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      Continuity in primary care: a critical but neglected component for achieving high-quality universal health coverage

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          Abstract

          Summary box Continuity is a critical but often neglected function of high-quality primary care and has three core domains: relational, informational and managerial continuity. Improving continuity is feasible in low-income and middle-income country health systems by using comprehensive empanelment systems or community-based follow-up programmes to improve retention in care. Continuity must receive more attention, measurement and improvement efforts, in order to achieve equitable, high-quality health for all. Introduction Despite increasing attention to what the goals for universal health coverage are,1 the global health community still lacks clarity on how low-income and middle-income countries (LMICs) can strengthen health systems to reach these ambitious goals, while ensuring quality at the same time.2 In October 2018, the world commemorated the 40th anniversary of the Alma-Ata declaration and issued the Astana Declaration on Primary Health Care, clearly centralising the universal health coverage agenda within an overall framing of strong primary healthcare, and offering guidance for the way forward.3 To meet the Sustainable Development Goals4 and deliver quality universal health coverage, LMIC health systems will need more resources; they will need to be redesigned around the core elements of high-quality primary care. The Starfield ‘4C’ functions of effective primary care—first-contact access, continuity, care coordination and comprehensiveness—offer meaningful targets for policy and planning of primary care in LMICs.5 Unfortunately, health systems have not historically been designed or resourced to reliably provide these core functions of primary care. While access, coordination and comprehensiveness have garnered some policy focus, continuity of care has received relatively little attention within LMICs. Continuity refers to coherent, linked care, between patients, families, communities and providers, across lifetimes. Continuity consists of understanding individuals’ contexts, with longitudinal clinical information, and using this knowledge to build trusting relationships over time.6 In higher-income settings, improved continuity has been associated with greater patient satisfaction, improved medication adherence, lower hospitalisation rates and lower mortality.7 However, in LMIC literature, there are scant systematic efforts to measure or improve continuity.7 Many LMIC health systems provide acute, episodic care, delivered by different providers at different facilities, on a condition-by-condition basis. Such care delivery is increasingly inadequate and outdated as the burden of non-communicable diseases, including mental and behavioural disorders, grows within LMICs.6 In this context, a reorientation of care delivery to provide more continuity will be essential to achieve the Sustainable Development Goals and universal health coverage. Continuity in primary care: a neglected component of the development agenda Haggerty and colleagues describe three types of continuity: relational, informational and managerial (table 1).6 These three domains of continuity are key components of high-quality primary care, and LMIC health systems must make investments and progress in each in order to achieve high-quality universal health coverage. Table 1 The three types of continuity and examples of each* Definition Intervention Examples Relational An ongoing therapeutic relationship between a patient (and often their family) with one or more providers Empanelment and multidisciplinary team-based care Costa Rica’s Equipos Básicos de Atención Integral en Salud care team structure13 Informational The use of information on past events and personal circumstances to make current and future care appropriate for each patient and family Electronic data systems that are interoperable with unique patient identifiers across settings Public-sector electronic health record system in Nepal9 Managerial A consistent and coherent approach to the management of a patient’s health conditions, that is responsive to changing needs over time Management standards of care and multidisciplinary team-based care Patient-centred medical home models in tribal populations in Alaska14 *Adapted from Haggerty, et al (2003).6 Relational continuity Of the three types of continuity, relational continuity is most prominently experienced by patients and communities. Relational continuity refers to sustained, healing relationships between patients and providers that cultivate trust and engagement. These relationships are foundational to the improvements in health and well-being that primary care can provide.6 In many historically marginalised and impoverished communities within LMICs, the formal health system is often unfamiliar and distrusted. Strong relational continuity can engage these communities, building trust and involving them in improving their health over time.6 Recent data from Ghana show improved patient-reported responsiveness outcomes with greater continuity, helping to address negative perceptions of transactional, episodic and fragmented care delivery.8 By improving provider consistency, LMICs can build