the comprehensive nature of PHC. A diagonal approach to scale-up of PHC system effectively improved maternal and child health [ 148 ]. This approach was also effective in the progress of UHC to care for chronic illness in the overall health system [ 149 ].

Adequate health workforce development accelerates PHC progresses [ 150 ]. Improving health workforce adequacy, like numbers with different skills, education, engaging interpreters and gender-concordant providers improved PHC. In a country where interpreters were included in the health workforce, PHC performance was improved. However, a PHC system should be careful in recruiting and using interpreters. For example, an interpreter may provide much information to patients with lower English proficiency at a time, but a patient may not grasp all information at once [ 151 ]. Gender-concordant health care providers improved PHC. Patient-physician gender concordance might impact patients’ perception (felt treated with respect), especially during sensitive health issues [ 152 ]. Despite its effectiveness, the disparity of PHC team composition between regions or institutions, lack of qualified health workers in the community, unbalanced population-to-physician ratio, and health workers’ lack of training interrupted the provision of continuous, coordinated and quality PHC. The absence of a quality circle interrupted PHC continuum of effective progress. In the absence of ‘quality of care circle’, there could be no way to a group of health team who meet regularly to discuss how to adhere with the standard of care, and quality of PHC is disrupted as a result [ 135 ]. Inadequate incentives for health workers also impeded accountable health care providers [ 153 ].

After-hour service is helpful when medical problems are addressed by few professionals or when health professionals are few due to high health care demands. When working hours are extended beyond eight hours per day, clients can get skilled personnel at PHC centre at any time. As a result, after-hour services reduced demand for acute care and reduced costs [ 154 ].

A ‘Scheduling Model’ improved PHC performance through accessibility and quality of PHC by which clients make an appointment to care based on their preference for the type of care and skilled personnel. It also has the power to change the perception of clients whereby clients perceived as they received better care [ 107 ]. Similarly, the probabilistic patient scheduling model was effective in a hospital by increasing annual cumulated profit, and decreasing waiting list and waiting times [ 155 ]. A scheduling model is an important procedure in a patient-referral system. Approaching this model helps primary care providers not to refer a patients to a physician with numbers of clients on the waiting queue [ 156 ].

A strong referral system shape health care system functionality and community perception of care. Moreover, the presence of referral system prevents health care service interruption [ 157 ]. In advancing technology, transition from paper-based referral to e-referral system partly solve conundrum of health workforce by using skewed physicians [ 158 ].

Telephone access and telephone appointments maintain an effective PHC system. Health technology and supply are the building blocks of health system [ 159 ]. Therefore, the absence of health technologies and lack of health system digitalisation lagged behind the successful progress of PHC [ 61 , 121 ].

The availability of appropriate tools, indicators and data supports the PHC system. Health information-related strategies allow measuring and disseminating health-related data that improves the PHC system [ 160 ]. In addition, it is known that offering tools and creating feedback mechanisms for the community reinforce the PHC system [ 161 ]. Therefore, a need to have agreed method of PHC cost measurement tool is required, for example, in Australia [ 6 ].

Community participation was an effective strategy. It is taken as a specific strategy in capacitating core principles of primary care and improving PHC outcomes. It helps to provide culturally safe care that promotes patients to attend health services for the next care [ 162 ]. Community participation improves clients’ perception towards care. In the current review, having better perception and client’s trust to health services supported PHC capacity, whereas bad perception found in contrast.

As to policy implication, a well-functioning health system—health leadership and governance, health finance, appropriate health workforce and availing proper health technology—pushes forward the PHC progress and maintains enacted PHC systems. Researchers can further examine the techniques to solve barriers and advancing emerging strategies. For example, ‘Quality of Circle’, ‘Scheduling Model’ and ‘Diagonal investment’.

Studies exclusively published in English are included in this review. This review might lack the chance of getting more advantageous by including non-English language articles. This scoping review, due to its design nature, lacks a quality appraisal of the included documents, and the current results may need caution in interpretation. Furthermore, a search from a single academic database (PubMed) may miss some important articles in other databases.

A country with a higher UHC effective service coverage index does not reflect its effectiveness in all aspects of PHC. Strengths of PHC are less costly community health workers services, presence of quality indicators and improved quality of care (e.g., maternal and child health), increased health care coverage, improvement of health outcome due to community participation, provision of comprehensive care and improved resource and service efficiency.

PHC is, beyond the technical practice given at health care spots, a system thinking that entertains multiple strategies towards health system impacts. Continues investment in PHC infrastructure, sustainable financing to reduce health care costs, appropriate workforce planning and training, construction of new PHC institutions in regions of low accessibility and institutionalizing quality of circle will accelerate PHC progress. A valid and agreed measurement tool for PHC attributes is also relevant. Additionally, the research did not address the wholistic concepts of PHC; almost all studies on PHC were only on integrated public and essential health services.

Availability of data and materials

The data set is available within this manuscript.

Abbreviations

Non-governmental Organisations

Primary Health Care

United Kingdom

United States of America

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AE and YA conceptualised the study design, retrieved relevant articles, screening and data extraction, analysed, interpreted the results, and drafted the manuscript. RBK and DE contributed to the research aim and manuscript draft, and critically revised the drafted manuscript. AZ, EW and FN contributed to critically revised the drafted manuscript. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1:.

Supplementary file 1. Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviewschecklist.

Additional file 2:

Table S1. Characteristics of articles.

Additional file 3:

Table S2. UHC effective service coverage index for countries included in the review.

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Endalamaw, A., Erku, D., Khatri, R.B. et al. Successes, weaknesses, and recommendations to strengthen primary health care: a scoping review. Arch Public Health 81 , 100 (2023). https://doi.org/10.1186/s13690-023-01116-0

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DOI : https://doi.org/10.1186/s13690-023-01116-0

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Implementing the primary health care approach: a primer

This Primer is about the 'how' of primary health care (PHC) and brings together best practices and knowledge that countries have generated through 'natural experiments' in strengthening PHC with the best available research evidence. Despite the progress made towards PHC globally, the concept is still often misunderstood, even within the public health community. The Primer offers a contemporary understanding of PHC and more conceptual clarity for strengthening PHC-oriented health systems. It does so by consolidating both scientific evidence and an extensive sample of practical experiences across countries for the needed evidence to address practical implementation issues. 

The Primer is organized in three parts. Part I explains the PHC approach, its history, core concepts and rationale, and draws out lessons for transformation. Part II addresses operational and strategic levers that make PHC work. It covers governance, financing and human resources for health, medicines, health technology, infrastructure and digital health, and their role in implementing change. Part III concludes with a cross-cutting view of the impacts of PHC on the health system, efficiency, quality of care, equity, access, financial protection and health systems resilience, including in the face of climate change. 

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Commitment to Primary Health Care (PHC) has been reinvigorated in recent years by the 2018 Declaration of Astana and the World Health Assembly resolution of recognizing its role in providing the full range of health services needed throughout the life course, including prevention, treatment, rehabilitation and palliative care.

Why is this an important issue?

PHC has three essential elements. Firstly, the delivery of primary care, that is the delivery of integrated health services. Secondly, PHC is made possible through actions and policies that cut across different sectors – such as health, agriculture and financing. At the heart of PHC, and thirdly, are empowered people and communities supporting the drive for better health.

PHC is a whole-of-society approach to health. It aims to ensure the highest possible level of health and well-being and their equitable distribution by focusing on people’s needs and preferences as early as possible along the continuum of care and as close as feasible to people’s everyday environments. It can improve equity by reaching disadvantaged groups with essential health services. These services are close to communities and can help to identify changing needs among service users, increasing the resilience of health systems.

Alliance track record

We now have a firm global commitment to PHC, what is needed is greater evidence and guidance to enable us to address the financial, infrastructural, political and technical challenges of policy implementation in different LMIC settings.

The Alliance has demonstrated its commitment to PHC in mutually reinforcing ways. We have brought together policy-makers and other stakeholders from low- and middle-income countries to form consensus on priority areas for support and research. We have funded the generation of new knowledge to track progress and identify gaps in our understanding of how to strengthen PHC. Finally, we have brought thinkers together to publish their research.

In 2015 the Alliance convened a group of PHC experts to explore the dynamic processes and contextual factors that impact health systems performance. Presentations on experiences and lessons learned from decentralized health systems and health reforms in Thailand, Brazil, India and Malawi were shared. In 2017 a rapid scoping review of PHC research in LMIC was conducted. This created a research agenda focused on: Quality, safety and performance; PHC policies and governance; organization and models of care and; financing for PHC. Policy-makers and researchers were brought together to identify areas where new knowledge is needed and potential research questions.

This consultation led to the creation of Primary Care Systems Profiles and Performance (PRIMASYS) to develop case studies to support policy-makers, practitioners, development agencies, and other similar entities think strategically about health systems issues in relation to PHC. It also catalysed the publication of a ground-breaking Supplement of BMJ Global Health - Strengthening Primary Health Care Through Research: Prioritized knowledge needs to achieve the promise of the Astana Declaration.

More recently, in 2020, a new project - Embedded primary health care research to engage communities and build learning health systems – led to two regional consultations with policy-makers representing more than 20 countries. In part this responds to the COVID-19 pandemic and the need for health systems to build and develop the ability to learn in their responses, address the broader determinants of health, as well as engage communities in efforts to mitigate and contain the outbreak.

Future commitment/call to action

Building PHC for the future will require clear regional and national coordination and work across sectors such as water and sanitation, urban development and design, commercial regulation, education. These efforts should have equity, and a gendered approach at their core.

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The Importance of Primary Care Research in Understanding Health Inequities in the United States

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Eliminating health and health care inequities is a longstanding goal of multiple United States health agencies, but overwhelming scientific evidence suggests that health and health care inequities persist in the United States, despite decades of research and initiatives to alleviate them. Because of its comprehensiveness, studying health inequities in the context of primary care allows for the use of multiple paradigms and methodologic approaches to understanding almost any state of health, disease, social challenge, or societal circumstance a patient or group of patients might face. We argue in this special communication that the many features/advantages of primary care research have valuable contributions to make in reducing health inequity, and scientists, journals, and funders should increase the incorporation of primary care approaches and findings into their portfolios to better understand and end health inequity.

• Delivery of Health Care
• Health Care Disparities
• Primary Health Care

Health inequities are differences in health status or the distribution of health resources between different population groups, arising from the social conditions in which people are born, grow, live, work and age. 1 Eliminating health and health care inequities is a longstanding stated goal of multiple United States health agencies, but overwhelming evidence suggests that these inequities persist in the United States, despite decades of research and initiatives to alleviate them. This stasis has led to calls for advancement in health inequities research methods and content by several US federal organizations. In 2012, the National Institutes of Health (NIH) convened a summit calling for a broadening of approaches to address health inequities, 2 and the National Institute of Minority Health and Health Disparities (NIMHD) has led visioning exercises to identify health inequity research priority areas. 3 , 4 While these renewed calls are needed, there are still gaps to better study health inequity. Overall, US health inequities research has been frequently described as a subdiscipline of public health research, 5 and major federal health inequities initiatives have relied on surveys initially developed around the mid-20 th century. 6 While a survey-based, public health approach benefits understanding region and society-wide trends and intervention efforts to reduce inequities, definitive progress on fully understanding and eliminating health inequities remains unfulfilled. An essential avenue for understanding and addressing health care inequities may be to more directly observe how vulnerable populations interact with the US health care system. Primary care providers are the front door to this system-even in a nation without universal primary care access- to which a wide swath of the United States, including vulnerable populations, access at multiple points throughout their life. 7 , 8 The addition of primary care research perspectives, approaches, and data into health inequities research may be a crucial step toward understanding, improving, and ultimately helping end health inequity in the United States.

• The What and Why of Primary Care Research

Primary care is first contact health care that is comprehensive, continuous, and coordinated. 9 Primary care research is research done in the primary care environment, 10 therefore, involving primary care patients, practitioners, perspectives, and priorities. Because of its comprehensiveness, studying health inequities in the context of primary care allows for the use of multiple paradigms and methodologic approaches to understanding almost any state of health, disease, social challenge, or societal circumstance patients might face. Further, while most research methods can be used in primary care, some methods such as pragmatic trials, 11 , 12 dissemination and implementation research, 13 and patient-investigator partnerships 14 are especially appropriate for primary care settings. Primary care delivery will not solve inequity alone, but observational and interventional research in the primary care setting is an essential and overlooked piece of the science to understand and reduce health inequity. Research in the primary care setting is a window that displays disease and health care and a wide representation of the issues relevant to inequity: the experience of violence, poverty, addiction, racism, cultural factors, and disadvantage, among others, throughout a lifetime. 7 , 8 , 15 , 16 The beneficial relationships forged in primary care 17 , 18 may, in part, start to mitigate the effects of violence perpetrated by researchers in the past. 19 There have been calls to examine inequities over the life course, 20 and primary care disciplines, especially family medicine, are well-positioned to do this given their comprehensiveness in scope.

The Reach of Primary Care for Health Inequities Research

For the researcher interested in health inequities research, a context-specific discipline might elicit sampling concerns: does the US primary care environment contain enough patients experiencing inequities to produce meaningful understanding on these issues? Is not studying those in the US primary care environment just the study of care quality for a subpopulation with unlimited access to resources and all the health care they need? Are vulnerable people—with poor access to services and resources—represented in a context that requires access a priori ? Historically, in the United States, these questions may have resulted in caution in evaluating health inequities in primary care settings, but this is rapidly changing. Even in a society that does not have universal health care coverage, a large proportion of the population does have contact with primary care providers; in national surveys, more than 85% of US individuals, across demographic groups, have at least some usual source of care (doctor's office or clinic/health center—not the emergency department). 21 Specifically, vulnerable and marginalized populations do see primary care providers, especially in the nation's network of community health centers (CHCs). CHCs (clinics receiving federal funding to provide comprehensive primary care) serve ∼30 million patients in the United States, approximately 10% of the country, regardless of citizenship, income, insurance status, language spoken, or other socioeconomic criteria, and especially serve low-income patients and racial/ethnic minorities. 8 Whether a patient accesses a CHC or not, numerous primary care networks, many of them now interconnected, widely represent those who might experience health inequities. For instance, primary care practices nationwide are increasingly part of data-connected networks – research networks, networks with shared administrative resources, and networks that share electronic health records and their functionalities for innovation and data aggregation. 22 , 23 These networks join the existing core resource of practice-based research networks (PBRNs) in primary care. 24 Though large connected primary care networks (data networks and PBRNs) may not have the representativeness of national surveys, they contain large patient samples with richer information on objectively measured health outcomes, care utilization, and increasingly, robust social determinants of health data. 25 All this is routinely collected in primary care clinics, which is challenging to collect or subject to recall bias in public health surveys. Amid calls for the integration of social care and the evaluation of social determinants of health into health care, 26 , 27 and calls for multi-level and “complex system analysis reflective of real-world settings” 4 to better understand inequity, these reports have missed an opportunity to explicitly recommend primary care research as a viable and necessary response to these calls. The primary care setting sits at the nexus of complex system factors, is already in the “real world” and therefore may have enhanced external validity, is where most social needs are witnessed in health care, and is where research into these aims is likely to be most effective. In addition, primary care data are already multi-level and routinely collected: multiple visit observations for a patient over time, patients nested within providers, providers nested within clinics, and clinics nested in neighborhoods, cities, and states. 22 , 25

• Recommendations to Improve Health Inequity Research

Researchers interested in US health inequities should consider primary care settings as a crucial avenue for understanding the full picture of health inequity and developing real-world interventions to end this inequity. The published opportunities of the NIMHD Health Disparities Science Visioning Initiative 3 all rely on studying the primary care environment. Still, primary care is not explicitly mentioned in this list. We would continue the call for an enhanced partnership between primary care and public health in a manner that leverages the research strengths of both fields to take advantage of these opportunities optimally. This outcome would mean a concerted and longitudinal integration of national US survey data with primary care-related datasets to even more fully capture the exposures, experiences, and care of those most at risk for poor health outcomes. Second, it would mean sustained collaboration in developing and testing scalable health-related interventions that span boundaries: boundaries between regions, care settings, and between “community” and “health care” settings. In the long-term, funding agencies and health systems could invest even more in primary care centered networks to continue building data sources that have the potential to aggregate significant data on the longitudinal experience and outcomes of vulnerable populations over the entire life course. While Congress has designated the Agency for Health Care Research and Quality (AHRQ) as the “principal source of funding for primary care research,” the AHRQ's 2021 budget was 0.5% of the NIH's budget, 28 , 29 and a very small proportion of the NIH budget is awarded to disciplines in primary care research. 30 In response to all these issues, we make the following recommendations:

Funding agencies in the United States should increasingly fund research projects that utilize broad primary care settings to study health inequity.

