The transfer of patient information between the domains of community and hospital influences the quality, continuity and cost of health care. To supply the need for information flow between community and hospital, computerized Health Information Exchange (HIE) systems have evolved. This paper examines the institutional forces that shape HIE development in Israel and in the United States.In Israel, the vertically integrated Clalit health services developed a different solution for HIE than was developed in the non-vertically integrated Maccabi and Meuhedet health funds. In the United States the fragmented nature of providers – outside of specific networks such as parts of the Kaiser Permanente and Veterans Administration system – have dictated a very different evolution of information flow between community and hospital. More broadly, we consider how institutional factors shape (and will shape) the development of HIEs in different contexts.This paper applies institutional analysis to explain the emergence of different patterns of development of HIE systems in each of the environments. The institutional analysis in this paper can be used to anticipate the future success or failure of incentives to promote digital information sharing at transition of care. Patients with chronic diseases and complex treatment regimens are most at risk when transitioning between hospital and community. Miscommunication or just lack of information may trigger unwarranted and potentially dangerous treatment ,2. DischThe paper discharge summary issued to patients when leaving the hospital, like the paper referral letter from a physician in the community, is an attempt to provide information flow between the hospital and the community. In general, traditional paper documents rely on patients to pass on the documents to the community physician. In practice, documents are often not passed on in a timely fashion. Kripalani et al. showed that in 47% of cases, the discharge letter took more than one week to reach the primary care physician in the community .The paper discharge summary suffers from additional deficits. Test results that were not received at the time when the discharge summary is produced will, often, never be seen by the primary care physician in the community. Kripalani et al. reported that at the date of discharge 88% of patients had tests still waiting for results. In addition, paper discharge summaries are not incorporated in the primary care physician’s EMR (if he has one!) and will, effectively, be lost for purposes of follow-up by the primary care physician. In addition, rich patient information (such as the patient’s response to specific drugs) will be lost to the record. This is further complicated by the growing but still limited adoption rate of EMRs by office-based physicians which in the US was only at 57% in 2011 .The paper referral letter from a physician in the community to the hospital is even more problematic. In emergency cases, no letter at all will be available and the admitting hospital will have no reliable record of existing diagnoses, current medications, symptoms or test results from the community. When a paper referral letter is available, the referring physician typically focuses on the immediate reason for referral. In many cases, the referral letter will not include background information. Digital information flows have been advocated to address the limitations inherent in paper-based information exchange . In the Often, problems that seem purely technical, such as data flow through a health provider network, are addressed by discrete technical fixes. Ideally, technical solutions to technical challenges will factor in the institutional context that will both influence and be influenced by the introduction of technical interventions. To place health information exchanges in institutional context, we present some factors to consider in planning, as well as simulating what is likely to happen in the implementation process .The arrangement of healthcare institutions in a society or geographic unit will directly affect the factors that influence the rate and cost of adopting health information exchange between partners in a coalition. These factors include the level of centralization of decision making in the coalition, the starting point for electronic medical records, the number/diversity of records systems that need to be integrated into the exchange, and the scale (how many records).Despite seeming to be an obvious consideration, it is often overlooked that introduction of Health Information Technology (HIT), not to mention attempts at inter-organizational transfer of data, will be impacted by the status of such systems at the outset of policy intervention. In organizations and health systems with strong histories of HIT development and utilization, information transfer issues will play out differently than in systems with nascent HIT systems. On one hand, a tradition of information technology at the individual provider – for example, health plan or hospital – level can serve as an infrastructure which facilitates information transfer. On the other hand, organizational level weddedness to existing systems, as well as proprietary and political interests may block smooth integration of systems across organizations. Alternatively, systems seeking to introduce both new information systems and guarantee transferability of data may be biting off more than they can chew.Rate and cost of HIE adoption is also a function of the starting point: the level of computerization within each of the partners and between the partners in a network before foundation of the HIE. In a case where some members of the network have no electronic medical records, HIE implementation will be delayed until the member organizations adopt computerized systems. In Israel, the nature of affiliation between community physicians and the Health Maintenance Organizations (HMOs) enabled the HMOs to require almost totally electronic records for community care. In the United States where the relationship between payers and providers is much looser, in general, individual practices and hospitals have been slower to adopt electronic records ,8. RelatThe institutional linkages among organizations will also have an impact. All information exchange solutions carry costs, startup implementation costs and ongoing transaction costs. Transaction costs will be higher, and rate of adoption will be slower, in loosely grouped coalitions of providers. An institutional arrangement where one overarching framework is available to allocate funds for paying costs and mandate implementation will move faster than an institutional arrangement with many completely independent actors . For exaHIE adoption costs will also be a function of the number of local systems that need to be integrated. In Israel, each of the HMOs has implemented standards-based records for community care across the network of providers. In the United States, individual practices who have adopted a software system have chosen from a plethora of available systems, making integration more expensive and more difficult.The scale of institutions affects the unit cost for information exchange systems. In Israel, with relatively large HMOs covering millions of subscribers, information exchange systems within each HMO were adopted relatively early and did not require government subsidies or regulations. In the United States, with many independent physicians and many small groups of physicians, the unit cost has been prohibitive and – prior to the era of Accountable Care Organizations (ACOs) – HIEs have withered as soon as government or philanthropic subsidies for the exchanges became exhausted.Finally, the contingencies of national policy regarding health information systems will influence the type of systems that emerge, the incentives of individual organizations and players to cooperate, and the smoothness of implementation.In the remainder of this paper, we employ this institutional lens in a comparative context, discussing the evolution of health information systems, in particular attempts at information transfer, in the US and Israel.The state and prognosis of health information exchange in the United States is a direct function of institutional and other non-technical factors, including factors that are unique to the American experience.When different providers operate different medical record software need to exchange data, ideally each software system would adopt a single standard to enable interoperability. In fact, multiple standards compete for the position of “the” standard, such as the Health Level 7 (HL7) Clinical Document Architecture (CDA) and The American Society for Testing and Materials  Continuity of Care Record (CCR) . Even whStandards alone have not been sufficient to promote widespread health information flow. Institutional factors and economic incentives have been key players in those cases where health information is shared between providers.Prior to 2010, the focus of the Nationwide Health Information Network (NwHIN)  was on fThe RHIOs had critical challenges, including compensating for the American resistance to using a unique identifier for people and dealing with a fragmented health care delivery system, even within small geographical area Table .RHIOs, initially supported by grants, were supposed to become self-supporting by charging fees to providers and insurers. However, starting in 2010, before many RHIOs became self-supporting, the Office of the National Coordinator (ONC) has been sponsoring policies that promote Health Information Exchanges (HIEs), rather than RHIOs. Grant funding for RHIOs has largely ended, forcing RHIOs to rely on fees from providers and insurers . As demoIn general, the RHIOs do not have a sustainable business model and are probably not viable without ongoing government funding. The geographical focus of RHIOs carried no inherent business incentives for providers and insurers. The costs of information exchange within an RHIO are not tied to possible gains, because the members in the organization do not share profits and losses. With government support under the Bush administration, the number of RHIOs expanded between 2005 and 2008 to over 100 active organizations. Even under this period, however, achievements of the RHIOs were limited because of inadequate funding. Under the Obama administration, support for RHIOs has been reduced and RHIOs have shrunk. By December 2009, only 75 RHIOs were operational with coverage of approximately 14% of U.S. hospitals and only 3% of ambulatory practices. Of the 75 operational RHIOs, only 25 met criteria for financial viability, only thirteen RHIOs supported stage 1 meaningful use and none met an expert-derived definition of a comprehensive RHIO .While RHIOs may be useful in improving information flows between providers, and may improve patient care, institutional factors were not aligned to provide viable ongoing sustenance for RHIO. Essentially, RHIOs are on artificial life support, connected to government or philanthropic largesse and when the flow of support is cut off, the RHIO shuts down.Regenstrief Institute.  