relational continuity that promotes better overall care. However, attempts to improve continuity exist in tension with access. Increasing continuity may decrease access, and vice versa, leading to a difficult ‘either/or’ dynamic of access versus continuity. For example, to increase continuity for a particular group of patients, their providers may need to limit the overall number of patients they are responsible for, thus potentially decreasing access for some, while improving continuity for others. Understanding these tensions, and aiming for improved—but not perfect—continuity is strategically advisable. Informational continuity Patients experience continuity as a seamless integration of their information by their known providers over time,6 promoting a sense of security and trust within these relationships. Without clearly documented and easily accessible information about medical history and demographics—including information about social determinants of health—health systems and providers cannot ensure safe, high-quality services over time. In many LMICs, where civil registration, vital statistics and health record systems are weak or entirely non-existent, this type of informational continuity is difficult to achieve.9 While some LMICs do have robust health management information systems, these serve primarily facility or population-level purposes and do not provide patient-level records, which are commonly relegated to ‘cards’ or ‘booklets’ with scant, encounter-based information that is insufficient to provide informational continuity. Alternatively, these health record systems may exist for certain vertical programmes such as HIV or tuberculosis, but are not well integrated between programmes. Electronic health records are nascent in some places, but rarely scaled across the entire population. Where present, they are often disconnected such that patients have multiple records across care settings, leading to further fragmentation.9 Looking forward, significant attention and investments are required to strengthen the infrastructure for patient-level information systems, including centralised, interoperable electronic health records. While these systems by themselves do not guarantee strong informational continuity—and indeed, many high-income settings still struggle to optimise use of electronic health records10—they can help to connect individuals’ records across settings as patients are referred or migrate between locations. Empowering both patients and providers, stronger data architecture, whether paper-based or digital, can make informational continuity a feasible reality in settings where, currently, each care episode is distinct and disconnected from past and future. Managerial continuity Managerial continuity is the consistent, coherent management of patients’ health conditions, responsive to changing needs over lifetimes, and across different levels of care.6 This requires integrating experiences of care in ways that make sense for patients and families, thus enabling adherence to care plans. Many LMIC health systems, however, already grappling with insufficient workforces and fragmented referral systems, struggle to provide managerial continuity. Accordingly, both additional resources and system redesign with an aim towards integration are required. Mid-level providers11 and community health workers,12 operating in multidisciplinary teams,5 are practical options to improve access and continuity. Multidisciplinary teams bolster facility-based workforces, and when properly supervised and trained, enable more standardised, integrated care.11 12 Examples from Costa Rica13 and indigenous communities in Alaska14 demonstrate that these multidisciplinary teams can serve poor, rural populations, providing high-quality community-based services, including regular follow-up, referral tracking, medication adherence counselling, risk modification and early warning to clinical worsening.12 This type of team-based care enables improved managerial continuity, and is particularly important for patients with multiple chronic conditions, for whom many disconnected episodes of care can lead to complex treatment plans that are difficult to incorporate into their lives.5 15 Pragmatic strategies to improve continuity in primary care For many LMIC health systems, improving continuity will be greatly challenging, requiring long-term, iterative improvement initiatives aimed at both increasing resources and redesigning care delivery. Nonetheless, there are practical, feasible strategies to improve continuity in these settings, with promising examples already in place in many settings globally. Empanelment Empanelment (also known as ‘rostering’) is the active and ongoing assignment of an individual or family to a primary care provider or team.5 16 Empanelment establishes a regular point of care for patients, and holds providers accountable for actively tracking and managing the health of a specific group of individuals. Empanelment also provides a population denominator to enable data tracking, interpretation and iterative improvement of care plans.16 17 When done well, empanelment enables both managerial and relational continuity and, when combined with strong data systems, informational continuity as well. For these reasons, empanelment has been heralded as a priority for LMIC health systems by global partnerships such as the Primary Healthcare