Journal editorial boards should recognize the importance, scientific merit, and enhanced external validity of utilizing primary care settings in health inequity research. They should prioritize the inclusion of primary care researchers—especially those with experience in health equity research— on board rosters.

Researchers should consider multi-level, etiologic, and complex system analyses 4 and understand that primary care sits at a nexus of multi-level investigations into health inequity (primary care is the bridge between biology, behavior, health care, and community); researchers should utilize the existing multi-level data in primary settings and networks for observational and intervention studies.

Primary care providers treat and health inequities affect every organ, every system, every malady, in every family, and every community. Primary care researchers, along with public health researchers, may bring about understanding and intervention to end health inequity in the United States together.

• Acknowledgments

The authors would acknowledge our home institutions and the patients and staff of the OCHIN Practice-Based Research Network, who support our work in general.

This article was externally peer reviewed.

Funding: National Institute on Aging and National Institute on Minority Health and Health Disparities.

Conflict of interest: None.

To see this article online, please go to: http://jabfm.org/content/33/5/849.full .

• Received for publication February 12, 2021.
• Revision received April 12, 2021.
• Accepted for publication April 13, 2021.
• 1. ↵ World Health Organization . 10 Facts on Health Inequities and their Causes . Available from: https://www.who.int/features/factfiles/health_inequities/en/ . Published 2017 . Accessed December 2019 .
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• 6. ↵ National Center for Health Statistics . Healthy People 2020 Data Issues 2015 . Available from: https://www.cdc.gov/nchs/healthy_people/hp2020/hp2020_data_issues.htm . Published November 6, 2015 . Accessed November 1, 2020 .
• Stange KC ,
• Flocke SA ,
• Miller WL ,
• Crabtree BF ,
• Zyzanski SJ
• 8. ↵ National Association of Community Health Centers . Community Health Center Chartbook . Available from: http://www.nachc.org/wp-content/uploads/2019/01/Community-Health-Center-Chartbook-FINAL-1.28.19.pdf . Published 2019 . Accessed November 1, 2020 .
• 9. ↵ World Health Organization . Primary Health Care . Available from: https://www.who.int/news-room/fact-sheets/detail/primary-health-care . Published April 1, 2021 . Accessed June 15, 2021 .
• Starfield B
• Carroll JK ,
• Dickinson LM ,
• Eichner B ,
• Michaels LAC ,
• Heintzman J ,
• Crosson J ,
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• Warren NT ,
• Gaudino JA Jr . ,
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• Bradley B ,
• Cottrell EK ,
• Hendricks M ,
• Dambrun K ,
• Blumenthal DME ,
• Nutting PA ,
• Goodwin MA ,
• Zyzanski SJ ,
• 19. ↵ Office of Environment, Health, Safety and Security, US Department of Energy . Chapter 12: the iodine 131 experiment in Alaska . In: Final Report of the Advisory Committee on Human Radiation Experiments . Washington, DC : US Government Printing Office ; 1995 . (Stock no. 061-000-008489) Available from: https://bioethicsarchive.georgetown.edu/achre/final/chap12_4.html . Accessed March 29, 2021 .
• Gilman SE ,
• Geronimus AT
• 21. ↵ Centers for Disease Control and Prevention . Early Release of Selected Estimates Based on Data From the National Health Interview Survey, January–September 2017 . Available from: https://www.cdc.gov/nchs/data/nhis/earlyrelease/EarlyRelease201803.pdf . Published March, 2021 . Accessed November 1, 2020 .
• Cottrell E ,
• 23. ↵ Kaiser Permanente Center for Health Research . Data & Analytics Expertise . Available from: https://research.kpchr.org/About/Capabilities/Data-Analytics-and-Expertise . Published 2018 . Accessed November 1, 2020 .
• 24. ↵ Agency for Healthcare Research and Quality . Practice-Based Research Networks . Available from: https://pbrn.ahrq.gov/about . Accessed March 25, 2021 .
• Bazemore AW ,
• 26. ↵ Lancet Editorial Staff . No health care without social care . The Lancet 2019 ; 394 : 1206 . OpenUrl
• 27. ↵ National Academy of Medicine . Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health . Available from: http://nationalacademies.org/hmd/Reports/2019/integrating-social-care-into-the-delivery-of-health-care . Published September 25, 2019 . Accessed November 1, 2020 .
• 28. ↵ American Institute of Physics . Final FY21 Appropriations: National Institutes of Health . Available from: https://www.aip.org/fyi/2021/final-fy21-appropriations-national-institutes-health2021 . Published November 19, 2020 . Accessed March 25, 2021 .
• 29. ↵ Agency for Healthcare Research and Quality . Budget Estimates for Appropriations Committees, Fiscal Year 202 . Available from: https://www.ahrq.gov/cpi/about/mission/budget/2021/index.html . Published February 2021 . Accessed March 25, 2021 .
• Cameron BJ ,

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Why Primary Care Providers Are Failing With Value-Based Contracts

Analysis  |  By Christopher Cheney    |    September 03, 2024

With quality measure saturation in value-based contracts, many primary care physicians feel set up to fail.

One of the primary criticisms of payers' value-based contracts is that there is little to no coordination of quality measures for which clinicians are held accountable. In addition, value-based contracts have been adopted for quality improvement in primary care despite mixed evidence of their positive impact.

A research letter published recently by JAMA Health Forum found that primary care providers at Providence have been saddled with an overwhelming number of quality measures in value-based contracts. The research features data collected from more than 800 primary care providers from 2020 to 2022.

The research letter includes the following key findings:

• Value-based contracts contained a mean of 10.24 quality measures.  
• Primary care physicians faced a mean of 57.08 quality measures across 7.62 value-based contracts.  
• Medicare value-based contracts had more quality measures than commercial or Medicaid value-based contracts. The mean number of quality measures in Medicare value-based contracts increased from 13.14 in 2020 to 15.04 in 2022.

"Value-based contracting is intended to incentivize care improvement, but it is unlikely a clinician or practice can reasonably optimize against 50 or more measures at a time," the co-authors wrote.

Impact of quality measure saturation

"We were shocked by what we found," said Ari Robicsek , MD, chief analytics and research officer at Providence. "Effectively, the level of industry disorganization leads to a situation where individual physicians have way more quality metrics than they can possibly be expected to manage."

Robicsek compared the situation to the Olympic biathlon event, where competitors cross-country ski as fast as they can, then stop to try to hit targets with a rifle.

"Primary care physicians are expected to provide care to as many patients as they possibly can and deal with their patients' problems in the office," Robicsek said, "then all of a sudden, the physicians have to interrupt the flow of patient care to hit targets on quality metrics."

Most primary care practices are not configured to do both things simultaneously, according to Robicsek.

"It is disruptive, frustrating, and ultimately, when you have a large number of quality metrics, they become white noise," Robicsek said. "The quality metrics either become ignored or they create a sense for providers that they are set up to fail."

One way is to put the burden of the work on administrators, which requires hiring staff to look at lists of patients who have not yet received their breast cancer screening. Administrative staff will reach out to these patients, communicate back and forth with the patients if they have questions, place the screening orders, and follow up to make sure the screening was conducted.

"There is administrative cost to hitting a target on a quality metric this way," Robicsek said.

The other way is to expect primary care physicians to hit quality metric targets as part of their daily workflow. Under this system, when patients come in to see primary care physicians, the physicians must talk to the patients about quality metrics such as breast cancer screening or diabetes screening.

"The challenge is that primary care physicians are working with patients to address their problems in relatively short visits, then we are expecting them to fit in the administrative task of hitting quality metrics," Robicsek said.

Instead of expecting primary care physicians to remember to "close gaps" in quality measures, many practices have reminders in the electronic medical record (EMR) to prompt physicians to talk with their patients about quality metrics such as breast cancer screening. As the doctor is trying to go through their workflow and the patient wants to talk about a particular clinical problem, the EMR interrupts with quality metrics that need to be addressed.

Quality metric saturation contributes to physician burnout, according to Robicsek.

"If you feel set up to fail, and you feel that you can't do your job, that is going to contribute to feelings of moral distress and exhaustion," Robicsek said.

Reforming value-based contracts

"There is no way that any physician is going to be able to manage 57 different quality metrics in their practice, while also trying to be a doctor," Robicsek said.

In principle, quality metrics that create incentives to provide great care are a good thing, but the current execution of quality metrics in value-based contracts is counterproductive, Robicsek explained.

"When they are done in a completely uncoordinated way such that a physician has way more metrics than can possibly be salient in their day-to-day practice," Robicsek said, "you do something worse than having no quality metrics at all."

The first step in the redesign process would be coordination among the different payers about what a limited set of quality metrics would be for use across the board for primary care physicians, according to Robicsek.

"Ideally, metrics would be chosen where there is an evidence base that demonstrates that incentives for metrics improve outcomes for patients," Robicsek said. "There also needs to be an evidence base suggesting that the set of metrics when used simultaneously benefits patients."

Christopher Cheney is the CMO editor at HealthLeaders.

KEY TAKEAWAYS

Primary care physicians at the Providence health system face 57 quality measures in their value-based contracts.

The strain of managing dozens of quality measures contributes to physician burnout.

There is an urgent need for payers to coordinate quality measures in value-based contracts.

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Trends in clinical workload in UK primary care 2005–2019: a retrospective cohort study

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Background Substantial increases in UK consulting rates, mean consultation duration, and clinical workload were observed between 2007 and 2014. To the authors’ knowledge, no analysis of more recent trends in clinical workload has been published to date. This study updates and builds on previous research, identifying underlying changes in population morbidity levels affecting demand for primary health care.

Aim To describe the changes in clinical workload in UK primary care since 2005.

Design and setting Retrospective cohort study using GP primary care electronic health records data from 824 UK general practices.

Method Over 500 million anonymised electronic health records were obtained from IQVIA Medical Research Data to examine consulting rates with GPs and practice nurses together with the duration of these consultations to determine total patient-level workload per person–year.

Results Age-standardised mean GP direct (face-to-face and telephone) consulting rates fell steadily by 2.0% a year from 2014 to 2019. Between 2005 and 2019 mean GP direct consulting rates fell by 5.8% overall whereas mean workload per person–year increased by 25.8%, owing in part to a 36.9% increase in mean consultation duration. Indirect GP workload almost tripled over the 15 years, contributing to a 48.3% increase in overall clinical workload per person–year. The proportion of the study population with ≥3 serious chronic conditions increased from 9.7% to 16.1%, accounting for over a third of total clinical workload in 2019.

Conclusion Findings show sustained increases in consulting rates, consultation duration, and clinical workload until 2014. From 2015, however, rising demand for health care and a larger administrative workload have led to capacity constraints as the system nears saturation.

• consultation
• primary care
• staff workload
• electronic health records
• retrospective study
• Introduction

Strong primary care is associated with better population health, lower healthcare expenditure, and a more equitable distribution of health resources. 1 In the UK, primary care plays an essential role in the provision of health care, accounting for approximately 90% of all NHS contacts. 2 Although NHS activity data indicate that general practices delivered a record 356 million appointments in 2023, demand continues to outstrip capacity. 3 A recent survey reported that 71% of GPs in the UK found their job to be very or extremely stressful, with the highest proportion among the 10 high-income countries surveyed. 4

Fears that primary care in the UK is in crisis or nearing breaking point are nothing new. 5 Although pressures on general practice were undeniably exacerbated by the COVID-19 pandemic, the current situation is the outcome of many years of underinvestment, a shrinking of the GP workforce, an ageing and growing population, and national strategic objectives that sought to shift care out of hospitals and into the community. Analysis of 2023 workforce data showed an 11.8% fall in the number of full-time equivalent (FTE) GPs (excluding locums, trainees, and retainers) and a 41% increase in the number of patients per FTE GP since 2014. 1 , 6 The proportion of NHS funding directed to general practices declined from 10.6% in 2005/2006 to 6.8% in 2020/2021 as secondary care services secured a greater share of increases in healthcare spending. 7 The UK population increased by 11.4% over the same period and its median age rose from 38.7 to 40.7 years. 8

Analysis of a large database of electronic health records described a 10.5% increase in annual consultation rates per person between 2007 and 2014, mainly accounted for by an increase in GP consultations. 9 The same period also saw an increase in consultation duration. In cross-sectional analysis, consultation rates were higher in older patients, females, and those living in more deprived regions. 10 A similar analysis of duration found GP consultations were longer in older patients and females, although the differences were small. 11 The focus of much of the literature on GP workload is on direct patient care, an activity that typically accounts for 75% of patient-related clinical workload. 12 Time spent on indirect patient care (for example, referral letters or repeat prescriptions) was not included, implying that primary care workload data may under-represent total patient-related clinical activity by a third.

How this fits in

Many questions remain unanswered. There is limited understanding of the factors driving long-term trends in consultation rates. The aim of this analysis of the volume and nature of GP and practice nurse consultations was to obtain objective data on changes in clinical workload between 2005 and 2019. Overall clinical workload over time, workload by clinical role, and by multimorbidity level are examined.

Study design

A retrospective cohort study was carried out using data obtained from IQVIA Medical Research Data UK (IMRD) incorporating data from The Health Improvement Network, a Cegedim database. IMRD includes anonymised electronic primary health care records from approximately 6% of the UK population in over 800 UK general practices. General practices are largely representative of UK primary care practices in size, age, and the sex of patients, and prevalence of chronic conditions. 13

Data were extracted for all patients registered with practices contributing to IMRD, covering the period 1 January 2005 to 31 December 2019. Data extraction was facilitated using the Data Extraction for Epidemiological Research (DExtER) tool. 14

The primary outcome is individual patient clinical workload, defined as the total number of contact minutes per year that the patient has with their general practice, coded by staff role and by type of contact. A GP contact is defined as any file opening by a GP and includes face-to-face consultations, telephone calls to or from a patient, results recording, or issuance of a repeat prescription. Similarly, a nurse contact is defined as any file opening recorded as being made by a practice nurse. Patient consultations with nurses are mainly separate from those with doctors. In the UK, primary care nurses’ responsibilities include immunisation, cervical screening, health promotion, and chronic disease management. 15 All non-clinical work by a GP or practice nurse was excluded from the workload calculations, as was any work done by other clerical or administrative staff or other providers of direct care such as physiotherapists or dieticians.

File openings of 0 min have been rounded up to 30 s. File openings of ≥30 min were truncated at 30 min as long openings were considered unlikely to reflect patient work. Consultation rates are defined as the number of times a patient’s file is opened per person–year, by a nurse or a GP. Consultation rates for direct patient contacts (face-to-face surgery consultations and telephone consultations) are also reported. Clinical workload per person–year is defined as the sum of all GP and nurse contact minutes for a given patient in a given year.

Multimorbidity status

Information about patients’ long-term conditions was obtained from IMRD with medical diagnoses of these conditions recorded using the Read code clinical classification system. Read codes are a hierarchical clinical terminology system used within both primary and secondary care to record a wide range of information relating to a patient’s demography, symptoms, tests, results, and diagnoses.

Previous work by Barnett et al identified 40 long-term conditions that had a significant impact on a patient’s quality of life, risk of mortality, and need for health care. 16 In the current study the code lists associated with each of these conditions as determined by a multimorbidity research joint project between the Universities of Cambridge and Birmingham was used. 17

Consulting patterns from 2015 until 2019 were examined, comparing individual workload at 1-year pre-diagnosis to workload 1 year, 3 years, and 5 years post-diagnosis for each condition to determine the length of time that conditions should be shown as present following diagnosis.

Person–years for each age group were calculated for each year. Workload per person–year and consultation rates were age standardised to the population of the 2005 IMRD dataset to allow comparison over time. Mean annual clinical consulting rates and mean duration of file openings were calculated for all types of consultations with a GP, face-to-face and telephone consultations with a GP, and consultations with a practice nurse. Patients were grouped according to how many chronic conditions they had (0, 1, 2, and ≥3 conditions) and average workload per person–year calculated for each group over the period. Summary statistics are presented in the following section, either graphically or in tables.