Without funding from the Regenstrief Institute, the INPC would not be able to present information about patients as one virtual medical record regardless of where they received prior care.The Indiana Network for Patient Care (INPC), a city-wide clinical informatics network in Indianapolis, is an excellent example of a RHIO that works, and illustrates how the RHIO is dependent on external funding. The INPC is substantially dependent on funding from the In the United States, with the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009 under the Obama administration, Health Information Exchanges (HIEs) emerged as a response to the need for better information flow. In particular, the HITECH Act allocates US$564 million for state-level health information exchanges . HIEs faWhile RHIOs were typically focused on state or municipal boundaries, privately-sponsored HIEs are likely to draw patient data across population catchment areas and will pay less attention to state or municipal boundaries. .The emergence of HIEs has been driven by push factors , has been enabled by the emergence of lower cost distributed computing solutions, and – like RHIOs – have been promoted by government grants. The divergence of government grants from RHIOs to HIEs effectively promotes HIEs in most areas of the country.As shown in Figure HIEs carry the promise of improved care, but also provide strong business incentives for adoption. The motivation for adoption of HIEs by private operators is increasing/maintaining patient volume. Providers who cooperate in the HIE hope to increase referrals within the network of partners in the HIE, driving patients to specialists, outpatient facilities and hospitals in the HIE network . For praThe shape of networks in different locations will drive demand for record sharing via a HIE. A hospital that owns a large network of community physicians and requires all physicians to use the same EMR would have less need for a HIE. But a similar hospital with fewer community physicians on the same EMR would have a greater need for a HIE.As described in the Bay Area case study (next page), the most important driver for major players adopting the HIE was the ability to generate referrals.HIEs, and the interoperability standards needed to support HIEs, are promoted by Medicare and Medicaid health IT incentives for healthcare organizations to achieve “meaningful use” of EHRs. Between 2011 and 2015, American Recovery and Reinvestment Act of 2009 (ARRA) incentives may total as much as $48,400 for eligible professionals and up to $11 million for hospitals.HIEs dependent on government incentives will always be vulnerable to changes in government regulations. Hospitals and community providers, aware of the fickle nature of government incentives and rules, will be reluctant to make the needed investments under conditions of uncertainty. Should the prevailing attitudes among policy makers shift – as was the case for RHIOs with the shift to the Obama administration – the move to HIEs will lose momentum, for those HIEs with insufficient business incentives.In addition to business drivers and meaningful use incentives, the Health Information Exchange Cooperative Agreement Program alone has distributed nearly $550 million dollars in funding. As of March 2010, the number of State HIE Cooperative Agreement Program awardees totaled 56.  is a legal entity composed of a network of doctors and hospitals that share responsibility for providing care to patients. The ACO is a provision of the Accountable Care Act intended to bring down costs in the Medicare program (ACA section 3022). Under current legislation, an ACO would agree to manage all of the health care needs of a minimum of 5,000 Medicare beneficiaries for at least three years.To win and keep customers, ACOs will need to prove that the overall health care product they deliver works better. To make profits, ACOs will need to be efficient low-cost providers. Unlike traditional fee for service Medicare payments, ACOs incentivize providers need to reduce costs Table . In thisWhile physicians will likely want to refer patients to hospitals and specialists within the ACO network, patients will be free to see doctors of their choice outside the network without paying more. Unlike in an HMO, an ACO patient is not required to stay in the network. As a result, physicians will need to balance patient satisfaction with low-cost per patient. Use of an HIE to maintain information flow across the provider spectrum within the ACO contributes to both goals: patient satisfaction and low-cost operation .and in better coordinating care between different providers in the community and in hospitals. Care coordination requires two-way communication between community and hospital providers. For this reason, we anticipate that a shift to ACOs will accelerate the movement towards HIE and will promote improved information flow at the transition of care between community and hospitals.To reduce costs over a multi-year period, ACOs have to make up-front investments in improving care (such as adding case managers) The advantage of large numbers of patients for players in the ACO market will encourage growth of large multispecialty physician groups and will encourage hospital systems to buy up physician practices. The larger the grouping, and the greater the vertical integration of the providers from physicians in the community to hospitals, the easier it becomes to provide a single Electronic Health Record (EHR) with information that flows across traditional boundaries.Medication reconciliation programs are endorsed by the Institute for Healthcare Improvement, are part of the United States’ Joint Commission’s National Patient Safety Goals, and are part of the “meaningful use” criteria under the American Recovery and Reinvestment Act. As the Centers for Medicare and Medicaid Services Process of Care (Core) Measures move from a pay-for-reporting structure to reimbursement based on performance  , hospitaIn Israel, universal national health insurance was legislated in 1994. There are 4 HMOs: Clalit, Meuhedet, Maccabi and Leumit. The HMOs are both the insurer and the provider of at least primary care in the community. The citizen pays health taxes according to income, and can choose freely between the HMOs without regard to their income and without limitation based on their health status.The HMO differ in structure (how primary care physicians are compensated/employed), size (number of patients), and scope. All of the HMOs offer networks of primary care physicians, at least some secondary care, and operate laboratories. The HMOs operate at least basic radiology services  with supplementary imaging and diagnostics provided by independent imaging centers or imaging centers in hospitals. In some areas, HMOs operate day-care units. Clalit is the only HMO to operate a network of hospitals, including some of the most advanced and largest hospitals in Israel.  Clalit makes use of other hospitals, especially in areas where there is no Clalit hospital . The other HMOs direct their patients to Clalit or non-Clalit hospitals based on location, specialty and the financial arrangements that each HMO negotiates with each hospital.This situation where patients cross organizational lines, with manual information flow between hospital and community, leads to transition of care problems. Patient medical records, traditionally, existed in a separate silo for each HMO, with additional silos in specific hospitals where the patient may have been hospitalized. When a patient is admitted, the physician at the hospital has to get the medical information from the patient himself or from a referral letter (if available). Information flow stops at institutional boundaries Figure .Unlike in the United States, in Israel virtually all community care records are computerized in a single system for each HMO. In addition, patient records in Israeli hospitals are – for the most part – fully electronic. Within the HMO, physicians – and patients – have electronic access to a range of medical records including lab results and imaging. In the United States, a move towards Health Information Exchanges (HIEs) may first require universal adoption of electronic medical records in community care and in hospitals. In comparison, HIE implementation in Israel starts from fully functioning hospital systems and comprehensive in-place community medical records.In 2004 the Ministry of Health decided to establish a country-wide HIE, the National Electronic Medical Registry (NEMR). Using the proposed NEMR, any admitted hospital could get critical information from other hospitals and HMOs, including lists of chronic diseases, allergies and chronic medications. The NEMR project has not come to fruition yet and, to the best of our knowledge, has been effectively stalled for approximately eight years. Even should the NEMR move ahead, the NEMR as currently envisioned would not provide full two-way information flow. The NEMR would not address the need to share hospital information with community caregivers.In the vacuum left by the absence of a top-down NEMR, the HMOs and the hospitals established, separately, HIEs. Initially, the HIEs were restricted to vertical integration within a single provider. Gradually, the HIEs began to cross institutional boundaries. The pioneer in cross-institution medical record exchange was the OFEK system, established by Clalit. Initially, OFEK bridged community and hospital care within the Clalit network. The Ministry of Health widened the scope of exchange by funding connection of government owned facilities to OFEK. Later, Hadassah Medical Center connected to the Maccabi and Meuhedet HIEs.The Clalit HMO has the full spectrum of health services from primary care through tertiary medical. To improve the information flow within the network of Clalit community care and hospitals, in 2005 Clalit launched an innovative system of hospital-community on-line medical records called OFEK (a