Performance Initiative (www.improvingphc.org) and the Joint Learning Network (www.jointlearningnetwork.org).16 17 In Costa Rica, empanelment systems have been in place for decades, even in the absence of digital data architecture, using community health systems and multidisciplinary teams to provide strong relational, informational and managerial continuity.13 Nonetheless, to date, for most LMICs, empanelment is either very weak or completely non-existent.5 To achieve the goals of patient-centred universal health coverage, this should be a priority for development partners and governments in future agenda-setting. Integrated community-based follow-up In many LMIC health systems, while empanelment may be a long-term goal, it may not be feasible in the immediate short term. In such settings, programmes aimed at improving retention in care and community-based follow-up after hospitalisation or outpatient visits can offer meaningful improvements in continuity. Historically, many vertical, disease-specific programmes for HIV/AIDS18 and tuberculosis19 delivered impressive results in community-based follow-up of patients, improving retention in care over time. More recently, initiatives to ‘horizontalize’ these programmes across a wider spectrum of conditions and patient groups have emerged as well. Data from Uganda20 show the feasibility of community outreach workers improving retention in care across a broad range of conditions, including non-communicable diseases, and decreasing overall loss-to-follow-up (LTFU) rates. A public-private partnership in rural Nepal has similarly shown strong continuity over time in a cohort of patients with diabetes, hypertension and chronic obstructive pulmonary disease, using an integrated system of community-based and facility-based care delivery.21 These programmes, while not yet at the national level, nor as comprehensive as full empanelment systems, demonstrate feasible, practical short-term options for LMICs to improve continuity in a stepwise manner over time. You cannot improve what you do not see Given that many LMICs currently have minimal primary care continuity, developing better metrics, and then acting on resulting data to improve continuity, is an urgent priority for achieving high-quality universal health coverage. The most commonly used LMIC continuity measure has been LTFU,20 22 a metric of success for vertical programmes such as HIV/AIDS and tuberculosis. Policy makers and donors have staked significant resources on achieving low LTFU rates, demonstrating multisectoral alignment for this type of continuity, though the metric has rarely been applied to primary care services. LTFU rates are, however, a rather crude metric, providing only limited insights into informational and managerial continuity, and little data describing relational continuity. In higher-income settings, more holistic metrics have been developed, attempting to describe all three continuity domains (table 2). While still imperfect, expanded use of these metrics can advance a health system’s understanding of its continuity. To be applicable in LMIC settings, these measurements will need adaptations, including a recognition of the role that multidisciplinary teams play in LMICs, and a concomitant shift away from physician-specific care that is more common in much of Europe and USA.5 7 Furthermore, to advance this work for the future, there is a critical need for a dedicated research agenda to better understand both the measurement of, and improvement strategies for, continuity within LMIC health systems. Table 2 Measures of patient continuity* Measure Calculation Provider-sided continuity % of total visits for a provider with patients on the provider’s panel Patient-sided continuity % of total primary care visits for a patient in which the patient sees their empaneled provider Usual provider continuity % of all provider visits seen with “usual provider” Known provider continuity % of time a patient saw a provider that they had seen previously in the prior year Continuity of care index (n2 – N) / (N*(N −1))n = # of visits to a specific provider; N=total # of visits Sequential continuity % of time a patient saw the same provider from their previous visit Modified, Modified Continuity Index (MMCI) (1 − (P/(V+0.1)))/(1 − (1/(V+0.1))P=total # of providers; V=total # of visits *Adapted from Paul, et al (2018).23 Conclusion As the global health community strives towards universal health coverage and the Sustainable Development Goals by 2030, there must be a focus on the key competencies of high-quality, patient-centred primary care delivery. The three domains of continuity deserve more attention, measurement and improvement efforts. Stronger continuity is feasible for LMIC health systems in the short-term and long-term through comprehensive empanelment systems and initiatives such as community-based follow-up programmes to improve retention in care. Continuity enables safer, more effective and more patient-centred care, which are core components of both high-quality healthcare and the universal health coverage agenda. Without improvements in continuity, quality universal health coverage will be impossible. As such, continuity is no longer a goal for high-income countries only; it must become a tracked priority of all health systems to meet their stated ambitions, in order to achieve equitable, high-quality health for all.