Overall, data for over 550 million file openings for 10 098 454 patients from 824 practices were examined in this study, representing over 69 million person–years of observation. Descriptive statistics are given for 2005 and 2019 ( Table 1 ).

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Descriptive statistics of dataset

A comparison of the population by age group for the dataset and for the UK population as a whole in 2005 and 2019 shows that the sample is broadly similar to UK national data obtained from the World Bank databank. 18 For example, in 2005, 59.7% of the UK population was aged <45 years compared with 55.5% for the sample ( n = 2 861 740/5 159 933). In 2019, 55.7% of the UK population was aged <45 years compared with 50.2% for the sample ( n = 1 399 167/2 785 796) (see Supplementary Table S1).

GP face-to-face/telephone consulting rates

After an initial drop in the age-standardised mean consulting rate, rates climbed to a high of 3.84 (95% confidence interval [CI] = 3.84 to 3.85) direct consultations per year in 2014. From 2014 mean consulting rates fell steadily by 2.0% a year to 3.47 (95% CI = 3.46 to 3.47) consultations per year by 2019. Between 2005 and 2019 mean consulting rates fell by 5.8% overall (see Supplementary Figure S1).

Duration of file openings

Duration of file openings by practice nurses increased at a relatively constant rate over the period from a mean of 6.83 (95% CI = 6.82 to 6.83) min in 2005 to 8.99 (95% CI = 8.98 to 9.00) min in 2019, a rise of 31.7% overall (see Supplementary Figure S2).

For GP face-to-face or telephone consultations, mean duration increased by 36.0% between 2005 and 2011. From 2011 onwards, the rate of increase in mean duration of GP face-to-face consultations plateaued, remaining between 8.21 (95% CI = 8.21 to 8.21) min and 8.46 (95% CI = 8.45 to 8.46) min until 2019. The biggest change was in all GP file openings where mean duration increased by 68.4% from 4.57 (95% CI = 4.57 to 4.57) min in 2005 to 7.69 (95% CI = 7.69 to 7.70) min by 2019 (see Supplementary Figure S2). From 2005 to 2019, mean duration of GP direct consultations increased by 36.9% overall.

Clinical workload

Age-standardised mean clinical workload per person–year increased by over 48% from 39.06 (95% CI = 39.03 to 39.10) min in 2005 to 57.61 (95% CI = 57.55 to 57.66) min in 2014. From 2014 to 2019 it remained relatively stable, fluctuating between 56.98 (95% CI = 56.93 to 57.03) and 57.98 (95% CI = 57.93 to 58.03) min ( Figure 1 ).

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Mean age-standardised workload per person–year by staff role. F2F = face to face.

GP workload

In the study, GP workload was separated into two parts: patient-facing workload (GP F2F: all face-to-face consultations and telephone consultations) and patient-related administrative work (GP admin). Mean GP F2F workload per person–year increased every year between 2005 and 2012 to a maximum of just under 33 min. From 2012 to 2019 it fell by 10.6% to just below 30 min. Mean GP admin workload stood at 4.60 (95% CI = 4.60 to 4.60) min per person–year in 2005 rising to 12.53 (95% CI = 12.52 to 12.55) min by 2019, an increase of 172.4%. Administrative workload as a proportion of total GP workload nearly doubled from 16.3% in 2005 to 29.6% in 2019 ( Figure 1 ).

Practice nurse workload

Age-standardised mean practice nurse workload per person–year rose consistently over the period from 10.75 (95% CI = 10.74 to 10.76) min in 2005 to 15.58 (95% CI = 15.56 to 15.59) min in 2019, an increase of 44.9% overall ( Figure 1 ).

Changes over the period in age-standardised mean workload by staff role and type of consultation are shown in Table 2 .

Age-standardised mean workload (in minutes per person–year) by staff role

Multimorbidity levels

Analysis of the impact of a diagnosis on workload found that for most conditions clinical consultation time increased considerably in the year of diagnosis compared with the year before diagnosis, however, consultation time returned to below pre-diagnosis levels within 5 years. For 11 conditions, consultation time increased considerably in the year of diagnosis and remained at a higher level even after 5 years. These conditions were coded to show as present indefinitely, whereas all the other conditions were coded to show as present for 5 years only following diagnosis (see Supplementary Information S1 for details).

Multimorbidity increased across all older age groups between 2005 and 2019 ( Figure 2 ). Overall, 51.5% of the study population had no serious chronic conditions recorded in 2005 and accounted for 27.9% of total clinical workload. Patients with multimorbidity with ≥3 serious chronic conditions represented just 9.7% of the study population but 24.2% of the workload ( n = 499 998/5 159 933). By 2019 the share of the population without any serious chronic conditions had fallen to 43.6% whereas that for patients with multimorbidity with ≥3 conditions had increased to 16.0% ( n = 447 060/2 785 796). The share of total clinical workload accounted for by these patients was 34.5%.

Prevalence of chronic conditions by age group. a) 2005; and b) 2019.

The mean clinical workload associated with patients with no chronic conditions was 21.71 (95% CI = 21.67 to 21.75) min in 2005. Clinical workload increased linearly with the number of chronic diseases: mean workload was 41.13 (95% CI = 41.04 to 41.22) min for patients with one condition, 62.54 (95% CI = 62.37 to 62.71) min for two conditions, and 97.14 (95% CI = 96.86 to 97.42) min for ≥3 conditions. In 2019, mean workload was 31.08 (95% CI = 30.99 to 31.16) min, 54.83 (95% CI = 54.67 to 54.99) min, 79.73 (95% CI = 79.45 to 80.00) min, and 131.03 (95% CI = 130.63 to 131.42) min for 0, 1, 2, and ≥3 conditions, respectively ( Figure 3 ).

Clinical workload by number of chronic conditions.

The rate of increase in clinical workload per person–year over the study period was highest for patients with no chronic conditions at +43.2%, compared with a +33.3% increase in workload for those with one condition, +27.5% for two conditions, and +34.9% for ≥3 conditions ( Figure 3 ).

This study examined trends in consulting rates and duration of consultations for GPs and practice nurses from 2005 to 2019. To capture the full scope of patient-level activity, all aspects of GP workload were studied: both time spent in face-to-face and telephone consultations as well as patient-related administrative work, such as results recording or third-party consultations. Direct patient workload has considerably increased over the period for both GPs and practice nurses by roughly the same amount. However, the amount of time spent by GPs doing patient-related administrative work has increased enormously.

Many factors are likely to have contributed to the increased admin workload of GPs observed over the study period, including the increased ability of GPs to access diagnostic services directly, the transfer of work from secondary to primary care, as well as the introduction of the Quality and Outcomes Framework (QOF) in 2004. 19 , 20 The QOF is a pay- for-performance scheme intended to reward primary care providers for improvements in the management of long-term conditions, representing over 8% of total practice income on average in 2019/2020. 21 Little existing literature on the QOF examines its impact on administrative work undertaken by GPs. There is evidence to suggest, however, that its introduction led to a substantial increase in non-consultation GP workload, in particular that associated with tests. A study of changes in diagnostic testing in UK primary care reported a 3.3-fold increase in test use between 2000/2001 and 2015/2016, and estimated that the average GP spent 1.5 to 2 h each day reviewing test results. 22

The current study recorded a plateauing of the rate of increase in clinical workload from 2014 onwards, with the higher levels of GP admin workload making up for the decline in the GP face-to-face or telephone consulting rate.

Strengths and limitations

The main strength of this study is that it is the first, to the authors’ knowledge, to report on trends in overall clinical workload, examining duration and frequency of clinical consultations, for both patient-facing and administrative activity related to a direct patient contact, such as a repeat prescription or recording of test results. Its findings are based on nearly 70 million person–years of observation covering a 15-year period for practices throughout the UK, making it, to the authors’ knowledge, the largest analysis of clinical workload to date.

This study has several limitations. The most important limitation is that it was not possible to include data from 2020 onwards in the analysis. However, it was felt that the considerable disruption in primary care use during the COVID-19 pandemic was unlikely to be permanent and consequently that the use of data from that period and shortly after would not be representative of any underlying trend. Although the IMRD database is one of the most comprehensive data sources worldwide, as is the case for many observational studies using electronic health records, the accuracy of the recording of consultation durations and types is variable. Short file openings for face-to-face consultations may not accurately reflect the actual work associated with a particular patient if the practitioner does not open the file at the beginning of a consultation, underestimating workload. Similarly, workload will be overestimated if a practitioner forgets to close a patient’s file at the end of the session (all consultations were truncated at 30 min to mitigate this problem).

The list of chronic conditions selected to determine morbidity levels is based on highly regarded previous work: the Read codes used to define these conditions for the present study closely mirror those used by Barnett et al and Cassell et al but may differ slightly. 16 , 23 Using a different set of conditions may have given different results in terms of prevalence and workload associated with the different levels of multimorbidity.

Comparison with existing literature

This study supports previous literature that showed an increase in face-to-face and telephone GP and practice nurse workload between 2007 and 2014 in English general practices, observing both a rise in the mean number of consultations per year and a 4.9% increase in consultation duration. 9 Research by Kontopantelis et al described an increase in the number of GP consultations per year from a median of 5.3 to 8.3 between 2000 and 2019, whereas the number of face-to-face GP consultations per year per patient fell from 3.7 to 3.1. 24 Analysis of the use of primary care by children in England reported a fall in general practice consulting rates of 1% per year in all age bands (except for infants) between 2007 and 2017 while observing a corresponding rise in urgent care use. 25

Whereas literature examining overall trends in clinical workload is scarce, considerable research has examined the association between primary care use and multimorbidity. The crude prevalence rate of multimorbidity (defined as the presence of ≥2 long-term conditions) was 31.6% in the present study in 2019 compared with 22.5% in 2005, rates that are broadly consistent with previous studies of similar populations in the UK. 16 , 23 Multimorbid patients consulted a GP 2.6 times more frequently 23 and each consultation lasted 0.2 min longer on average than for patients without multimorbidity. 26 Using a different definition of multimorbidity (≥2 chronic conditions of the 17 conditions included in the QOF), the first comprehensive study published on the prevalence of morbidity in England identified 16% of patients as being multimorbid in 2008 and these patients accounted for almost a third of all primary care consultations. Patients with multimorbidity had on average 9.4 consultations per annum compared with 3.8 for those without multimorbidity. 27

Implications for research and practice

Primary care practices have had to adjust to consistent increases in the duration of nurse and GP contacts since 2005 in the face of higher numbers of patients with multimorbidity with complex care needs and a greater administrative load per patient. With fewer FTE GPs per head of population, many practices have been unable to keep pace with these changes, leading to a drop in consulting rates since 2015.

The implications of this for practice funding and access to care are important. Approximately half of practice revenue is from the global sum payment, with the amount allocated based on an estimate of a practice’s patient-level workload using demographic data that is over 20 years old. The statistical model used is commonly known as the Carr-Hill formula and it includes factors relating to patient age and gender, morbidity and mortality measures, the number of newly registered patients, staff expenses, practice rurality, and the number of patients living in nursing and residential homes. It is widely recognised that the Carr-Hill formula does not adequately reflect population healthcare needs, particularly need associated with socioeconomic deprivation. 28 , 29 Previous research reported that practices in areas of greater deprivation received 7% less funding per need-adjusted patient than those in more affluent areas. 30 An analysis of primary care funding in England for 2015–2016 found only a modest association between practice funding and morbidity burden at the regional level, with the North East and North West regions appearing to be particularly under-resourced. 28

Repeated calls on the government to replace the Carr-Hill formula with a more equitable formula that better reflects the greater workload associated with deprivation and morbidity have resulted in little progress. Acknowledging in 2015 that the current formula is ‘out of date and needs to be revised’, NHS England and the British Medical Association committed to review the Carr-Hill formula, anticipating that the work would be completed by the summer of 2016. 31 The timeline for reporting findings has since been extended several times but no details of any proposed changes to the formula have been reported to date.

• Acknowledgments

Thank you to the DExtER team at the Institute of Applied Health Research, University of Birmingham, for their assistance in extracting the data from IMRD.

Ethical approval

This article is based on independent research carried out as part of Lyvia de Dumast’s PhD thesis. Analysis of IQVIA Medical Research Data (IMRD) was approved by London — South East Research Ethics Committee pm 5 Jul 2018 (reference: 18/LO/0441), subject to independent scientific review of the analysis. Scientific Review Committee approval for this analysis of the IMRD-UK data was received in January 2021 (reference: 20SRC076).

The IMRD-UK dataset cannot be shared under the data-sharing agreement with the University of Birmingham on behalf of IQVIA.

Freely submitted; externally peer reviewed.

Competing interests

The authors have declared no competing interests.

Discuss this article:

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• Received November 14, 2023.
• Revision requested February 29, 2024.
• Accepted March 27, 2024.
• © The Authors

This article is Open Access: CC BY 4.0 licence ( http://creativecommons.org/licences/by/4.0/ ).

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Study reveals ‘patchy and inconsistent’ end-of-life care

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One in three dying people in England and Wales was severely or overwhelmingly affected by pain in the last week of life, with bereaved people reporting how difficult it was to get joined-up support from health and care professionals at home.

This report highlights the need for a radical repurposing of NHS funding to resource primary care for that ambition to be achieved Stephen Barclay

These are among the conclusions of Time to Care: findings from a nationally representative survey of experiences at the end of life in England and Wales , a new report funded by end-of-life charity Marie Curie and produced by King’s College London’s Cicely Saunders Institute, Hull York Medical School at University of Hull, and the University of Cambridge.

Time to Care aims to describe the outcomes, experiences, and use of care services by people affected by dying, death, and bereavement in England and Wales. It is the final report from the Marie Curie Better End of life programme.

The report found one in five dying people had no contact with their GP in the last three months of life.

Half of people surveyed (49%) said their dying loved one visited A&E at least once in their final three months of life, and one in eight people who died in hospital had been there less than 24 hours. 

Half of respondents (49%) in the study were also unhappy with at least one aspect of the care the person who died received and of those one in eight people made a formal complaint. Fewer than half of respondents said they had a key contact person to co-ordinate their care. This meant responsibility for care fell on informal carers (family and friends), who often felt unprepared and unsupported.

Professor Stephen Barclay, from the Department of Public Health & Primary Care at the University of Cambridge, a researcher on the project and a practicing GP, said: “GPs, Community Nurses and the wider Primary Care Team have a central and often under-recognised role in the care of people approaching and at the end of their lives. But they are under enormous pressure with increasing workloads, diminishing workforces and inadequate investment over recent years.

“Increasing numbers of people have been dying in the community during and following the COVID-19 pandemic, at home or in care homes. This important survey, undertaken at a time when the NHS was beginning to recover from the worst of the pandemic, reveals how clinical teams in all settings are struggling to meet the needs of this vulnerable patient group.

“The out-of-hours period, which comprises two-thirds of the week, is particularly difficult for patients and their families. Across the UK, GPs and Community Nurses want to provide excellent palliative and end of life care, but the necessary ‘time to care’ is currently often squeezed. The new UK Government’s focus on care close to home is welcome. This report highlights the need for a radical repurposing of NHS funding to resource primary care for that ambition to be achieved.”

The research report is based on a survey sent by the Office for National Statistics in 2023 to a nationally representative sample of people who had registered the death of a family member in the prior six to 10 months. Only non-sudden causes of death were included. Responses were received from 1179 people, making this the largest nationally representative post-bereavement survey in England and Wales for a decade.

Professor Katherine Sleeman, from King’s College London and lead researcher on the project, said:  “This study reveals patchy and inconsistent provision of care for people approaching the end of life. While there were examples of excellent care - including in the community, in care homes, and in hospitals - the overall picture is of services that are overstretched, and of health and care staff lacking the time they need to consistently provide high-quality care. This means that dying people miss out on treatment and care for their symptoms, and families are left feeling unprepared and unsupported which has lasting emotional repercussions into bereavement.

The researchers say the findings are concerning, considering the ageing population and the expected increase in palliative care needs across the UK. By 2048, there will be an additional 147,000 people in the UK who need palliative care before they die, an increase of 25%.

“Without a corresponding increase in capacity of primary and community care teams to support these people as they approach the end of life, the quality of care is likely to further suffer,” said Professor Sleeman. “It has never been more important to ensure high-quality palliative care for all who need it.”