dbMotion software product). Through the OFEK system, all Clalit clinics and hospitals can communicate and get information on all the patients in Clalit Figure .In addition to the Clalit hospitals, three government-owned hospitals joined the OFEK system: Rambam, Sheba and Wolfson. Rambam and Sheba are major tertiary care centers. Currently, there is no connection between OFEK and other non-Clalit HMOs. Nirel et al. found that “at clinics in catchment areas of hospitals using OFEK extensively, OFEK reduced the number of imaging tests and, to a lesser extent, laboratory testing and improved several quality measures” .Unlike physicians in other HMOs, Clalit physicians uniquely have universal access to records for their patients everywhere – in the community and in Clalit hospitals. When Clalit embarked on the OFEK project the other HMOs led the field in sharing medical records across the community. With OFEK, Clalit has been able to deliver a single solution for both community and hospital records.The vertical integration across hospital and community care explains why Clalit has been a pioneer in sharing electronic health information across the hospital-community divide.Hadassah Medical Center is a non-profit independent organization which operates two hospitals in Jerusalem - Hadassah Ein Kerem Hospital and Hadassah Mount Scopus Hospital (1000 beds). Hadassah is a tertiary medical center that serves patients from all over the world but mainly from Jerusalem. All four HMOs direct their patients to Hadassah as well as to other hospitals in Jerusalem. In order to improve the connection between Hadassah and the HMOs, Hadassah established a computerized-connection between Hadassah and two HMOs: Maccabi (2009) and Meuhedet (2011). This connection enables hospital physicians to see critical details from the patient’s electronic record in the HMO  Figure . For priThe HMOs have an incentive to invest in improved information flow with Hadassah more than in hospitals in other cities because of the absence of a Clalit hospital in Jerusalem. However, the same competition for patients, and the same potential gains from improved transition of care for patients transitioning between hospitals and community, will drive the HMOs to seek similar arrangements with other hospitals and in other cities. For Hadassah, the desire to earn more referrals from community physicians is an incentive to invest in the system, as well as a desire to leverage information flow to provide the safest, most optimal care. In addition, we attribute success to a fortuitous combination of key personnel involved at Hadassah, Maccabi and Meuhedet. Planning and implementation were supported by strong lobbying on the part of staff physicians, managers and the Hadassah Center for Clinical Quality and Safety.The institutional analysis in this paper can be used to explain and to anticipate the success or failure of digital information sharing at transition of care, as well as the course of developments in the evolution of health information exchanges.In the US and Israel, the nature and direction of information flow at the transition of care between hospital and community can be understood in the context of the institutional relationships between providers in the community, and between hospital and community providers Table . ConsistIn this context, at first glance the information flow from Maccabi and Meuhedet to Hadassah seems somewhat of an outlier. In addition to improvements in the quality of care, the incentives for Maccabi and Meuhedet to invest in the system are the hoped for gains from improved transition of care for patients transitioning to one of the Hadassah hospitals. Leumit is not involved, at least not yet, because its smaller size will yield a smaller payoff. Hadassah’s incentive to invest is to earn more referrals from community physicians . In addition, we attribute Hadassah’s willingness to invest to strong lobbying by staff physicians involved with the Hadassah Center for Clinical Quality and Safety .Building on our institutional analysis we can anticipate the success or failure of incentives, and can offer recommendations to shape the evolution of digital information sharing at transition of care in different environments. For both Israel and the US, a key insight is that single-minded focus on the technical and logistical challenges of implementing Health Information Exchanges will miss the important market structure, legal and cultural contingencies that are as important. Moreover, a top-down orientation, by which national government seeks to promote deployment of HIT may not only run afoul of local contingencies, but also miss out on the potential of grass roots initiatives. Too often, it seems that well intentioned pushes for using HIT to, for example, reduce administrative costs, fail to give adequate attention to such considerations  .Regarding the United States, where fragmentation of providers along the spectrum of care and separation between providers and payers is the norm, we do not expect information flow at transition of care between community and hospital environments to evolve organically. While external incentives such as grants may stimulate development of information exchanges, sustainability in information exchanges beyond a grant period requires structural changes that will increase the payoff from sharing information. The single most important change that we anticipate is change in reimbursement policy – such as the ACOs – that provide payoffs sufficient for providers to cooperate in information flow. The question will be whether ACOs can create the institutional conditions for collaboration among erstwhile separate providers in order to justify, from a cost point of view, the development and maintenance of information exchanges. This is crucial, as government and private grant support will be insufficient to create long-term sustainable HIEs Table .In addition to government grants and incentives, private sector funds (such as those provided by the Regenstrief Institute) and insurance rules can trigger development and promote adoption of solutions that facilitate information flow at the transition of care. However, fragmentation of providers means that solutions in the United States will be more difficult to implement, more costly to develop, and more expensive to maintain over time. The policy implication is that substantial ongoing incentives will be needed to create and sustain HIE systems over time. ACOs are an example of one instance where structural change provides incentives to promote and support information exchange. ACOs will, we anticipate, lead to development of sustainable HIE, albeit within the confines of each ACO. The Veterans Health Administration (VHA) provides an instructive example of how institutional factors can support information flow. The integrated nature of the VHA, the long term view that the VHA adopted, as well as the VHA drive for quality, have all contributed to successful health information exchange within the VHA.In Israel, where community health records are fully computerized for all four HMOs, it seems that computerized information flow will evolve faster, more effectively and relatively less expensively – at least in the near term – than health information exchange in the US, where the starting point for community records is less integrated. The health services in Israel will continue to bridge the divide separating hospital and community care, but in the future will do so less by developing ad hoc solutions for each HMO based on linking each HMO to medical records in individual hospitals or hospital networks. Instead, the divide separating hospital and community care will be, increasingly, bridged by the expansion of OFEK to other non-Clalit hospitals. OFEK will be used for all HMOs to share information with hospitals. After OFEK implantation becomes universal, there will be institutional pressure to expand the functionality of OFEK. For example, we expect the HMOs, including Clalit, to expand OFEK to deliver alerts and warning for information from hospital information to community care. If OFEK does not respond to institutional pressures, we would expect the reemergence of various ad hoc hospital-by-hospital direct information exchange between HMOs and individual hospitals .In this institutional analysis we described the development of RHIO and HIE systems in the United States and in Israel. We detailed the multi-layered relationships between the health institution structure and the process of development health information exchange between different community and hospital health providers.Worldwide, HIE development is key to improving quality of care and containing costs. The very different experiences of the United States and Israel provide lessons that can be applied in different countries with very different institutional arrangements for health care.Before trying to develop or improve an HIE system in any country, health policy authorities should consider how local health institution structures will affect the main players in the field. Understanding the whole ecosystem of institutional relationships, incentives and interests will aid decision makers in formulating policies that will be effective for optimal HIE development and implementation.MF is a salaried employee in Hadassah Medical Center & Clalit Health Services. DC is a salaried employee in Hebrew University. CS is a PhD student in Johns Hopkins University. KR is a self-employed software engineer. None of the authors got funding for this research paper.MF collected, analyzed and summarized the information on computerized HIE between hospitals and community in Israel, and read and approved the final manuscript. DC proposed the initial idea of the paper, made critical revisions, and read and approved the final manuscript. CS made critical revisions and wrote part of the section on HIE in the US. KR analyzed and summarized the information on RHIOs and HIEs in the US, and wrote, read and approved the final manuscript.MF is a senior physician in the Emergency Medicine Department in Hadassah Ein Kerem Hospital and practices internal medicine at a Clalit Health Services clinic in Jerusalem. DC is a Professor of health policy and management in the Braun School of Public Health, Hebrew University, Jerusalem. CS is a PhD student in the Department of Health Policy and Management of the Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. KR is a medical informatics specialist and a PhD candidate in the Braun School of Public Health, Hebrew University, Jerusalem.