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          Continuity of care with doctors—a matter of life and death? A systematic review of continuity of care and mortality

          Objective Continuity of care is a long-standing feature of healthcare, especially of general practice. It is associated with increased patient satisfaction, increased take-up of health promotion, greater adherence to medical advice and decreased use of hospital services. This review aims to examine whether there is a relationship between the receipt of continuity of doctor care and mortality. Design Systematic review without meta-analysis. Data sources MEDLINE, Embase and the Web of Science, from 1996 to 2017. Eligibility criteria for selecting studies Peer-reviewed primary research articles, published in English which reported measured continuity of care received by patients from any kind of doctor, in any setting, in any country, related to measured mortality of those patients. Results Of the 726 articles identified in searches, 22 fulfilled the eligibility criteria. The studies were all cohort or cross-sectional and most adjusted for multiple potential confounding factors. These studies came from nine countries with very different cultures and health systems. We found such heterogeneity of continuity and mortality measurement methods and time frames that it was not possible to combine the results of studies. However, 18 (81.8%) high-quality studies reported statistically significant reductions in mortality, with increased continuity of care. 16 of these were with all-cause mortality. Three others showed no association and one demonstrated mixed results. These significant protective effects occurred with both generalist and specialist doctors. Conclusions This first systematic review reveals that increased continuity of care by doctors is associated with lower mortality rates. Although all the evidence is observational, patients across cultural boundaries appear to benefit from continuity of care with both generalist and specialist doctors. Many of these articles called for continuity to be given a higher priority in healthcare planning. Despite substantial, successive, technical advances in medicine, interpersonal factors remain important. PROSPERO registration number CRD42016042091.
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            The case for a global focus on multiple chronic conditions

            Summary box Consensus agreement on the taxonomy of patients living with more than one chronic condition is needed. Prevention of chronic diseases through improvements to lifestyle behaviours remains paramount to tackling multiple chronic conditions (MCC); evaluations to establish cost-effective interventions are required. Data reporting and monitoring systems, such as hospital coding, should specifically address and monitor MCCs. Healthcare delivery systems need to adapt to manage more than one chronic condition simultaneously; this will be facilitated by innovative changes in healthcare payment mechanisms. An increase in the resources to further the evidence base must be part of any strategic solution for MCCs. Introduction One in three adults globally suffers from multiple chronic conditions (MCC).1 This figure is closer to three out of four in older adults living in developed countries1 and is predicted to rise dramatically, with the proportion of patients with four or more diseases almost doubling between 2015 and 2035 in the UK.2 Yet the response from the health community is non-commensurate. In our 2017 overview of the burden from MCCs, presented at a World Economic Forum event in September 2017 alongside the United Nations General Assembly, we concluded that despite MCCs being so prevalent, there is a dearth of information on their prevalence, clustering, cost and patient burden.3 A 2018 systematic review published in BMJ Global Health by Sum et al aimed to quantify out-of-pocket expenditure (OOPE) on medicines in patients with MCCs.4 Using the context of the findings from Sum et al, we argue why MCCs warrant specific focus by the health community. Terminology and definitions Challenges persist in framing the topic of MCCs. First, there is no agreed taxonomy. Our research found that several terms including MCCs, multimorbidity, comorbidity and polychronic diseases are all used interchangeably to mean ‘more than one chronic condition’.3 However, experts deemed multimorbidity to signify end-stage disease and comorbidity to signify only one main condition.3 Second, there is no agreed definition of which ‘chronic’ conditions should be included in the context of MCCs, with studies including between 4 and 147 conditions.3 We previously found this lack of consensus can lead to variation in prevalence rates by up to threefold,3 and in the review by Sum et al resulted in too much heterogeneity to perform a meta-analysis.4 Also of note, the review by Sum et al focused on MCC but did not include the broader definition including long-term communicable conditions such as tuberculosis (TB) and HIV/AIDS.5 6 Current gaps in the evidence base In addition to the noted dearth of academic literature,3 commonly used health, epidemiological and administrative data sources such as WHO, Global Burden