Annette Weatherley, Marie Curie Chief Nursing Officer, added: “The findings are shocking.  Too many people are dying in avoidable pain, struggling with breathlessness and other debilitating symptoms because of the difficulties they face accessing the end-of-life care they need from overstretched GPs and other health and care workers.

“Without urgent action, gaps in access to palliative and end of life care will only grow.

“It is a critical time to improve palliative and end of life care. People at the end of life should be able to have the very best possible care. There is only one chance to get it right at the end of life.  Yet, as the evidence shows, too many people are being failed by a system faced with extreme financial and workforce pressures.  It’s time for Governments to step up and fix care of the dying.”

Professor Stephen Barclay is a fellow at Emmmanuel College, Cambridge.

Adapted from a press release by Marie Curie

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Welsh research aims to investigate the risk of respiratory tract infections in patients with resolved asthma

Researchers at Wales Centre for Primary and Emergency Care Research (PRIME Centre Wales), funded by Health and Care Research Wales, have discovered that primary care patients with a history of resolved asthma face a higher risk of respiratory tract infections and antibiotic use, compared with the general population.

The study is led by Dr Harry Ahmed , a Health and Care Research Wales Faculty member and Dr Rebecca Cannings-John, both co-lead of PRIME's research in infections and antimicrobial resistance.

Dr Ahmed said currently there is no guidance for regular assessment or review of people deemed to have resolved asthma, and little is known about their ongoing risk of adverse respiratory events.

He added: “The project originated from a query by a patient who had suffered from asthma since childhood. Over time, their condition improved to the point where they no longer needed medication and could engage in exercise and other activities without any problem.

“However, they noticed they were experiencing frequent respiratory infections. After some years, they requested lung function tests and were informed that they had asthma again, prompting them to restart treatment.

“Once treatment had been in place for a few months, they noticed that the respiratory infections ceased.”

Asthma is a chronic respiratory condition affecting around 339 million people worldwide. In a proportion of people, asthma symptoms will resolve and these patients will no longer have the same follow-up they had whilst they needed asthma medications.

The team used data from patients registered with GPs across England to look at whether individuals whose asthma has resolved continue to face a risk of respiratory infections.

The team found that patients with resolved asthma had significantly more general practice visits for respiratory infections and received more antibiotic prescriptions compared to those without a history of asthma.

However, the rates of serious respiratory infections requiring hospital admission were similar between those with resolved asthma.

Dr Ahmed said “Our overall conclusion was that we may need to undertake a more comprehensive respiratory assessment if a patient with resolved asthma presents with respiratory infection symptoms, in order to evaluate symptom burden, airway obstruction and the potential benefit of restarting inhalers.”

Sign-up to the weekly bulletin  for the latest research news in Wales.

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This paper is in the following e-collection/theme issue:

Published on 3.9.2024 in Vol 26 (2024)

This is a member publication of University of Oxford (Jisc)

Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies

Authors of this article:

Original Paper

• Sarah M Goodday 1, 2 , MSc, PhD   ; 
• Emma Karlin 1 , MSc   ; 
• Alexa Brooks 1 , MS, RD   ; 
• Carol Chapman 3 , MPH   ; 
• Christiana Harry 1 , MPH   ; 
• Nelly Lugo 1 , BS   ; 
• Shannon Peabody 1 , BA   ; 
• Shazia Rangwala 4 , MPH   ; 
• Ella Swanson 1 , BS   ; 
• Jonell Tempero 1 , BS, MS   ; 
• Robin Yang 1 , MS   ; 
• Daniel R Karlin 1, 5, 6 , MA, MD   ; 
• Ron Rabinowicz 7, 8 , MD   ; 
• David Malkin 7, 9 , MD   ; 
• Simon Travis 10 , Prof Dr   ; 
• Alissa Walsh 10 , MD   ; 
• Robert P Hirten 11 , MD   ; 
• Bruce E Sands 11 , MS, MD   ; 
• Chetan Bettegowda 12 , MD, PhD   ; 
• Matthias Holdhoff 13 , MD, PhD   ; 
• Jessica Wollett 12 , MS   ; 
• Kelly Szajna 12 , BSc, RN   ; 
• Kallan Dirmeyer 12 , BS   ; 
• Anna Dodd 14 , MS   ; 
• Shawn Hutchinson 14 , MS   ; 
• Stephanie Ramotar 14 , BSc   ; 
• Robert C Grant 14 , MD, PhD   ; 
• Adrien Boch 15 , MA   ; 
• Mackenzie Wildman 16 , PhD   ; 
• Stephen H Friend 2, 4 , MD, PhD  

1 4YouandMe, Seattle, WA, United States

2 Department of Psychiatry, University of Oxford, Oxford, United Kingdom

3 Crohn's & Colitis Foundation, New York, NY, United States

4 Section of Urology and Renal Transplantation, Virginia Mason Francisan Health, Seattle, WA, United States

5 MindMed Inc, New York, NY, United States

6 Tufts University School of Medicine, Boston, MA, United States

7 Department of Paediatrics, University of Toronto, Toronto, ON, Canada

8 Department of Pediatric Hematology/Oncology, Schneider Children's Medical Center of Israel, Petach-Tikva, Israel

9 Department of Pediatrics, University of Toronto, Toronto, ON, Canada

10 Gasteroentology Unit, Oxford University Hospitals NHS Foundation Trust and Biomedical Research Centre, Oxford, United Kingdom

11 The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

12 Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States

13 The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, United States

14 Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada

15 Evidation Health Inc, Santa Mateo, CA, United States

16 Sage Bionetworks, Seattle, WA, United States

Corresponding Author:

Sarah M Goodday, MSc, PhD

2901 3rd Ave

Seattle, WA, 98121

United States

Phone: 1 (206) 928 8243

Email: [email protected]

Background: Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor.

Objective: The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs.

Methods: Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported.

Results: The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies.

Conclusions: Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.

Introduction

Wearable digital health technologies (DHTs) [ 1 , 2 ] and mobile apps facilitate the remote, real-world assessment of health including objective signs of disease that are typically confined to health care visits and health care provider interpretation. These specific categories of DHTs, herein referred to as “personal DHTs,” hold promise for transforming health research through the new ability to capture high-resolution, high-frequency, in-the-moment health-related multimodal information in decentralized ways. Through the provision of personal DHTs in clinical care, individuals could be better empowered to navigate their health outside the health care system with greater accessibility, agency, and accuracy than currently possible [ 1 , 2 ]. One of the largest challenges in the future of digital health that involves the use of personal DHTs is end-user engagement. While direct comparisons of engagement in personal DHT research are challenging due to the heterogeneous reporting of retention and adherence factors, and a lack of consensus on a definition of “engagement” [ 3 - 6 ], accumulating evidence supports that so far engagement in the use of personal DHTs has been poor. Specifically, retention in personal DHT research studies and the use of health-related apps is low across diverse populations and applications [ 7 - 9 ]. Further, there is evidence of attrition biases in personal DHT research resulting in insufficient representation of minority populations [ 7 ]. In addition to poor retention, personal DHT research studies have low adherence to completing active app-based tasks resulting in large amounts of missing data. This missing data problem results in challenges in artificial intelligence models from insufficient volumes of data to follow individual patterns, and limits app-based context “label” data. This “label” data is crucial for validating passively collected information from personal DHTs, particularly given the early state of the field and as the utility of certain approaches such as knowledge graphs and large language models emerge.

Several personal DHTs health research studies have started to surface [ 7 - 12 ], resulting in the identification of barriers to engagement. These barriers include technical problems with the technology and in collecting the data, usability, privacy concerns, and digital literacy. Many of these barriers point to a need to retain a human element in the research process, and to include an aspect of co-designing with end users. Emerging personal DHT research studies that show better engagement retain some form of “human-in-the-loop” (regular contact with research staff) and co-design or end-user approach [ 11 - 15 ]. Among these studies, retention rates of 80% and higher have been observed, while average adherence to wearable device use and daily app surveys have been shown to be >90% and 70%, respectively [ 11 - 15 ].

The promise of digital health rests on the assumption that end users can be engaged in the long-term use of personal DHTs for health monitoring, yet this remains to be seen among most existing research applications. There have been increasing international calls for the inclusion of patients in the design and conduct of health research [ 16 - 18 ], and this seems particularly relevant for digital health research where the patient is the end user of these new remote tools. In this paper, we report on engagement across 6 unique personal DHT health research studies that adopted different aspects of a participant-centric design, but each with distinct population and design features. The objective is to describe how participant engagement techniques and different personal DHT designs affect participant adherence, retention, and overall engagement in personal DHT health research.

Study Design

In total, 6 personal DHT research studies are included in this quantitative and qualitative analysis of engagement that span diverse populations including a frontline health care population (the stress and recovery in frontline health care workers study) [ 11 ]; a conception, pregnancy, and postpartum population (Better Understanding the Metamorphosis of Pregnancy [BUMP] study) [ 19 ]; and populations with different diseases including Crohn disease (stress in Crohn: forecasting symptom transitions study), Li-Fraumeni syndrome (stress and LFS: a feasibility study of wearable technologies to detect stress in families with LFS), and patients with pancreatic and central nervous system (CNS) tumors (help enable real-time observations [HERO] in pancreatic [PANC] and CNS tumors studies) [ 20 ].

All of these studies were conducted by 4YouandMe—a US-based nonprofit (charitable) organization. 4YouandMe specializes in open-source research into the application of personal DHTs for health and wellness [ 20 ]. 4YouandMe has a particular focus on leveraging personal DHTs to empower the patient in navigating their unique disease or life transitional period. These 6 studies were included in this analysis as they reflect all of the completed studies by 4YouandMe at the time of this analysis. Characteristics of these studies can be found in Table 1 and additional methodological detail can be found in Multimedia Appendix 1 . All studies involved the use of a bespoke study smartphone app built by 4YouandMe and the use of the Oura smart ring, the Garmin smartwatch, the Apple smartwatch, an Empatica smartwatch, and the Bodyport Cardiac Scale. Details of these devices can be found in Multimedia Appendix 2 ).

a MSSM: Mount Sinai School of Medicine.

b HERO-CNS: help enable real-time observations—central nervous system.

c CNS: central nervous system.

d HERO-PANC: help enable real-time observations—pancreatic cancer.

e n=24, 2 unknown.

f Until withdrawal, progression, death, or study completion (October 31, 2022).

g LFS: Li-Fraumeni syndrome.

h BUMP: Better Understanding the Metamorphosis of Pregnancy.

i BUMP-C: Better Understanding the Metamorphosis of Pregnancy—Conception.

Ethical Considerations

All included studies were approved by the local institutional research ethics boards (REB) at their local sites ( Multimedia Appendix 1 ): stress and recovery in frontline health care workers study (institutional review board [IRB], Advarra [4UCOVID1901, Pro00043205]), BUMP study (IRB Advarra Pro00047893), stress in Crohn (Oxford site: Hampshire-A IRAS ID: 269286, Mount Sinai School of Medicine [MSSM] site: IRB of MSSM: GCO 19-1543 | IRB-19-02298), stress and LFS (Sick Kids: REB: 1000072240), HERO-CNS (John Hopkins Medicine IRB IRB00253818), and HERO-PANC (University Hospital Network REB: 20-5211).

Statistical Analysis

Definitions of adherence in digital health research studies are heterogeneous [ 3 - 6 ]. Consistent criteria for adherence across all included studies were attempted. While many different wearable features could be used as the basis for the use of the device, features that were most reliably monitored were selected. For studies using the Oura smart ring, daily adherence was defined as at least one sleep data event present for the prior night. The Oura ring was only expected to be worn at night for many of the included studies, which is why sleep data were used as the indicator for adherence. For studies using the Garmin smartwatch, daily adherence was defined as step data present for that day. For the Empatica smartwatch, daily adherence was defined as at least one data event (worn properly in a day). Adherence to the Bodyport Cardiac Scale was defined as the proportion of days where a weight event was present divided by the total number of expected follow-up days. Adherence to in-app task completion was defined as the proportion of tasks completed when prompted in the app divided by the total number of tasks that should have been completed over study follow-up. For example, all included studies had a daily survey. In a study with a minimum of 4 months of follow-up expected from participants, the total number of expected daily surveys is approximately 120. For a weekly app survey, the total number of expected surveys for a 4-month study follow-up would be 16. Adherence to biweekly check-in calls was defined as the proportion of calls completed divided by the total number of expected calls over study follow-up. Medians and ranges are described since the adherence distributions were nonnormally distributed. All adherence estimations were performed only among retained participants.

Differences in adherence and retention by sociodemographic characteristics were estimated using χ 2 , Fisher exact, Mann-Whitney U , and ANOVA tests where appropriate among studies that have sufficient sample sizes (stress and recovery, BUMP, and stress in Crohn). Survival probabilities using the Kaplan-Meier approach were calculated to display the probability of retention over the course of each included study. Retention (total proportion of participants completing the study among all enrolled) is also reported. Additional information on how retention was calculated for each unique study can be found in Multimedia Appendix 3 .

Description of Included Studies

Study design characteristics of all studies are described in Table 1 . All studies included the use of at least one wearable device plus a study app that involved daily, as well as intermittent surveys (daily question prompts, validated questionnaires) and active tasks (cognitive active or physical function tasks [eg, walk tests], video diaries). In all included studies, participants were required to use their own Android or iPhone smartphone for study activities. Recruitment mechanisms differed across studies with some including remote recruitment through digital advertisements on social media, professional organizations and newsletters, and patient portals (stress and recovery, and BUMP), while others recruited patients in-person through specialty clinics (stress in Crohn, HERO studies, and stress and LFS). The daily burden of app active tasks across studies ranged from 2 to 7 minutes. Study follow-up periods across studies ranged from 4 to 18 months. Across all studies except the stress and LFS study, participants were offered to keep some of the study wearable devices (most often the ring and the watch). Further, 2 studies included the option for modest financial compensation (BUMP and stress in Crohn).

All studies included an engagement strategy that centered around a biweekly phone check-in with a consistent engagement specialist that served the purpose of supporting participants, helping them with onboarding, resolving potential technological problems, and discussing and collecting study experience feedback. Additionally, all included studies implemented different strategies that focused on working with participants as co-designers. These strategies included making app changes that were driven by direct participant feedback during active follow-up, offering a “your data” section in the app that allowed participants to track key symptoms over time, hosting optional investigator-participant Zoom calls where participants could meet the study team, receive study updates, preliminary results, and could offer more feedback, and inviting participants to contribute to and be listed as coauthors on published work.

Adherence by Study Population

Median adherence in engagement phone check-in calls, wearable device use, daily app survey completion, and in-app active tasks can be found in Table 2 . Median adherence varied across study populations. The stress in Crohn–MSSM site had a lower adherence on the engagement check-in calls (50%) compared to other studies, many of which had 100% adherence on these calls ( Table 2 ). This study site is herein referred to as the low-engagement cohort. In this low-engagement cohort, median adherence to completing daily app surveys, to wearing the Empatica smartwatch, and to using the Bodyport Cardiac Scale were lower than all other study cohorts that included these studies’ activities (except the BUMP-postpartum cohort). Further, median adherence to using the Oura smart ring was lower in the low-engagement cohort compared to other cohorts except for the postpartum and severely ill cancer populations.

The HERO studies included the most severely ill participants including patients with active diagnoses of CNS and pancreatic tumors. Some HERO participants were undergoing chemotherapy, some had therapy-related complications, some had infections, and some had progressive, life-threatening tumor growth. While the total number of participants in these studies was low, these studies showed low adherence on the daily survey (<55%) and wearable device use (<65% HERO-CNS only). Interestingly, HERO-PANC participants exhibited high wearable device use median adherence (83.3%, IQR 51%-93.2%, Oura and 95.5%, IQR 75.2%-99.2%, Garmin), despite the health status of this population. Further, median adherence to in-app cognitive active tasks was higher among the HERO studies compared to most other studies. Engagement check-in call adherence was also high in the HERO studies. Among the BUMP postpartum cohort, there was consistently lower adherence on all study tasks except for the engagement check-in calls compared to other studies, particularly in comparison to the BUMP prenatal cohort. Specifically, median adherence to the Oura ring, Garmin smartwatch use, and the Bodyport Cardiac Scale in the BUMP-prenatal cohort compared to the BUMP postpartum cohort dropped from 87.2% (IQR 68.7%-96.7%) to 55% (IQR 5.5%-83.7%), 96.7% (IQR 82.9%-100%) to 62.5% (IQR 12.3%-96.4%), and 74.7% (IQR 52%-87.3%) to 33.1% (IQR 8.9%-67.7%), respectively ( Table 2 ).

a BUMP-C: Better Understanding the Metamorphosis of Pregnancy—Conception.

b BUMP: Better Understanding the Metamorphosis of Pregnancy.

c BUMP-POST: Better Understanding the Metamorphosis of Pregnancy—Postpartum.

d SINC: stress in Crohn.

e MSSM: Mount Sinai School of Medicine.

f HERO-CNS: help enable real-time observations—central nervous system.

g HERO-PANC: help enable real-time observations—pancreatic cancer.

h LFS: Li-Fraumeni syndrome.

i ES: engagement specialist.

j Not available.

k EBT: emotional bias test.