Dissemination is a critical facet of implementing quality improvement in organizations. As a field, addiction treatment has produced effective interventions but disseminated them slowly and reached only a fraction of people needing treatment. This study investigates four methods of disseminating quality improvement (QI) to addiction treatment programs in the U.S. It is, to our knowledge, the largest study of organizational change ever conducted in healthcare. The trial seeks to determine the most cost-effective method of disseminating quality improvement in addiction treatment.The study is evaluating the costs and effectiveness of different QI approaches by randomizing 201 addiction-treatment programs to four interventions. Each intervention used a web-based learning kit plus monthly phone calls, coaching, face-to-face meetings, or the combination of all three. Effectiveness is defined as reducing waiting time (days between first contact and treatment), increasing program admissions, and increasing continuation in treatment. Opportunity costs will be estimated for the resources associated with providing the services.The study has three primary outcomes: waiting time, annual program admissions, and continuation in treatment. Secondary outcomes include: voluntary employee turnover, treatment completion, and operating margin. We are also seeking to understand the role of mediators, moderators, and other factors related to an organization's success in making changes.We are fitting a mixed-effect regression model to each program's average monthly waiting time and continuation rates (based on aggregated client records), including terms to isolate state and intervention effects. Admissions to treatment are aggregated to a yearly level to compensate for seasonality. We will order the interventions by cost to compare them pair-wise to the lowest cost intervention . All randomized sites with outcome data will be included in the analysis, following the intent-to-treat principle. Organizational covariates in the analysis include program size, management score, and state.The study offers seven recommendations for conducting a large-scale cluster-randomized trial: provide valuable services, have aims that are clear and important, seek powerful allies, understand the recruiting challenge, cultivate commitment, address turnover, and encourage rigor and flexibility.govNCT00934141ClinicalTrials.  National Survey on Drug Use and Health estimates that 23.5 million Americans age 12 and older needed treatment and 2.6 million received treatment from addiction treatment facilities [The field of addiction treatment serves only a small proportion of people who need its help. The 2009 Several barriers stand in the way of people getting treatment. Individuals who call a program for help often encounter problems such as complicated admission systems, poorly designed phone systems, and a rude reception . As a reJust as several barriers impede access to treatment, so do various factors reduce retention. Clients most often leave treatment because of inconvenient treatment services , dissatiProgram leaders set organizational policies and processes that affect all of their clients, and they have the ability to change them. Given that the literature provides solutions for the access and retention problems in addiction treatment, what is the best way to disseminate improvements? Here, the literature provides surprisingly scant information . One way1. Sample size is a frequent limitation. Because randomization and analysis usually happens at the organizational rather than individual level, randomized trials of organizational change require a large number of organizations. Compared to individuals in clinical trials, it can be difficult to get organizations to participate, stay involved, and pursue the same aims.2. Organizations differ and interventions, if they are to succeed, need to be tailored to the organizations. Differences in organizations make it hard to specify treatment as usual at the start of a study, so the researcher needs another baseline.3. Participating organizations may be only those that volunteer and therefore not be representative of organizations in the field.4. The intervention is hard to hold constant. In drug trials, the same pill is given to every subject in the intervention. But if, for instance, the intervention includes consulting, it is hard to hold consultation characteristics constant.5. The environment is also hard to hold constant in studies that take months or years to complete.The complexities of organizational change explain, in part, two criticisms often made of QI studies: the majority of QI efforts involve single organizations and a simple 'before and after' analysis, and QI efforts are multifaceted and studies of QI do not determine the active ingredients . The scaImprovement collaboratives are one tool commonly used to deliver QI to organizations. Improvement collaboratives involve multiple organizations working together  to make The study described here uses improvement collaboratives along with previously described principles and techniques developed by NIATx, formerly the Network for the Improvement of Addiction Treatment ,39. LikeThe primary research question of NIATx 200 is to determine which of four collaborative service combinations produces the greatest improvement in waiting time (days to treatment from first contact), rates of admissions to treatment, and continuation in treatment. The secondary research question is: What is the impact of the study interventions on treatment completion rates, the level of adoption and sustainability of the recommended practices, organizational readiness to adopt and sustain the new practices, voluntary employee turnover, and program margin? Interventions were delivered to and outcome analyses are performed at the program level.We are also examining the cost of delivering each combination of services. The intent of the cost analysis is to assess the cost of disseminating different QI methods to state agencies that regulate substance abuse treatment. Our budget did not allow us to assess the cost to the treatment programs of implementing the services.NIATx 200 is a cluster-randomized trial with four interventions: interest circle calls, coaching, learning sessions, and the combination of interest circle calls, coaching, and learning sessions. Figure Interest circle calls are regularly scheduled (monthly in our case) teleconferences in which change team members from different programs discuss issues and progress and get advice from experts and one another. Interest circle calls are inexpensive and provide a regular meeting time for members to continue collaborating. If interest circle calls produce QIs, then change can be made widely at relatively low cost. But the quality of interest circle calls may vary by facilitator, and because the teleconferences are scheduled at particular times, they sometimes compete with other priorities, limiting participation. Although participants can listen to recordings of calls they miss, they lose the chance to interact.Coaching assigns a process improvement expert to work with program leaders and change teams to help them make and sustain process improvements. In our study, coaching involved one site visit, monthly phone conferences, and e-mail correspondence. Coaches give a program ongoing access to an expert who tailors advice to the program and makes contacts with other experts and programs that have already addressed the same issue. However, coaching is expensive, and the match between program and coach may not be ideal. Program staff may also miss the camaraderie that comes from learning sessions and interest circle calls. Just as facilitator quality in interest circle calls may vary, so may the quality of coaching.e.g., creating business cases for improvements). Learning sessions raise interest in making changes and provide opportunities for program staff to share plans and progress. The sessions promote peer learning, increase accountability and competition, and give program staff the time to focus and plan as a team without distraction. Learning sessions also are costly, and the knowledge and excitement they produce can fade.Learning sessions occur periodically-in our case, twice a year. These face-to-face multi-day conferences brought together program change teams to learn and gather support from outside experts and one another. Participants learn what changes to make and develop skills in QI , ways to measure change, case studies, and other features. What varied were the methods of instruction and support that were wrapped around the learning kit: interest circle calls, coaching, learning sessions, or the combination of all three.The protocol called for all four interventions to have the same goals during the three six-month intervention periods. For example, each program concentrated on reducing waiting time during months 1-6 of their involvement. During each intervention period, programs chose which practices to implement from those shown in Table The study received approval from the institutional review boards at the University of Wisconsin-Madison and Oregon Health and Science University and is registered at ClinicalTrials.gov (NCT00996645).The Center for Health Enhancement Systems Studies (CHESS), located at the University of Wisconsin-Madison, is a multidisciplinary team addressing organizational change to improve healthcare. NIATx is part of CHESS. The Center for Substance Abuse Research and Policy  works at the nexus of policy, practice, and research for the treatment of alcohol and drug dependence to improve evidence-based addiction treatment. The Health Economics Research Group (HERG) at the University of Miami conducts research on the economics of substance abuse treatment and prevention, HIV/AIDS, criminal justice programs, and health system changes. This core research team worked with five states through each state's single state agency (SSA) (the authority for substance-abuse treatment at the state level).SSA administrative systems identified eligible programs. To be eligible to participate in NIATx 200, programs had to have at least 60 admissions per year to outpatient or intensive outpatient levels of care as defined by the American Society of Addiction Medicine (ASAM) and have received some public funding in the past year. Programs that had worked with NIATx in the past were excluded from participating.  