of Disease and the International Classification of Diseases coding systems do not report specifically on MCCs which would otherwise contribute significantly to the available data.3 The evidence base is particularly scarce in younger populations and low/middle-income country settings. It is worth noting that development assistance for non-communicable diseases (NCD) has historically been disproportionately low: NCDs accounted for 49.8% of the disease burden whereas the level of health assistance allocated was just 2.3% to NCDs for low/middle-income countries in 2010.3 The amount of funding for MCCs is unknown but is likely to represent only a small proportion of that for NCDs overall. We are unlikely to see a change in how chronic conditions are managed until we increase funding for them. Financial and social burden MCCs accrue a markedly elevated cost and personal burden with some evidence of a doubling of healthcare expenditure with each additional chronic condition.7 In addition, individual patients are also impacted by elevated costs of MCCs if they are responsible for expenses associated with their healthcare usage. For example, OOPE is 2.1 times higher for older patients with MCCs than those without MCCs.8 Sum et al report that the greater the number of conditions, the higher the OOPE on medicines as a proportion of total healthcare expenditure.4 Furthermore, OOPEs for people with MCCs are increasing at a faster rate than wage growth or broader consumer inflation levels4 and elderly and low-income families are disproportionately affected.4 MCCs have the potential to adversely affect the lives of the most vulnerable members of society, particularly those without adequate health insurance coverage or access to universal healthcare systems.7 It is difficult to quantify OOPE accurately due to the sheer complexity of healthcare payment systems, including: large variations in copays, coinsurance, absolute caps and other payment models. As the studies arise from several differing health systems, the variation is likely to be even greater. Nevertheless, as the authors rightly emphasise, this finding highlights an urgent need to review and revise existing OOPE payment requirements for chronic conditions to move from single-disease frameworks to ones that take MCCs into account. Finally, the burden of MCCs also increases the burden of care on individuals while also reducing patients’ abilities to care for themselves. Clusters of chronic conditions Certain conditions occur together more frequently by virtue of high prevalence rates, shared risk factors or due to causation of one condition by another. A cluster of chronic conditions is when two or more chronic conditions occur together more often than expected. Common clusters of conditions are shown in table 1: strong associations exist between depression associated with stroke and Alzheimer’s disease, and communicable conditions such as TB and HIV/AIDS with diabetes and cardiovascular disease (CVD), respectively. Table 1 Commonly occurring clusters of chronic condition Condition 1 Condition 2 Association* Ischaemic stroke Depression Relative risk: 3.1910 Diabetes Tuberculosis OR: 3.115 COPD Tuberculosis OR: 2.4711 HIV/AIDS CVD OR6: On ART: 2.0 Not on ART: 1.6 Diabetes Ischaemic stroke Relative risk: 1.912 Depression Alzheimer’s and other dementias Relative risk: 1.8513 *OR or relative risk (RR) of having condition 2 for those with condition 1. ART, antiretroviral therapy; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease (includes ischaemic stroke and ischaemic heart disease). Sum et al reported that some clusters of conditions yielded greater OOPE for medications.4 This aligns with previous findings of greater healthcare expenditures on specific clusters of conditions.3 However, only one of the 14 studies that were included investigated this issue and it was limited to only two clusters of conditions, namely those including diabetes and acute coronary syndrome. While this issue warrants considerable further investigation, caution must be made in the interpretation of the findings from a single study from Australia9 in terms of making policy recommendations. A thorough investigation of all chronic conditions, including both NCDs and long-term communicable conditions, and covering different geographical health systems, would be required to truly identify which clusters of conditions account for higher OOPE. In low/middle-income countries, where conditions such as diabetes and TB are highly prevalent, the failure to take clustering into account risks serious failures in the epidemiological estimates and clinical management of these conditions. Clusters are also highly amenable to large improvements in health and cost outcomes through relatively simple shifts in healthcare delivery such as the use of joint disease guidelines that tackle more than one common condition in a cluster, tailored screening and prevention. Healthcare payment mechanisms in developed countries often reward activity rather than desirable outcomes; shifting towards payment for quality or outcomes would facilitate better management of MCCs. As the majority of chronic conditions that are part of MCC clusters are amenable to prevention, interventions that tackle