Adherence by Study Activity

There were differences in adherence rates across different study activities. Adherence to wearable device use was consistently higher across studies compared to in-app activities, which is not surprising given the passive nature of these devices. Excluding the postpartum and HERO-CNS study, median adherence to Oura ring use was >80% across all studies, and as high as 99% (IQR 94.9%-99.6%; stress in Crohn-Oxford site; Table 2 ). There were also differences in adherence across specific wearable devices. Garmin and Apple smartwatch adherence was >95% in BUMP pregnant individuals and HERO-PANC participants, while median adherence for the Empatica Watch was lower among the studies that used this device (stress in Crohn-Oxford, 72.5%, IQR 37.1%-96.8%; stress in Crohn-MSSM, low-engagement cohort, 26%, IQR 6.2%-64.1%; and stress in LFS, 86.8%, IQR 0.7%-0.9%). Median adherence to the Bodyport Cardiac Scale was 74.7% (IQR 52%-87.3%) among BUMP pregnant individuals and 79.5% (IQR 52.7%-88.4%) in HERO-PANC participants ( Table 2 ). Excluding the postpartum and HERO study populations and the low-engagement cohort, in-app daily survey adherence was >60% for all studies ( Table 2 ). Finally, adherence to in-app active tasks was lower in general compared to other activities such as wearable device use or in-app surveys. Tasks that involved walking (gait and walk task) or speaking (video diaries) showed lower adherence compared to other active tasks (eg, cognitive and emotional bias tasks; Table 2 ).

Adherence by Study Recruitment and Engagement Strategy

There did not appear to be any meaningful difference in median adherence rates across study activities by study recruitment methods (in-clinic vs remote) or follow-up time. Further, 2 studies that included modest financial compensation in addition to engagement strategies showed higher adherence rates compared to some of the other studies (ie, BUMP and stress in Crohn), but the impact of compensation is difficult to disentangle from other study characteristics such as population differences, and these studies did not show superior adherence rates compared to the stress and recovery study that did not offer financial compensation.

The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%; Table 3 ). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies ( Multimedia Appendix 4 ).

b Only includes participants who were enrolled in the Better Understanding the Metamorphosis of Pregnancy—Conception-specific app.

c BUMP: Better Understanding the Metamorphosis of Pregnancy.

d MSSM: Mount Sinai School of Medicine.

e HERO-CNS: help enable real-time observations—central nervous system.

f HERO-PANC: help enable real-time observations—pancreatic cancer.

g Help enable real-time observations—pancreatic cancer has unique factors to consider when interpreting the proportion retained until study completion, since the study aimed to monitor patients until they developed progressive disease or died, or the study end date (October 31, 2022; see Multimedia Appendix 3 ).

Adherence and Retention by Participant Sociodemographic Characteristics

Median adherence for the Oura smart ring, a smartwatch (Garmin, Apple, and Empatica), and the Bodyport Cardiac Scale was lower among younger participants compared to older participants across most studies ( Multimedia Appendix 5 ). Specifically, Oura smart ring adherence was significantly lower in those aged 18-25 years compared to those aged ≥26 years in the BUMP study ( P =.03) and stress in Crohn-MSSM studies ( P =.02), and was lower in the BUMP-C and stress and recover studies, but this difference was not statistically significant at P =.59 and P =.08, respectively. Median adherence for Apple smartwatch use was significantly lower in those aged 18-25 years compared to those aged ≥26 years in the BUMP study ( P =.02), while median adherence for Garmin smartwatch use was lower but not statistically significant ( P =.06). Median adherence for the Bodyport Cardiac Scale was significantly lower in those aged 18-25 years compared to those aged ≥26 years in BUMP ( P <.005) and stress in Crohn-MSSM ( P <.006).

In the BUMP study, Black or African American ethnicity had significantly higher median adherence to completing the in-app daily survey compared to other race or ethnicity groups ( P =.01). This trend was observed in the stress and recovery study ( P =.07) and the stress in Crohn-MSSM study ( P =.24), although the difference was not statistically significant. In contrast, median adherence to Oura smart ring, smartwatch, and Bodyport Cardiac Scale use was lower among Black or African American individuals compared to other race or ethnicity groups, although these differences were not statistically significant ( Multimedia Appendix 5 ).

Retention did not significantly differ by age group or gender ( Multimedia Appendix 6 ).

Retention likelihood was significantly different by race or ethnicity groups in BUMP-C ( P <.001) and BUMP ( P= .001). Specifically, participants of White ethnicity were more likely to stay in the study in both BUMP-C and BUMP, while participants reporting their race or ethnicity as either unknown or not reporting this item were less likely to be retained ( Multimedia Appendix 6 ).

Barriers to Engagement (Qualitative Synthesis of Participant Feedback)

Figure 1 describes key themes that impacted participant retention, adherence, and overall engagement that cut across all included studies. These themes include participant burden and forgetfulness, digital literacy, physical and mental barriers, personal and altruistic benefits, and privacy and confidentiality. Qualitative feedback from participants, research staff, and investigators across these 5 themes is summarized in Multimedia Appendix 7 . The top three barriers to engagement in active study tasks were (1) participant burden and in particular fatigue with the repetitiveness of tasks; (2) physical or mental and situational barriers that prevented the ability to complete tasks; and (3) personal and altruistic benefit, namely the perception that the use of the personal DHTs was not personally useful for a health benefit or a lack of understanding as to why and how certain features (eg, heart rate variability) could be useful to track for health benefit. Qualitative feedback from participants in the 2 cohorts demonstrating lower adherence (HERO-PANC and BUMP post partum) suggested that while participants were highly engaged, they were either too ill, distracted, or tired to complete many of the study activities while navigating a serious illness or the early postpartum period.

Principal Findings

Evidence across 6 unique and diverse studies involving the longitudinal use of personal DHTs supports that participant-centric engagement strategies aid in participant retention and maintaining good adherence in some populations. These strategies centered around (1) human contact with an engagement specialist as often as every 2 weeks, (2) investigator-participant meetings during active study follow-up, (3) offering returned symptom data in the app, (4) inviting participants to contribute as coauthors in published work, and (5) real-time modifications to the study app based on participant feedback.

In the majority of included studies, the probability of staying in the study stayed above 90% for the first month and stayed above 50% for active study periods for all studies. Lower retention or adherence was observed among studies that included a severely ill cancer population and a postpartum population. Barriers to participation in these cohorts were largely the result of physical and situational roadblocks. Excluding studies of a severely ill and postpartum population and the low-engagement cohort in the stress in Crohn study, adherence to Oura smart ring and Garmin smartwatch use was 80% and as high as 99% in some cohorts, while adherence to the Bodyport Cardiac Scale was 75% in a pregnant population. This supports that different populations can successfully be engaged in the use of active app assessments and wearable devices in the long term with adequate support.

Retention and adherence rates observed in these studies are higher than typically reported by other personal DHT research studies [ 7 - 9 , 12 , 13 , 21 ]. For example, a review of 8 large app-based DHT research studies in the United States reported that the probability of staying in the study dropped to or below 50% after the first 4 weeks of participation for all included studies [ 7 ]. Further, across the 8 included studies in this review, >50% of participants did not engage with the app for at least 7 days. Another large app-based study in the United States, the Warfighter Analytics Using Smartphones for Health study that collected daily active and passive app data reported a median retention of 45.2% (38/84 days), while the probability of staying in the study hit 50% at approximately 5.5 weeks [ 10 ]. A large app-based study in the United Kingdom (cloudy with a chance of pain study) involving daily active app assessments reported that 64% of participants fell into the low engagement or no engagement categories after baseline [ 12 ]. The RADAR study [ 14 ], a multinational study involving active and passive assessments from an app, and a Fitbit reported comparable retention results among participants with major depression to those reported here. This study reported a retention rate of 54.6% for 43 weeks of study participation; however, the probability of staying in the study stayed above 75% for the first several months of participation (~6 months). While the active app assessments in this study only included assessments every 2 weeks as opposed to daily assessments, this study additionally included aspects of a participant-centric design, which may have contributed to the higher reported retention [ 15 ].

Taken together, in comparison to other published personal DHT research studies, the 6 studies included in this paper reflect higher levels of engagement. Importantly, the included studies in this analysis involved high burden designs in comparison to other studies that request, for example, weekly or biweekly active tasks of participants [ 14 ] or only involve the use of a smartwatch. Specifically, across the included studies here, participants were expected to complete on average 4.6 (SD 1.62) minutes a day of app activities in addition to continuously using multiple wearable devices.

While different variations of participant-centric strategies were used across the 6 included studies, a key common feature was a biweekly check-in call with an engagement specialist. These calls served the purpose of providing support and building rapport with participants, working through onboarding and technological issues with study devices, tracking adherence, and receiving study-related feedback from participants. Numerous challenges arise in the conduct of remote, personal DHT research, and without frequent check-in and semiregular data monitoring by research staff, knowledge of these issues is a black box. The most significant drop in retention in personal DHT research studies tends to be during the first few weeks of participation [ 7 ]. These early onboarding weeks are crucial in working with participants to ensure they can get into a rhythm of participation. The passive sensing nature of personal DHTs has much potential to inform new objective measures of health, however, are not always intuitively understood as personally important for unique diseases (eg, heart rate variability or phone screen time). Personal DHT studies allow for “light touch” research approaches that enable data collection without traditional research coordinator contact, but this may come with a cost that inadvertently creates a less engaging study environment for participants and limits the opportunity to help participants understand the value in their participation. Of the included 6 studies, 1 cohort had much lower engagement on the check-in calls (50% adherence) compared to other included studies and, in turn, consistently demonstrated lower adherence to study-related activities. Still, even with extensive engagement designs, populations that had physical, mental, and situational barriers to study task completion (ie, severely ill, postpartum mothers) showed lower adherence to wearable device use and active smartphone tasks compared to other study populations. Top reported barriers to engagement included participant burden, physical, mental, and situational barriers, and low perceived value of personal DHTs for health care. These engagement barriers have been reported in previous literature [ 8 , 9 ] relating to DHT research and in the use of DHT interventions. However, the conveyed importance of the perceived value of the approach among participants in the current analysis is noteworthy. Given the foreign nature of personal DHTs for many individuals, particularly older populations, further work is needed to co-design and educate end users on the potential value of self-monitoring unique health-related data.

Irrespective of the engagement approach, adherence to in-app surveys and tasks was lower than wearable device use, which is not surprising given the higher burden related to in-app activities. The self-reported information captured from frequent or momentary in-app assessments is extremely valuable as context information. This context information or “label” data is useful for validating objectively captured information, yet remains the most difficult to capture in sufficient detail. Further, certain in-app activity adherences were consistently lower than others. Namely, activities that required the user to be active (walk in a straight line or complete a video diary) were low across studies. Still, adherence to daily in-app surveys was >60% for all studies excluding the postpartum and HERO study populations.

Limitations

This quantitative and qualitative analysis compared observational data across different digital health studies. However, no true comparison cohort that did not include engagement strategies was included. Therefore, the inferred casualty of participant check-ins with engagement specialists on retention and adherence rates cannot be not concluded. We are formally testing whether the biweekly check-in significantly increases adherence and retention in an ongoing study with an appropriate comparison arm without check-in support (NCT05753605). One of the included studies (stress and recovery) was conducted during the early 2020 COVID-19 pandemic. There is some evidence that engagement in research was higher during the early pandemic time periods [ 22 ]. It cannot be ruled out that the higher observed retention and adherence in this study compared to others was not due to this potential time period bias. The stress in the Crohn-Oxford site included a population of patients some of whom were already engaged in the use of web-based monitoring of symptoms. In turn, this could have contributed to the high retention and higher adherence observed at this site compared to the other stress in the Crohn-MSSM site. The results presented on barriers to engagement were primarily qualitative and collected from conversations with participants, research staff, and investigators across studies.

Conclusions

Globally, mobile apps are used for a variety of purposes in everyday life, while the use of smartwatches for activity monitoring is gaining increasing popularity. However, the use of these tools for health remains a challenge. These findings support that human support via phone and other participant-centric engagement strategies centered on giving back to participants and working with them as co-designers can support sufficient retention and adherence in personal DHT research across diverse populations. This has implications for the utility and potential necessity of a digital support worker in digital health care, as highlighted by others [ 23 ]. A power of personal DHTs is enabling the patient to be in control of their health through self-monitoring, but this new role comes with a responsibility. This important shift in role from doctor to patient outlines how crucial it is to include patients in the early design phase of personal DHT health research. Further work is needed to inform app designs that support habitual forming activities around task completion so that app-related activities become a part of participants’ daily routine and are perceived as personally valuable.

Acknowledgments

The stress and recovery study was supported in part by the Bill & Melinda Gates Foundation (INV-016651). The stress in Crohn study was funded by the Leona M. and Harry B. Helmsley Charitable Trust (1911-03376). The help enable real-time observation (HERO)–central nervus system study was funded by the Mark Foundation for Cancer Research through an ASPIRE award (19-024-ASP). The HERO–pancreatic cancer study was funded by the Mark Foundation for Cancer Research through an ASPIRE award (19-024-ASP), Pancreatic Cancer Canada, the Princess Margaret Cancer Foundation, and 4YouandMe. The Better Understanding the Metamorphosis of Pregnancy (BUMP) study was funded by 4YouandMe and Sema4 along with supplemental in-kind contributions from coalition partners (Evidation Health, Vector Institute, Cambridge Cognition, and Bodyport). The stress and LFS study was funded by in-kind contributions from 4YouandMe, SickKids Hospital, and the Vector Institute.

Conflicts of Interest

CB is a consultant for Depuy Synthes, Bionaut Labs, Galectin Therapeutics, Haystack Oncology, and Privo Technologies. CB is a cofounder of Belay Diagnostics and OrisDx. DRK is an officer, employee, and shareholder of MindMed; a consultant at Tempus, Nightware, and Limitless; and board member of Sonara. RPH is an advisory board member at Bristol Meyers Squibb. MH is an advisory board member for Servier, AnHeart, and Bayer; steering committee member for Novartis; honoraria from Novartis; data safety monitoring committee member for Advarra and Parexel. RG received a graduate scholarship from Pfizer and provided consulting or advisory roles for Astrazeneca, Tempus, Eisai, Incyte, Knight Therapeutics, Guardant Health, and Ipsen. The others declare no conflicts of interest.

Study descriptions.

Study wearable devices.

Retention calculations.

Probability of retaining in the study across studies.

Median adherence to study activities stratified by sociodemographic characteristics.

Sociodemographic differences in participants who were retained versus not retained.

Qualitative feedback from participants, research staff, and investigators surrounding barriers to engagement in digital health research, summarized across 6 unique studies.

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• Goodday SM, Karlin E, Brooks A, Chapman C, Karlin DR, Foschini L, et al. Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum. NPJ Digit Med. Mar 30, 2022;5(1):40. [ CrossRef ] [ Medline ]
• 4YouandMe. URL: http://4youandme.org [accessed 2023-06-07]
• Nowell WB, Curtis JR, Zhao H, Xie F, Stradford L, Curtis D, et al. Participant engagement and adherence to providing smartwatch and patient-reported outcome data: digital tracking of rheumatoid arthritis longitudinally (DIGITAL) real-world study. JMIR Hum Factors. 2023;10:e44034. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Vargo D, Zhu L, Benwell B, Yan Z. Digital technology use during COVID-19 pandemic: a rapid review. Human Behav and Emerg Tech. Dec 28, 2020;3(1):13-24. [ CrossRef ]
• Perret S, Alon N, Carpenter-Song E, Myrick K, Thompson K, Li S, et al. Standardising the role of a digital navigator in behavioural health: a systematic review. Lancet Digit Health. 2023;5(12):e925-e932. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach, T de Azevedo Cardoso; submitted 06.03.24; peer-reviewed by C Godoy Jr; comments to author 05.04.24; revised version received 12.04.24; accepted 29.05.24; published 03.09.24.