All clients treated within an eligible program were deemed eligible and included in the analysis. SSAs and state provider associations promoted the study and helped recruit participants. Before randomization, NIATx assessed programs' readiness for change and management strength, and asked about which other treatment programs most influenced their own operations. Then programs were randomized to study interventions.NIATx 200 has three primary outcomes: waiting time (days from first contact to first treatment), annual program admissions, and continuation in treatment through the first four treatment sessions. Data for the waiting time and continuation outcomes come from patient information collected, aggregated, and sent in by the SSAs at approximately 9, 18, and 27 months after the start of the intervention. Annual program admissions and other secondary outcomes  were collected through surveys of executive directors conducted at baseline, mid-intervention, and project completion. The research team also surveyed staff members at the treatment programs and is using other measures, described below, to understand the role of mediators, moderators, and other factors that contribute to an organization's success in making changes. As others have demonstrated, QI efforts are much more likely to improve quality if they take place in a supportive context ,41.The Organizational Change Manager (OCM) measures an organization's readiness for change. Staff members at treatment programs completed the OCM. The OCM had good inter-rater reliability among respondents in field tests .Organizations adopt many changes, but sustain few . The 10-The management survey measured 14 management practices at the beginning of the study. The survey was based on the instrument developed by Bloom and Van Reenan . The pubThe Drug Abuse Treatment Cost Analysis Program (DATCAP) is a data-collection instrument and interview guide that measures both direct expenses and opportunity costs. Although DATCAP was initially used in the field of drug abuse treatment, the instrument is now used in treatment programs in many social-service settings. DATCAP was modified for this study to capture the economic costs to an SSA of developing and providing services .Power calculations were predicated on the idea that the unit of analysis is the program rather than the client. Power was calculated for various sample sizes, with consideration given to anticipated recruitment levels in each state. An attrition rate of 20% was assumed in the sample size calculations. It was determined that a sample size of 200 programs would provide 80% power to detect a difference of 3.2 days in waiting time, 7.5% difference in continuation, and 14.2% difference in the log of admissions with alpha = 0.05. These levels of improvement for each outcome were deemed to be clinically or organizationally meaningful.The study design calls for nesting of agencies within states; as such, randomization of programs took place state by state. Though recruitment took place over a period of several months, all programs were randomized at a single point in time at the end of the recruitment period in each state. The randomization was stratified by program size and a quality-of-management score generated during a baseline interview with program leaders . The proThe five states participating in NIATx 200 were divided into two cohorts. Cohort one had three states; cohort two had two states. For each cohort, randomization took place in the two to three weeks before the first six-month intervention began. See Table The state authorities recruited programs to participate in NIATx 200. They promoted the study at meetings and by word of mouth, and wrote letters to the CEOs of programs. They also conducted meetings so the leaders of eligible programs could learn more about the study. At these meetings, the research team and SSA directors explained that programs would use one of four methods to improve processes that affect access and retention. Peer programs with NIATx experience explained the improvements they had made using the same methods and showed changes in data that resulted from the improvements. SSA directors outlined the benefits and responsibilities of programs in the study. Programs would gather pretest data, be randomly assigned to one of four study interventions, and receive 18 months of support. During months 1 to 6, they would focus on reducing waiting time; in months 7 to 12, increasing continuation rates; and in months 13 to 18, increasing admissions. Afterward, the program could join state-led activities to sustain changes. The SSA would send client data to the researchers about waiting time, admissions, continuation, and treatment completion. The program CEO would name from the staff an influential change leader. The leader and staff at each program would complete surveys during the pretest period and at 9, 18, and 27 months. These surveys addressed employee turnover, new practices initiated, number of employees, revenue, and operating margin. Staff would complete surveys about how the program makes and sustains changes. The program would receive minimal compensation for reporting these data.The treatment program comprises both the unit of randomization and the unit of analysis in the study. For the primary analysis, the protocol calls for us to aggregate client records to compute monthly averages for each program's waiting time and continuation rates. The unit of analysis will be a vector of program-month results based on these aggregated values. We will fit a mixed-effect regression model to these monthly observations, including terms to isolate state and intervention effects. We are aggregating admissions to a yearly level to compensate for seasonality. We will use random effects to model the correlation among outcomes from the same program. Organization-level random effects will be included to model the correlation among programs within the same organization. Interventions will be ordered by the cost of implementation and compared in a pair-wise fashion to the lowest cost intervention . All randomized programs with available outcome data will be included in the analysis according to the intent-to-treat principle. The analysis will be conducted by originally assigned intervention regardless of how much programs participated in the learning services. Organizational covariates accounted for in the analysis will include program size, management score, and state affiliation.The following scenario shows what a program assigned to the coaching intervention might have experienced. Assignment to the coaching intervention meant that the program had a process improvement coach visit at the beginning of the study, call every month for the next 18 months, and communicate via e-mail.A program began its work once the CEO named a change leader and change team. This group learned from the research team in the first week of their participation how to do a walk-through, which is a tool that shows staff members what it is like to experience dealing with the program as a client does. The program also had a coach assigned to it by the research team. The coach phoned the CEO, change leader, and change team members to introduce herself and set an agenda for a site visit. The coach also reviewed with the change team the results of the walk-through, an experience that revealed many issues to the change team, including long waiting times. The coach also encouraged change team members to examine case studies on the study website.The site visit allowed the coach and change team members to get to know each other. The coach explained evidence-based improvement principles to the change team and different ways of reducing waiting time, the goal of the study in the first six months. The change team had to decide on one change to make first to reach this goal. In this example, the program decided to adopt a change they learned about from a case study on the study website: Eliminate appointments and instead invite callers to walk in the next morning, complete intake and assessment, and start treatment by noon. Among other things, the change team learned that making this change had resulted in the outpatient program's significantly increasing its revenue. Using information from the case study, the coach helped the change team review in detail how the program made this change-what data they collected, what steps they took, and what protocols they used to train staff for handling the high volume of walk-ins. Finally, the coach helped the change team figure out how to collect pretest data and start the rapid-cycle change process. In two weeks, the program would have enough pretest data (on about 25 clients) to start the change process.Once pretest data were collected, the first rapid-cycle change (or plan-do-study-act cycle) began. The first three callers on Monday were invited to come anytime the next morning to be seen right away. Two callers jumped at the chance. One had to work but offered to come after work. The change leader  did the intake and assessment with one client to experience the new process. The clinician did the same with the other client. At the end of the day, the medical director and clinician modified the change to allow walk-ins at the end of the day too. The next version of the change started Thursday and involved the first five people who called and two clinicians. After this change, the staff identified additional concerns and made other adaptations. Throughout the rapid-cycle changes, the