both primary and secondary prevention must be part of the longer term strategy to reducing the MCC burden. Recommendations Key recommendations for moving forward with the science and interventions for MCCs include: Consensus agreement on the taxonomy of MCCs is needed including the terminology, the definition and even what constitutes a chronic condition. Prevention of chronic diseases through improvements to lifestyle behaviours remains paramount to tackling MCCs and economic evaluations to establish the most cost-effective interventions to reduce the MCC burden are required. Data reporting and monitoring systems should specifically address and monitor MCCs; the ability to report on multiple primary diagnoses in future coding systems should be considered. Healthcare delivery systems need to adapt to move away from single-disease frameworks to managing more than one chronic condition simultaneously, examples of which are emerging for HIV and CVD management. Healthcare payment mechanisms that reward positive health outcomes rather than activity-based funding may help in achieving improved patient outcomes, such as the shift to outcome-based reimbursement in the USA. Conclusion The authors agree with the recommendations of Sum et al that urgent reforms are required in the healthcare delivery and financing mechanisms applied to the management of chronic conditions to move away from a single-disease framework towards one that considers clusters of chronic conditions. By tackling clusters rather than single conditions, interventions and systems can directly address particular difficulties faced by such patients, through medication design (eg, fixed-dose combination pills), approaches for screening and detection (eg, mental health screening in diabetes clinics) and joint care guidelines (eg, for diabetes and TB). These relatively small changes in the way that such care is delivered may lead to considerable changes in the ability to deliver greater patient-centric care. However, the current gaps in evidence base related to MCCs prevent such policy changes being fully informed. An increase in the resources to further the evidence base must be part of any strategic solution for MCCs. The rapid ageing of populations globally and the alarming predictions of increased MCCs render these changes urgent.
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              Primary Health Care That Works: The Costa Rican Experience.

              Long considered a paragon among low- and middle-income countries in its provision of primary health care, Costa Rica reformed its primary health care system in 1994 using a model that, despite its success, has been generally understudied: basic integrated health care teams. This case study provides a detailed description of Costa Rica's innovative implementation of four critical service delivery reforms and explains how those reforms supported the provision of the four essential functions of primary health care: first-contact access, coordination, continuity, and comprehensiveness. As countries around the world pursue high-quality universal health coverage to attain the Sustainable Development Goals, Costa Rica's experiences provide valuable lessons about both the types of primary health care reforms needed and potential mechanisms through which these reforms can be successfully implemented.
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                Author and article information

                Journal
                BMJ Glob Health
                BMJ Glob Health
                bmjgh
                bmjgh
                BMJ Global Health
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2059-7908
                2019
                23 May 2019
                : 4
                : 3
                : e001435
                Affiliations
                [1 ] departmentPrimary Health Care , Ariadne Labs , Boston, Massachusetts, USA
                [2 ] departmentDivision of Global Health Equity , Brigham and Women's Hospital Department of Medicine , Boston, Massachusetts, USA
                [3 ] Northwestern University Feinberg School of Medicine , Chicago, Illinois, USA
                [4 ] departmentDivision of General Internal Medicine , Brigham and Women's Hospital Department of Medicine , Boston, Massachusetts, USA
                Author notes
                [Correspondence to ] Dr Dan Schwarz; daschwarz@ 123456gmail.com
                Author information
                http://orcid.org/0000-0002-6975-4519
                http://orcid.org/0000-0002-4355-7437
                http://orcid.org/0000-0002-3633-3806
                Article
                bmjgh-2019-001435
                10.1136/bmjgh-2019-001435
                6570977
                31263586
                0cd54151-1643-49d8-a350-54d4919bfcc3
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 January 2019
                : 07 April 2019
                : 27 April 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
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                continuity,primary care,primary health care,low- and middle-income countries,health systems,universal health coverage,quality,equity

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