©Sarah M Goodday, Emma Karlin, Alexa Brooks, Carol Chapman, Christiana Harry, Nelly Lugo, Shannon Peabody, Shazia Rangwala, Ella Swanson, Jonell Tempero, Robin Yang, Daniel R Karlin, Ron Rabinowicz, David Malkin, Simon Travis, Alissa Walsh, Robert P Hirten, Bruce E Sands, Chetan Bettegowda, Matthias Holdhoff, Jessica Wollett, Kelly Szajna, Kallan Dirmeyer, Anna Dodd, Shawn Hutchinson, Stephanie Ramotar, Robert C Grant, Adrien Boch, Mackenzie Wildman, Stephen H Friend. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.09.2024.

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The Directly Observed Therapy Short-Course (DOTS) strategy in Samara Oblast, Russian Federation

Affiliation.

• 1 HPA Mycobacterium Reference Unit, Clinical TB and HIV Group, St Bartholomew and Queen Mary School of Medicine, London E1 2AT, UK. [email protected]
• PMID: 16556324
• PMCID: PMC1440858
• DOI: 10.1186/1465-9921-7-44

Background: The World Health Organisation (WHO) defines Russia as one of the 22 highest-burden countries for tuberculosis (TB). The WHO Directly Observed Treatment Short Course (DOTS) strategy employing a standardised treatment for 6 months produces the highest cure rates for drug sensitive TB. The Russian TB service traditionally employed individualised treatment. The purpose of this study was to implement a DOTS programme in the civilian and prison sectors of Samara Region of Russia, describe the clinical features and outcomes of recruited patients, determine the proportion of individuals in the cohorts who were infected with drug resistant TB, the degree to which resistance was attributed to the Beijing TB strain family and establish risk factors for drug resistance.

Methods: Prospective study.

Results: 2,099 patients were recruited overall. Treatment outcomes were analysed for patients recruited up to the third quarter of 2003 (n = 920). 75.3% of patients were successfully treated. Unsuccessful outcomes occurred in 7.3% of cases; 3.6% of patients died during treatment, with a significantly higher proportion of smear-positive cases dying compared to smear-negative cases. 14.0% were lost and transferred out. A high proportion of new cases (948 sequential culture-proven TB cases) had tuberculosis that was resistant to first-line drugs; (24.9% isoniazid resistant; 20.3% rifampicin resistant; 17.3% multidrug resistant tuberculosis). Molecular epidemiological analysis demonstrated that half of all isolated strains (50.7%; 375/740) belonged to the Beijing family. Drug resistance including MDR TB was strongly associated with infection with the Beijing strain (for MDR TB, 35.2% in Beijing strains versus 9.5% in non-Beijing strains, OR-5.2. Risk factors for multidrug resistant tuberculosis were: being a prisoner (OR 4.4), having a relapse of tuberculosis (OR 3.5), being infected with a Beijing family TB strain (OR 6.5) and having an unsuccessful outcome from treatment (OR 5.0).

Conclusion: The implementation of DOTS in Samara, Russia, was feasible and successful. Drug resistant tuberculosis rates in new cases were high and challenge successful outcomes from a conventional DOTS programme alone.

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The Directly Observed Therapy Short-Course (DOTS) strategy in Samara Oblast, Russian Federation

• Y Balabanova 1 , 3 ,
• F Drobniewski 1 ,
• I Fedorin 2 ,
• S Zakharova 3 ,
• V Nikolayevskyy 1 ,
• R Atun 4 &
• R Coker 5  

Respiratory Research volume  7 , Article number:  44 ( 2006 ) Cite this article

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The World Health Organisation (WHO) defines Russia as one of the 22 highest-burden countries for tuberculosis (TB). The WHO Directly Observed Treatment Short Course (DOTS) strategy employing a standardised treatment for 6 months produces the highest cure rates for drug sensitive TB. The Russian TB service traditionally employed individualised treatment.

The purpose of this study was to implement a DOTS programme in the civilian and prison sectors of Samara Region of Russia, describe the clinical features and outcomes of recruited patients, determine the proportion of individuals in the cohorts who were infected with drug resistant TB, the degree to which resistance was attributed to the Beijing TB strain family and establish risk factors for drug resistance.

prospective study

2,099 patients were recruited overall. Treatment outcomes were analysed for patients recruited up to the third quarter of 2003 (n = 920). 75.3% of patients were successfully treated. Unsuccessful outcomes occurred in 7.3% of cases; 3.6% of patients died during treatment, with a significantly higher proportion of smear-positive cases dying compared to smear-negative cases. 14.0% were lost and transferred out. A high proportion of new cases (948 sequential culture-proven TB cases) had tuberculosis that was resistant to first-line drugs; (24.9% isoniazid resistant; 20.3% rifampicin resistant; 17.3% multidrug resistant tuberculosis). Molecular epidemiological analysis demonstrated that half of all isolated strains (50.7%; 375/740) belonged to the Beijing family. Drug resistance including MDR TB was strongly associated with infection with the Beijing strain (for MDR TB, 35.2% in Beijing strains versus 9.5% in non-Beijing strains, OR-5.2. Risk factors for multidrug resistant tuberculosis were: being a prisoner (OR 4.4), having a relapse of tuberculosis (OR 3.5), being infected with a Beijing family TB strain (OR 6.5) and having an unsuccessful outcome from treatment (OR 5.0).

The implementation of DOTS in Samara, Russia, was feasible and successful. Drug resistant tuberculosis rates in new cases were high and challenge successful outcomes from a conventional DOTS programme alone.

Since the 1990s the World Health Organization Directly Observed Therapy Short Course (DOTS) management strategy has become the internationally recommended approach for tuberculosis (TB) control programmes [ 1 – 3 ]. By the beginning of the new Millennium, 149 countries in the world had adopted the DOTS strategy to varying degrees and important measures of DOTS success (case detection and treatment success) were included in the Millennium Development Goals framework [ 4 ]. In the former Soviet Union (FSU) only a limited number of WHO DOTS implementation programmes exist and currently countries of the FSU report the lowest case detection rates (22%) with 9% of cases failing treatment and a death rate of 7% during treatment [ 4 ]. WHO has acknowledged that until TB is controlled in Africa and Eastern Europe, this disease will remain of major world-wide concern; current analysis indicates that it is unlikely that the Millennium Development Targets for TB will be met in these regions [ 4 ].

Russia is one of 22 TB high-burden countries as defined by the WHO [ 5 , 6 ]. Russia has a highly-specialised tuberculosis health care system with a large organisationally-vertical network of specialized institutes, dispensaries, hospitals, outpatient clinics, sanatoria and rural feldsher points. Case detection is based largely on the presence of radiological abnormalities on chest X-rays with or without bacteriological confirmation detected through a national policy of compulsory annual fluorographic population screening [ 7 – 9 ]. In contrast to the WHO recommended tuberculosis control DOTS strategy, which favour minimising hospital stays, clinical guidelines and health system financing incentives, TB patients in Russia experience frequent and lengthy hospitalisations, and historically have received individualised treatment regimens with doses of the main first line drugs and duration of chemotherapy varying from internationally accepted standard treatment regimens. The system also included prolonged periods of follow-up and repetitive courses of anti-relapse therapy [ 10 , 11 ].

The rationale for implementing the DOTS strategy in Russia is to establish cost-effective tuberculosis control by reducing unnecessary care costs due to lengthy hospitalisations, while improving cure rates and reducing the development of drug resistant TB [ 3 , 7 , 12 – 14 ].

In 2002, with assistance from the UK Department for International Development, a TB control programme that adhered to internationally accepted norms and standards was launched by the regional Ministry of Health. We have reported elsewhere on the considerable body of research undertaken in Samara that explores the epidemiological profile, the health care system structures and processes, and public health challenges being faced by the oblast [ 7 , 9 – 12 ], [ 15 – 23 ]

This paper describes the clinical features and outcomes of patients recruited to a DOTS programme which was implemented in civilian and prison sectors in Samara Oblast.

At the initial stage of implementation of DOTS a standard protocol was agreed with the Regional Ministry of Health. This was followed by extensive training of medical doctors and TB nurses with the involvement of WHO and experts from Russian Federal TB Institutes. Two project medical co-ordinators based in Samara were appointed to oversee implementation which was rolled out in three phases.

Under phase one, initiated in April 2002, patient recruitment commenced at two pilot TB dispensaries in Samara City and at two TB prison colonies (one, an inpatient prison facility used for initial therapy, the second an outpatient facility where continuation of therapy occurred) that looked after all prisoners with TB in the oblast. Recruitment was expanded in January 2003, under phase two, to all TB facilities in Samara city (five dispensaries and three TB hospitals) and to the neighbouring city Togliatti. Under phase three, a further rollout occurred in January 2004 to the rural district of Krasny Yar. We report results through all three phases and include patients recruited up to the third quarter of 2004.

Patients were recruited into standard WHO categories (Table 1 ). In 2002, initially only new cases were recruited (category I and III). From April 2003, recruitment was extended to include relapse cases (category II). Because of the prevalence of drug resistance and concerns that resistance profiles would be further amplified [ 23 – 25 ] chronic cases were ineligible for recruitment.

Given implementation of the internationally supported programme ceased in third quarter 2004, clinical outcomes presented are until the third quarter of 2003. Outcomes for patients recruited subsequently were registered within the newly adopted Russian national system which continued following this programme [ 26 , 27 ].

Standard TB control treatment outcomes were recorded (Table 1 ). Treatment success under the DOTS strategy was determined by cures and treatment completions and unsuccessful treatment included patients who failed and defaulted [ 28 ].

A modified feature of the programme was introduced where patients registered initially under the DOTS cohort could be transferred to an "individual treatment regimen", an approach that reflected the Russian legacy of individualised approaches to treatment. According to the prevailing views of Samara phthisiatrists not only patients who were diagnosed with MDRTB but also some severely ill patients or those with severe co-morbidities or perceived adverse reactions would be removed from the DOTS programme and managed within the regional TB programme using an individualised approach in line withy earlier Russian traditions. Cases, following recruitment, which were subsequently determined to have MDR TB, were transferred out to an individually-tailored MDRTB drug regimen. A further feature of the modification of the DOTS programme was the continuation of the intensive phase of treatment beyond two months (for one more month) despite patients becoming smear-negative in the end of the second month of therapy intensive phase. This was done, in accordance with Russian traditions, where extensive radiological changes were present.

Standard technical approaches to documentation and diagnostic/treatment protocols were employed[ 28 ]. Sputum collection was performed at recommended intervals. During the intensive phase of therapy ethambutol was administered instead of streptomycin because a previous drug resistance survey had documented very high rates of primary resistance to streptomycin [ 9 , 23 ].

For all patients smear microscopy and culture was performed at recommended intervals. Smear microscopy and culture were performed using standard Ziehl-Neilsen microscopy and culture on Lowenstein-Jensen media. All positive isolates were tested for drug susceptibility to isoniazid, rifampicin, ethambutol and streptomycin. Quality-assured drug susceptibility testing (DST) was performed at three civil and one prison site using an absolute concentration method on Lowenstein-Jensen media. DST was assured by a period of training by staff from the WHO Supranational Reference Laboratory (SRL) in London (Health Protection Agency MRU) and in Samara. A blinded analysis of a test panel of TB cultures was performed. A proportion (10%) were retested by the SRL in London.

DNA was extracted and Beijing family strains were analysed in London and Samara by detection of the IS6110 insertions in the dnaA-dnaN intergenic region on a proportion of sequential isolates (n = 740).

Direct supervision of treatment adherence was completed by TB nurses at TB hospitals and dispensaries with out-patients receiving treatment daily or three times weekly. Upon release, ex-prisoners completed their treatment upon transfer of their care to the civilian service.

Medical co-ordinators performed regular visits to all participating DOTS sites to support implementation, ensure recruitment was maintained, and review documentation and adherence to the protocol. Over-arching project management group meetings which included all clinical stakeholders and the project directors from each DOTS site occurred on a monthly basis.

Socially disadvantaged patients were identified by a responsible physician at each dispensary and offered additional support to encourage treatment adherence with weekly food packages at a cost of 100 Russian Roubles (3 Euros) per person per week.

Data were entered and stored into a password protected database. The statistical analysis was performed using Excel and SPSS 12. Proportions with 95% confidence intervals (CI), relative risks (RR), odds ratios (OR), and χ 2 test are used for comparison of categorical variables.

The study was approved by the Samara Regional Ethics Committee.

2,099 patients were recruited from 1 st April 2002 to 30 th September 2004, including 1,971 individuals with pulmonary tuberculosis (93.9%) and 128 patients with extrapulmonary disease (6.1%); 1,684 of recruits were men (80.2%) and 415 (19.8%) women. 640 patients were recruited in the prison sector and 1459 were civilian TB patients.

One third (33.1%; 694/2,099) of recruited patients were WHO category I and 24.3% (162/694) of these were smear-negative cases with extensive parenchymal involvement; 58.8% (1,234/2,099) of cases were WHO category III patients. Recruitment into WHO category II was limited to relapse cases only and 171 patients (8.1%) were recruited.

The mean age of patients was 38.5 years (95%CI 37.9–39.1 years; range: 16–90 years) with prisoners being significantly younger than civilians (mean age 30.9 years; 95%CI 30.2–31.6 years versus mean age 41.9 years; 95%CI 41.1–42.7 years). Female patients were older than male patients (mean age of men was 38.0 (95%CI 37.4–38.6) years and mean age of women with TB was 40.4 (95%CI 38.8–42.0) years).

Details of bacteriologically (smear and/or culture) confirmed cases are shown in Table 2 . Table 3 shows differences between the infectious status of civilian and prison populations with TB where civilians were more likely to have infectious disease whether determined by smear status or culture status. Overall the rate of laboratory diagnosed TB cases was slightly higher in civilian patients than prisoners.

Cultures from 948 sequentially new and 94 relapse cases were isolated and tested for susceptibility to first-line drugs. Of the new cases, 24.9% (236/948) new cases had isolates resistant to isoniazid, 20.3% (192/948) new cases had isolates resistance to rifampicin, and 17.3% (164/948) had MDRTB (vs 34.0% (32/94) of relapse cases being MDR (OR-2.5; 95%CI 1.6–3.9). Table 3 and Figure 1 show the differences between civilian and prison patients.

Rates of first-line drug resistance among civil and prison patients.

Molecular epidemiological analysis demonstrated that a half of all isolated strains (50.7%; 375/740) belonged to the Beijing family. Of note, seven isolates (0.9%) were mixed strains. The prevalence of the Beijing strain (60.9%; 117/192) among prisoners was significantly higher (OR-1.7; 95% CI 1.2–2.4) than in civilians (47.1%; 258/548) confirming earlier research findings in a drug resistance survey in the same region in the preceding year [ 19 ].

For 709 isolates data on both drug resistance and strain type were available (31 isolates were non-viable or were contaminated and DST could not be performed). Drug resistance including MDR TB was strongly associated with being infected with the Beijing strain (for MDR TB 35.2% in Beijing strains versus 9.5% in non-Beijing strains, OR-5.2 (3.4–7.9) (Table 5 ) confirming earlier research in a different population of patients treated under the Russian system in the same region[ 19 ].

Multivariate analysis suggests that being a prisoner (OR – 4.4; 95%CI 2.7–7.1), having a relapse of TB (OR-3.5; 95%CI 1.7–7.1), being infected with the Beijing family strains (OR-6.5; 95%CI 4.0–10.5) and having unsuccessful outcome of treatment (OR-5.0; 95%CI 1.1–22.7) were risk factors for MDR TB.

During the course of treatment the majority (97.7%; 284/290) of smear-positive new cases converted by the end of the intensive phase of treatment.