change leader worked side-by-side with clinical staff (a key part of the strategy) to understand problems and make modifications. After staff discussions, the group decided on one last change. Now the new process would be tested with anyone who called for the next week . In the space of three weeks, the overall goal of taking walk-ins was achieved by making and adapting small changes several times, until the process worked well. As a result, the program began serving more clients without adding staff.During the remainder of the six months when the goal was to reduce time to treatment, the program continued to introduce and refine other changes  to have enough power to produce reliable results. This determination gave us a sense of the scale and cost of NIATx 200. We recognize that such a study is costly to implement and evaluate, and it will be hard to replicate at a similar scale. It will, however, provide extensive data for the analysis of organizational change for years to come.i.e., access and retention). Second, the sheer scope of the study (201 treatment programs nested within five states) is unprecedented in QI research, and will provide insights on the effectiveness as well as the cost of delivering an organizational change intervention across multiple organizations. Third, in contrast to research that has examined the effects of a single tool or strategy (such as a checklist), NIATx 200 will advance the field by identifying the active ingredients in a multi-factorial QI strategy. Distance learning, coaching, and learning sessions have been key elements in many QI initiatives. Despite their widespread use, few rigorous studies have estimated their effectiveness. NIATx 200 provides a unique and specific test of their relative contributions to improvement.NIATx 200 advances organizational change and QI in several important ways. First, the findings will provide quantitative evidence of whether QI can improve fundamentally important elements of addiction treatment , the study will provide insights about how states manage data systems and how they are prepared to address information system-related issues in healthcare reform. The study also collected information on which treatment programs exert influence over others, making it possible to examine influence characteristics in addiction treatment and how these may affect the dissemination of change.The study examines methods for moving process improvement from a small number of organizations led by a national office to a state-run initiative involving many programs. Qualitative data will show the client experience in seeking care. Because states had to develop new data systems (It may also be possible to identify practices of organizations that are willing to make changes. The study will describe these practices in detail, assess the impact of the practices on clients, and give practical advice about how to implement them. The measures of management quality, readiness for organizational change, and the potential for sustainability allow validation of those instruments and further understanding of the conditions that contribute to lasting organizational change.The data also will allow for detailed studies of moderation. What type of programs benefit from which QI approach? What type of coaching works best with which types of programs? Under what conditions do specific promising practices have the greatest success? One key product of the study will be the resources developed to teach QIs. For example, in preparing for the study, many promising practices were described in 'how to' manuals. These manuals are available to organizations interested in learning about and using these QI techniques. The diverse, real-world results of the study should provide direction for state leaders, heads of programs, and others about how best to improve access to and retention in treatment.Finally, NIATx 200 shows that rigorous randomized trials of organizational change can be designed and conducted. The information produced will expand our understanding of treatment organizations and provide stakeholders in healthcare delivery a body of research they need to move our healthcare systems forward.The authors declare that they have no competing interests.DG and DM designed the study. AQ, JF, and AP contributed to the design and managed the acquisition of data and the implementation of the project. MF and KM led the economic design. AQ drafted the original manuscript. All authors read and approved the final manuscript.


Care of stroke patients costs considerably more in specialized stroke units (SU) compared to care in general medical wards (GMW) but the technology may be cost effective if it leads to significantly improved outcomes. While randomized control trials show better outcomes for stroke patients admitted to SU, observational studies report mixed findings. In this paper we use individual level data from first-ever stroke patients in four European cities and find evidence of selection by the initial severity of stroke into SU in some cities. In these cases, the impact of admission to SU on outcomes is overestimated by multivariate logit models even after controlling for case-mix. However, when the imbalance in patient characteristics and severity of stroke by admission to SU and GMW is adjusted using propensity score methods, the differences in outcomes are no longer statistically significant in most cases. Our analysis explains why earlier studies using observational data have found mixed results on the benefits of admission to SU. European health care systems have significant differences among them but a common feature is to typically rely on a coordinated or centrally planned decision about the adoption of new technologies and services. In that respect, cost-effectiveness analysis plays an important role in this decision. For instance, in the U.K., the National Institutes of Clinical Excellence (NICE) recommends that a new technology be adopted if the cost per quality adjust life year (QALY) is £30,000 or less. However, analysis based on observational data, as opposed to randomized control trials, may reach different conclusions about the relative effectiveness (or cost effectiveness) of a new service. In part this may be due to selective assignment of patients to new technologies or services relative to the standard care in different centers.In this paper we use observational data on patient outcomes from four European cities on stroke patients who are exposed to an expensive technology – admission to a specialized stroke unit ward (SU) or general medical wards (GMW), and demonstrate the importance of correcting for selection bias in measuring differences in outcomes . We find evidence of overt selection into SU in some cities, and, in these cases, find that the impact of admission to SU on outcomes is overestimated by standard multivariate logit models even after controlling for case-mix. Using propensity score methods to control for selection, we do not find evidence of differential outcomes by admission to SU or other wards. Our analysis explains why earlier studies using observational data have found mixed results on the benefits of admission to SU.de facto policy of admitting patients with given risk factors into the more intensive treatment group [overt selection bias occurs when there is an imbalance in the characteristics of patients assigned to the treatment or control groups and these characteristics also influence the outcome [Selection bias is said to occur when patients with specific characteristics are systematically assigned to treatment A or B and these characteristics independently also influence the outcome. Reasons for such selections may stem from a nt group . Alternant group ,3. For int group . Similar outcome ,5.covert bias can occur if patients in treatment or control groups differ in unobservables and these differences also influence outcomes.In the context of admission to stroke unit, this can happen if patients with certain characteristics, e.g., type of stroke, severity, age, pre-existing conditions, are either systematically admitted to stroke units or admitted to other wards since these characteristics also independently influence patient outcome. The bias may be favorable in one center , unfavorable in another (triaging those with worse case-mix into stroke units) and yet may not be present in other cases. Similarly, hidden or Regardless of the reasons for selection bias – gaming, triaging, defacto policies or constraints of the health care system – estimation methods that do not explicitly account for these differences lead to biased estimates of the treatment effect -8. TradiIn the present study, we compare outcomes for stroke patients admitted to specialist stroke units (SU) and other general medical wards (GMW) from four different cities in Europe. We pay particular attention to the issue of selection bias and explicitly model the likelihood of a patient being admitted to SU given their case-mix, and importantly, the measured severity of stroke prior to admission to SU or GMW. We use both the regression based and propensity score based methods and highlight the differences in estimated treatment effects across these methods. Pooling observations from multiple centers creates the added difficulty of appropriately accounting for heterogeneity of services in SU across centers. Thus, to account for heterogeneity of services in SU across the four centers, as well as to allow for possibly different forms of selection bias , we analyze data from each of the four cities separately. This then allows us to detect selection and account for it when comparing outcomes in SU versus GMW within each city rather than confound the results by comparing across non-homogeneous SU centers. For comparison, we also show results from pooling all observations across centers and ignoring the underlying heterogeneity across centers.Data for this study are drawn from the European Register of Stroke Collaborators (EROS) database and is a collection of population based registers established in six European cities including Dijon (France), Sesto Fiorentino , Kaunas (Lithuania), Warsaw (Poland), Mahon, Menorca (Spain) and London (UK). The database has been described in detail elsewhere and we restrict our description to the selected sample for this study ,14. The The three main outcome measures were mortality at one month, three months and one year and are analyzed by whether a patient is admitted to SU or GMW. Two centers, Warsaw and Menorca, were omitted from this analysis due to lack of variation in type of admission (SU or GMW). In Warsaw, all but four patients were admitted to SU, while in Menorca only one patient was admitted to SU. Within each center we restricted our attention to patients who either suffered cerebral infarction (CI) or primary intracerebral haemorrhage (PICH), as these patients would be typically eligible for SU care. Among the remaining four centers, 35 patients were not admitted to any hospital, the type of ward is not known for four patients, 49 patients had a subarachnoid haemorrhage (SAH), and 42 had an unknown or unclassified type of stroke. This led to an exclusion of 128 observations (there was some overlap) giving a potential sample of 1,693 first-ever stroke patients with either first-time stroke as CI or PICH. The final sample consisted of 1,492 observations, where the additional loss of observations was because complete information on case-mix variables (described below) was not available.Severity of stroke at initial diagnosis is measured using two alternative scales, the Glasgow Coma Scale (GCS) and the National Institutes of Health Stroke Scale (NIHSS) . GlasgowOther relevant controls include age, age square, gender, interaction of age and gender, type of stroke (CI or PICH), swallowing test, ability to lift both arms, ability to walk, pre-stroke comorbidities , Myocardial Infarction (MI), Diabetes Mellitus (DM), Hypertension (HT)), whether the patient was independent or needed assistance in the two weeks prior to stroke and living conditions prior to stroke . This last variable, living conditions prior to stroke, is not coded uniformly across centers and hence we accordingly use only categories that are available. For instance, in the case of London and Dijon, patients are coded according to all five categories but in the case of Florence all patients in the sample are either coded as home alone or home with others. Data are summarized in Table c = 1,2,3,4 and o = 1,2,3) we estimated,For each of the three outcome measures , multivariate logit models were estimated to compute the odds ratio of mortality for admission to SU relative to GMW by center, controlling for severity of stroke and other relevant risk factors . Thus for each of the four centers and three outcomes , Xi is a vector of other relevant risk factors  for the ith patient and the primary coefficient of interest is β1 which provides the odds ratio for admission to SU (by center c and outcome o).where Additionally, for each center, we also estimated odds ratio for admission to stroke unit as a function of severity of stroke. In this second model, the primary interest is in detecting whether the severity of stroke (GCS or NIHSS) predicts if a patient is admitted to SU or GMW, after controlling for other observable characteristics of the patients . Thus for each of the four centers, we estimatedSSi is the measure of severity of stroke at initial diagnosis, Zi is the vector of other controls, and θ1 is the coefficient of interest which provides odds of admission to SU as a function of severity of stroke. Results from multivariate logits are summarized in Table where We checked for the imbalance in the values of the observed characteristics for patients admitted to SU and GMW using univariate measures of differences in means. Large and significant differences were observed in many covariates. Hence we re-estimated the logits for admission to SU or GMW but also included interactions of variables that were identified in the previous univariate analysis to be significant. The predicted values from the logit, which is the probability that a patient will be admitted to SU, was then used as the propensity score for the patient in a given center. Patients admitted to SU were assigned a weight of the reciprocal of the predicted probability of admission to SU while those admitted to GMW were assigned a weight of the reciprocal of the probability of admission to GMW. Weighted differences in means between SU and GMW were then computed to test if the propensity scores achieved reasonable balance among observables between SU and GMW ,4,8,9. WBased on the propensity scores, two methods were used to assess the impact of SU on outcomes. In the first method, using observations on the common support, we stratified the observations based on the propensity score. In the case of London and Kaunas, five strata were defined (quintiles). For Dijon and Florence, observations were divided into three strata. Dijon has a very low mortality rate and hence division into five strata leads to zero mortality in some of the subgroups, thus making computation of odds ratio for the strata in question infeasible. Similarly, there are relatively few observations available for Florence  again making five strata infeasible. Hence for these two centers, only three strata were used. Nonetheless, in each case, whether three or five strata were used, the first two moments of the propensity score between SU and GMW observations were compared within each strata to further check for adequacy of balance. Odds ratio of mortality for admission to SU were computed within each strata and a weighted average across strata was derived based on weights proportional to the inverse of the variance of the estimates . In the Relevant summary statistics are provided in Table Overall 35.7% of the patients were admitted to SU (534 out of 1492). However, there is a significant imbalance across centers. Admission to SU in Dijon is 35.5% and in Florence it is 39.4%, but in London 80% of the patients are admitted to SU while in Kaunas only 11% are admitted to SU. Similarly, the mean value of GCS for patients admitted to SU versus those admitted to GMW in both Dijon and Florence are very close to each other  but there is a large difference in the mean values of GCS in both London and Kaunas. In London, the mean GCS value of patients admitted to SU is 2 points higher than those in GWM (13.19 vs. 11.18) while for Kaunas the mean value of GCS for patients in SU is 2.5 points lower than in GMW (11.47 vs 14).Panel A of Table Mortality odds ratios by admission to SU or GMW, adjusted by severity of stroke as measured by GCS, patient demographics and other case-mix variables listed earlier are given in Panel B of Table Based on these adjusted ORs by center, a tentative conclusion is that patients admitted to SU in London have better survival probabilities than their counterparts admitted to GMW, at least in the short and intermediate term (30 and 90 days). Similarly, patients admitted to SU in Kaunas tend to fare much worse when admitted to SU, at least in the intermediate and the long run  while patients in Dijon and Florence are at par in terms of survival in SU and GMW.However, these results are driven in part by the type of patient admitted to SU or GMW. The bottom part of Table Patients admitted to SU and GMW may differ in other characteristics as well. Table To adjust for these differences in observables, a propensity score for each patient was computed as the logit predicted probability of admission to SU. All the original covariates, along with the interactions among the covariates that were initially significantly different were used to predict the probability of admission to SU. Weighting observations based on the propensity score, the differences in covariates were no longer significant and are given in the columns labeled 'Post’ in Table The odds ratios based on the two propensity score methods described earlier are given in Table It is also worth comparing these ORs with those from randomized control trials. The most recent Cochrane review  compares death by the end of scheduled follow-up  for patients admitted to comprehensive stroke wards versus general medical ward from 11 randomized control trials , [see ppWe re-estimated the models using alternative functional forms or measures of stroke severity. The results were robust to other specifications such as the inclusion of quadratic and cubic terms of the GCS score, switching to categorical classification for the GCS as low < 9, medium 10–12, and high > 13, or alternatively to use individual components of GCS for eye opening score, motor response score, and verbal response score. Other changes in the specifications included replacing GCS with the Barthel score post initial stroke, omitting the swallowing test variable, dropping additional control variables , and including observations on patients with subarachnoid haemorrhage in the analysis. In each of the cases above, the point estimates for the earlier reported odds ratios were only slightly different, but in none of the cases did the results change in any substantial way.The odds ratios reported in this paper should not be taken at face value for at least two reasons. First, propensity score methods are best applicable in large data sets. In the current analysis, the total sample is sub grouped by cities to account for the underlying differences in care between cities. This leads to relatively small sub samples, especially for Florence, and hence the large confidence intervals for odds ratios could also be attributed to a small sample problems . Second, while the propensity score method accounts for overt selection, the underlying assumption is of strongly ignorable treatment assignment given the observed covariates. The assumption implies that no systematic unobserved pretreatment differences exist between the two groups that