Treatment outcomes among new cases confirmed by culture are shown in Table 6 . Because recruitment of relapses was initiated at a later stage, the number of these is small. Overall 85.4% (786/920) of newly diagnosed and recruited patients were treated according to the WHO protocol. Nearly fifteen percent (134/920) of patients were transferred out of the DOTS clinical protocol and this included patients transferred to individual regimens because MDRTB (17.3% of all new cases were MDR and 34.0% of all relapse cases) or extensive radiological abnormalities, adverse drug reactions, or co-morbidities). MDR TB patients were removed from the programme according to DOTS project criteria and further treated with tailored schemes using second-line drugs. More smear positive patients were transferred out than smear-negative cases (22.4% versus 11.6%; OR-2.2; 95%CI 1.5–3.2). In total 75.3% (592/786) of patients were successfully treated and in 7.3% (57/786) treatment failed or patients defaulted. The odds of failing treatment or defaulting were higher in smear positive patients (OR – 10.6; 95% CI 3.4–32.8).

There was no statistically significant difference in treatment outcomes between male and female patients.

The rates of unsuccessful treatment was higher among civilians compared to prisoners (OR-4.5; 95%CI 2.1–10.0)

Twenty-eight patients (3.6 %; 28/786) died during the course of treatment with a significantly higher proportion (11.3%) of smear-positive cases dying versus smear-negative (OR -12.5; 95%CI 5.0–31.3).

Discussion and conclusion

The WHO have argued that the introduction of DOTS cohort treatment strategies improves case detection and treatment and leads to a reduction in TB prevalence and death rates by cutting the duration of illness and case fatality.

Two examples from middle and high incidence countries (Peru and China) support this view. In Peru, the incidence rate of pulmonary TB has decreased annually by 6% after the nationwide implementation of DOTS[ 29 ]. In 13 provinces of China that implemented DOTS, the prevalence rate of culture-positive TB was cut by 30% between 1990 and 2000 [ 30 ]

The introduction of DOTS resulted in profound changes to the delivery of clinical care within the Samara TB Service. Although a direct observation component had, broadly. been present within the old system through lengthy hospitalisation periods, the strict adherence of physicians to standard regimens, the emphasis on laboratory diagnosis, and a robust system of recording and reporting of cohorts were new [ 7 ].

Similarly fewer than 70% of patients with TB were cured or completed treatment in Samara compared to 75.3% in the cohort groups. This is in keeping with the cure rates reported for DOTS programmes internationally (Table 7 ) and the global treatment success rate under DOTS has been high since the first observed cohort in 1994 (77%)[ 4 ].

The relatively high failure rates noted elsewhere in Eastern Europe, (9% of cases failed treatment and 7% died during treatment) are believed to be associated with high rates of multidrug resistance (which in itself is an indicator of a programme with low cure rates previously). In Samara, prior to the introduction of the DOTS cohort strategy we established that drug resistance was high in both new (approx 20%) and chronic cases in Samara [ 9 , 17 , 23 ].

Dye et al [ 4 ] further established that the prevalence will decrease sooner if case detection by DOTS programs (and hence the quality of treatment) can be improved more quickly, thus reducing the burden of illness during this period in future years. The DOTS programmes emphasise the importance of bacteriological confirmation. [ 7 ]Prior to the establishment of the cohort, there were more than 1.5 million flurographic examinations of the general population for early diagnosis of TB reflecting the national policy of fluorography screening of the population for TB for early diagnosis. We have reported on the subjective nature of radiological examination elsewhere [ 31 ] and emphasised the need for bacteriological confirmation of the diagnosis in line with international standards.

Previously, less than one-third (30.1%) of cases were bacteriologically confirmed (Coker et al, 2003 IUATLD) compared to the DOTS cohort where 49.6% of all cases (and 57.6% in civilian cases) were bacteriologically confirmed. [ 10 , 11 ]Overall the proportion of cases which had a bacteriological confirmation of the diagnosis was similar to rates reported from other regions of Russia [ 4 ];.

[ 31 ]Although the laboratory component of the TB service in Samara Oblast has been extensively upgraded and improved with prison and civil laboratory services working to these improved standards, maintenance and further quality improvement remains a priority. Without appropriate laboratory support, over-diagnosis of tuberculosis remains a possibility, resulting in unnecessary treatment and side-effects without benefit, and compounding service inefficiencies [ 13 ].

Relatively low default rates occurred with implementation of DOTS in Samara. This may be, in part, attributable to a programme of externally financed social support. This component was discontinued after external funding ceased, and it remains to be seen whether adherence rates will suffer. Of note, substance abuse, alcoholism, poverty and unemployment are common amongst patients with TB in Samara Oblast, co-factors likely to influence treatment adherence [ 22 ]. The sustainable success of DOTS in Russia is likely to be dependent on how care and support for these social and behavioural factors are integrated into TB care systems.

Effective responses in support of TB control demand political commitment and investment from local and federal budgets into non-medical support to patients and their families. However, few integrated social support systems for tuberculosis patients currently exist, and current laws and regulations have the potential to ensure that health and social care budgets remain disconnected from each other and from need [ 32 , 33 ]. Consequently, to compensate for inadequate social support systems for tuberculosis patients, providers use sophisticated practices to ensure lengthy admissions in the winter months – a response to social rather than medical need [ 32 , 33 ]. Whilst the two recent decrees on TB control issued in 2003 (#109 and #50) [ 26 , 27 ] support convergence of Russian TB control practices with WHO's DOTS strategy (with some specific differences reflecting Russia's clinical legacy), the sustainability of reforms needed to ensure cost-effective implementation such that DOTS implementation is allied to structural reform remains uncertain.

The rate of successful treatment (75.3% overall and 79.0% in civil sector) though below the 85% WHO target, was higher than reported from other several DOTS pilot regions in the former Soviet Union (68.1% according to the meta-analysis performed by Faustini et al, 2005 [ 25 ]). The zero mortality among prisoners may be misleading: several patients died after data censoring. Furthermore, policy that very severely ill patients are released from prison for treatment in the civilian sector means that deaths of these ex-prisoners are recorded as civilian deaths.

The high prevalence of drug resistance and the frequency of the Beijing strain family (previously shown to be associated with drug resistance) [ 19 ] remains a major clinical and public health challenge. Extremely high rates of drug resistance among prisoners despite significantly lower default rates in prison likely reflects on-going transmission of resistant strains. Rapid isolation of MDR TB cases, good co-ordination between the prison and civilian TB services and enhancement of infection control and treatment are needed to prevent further nosocomial and institutional spread of MDR TB and would increase the success of the current TB programme. This issue is likely to become considerably more of a problem as the emergent epidemic of HIV in Samara matures

Although the DOTS strategy does not include specific therapy for multi-drug resistant cases its effective implementation reduces the occurrence and further transmission of resistant strains [34]. However, in regions such as Samara with very high rates of MDR TB it is essential to ensure the availability of appropriate and timely diagnosis and treatment of existing cases as well as preventing the development of new ones. Rapid drug susceptibility techniques, with appropriate treatment to be tailored to circumstance may be necessary. Cost-effectiveness analysis of rapid methods in the post-Soviet context is required to inform investment and policy changes.

Abbreviations

Confidence Interval

Directly Observed Therapy Short-Course

multi-drug resistant tuberculosis

Supranational Reference Laboratory

tuberculosis

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Acknowledgements

The UK Department for International Development (DFID) funded this study, but the views and opinions expressed are those of the authors alone.

We would like to thank all doctors and nurses who took part in this study.

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Center for Health Management, Tanaka Business School, Imperial College London, South Kensington campus, London, SW7 2AZ, UK

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YB participated in the design of the study, its coordination, acquisition of data, statistical analysis and drafted the manuscript; RC participated in acquisition of funding, design of the study, its supervision and drafted the manuscript; IF participated in administration of the study, its design and revision of the manuscript; SZ participated in design and coordination of the study, data acquisition and manuscript revision; VN carried out laboratory work and revised the manuscript, RA participated in acquisition of funding, design of the study, its supervision and revision of the manuscript, FD participated in acquisition of funding, design of the study, its supervision and gave final approval of the version to be published. All authors read and approved the final manuscript.

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Balabanova, Y., Drobniewski, F., Fedorin, I. et al. The Directly Observed Therapy Short-Course (DOTS) strategy in Samara Oblast, Russian Federation. Respir Res 7 , 44 (2006). https://doi.org/10.1186/1465-9921-7-44

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DOI : https://doi.org/10.1186/1465-9921-7-44

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Centralized vs. decentralized cloud computing in healthcare.

1. Introduction

• RQ1: What are the benefits and limitations of the use of centralized cloud computing in healthcare that aims to improve HIE?
• RQ2: What are the benefits and limitations of the use of decentralized cloud computing in healthcare that aims to improve HIE?
• RQ3: What are the differences between centralized and decentralized cloud healthcare solutions regarding the system’s performance, response times, latency, the privacy of patient data, and cost efficiency?
• RQ4: What are the research gaps within centralized cloud healthcare solutions?
• RQ5: What are the research gaps within decentralized cloud healthcare solutions?

2. Health Information Exchange

3. cloud computing, 3.1. cloud computing deployment models, 3.2. cloud computing in healthcare, 4. research methodology.

• The identification of the research problem;
• The determination of the research questions.
• The determination of the search strategy;
• The determination of the study selection criteria;
• The determination of the study selection process.
• The definition of the dissemination strategy;
• The formatting of the report;
• The evaluation of the report.

4.1. Planning Phase

4.1.1. defining the need for a paper, 4.1.2. determining the research questions, 4.2. conducting phase, 4.2.1. search strategy.

• Scholarly databases and open-access platforms formed the foundation of this paper, including the following: IEEE Xplore, ACM Digital Library, SpringerLink, Elsevier, and MDPI. These papers were selected based on the most renowned and trusted publishers that researchers widely rely on. They provide subscriptions and free access to the majority of papers;
• To locate pertinent data on cloud computing systems in HIE, specific terminological queries were formulated: “Cloud computing”, “health information exchange in Cloud computing”, “centralized Cloud computing in healthcare”, “decentralized Cloud computing in healthcare”, “Healthcare Integration”, “Electronic Health Records (EHR) Integration”, “Unified Healthcare Records”. Alternative spellings were also taken into consideration while searching through the names of the libraries; the libraries’ basic search operators used OR and AND to allow for a broader search. The keywords in each group were connected using the OR operator, while between the groups of keywords, the connection was made using the AND operator.

4.2.2. Study Selection Criteria

• When choosing the best scientific paper from a pool of research papers, the process involves critically evaluating and refining the set of papers to include only those that are most relevant, high-quality, and suitable for the specific research objectives, which is essential for establishing the criteria for including or excluding them. These criteria usually consider factors such as the following: – The paper is relevant to the research questions; – The paper is highly related to the topic; – Methodological rigor and validity of investigative approaches; – The investigation focused on scholarly works disseminated, spanning from 2011 to 2024. The following ensured that the papers included in this review are relevant and met a certain level of quality:
• Redundant entries and cross-database duplicates were eliminated, ensuring a unique representation of each scholarly work in the final literature pool;
• Bibliographic analysis was conducted to find additional related articles.

4.2.3. Study Selection Process

• The initial phase involved querying predetermined terminological parameters to generate an initial foundational research set. We used Google Scholar to reach the research papers in famous journals. And for identifying the papers, we performed it two ways manually and using RAYYN. Over 324 original articles were reviewed after examining several models adopted by previous researchers. In order to obtain a reasonable number of reviewed articles, over 57 articles from the ACM digital library were downloaded, 73 articles from the IEEE digital library, 64 from MDPI, 49 from ELSEVER, and 81 from Springer. A small number of articles from less popular journals were also included. The articles reviewed were refined to 183, due to the similarity observed in some researchers’ models and approaches. Figure 3 illustrates the PRISMA flow diagram.
• In step two, the study titles were analyzed, the duplicates removed, and the inclusion/exclusion criteria were applied. In total, 141 studies were excluded, due to their irrelevant titles and because they were duplicates or included outdated solutions.
• In the third step, the resulting 98 studies were selected as their abstract and keywords complied with the selection criteria.
• In the fourth step, following a thorough full-text analysis, 53 studies were selected for this paper.

4.3. Reporting Phase

• Adherence to standardized documentation protocols in manuscript preparation;
• The assessment of the document’s accuracy and academic quality is thorough and rigorous.

5. Centralized Cloud Computing

6. decentralized cloud computing, 7.1. rq1: what are the benefits and limitations of the use of centralized cloud computing in healthcare that aims to improve hie, 7.1.1. benefits of the use of centralized cloud computing in healthcare, 7.1.2. limitations of centralized cloud computing in healthcare, 7.2. rq2: what are the benefits and limitations of the use of decentralized cloud computing in healthcare that aims to improve hie, 7.2.1. benefits of the use of decentralized cloud computing in healthcare, 7.2.2. limitations of decentralized cloud computing in healthcare, 7.3. rq3: what are the differences between centralized and decentralized cloud healthcare solutions regarding the system’s performance (response times, latency), the privacy of patient data, and cost efficiency, 7.3.1. architecture, 7.3.2. data management, 7.3.3. system performance and latency, 7.3.4. security and privacy of patient data, 7.3.5. cost efficiency, 7.4. rq5: what are the research gaps within centralized cloud healthcare solutions, 7.5. rq6: what are the research gaps within decentralized cloud healthcare solutions, 8. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Abughazalah, M.; Alsaggaf, W.; Saifuddin, S.; Sarhan, S. Centralized vs. Decentralized Cloud Computing in Healthcare. Appl. Sci. 2024 , 14 , 7765. https://doi.org/10.3390/app14177765

Abughazalah M, Alsaggaf W, Saifuddin S, Sarhan S. Centralized vs. Decentralized Cloud Computing in Healthcare. Applied Sciences . 2024; 14(17):7765. https://doi.org/10.3390/app14177765

Abughazalah, Mona, Wafaa Alsaggaf, Shireen Saifuddin, and Shahenda Sarhan. 2024. "Centralized vs. Decentralized Cloud Computing in Healthcare" Applied Sciences 14, no. 17: 7765. https://doi.org/10.3390/app14177765

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Tuberculosis in Healthcare Workers and Infection Control Measures at Primary Healthcare Facilities in South Africa

Mareli m. claassens.

1 Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Stellenbosch University, Parow, South Africa

5 Department of Clinical Epidemiology, Biostatistics and Bio-informatics, University of Amsterdam, Amsterdam, The Netherlands

Cari van Schalkwyk

2 The South African Department of Science and Technology / National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis, Stellenbosch University, Stellenbosch, South Africa

Elizabeth du Toit

Eline roest, carl j. lombard.

3 Biostatistics Unit, Medical Research Council, Parow, South Africa

Donald A. Enarson

4 The International Union Against Tuberculosis and Lung Disease, Paris, France

Nulda Beyers

Martien w. borgdorff.

Conceived and designed the experiments: MMC NB DAE. Performed the experiments: EDT MMC. Analyzed the data: MMC CVS ER CJL MWB. Wrote the manuscript: MMC CVS ER EDT CJL NB DAE MWB.

Challenges exist regarding TB infection control and TB in hospital-based healthcare workers in South Africa. However, few studies report on TB in non-hospital based healthcare workers such as primary or community healthcare workers. Our objectives were to investigate the implementation of TB infection control measures at primary healthcare facilities, the smear positive TB incidence rate amongst primary healthcare workers and the association between TB infection control measures and all types of TB in healthcare workers.

One hundred and thirty three primary healthcare facilities were visited in five provinces of South Africa in 2009. At each facility, a TB infection control audit and facility questionnaire were completed. The number of healthcare workers who had had TB during the past three years was obtained.

The standardised incidence ratio of smear positive TB in primary healthcare workers indicated an incidence rate of more than double that of the general population. In a univariable logistic regression, the infection control audit score was significantly associated with reported cases of TB in healthcare workers (OR=1.04, 95%CI 1.01-1.08, p=0.02) as was the number of staff (OR=3.78, 95%CI 1.77-8.08). In the multivariable analysis, the number of staff remained significantly associated with TB in healthcare workers (OR=3.33, 95%CI 1.37-8.08).

The high rate of TB in healthcare workers suggests a substantial nosocomial transmission risk, but the infection control audit tool which was used did not perform adequately as a measure of this risk. Infection control measures should be monitored by validated tools developed and tested locally. Different strategies, such as routine surveillance systems, could be used to evaluate the burden of TB in healthcare workers in order to calculate TB incidence, monitor trends and implement interventions to decrease occupational TB.

Introduction

The World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) have proposed practical low cost interventions to reduce nosocomial transmission of Mycobacterium tuberculosis in resource limited settings [ 1 , 2 ]. TB disease in healthcare workers can be used as a proxy to quantify nosocomial TB transmission in low and middle income countries such as Thailand and Malawi [ 3 ]. Evidence from systematic reviews reinforces the need to design and implement simple, effective and affordable TB infection control measures in healthcare facilities [ 3 , 4 , 5 ]. Such measures conserve resources in terms of direct and indirect costs and reduce the TB burden [ 1 ]. To evaluate the effectiveness of infection control measures, the CDC has developed a TB infection control audit tool [ 2 ] aiming to be applicable to different settings.

Studies have been published internationally and in South Africa about TB in healthcare workers. Five per cent of healthcare workers in a study from Uganda reported having had TB in the past five years [ 6 ]. In Nigeria 3.3% of healthcare workers were acid fast bacilli positive [ 7 ]. A study from India showed healthcare workers employed in medical wards who had frequent contact with any patients had a higher odds of developing TB [ 8 ]. A case series from KwaZulu-Natal in South Africa [ 9 ] reported the psychosocial impact of drug resistant TB on five human immunodeficiency virus (HIV) negative doctors who, after they recovered from their illness and because of their disease experience, had minimal or no involvement with TB patients. Another study [ 10 ] reported four of the ten extremely drug resistant TB cases had died by the time of publication. O’Donnell et al [ 11 ] reported an incidence rate ratio of 5.5 for multidrug-resistant (MDR) TB hospital admissions in healthcare workers compared to the general population. A tertiary hospital reported both drug sensitive and drug resistant TB were potentially transmitted nosocomially [ 12 , 13 ]. Other studies reported poor infection control measures at primary healthcare facilities [ 14 ] and TB hospitals admitting drug resistant cases [ 15 ].

Substantial challenges thus exist regarding TB infection control and TB in hospital-based healthcare workers in South Africa. However, few studies report on TB in non-hospital based healthcare workers such as primary or community healthcare workers. A standardised TB incidence ratio of 2.5 was shown amongst community-based healthcare researchers in comparison to the communities where they worked and lived [ 16 ] and a TB prevalence of 5% was documented amongst community healthcare workers in Cape Town [ 17 ] albeit in a small non-representative sample. TB in primary healthcare workers has not yet been described in the South African context.

The objectives of this study were to investigate the implementation of TB infection control measures at primary healthcare facilities in five provinces of South Africa, the smear positive TB incidence rate in healthcare workers and the association between TB infection control measures and all types of TB in healthcare workers.

Methodology

Ethics approval was obtained from Stellenbosch University (N09/02/038) and the Ethics Advisory Group of the International Union against Tuberculosis and Lung Disease (03/2009). Questionnaires were barcoded for confidentiality and quality control. Facility names were deleted from the database and anonymously linked by the data manager. Facility managers signed informed consent prior to enrollment. Permission to do the study in the provinces was obtained via the National Department of Health.

Study design

In a cross sectional ecological study 133 primary healthcare facilities were visited between May and September 2009 in five provinces of South Africa. The unit of investigation was a primary healthcare facility. The facilities were systematically sampled from a list of facilities supported by the University Research Corporation (URC) as part of their Technical Assistance and Support Contract II, Tuberculosis (TASC II TB) project [ 18 ] in districts identified by the National TB Crisis Plan as areas performing poorly with regards to the TB Programme and comprising 20% of the TB burden in South Africa. The TASC II TB project reached 659 facilities in 11 districts over a period of five years.

A healthcare worker was defined as any individual employed at the facility.

Exposures and outcome

At each facility, the research team completed a TB infection control audit and a questionnaire answered by the facility manager or another focal person, who was defined as a healthcare worker working in the TB room with knowledge of the TB programme.

The main determinant, TB infection control measures, was evaluated by using an audit tool at a single point in time at each facility. This tool included questions about administrative, environmental and personal respiratory protection measures as specified in the CDC template [ 2 ]. A score was calculated by counting the number of ‘yes’ responses (indicating a good infection control measure) which was scored 1, ‘no’ responses (indicating a poor infection control measure) which was scored -1 and ‘unknown’ or ‘not applicable’ responses which was scored 0. The total score was treated as a continuous variable with a higher score indicating better infection control. Items were not weighted. The research team received training on how to perform an infection control audit before the study commenced.

Information on smear positive TB and other types of TB amongst healthcare workers was captured in the questionnaire. The total number of healthcare workers who had had TB at each facility during the period January 2006 through December 2008 was obtained from the questionnaire. Individual data of healthcare workers were not captured. TB infection status or HIV data were not included in the study. The questionnaire included questions on the number of staff at each facility by calendar year, the geographical location of the facility (rural/urban and province), whether the facility had a fast track for TB suspects or patients and/or an area designated specifically for the treatment of TB patients, whether the facility had an occupational health policy and whether healthcare workers were screened for TB.

Sample size calculation

The hypothesis was that the Pearson correlation coefficient between the TB incidence rate in healthcare workers and the audit tool score would be ρ=0.3, with the assumption that a linear relationship exists. A 0.05 two-sided Fisher’s z-test had 90% power for ρ=0.3 to be significant when the sample size was 113; 30 facilities were systematically sampled from a random starting point on the URC list for each province except for province 5 where only 16 facilities were supported by URC (where all were selected). A total of 136 facilities were selected to account for the possibility of missing data. Of these facilities, 133 were visited of which 12 were excluded from the analyses as they were unable to provide any information about TB in healthcare workers. Three facilities in Mpumalanga province were not visited because of time constraints.

Data analysis

The smear positive TB incidence rate in healthcare workers was calculated as the number of healthcare workers who developed smear positive TB (numerator) divided by the total number of healthcare workers (denominator) for each of the three years included in the study. In order to compare the TB incidence rate among healthcare workers with the general population the incidence rate was combined for all facilities. For comparison with the general population we calculated a standardised incidence ratio, using indirect standardisation for age and sex, as was done in other studies [ 19 , 20 ].

As 64% of the facilities did not report any healthcare workers with TB (all types) for the study period, we then classified the facilities according to whether or not there was any healthcare worker with TB in the study period. This binary variable was used as the primary outcome in a logistic regression analysis. The audit tool score (as a continuous variable) was investigated as primary determinant. The association between the score and smear positive TB incidence for 2008 is graphically demonstrated by categorising the score into quintiles ( Figure 1 ). The variable for the number of staff per facility did not have a normal distribution and was logistically transformed (natural logarithm) before inclusion as a possible confounder. For the multiple imputation of missing values, the imputation model included all the variables investigated as predictors of TB in healthcare workers. Data were analysed using STATA 12 (StataCorp LP, College Station, TX, USA).

α Smear positive TB incidence did not differ significantly between quintiles. *A higher quintile is an indication of better infection control measures.

TB incidence rate

The smear positive TB incidence rate per 100,000 persons was 834 (95%CI 431-1457) in 2006, 1,092 (95%CI 647-1725) in 2007 and 887 (95%CI 517-1420) in 2008 across the five provinces for all facilities (including those where there were no TB cases in healthcare workers). The standardised incidence ratio ( Table 1 ) was 2.4 (95%CI 1.2-4.2) in 2006, 3.0 (95%CI 1.8-4.7) in 2007 and 2.3 (95%CI 1.3-3.7) in 2008.

Infection control

The range of the total score of the audit tool was -22 to +51 ( Table 2 ). The mean number of staff per facility was 17 (IQR 8-23) at the visit. Forty four facilities (36%) had had healthcare workers with TB disease in the study period. Sixty six facilities (55%) were urban. Facilities were distributed across provinces with the most facilities from province 1 (n=28) and fewest from province 5 (n=14). Seventy seven facilities (64%) had a TB area/room and 106 (88%) a fast track for TB suspects/patients. Forty eight facilities (40%) had an occupational health policy and 43 (36%) reported TB screening for healthcare workers.

Administrative control measures were assessed with the audit tool ( Table 3 ). The major differences between facilities were: 28 of 44 facilities with TB cases (64%) separated infectious patients from non-infectious patients compared to 32 of 77 facilities (42%) without TB cases. Forty facilities (91%) with TB cases trained healthcare workers on infection control compared to 50 facilities (65%) without TB cases. With regards to environmental controls, 33 facilities (75%) with TB cases reported the use of cross ventilation compared to 52 facilities (68%) without TB cases. 23 (52%) facilities with TB cases used propeller fans compared to 56 (73%) facilities without TB cases. Thirty facilities (68%) with TB cases reported exhaust ventilation systems compared to forty two facilities (55%) without TB cases. Eight facilities (18%) with TB cases had previously had audits compared to one facility (1%) without TB cases. Overall only four facilities (3%) had an area designed to separate possible or confirmed MDR-TB cases and six facilities (5%) had a written respiratory protection plan.

Infection control measures as risk factors for TB

The smear positive TB incidence for 2008 per audit tool score quintile is shown in Figure 1 , indicating a similar TB incidence in all quintiles. In the univariable logistic regression ( Table 4 ), the total audit tool score as a continuous variable was significantly associated (per unit of the score) with whether the facility had TB (all types) in healthcare workers (OR=1.04, 95%CI 1.01-1.08, p=0.02) implying that facilities with TB cases had better infection control measures. Significant associations were observed for environmental controls (OR=1.12, 95%CI 1.01-1.23), number of staff at a facility (OR=3.78, 95%CI 1.77-8.08) which means a 3.78 increase in odds for every 2.72 (natural logarithm) increase in the number of staff, and whether a facility had a TB room/area (OR=3.24, 95%CI 1.37-7.65).

Although facilities with more staff had a higher score (coeff 5.69, 95%CI 2.24-9.14, p=0.002), there was no interaction between the number of staff and the score in association with whether the facility had any TB cases (data not shown). The separate scores for administrative and personal controls, geographic location and whether a facility had a fast track for TB patients, an occupational health policy or health screening for staff were not associated with TB in healthcare workers. In the multivariable analysis, the number of staff (OR=3.33, 95%CI 1.37-8.08) remained significantly associated with TB in healthcare workers indicating a confounding effect.

In our study the standardised incidence ratio for smear positive TB in primary healthcare workers indicated an incidence rate more than double that of the general population for each of the three years. TB in healthcare workers (all types of TB) had a weak association with infection control measures in the unadjusted models, particularly with environmental measures, indicating the relative importance of these measures and mirroring findings showing the effectiveness of natural ventilation [ 21 ]. Our results indicate that (i) occupational TB is concerning amongst primary healthcare workers and (ii) the audit tool did not perform as expected as a measure of nosocomial transmission risk since we found inverse associations, in other words the presence of nosocomial transmission risk was associated with better infection control measures.

TB incidence in healthcare workers in South Africa was previously (1986-1997) reported [ 22 ] as significantly lower than in the general population [ 23 ], but in hospital-based healthcare workers it increased from 1,024 to 1,641 per 100,000 (1999-2003), significantly higher than in the general population [ 24 ]. In our study, we focused on healthcare workers at primary healthcare facilities, a cadre not previously investigated in South Africa. We showed an occupational risk of TB similar to the risk in hospital-based healthcare workers indicating an urgent need for interventions to limit occupational exposure. However, because of the high TB incidence in the general population in South Africa, ways of measuring occupational risk per se should be investigated.

Our study also focused on TB infection control. Infection control audits are characterised by a cycle of four parts [ 25 , 26 ]: (i) standards are set, (ii) infection control measures and outcomes are evaluated against these standards, (iii) measures are corrected if needed and (iv) re-auditing completes the cycle. In general, nosocomial infection rates are the outcome of primary interest for infection control audits [ 26 ], albeit the most difficult to evaluate. Repeated surveys could be used to measure and compare infection rates in healthcare workers [ 26 , 27 ]. Our study evaluated TB infection control measures taking into account that the South African Department of Health developed TB infection control and occupational TB guidelines in 2007 [ 28 ]. According to an infection control audit cycle, we were evaluating the measures and outcomes against the standards. However, data on TB in healthcare workers were not routinely captured and we depended on information gathered from a focal person at each facility. This is in contrast to other areas, for instance a routine electronic notification system used in Samara Oblast (Russia) capturing individual demographic, clinical and epidemiological data, including employment information [ 29 ] to calculate TB incidence in healthcare workers.

Of interest is the possible association between infection control measures and TB in healthcare workers. We could not identify studies where this association was investigated despite numerous studies on TB infection control [ 3 , 4 , 5 , 14 , 30 ] as proposed by the Stop TB 3 I’s strategy [ 31 , 32 ]. However, after adjusting for the number of staff per facility no association between the audit score and whether a facility had had healthcare workers with TB was found. These findings lead to speculation as to whether the tool was a sufficient indicator of infection control.

Firstly, the question should be asked whether the same audit tool could be used in different contexts. In countries with a low TB incidence like the United States of America (USA), the implementation of infection control guidelines has proven to be effective in the prevention of outbreaks of nosocomial disease and in decreasing the rate of infection in healthcare workers [ 2 ] but a recent study comparing China with the USA [ 33 ] indicated that an infection control manual developed by the China Centers for Disease Control did not include data from China and was based mostly on expert opinion. Ideally countries should study local infection control measures to inform their guidelines and tools, for instance studies to identify problems, evaluate new policies and monitor the implementation of policies [ 33 ], rather than using tools developed for different health systems, disease burdens and resources. For instance, a tool derived from the CDC template but focusing on specific elements (developed by the Wits Reproductive Health and HIV institute) [ 34 ] could be evaluated in the South African context. Secondly, the rationale for doing infection control audits should be re-evaluated. If audits do not give an indication of disease in healthcare workers as a proxy for nosocomial transmission but are used only as a process management tool when healthcare workers are overburdened already, is it worthwhile?

Infection control is a cardinal part of healthcare facilities, whether hospitals or primary facilities. However, when evaluating the effectiveness of infection control measures the measurement tools should be validated before programmatic implementation to ensure that a false sense of security amongst healthcare workers does not mask nosocomial transmission.

Limitations

Our study was limited by the measurement of TB in healthcare workers over a period of three years, while the infection control audit was done at a single point in time. Facilities may have changed their practices prior to the audit, for instance the facilities with the highest risk may have implemented infection control measures and these changes would not be reflected in our study, thereby introducing the possibility of reverse causality. Future investigators should consider prospective cohorts of healthcare workers with regular infection control audits and routine surveillance in health facilities.

We depended on information from a single source about TB in healthcare workers. This may have underestimated the number of healthcare workers with TB, but it also meant HIV status could not be captured. However, since other studies have indicated a similar HIV prevalence among healthcare workers as the general population [ 35 , 36 ] although not age/sex standardised, we do not expect that HIV has confounded the standardised incidence ratio or the absence of association with infection control measures.

Implications

Effective infection control measures are essential at all health facilities especially in high TB/HIV prevalence settings. These measures should be monitored by validated tools tested locally. In our study, the infection control audit tool did not perform well as a measure of nosocomial transmission risk and poor infection control measures were not associated with TB in healthcare workers. Other strategies to document and monitor TB in healthcare workers should be explored, for instance repeated surveys of TB in healthcare workers which could give an indication of how infection control measures are functioning and/or improving. If the TB burden in healthcare workers in comparison to the general population continues to rise, one would assume that the nosocomial transmission risk is increasing. Such surveys would be in addition to what is already occurring at facilities, requiring additional resources and planning in advance. A national TB prevalence survey would not give an adequate baseline estimate of TB in healthcare workers since too few healthcare workers would be included in such a survey. We thus recommend that the Department of Health should implement a confidential surveillance system for the routine documentation of TB in healthcare workers at facilities. Resources should be made available for diagnostic and therapeutic care for healthcare workers without subjecting them to stigmatisation. Such a routine surveillance system could be used to calculate TB incidence in healthcare workers and monitor trends.

Acknowledgments

The study was done with permission and in collaboration with the National Department of Health. We would like to thank the Desmond Tutu TB Centre staff and the communities where the study was undertaken. This article is in partial fulfillment of a PhD.

Funding Statement

This study was funded by University Research Corporation (URC) Grant FY09-G03-4710 under USAID Contract No. GHS-I-00-03-00032-00 (TASCII TB South Africa). The authors had full control over the data and did not have an agreement with the funders that may have limited the completion of the study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.