are related to the outcome measure. Nonetheless, patients who appear similar in terms of observed covariates may differ in other important dimensions not observed in the data. Thus, if in addition there is a hidden (or covert) bias, neither the multivariate regressions nor any of the propensity score methods can produce unbiased estimates of relative effectiveness of admission to stroke unit. To do so would require finding and using an instrumental variable that predicts reasonably well which patient is admitted to SU or GMW, but at the same time cannot predict the probability of death or poor outcome for the patient. Such a variable is not available in the EROS database but future work using other databases may be able to overcome this difficulty.Benefits of organized stroke care have been debated for the past three decades -19. RandThe foregoing analysis shows that traditional multivariate regression based methods tend to overestimate the impact of admission to stroke unit on outcomes in the presence of overt selection. When there is favorable selection of patients, e.g. London, the odds ratios are downward biased. When there is unfavorable selection, e.g., Kaunas, the odds ratios are upward biased. In both London and Kaunas, where selection is most severe, odds ratios based on multivariate logits are significantly below or above one. Correcting explicitly for this imbalance in covariate distribution, especially for the severity of stroke, leads to a much more modest impact of stroke unit on patient mortality. In the case of London, there is no statistical difference in mortality between SU and GMW. In the case of Kaunas, the mortality rates are higher in SU and the intermediate and long term (90 and 365 days) odds ratio stay significantly above one, though the magnitude decreases once selection of more severe and older patients in SU is accounted for via propensity scores. In the case of the other two cities, Dijon and Florence, where the distribution of severity is more balanced, the conclusions from the two methods, multivariate regressions and propensity score methods do not change, i.e., admission to stroke unit does not lead to statistically different outcomes as the confidence interval for the odds ratios always include one.Our analysis does not support the hypothesis that admission to SU leads to lower mortality compared to patients admitted to GMW. The lower odds of death in SU in London are driven by selection of patients with lower initial severity of stroke while the higher odds of death in SU in Kaunas are driven, in part, by selection of patients with greater initial severity of stroke. The other two centers exhibit neither any significant selection by severity of stroke, nor do they, consequently, show an difference in outcomes by SU or GMW wards.The different estimates of odds ratios presented in this paper highlight that alternative conclusions about the relative effectiveness of stroke units can be drawn depending on if overt selection is accounted for or not in the estimation methods. Moreover, these estimates show that the case for cost-effectiveness of stroke units, compared to care in other wards is not a closed case (even the earlier cited RCTs show ORs ranging from.11 to 1.50). For instance, Patel et al. compared outcomes and costs from a randomized control trial where patients either received care in a stroke unit, in the care of a stroke team (in other wards) or in domiciliary care . While tThe authors declare that they have no competing interests.CW is the coordinator of the study and provided feedback on drafts and results and is responsible for data collection and data integrity. AR, PL and MD provided background information based on their experience as experts and investigators in this field and commented on drafts. FB and IW wrote the initial draft and FB provided the data analysis. All authors read and approved the final manuscript.


Millennium Development Goal (MDG) 5 is focused on reducing maternal mortality and achieving universal access to reproductive health care. India has made extensive efforts to achieve MDG 5 and in some regions much progress has been achieved. Progress has been uneven and inequitable however, and many women still lack access to maternal and reproductive health care.In this review, a framework developed by the Commission on Social Determinants of Health (CSDH) is used to categorize and explain determinants of inequity in maternal and reproductive health in India.A review of peer-reviewed, published literature was conducted using the electronic databases PubMed and Popline. The search was performed using a carefully developed list of search terms designed to capture published papers from India on: 1) maternal and reproductive health, and 2) equity, including disadvantaged populations. A matrix was developed to sort the relevant information, which was extracted and categorized based on the CSDH framework. In this way, the main sources of inequity in maternal and reproductive health in India and their inter-relationships were determined.Five main structural determinants emerged from the analysis as important in understanding equity in India: economic status, gender, education, social status (registered caste or tribe), and age (adolescents). These five determinants were found to be closely interrelated, a feature which was reflected in the literature.In India, economic status, gender, and social status are all closely interrelated when influencing use of and access to maternal and reproductive health care. Appropriate attention should be given to how these social determinants interplay in generating and sustaining inequity when designing policies and programs to reach equitable progress toward improved maternal and reproductive health. Millennium Development Goal (MDG) 5 is focused on reducing maternal mortality and achieving universal access to reproductive health care. Under MDG 5, India has committed to reducing maternal mortality to 108 deaths per 100,000 live births by 2015. The latest estimates of maternal mortality rate (MMR) in India, from 2007 to 2009, show a national average of 212 deaths/100,000 live births, a decline of 89 deaths per 100,000 live births since 2001–2003  maternal and reproductive health and 2) equity, including possible categories of disadvantaged populations such as place of residence, race/ethnicity, occupation, gender, religion, education, socioeconomic status, and age, amongst others , and when similar findings on the same topic were found in two publications using data from different rounds of the NFHS, only the most recent publication was included. Five articles could not be identified and obtained. Sixty-six articles were included in the study. A thematic content analysis of these articles was conducted using a matrix developed to sort the information obtained from each article , and age (adolescents). These five determinants are closely interlinked, which is reflected in the literature. In a majority of the articles, economic status is the primary focus; where it is not the main focus, it is often brought up in the discussion. Social status, which in the context of India can be measured with the proxy of ‘caste’, is often discussed in terms of economic status. There is also an overlap between how gender and adolescence generates and sustains inequity and between economic status and education. Income, occupation, ethnicity, and religion are all determinants of equity that are reported in a few of the retrieved articles but no articles were found where these determinants were the primary focus or were discussed in-depth.As context is essential in understanding the underlying determinants of health inequity, each of these determinants will be preceded by a short description of the current situation in India, including at the subnational level where possible.According to the Tendulkar Committee report, which the Indian Government accepted in 2011, the proportion of people living in poverty was estimated at 37% of the population . This haUtilization of antenatal care (ANC) and skilled attendance at birth has increased among the general population in India in the last 15 years. However, progress among women belonging to economically disadvantaged segments of the populations has been slow. Results from a study looking at progress based on data from the three rounds of NFHS in 1992–1993, 1998–1999, and 2005–2006 showed that use of ANC services in the whole of India increased by 12 percentage points between 1992 and 2006 but that the increase among the poor was only 0.1 percentage points . The samA household survey from Chandigarh Union Territory comparing coverage of maternal health care showed that among the women studied, only 32% of the women living in urban-slum areas had an institutional delivery, compared to 93% of the non-slum urban women, and 79% of the women living in rural areas . The aveQuality of care in maternal health services has also been shown to differ according to economic and residence status. A cross-sectional study conducted in Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu showed significant differences in the quality of ANC between poor and non-poor groups . A studyThe urban population in India is one of the largest in the world, with many living in urban slums. A study on women's reproductive health showed that a significant lower proportion of women living in slum areas compared with women living in non-slum areas had ever used contraceptives, were less likely to use skilled attendants at delivery, and less likely to receive postpartum check-ups . A hospiFindings from a study conducted in the slums of Mumbai show that traditional birth attendants are the most common health professionals to assist at home births and that the direct cost of a home birth was not substantially less than the cost of an institutional delivery in the public health sector . CustomsWomen living in rural parts of India are considered a vulnerable group in terms of maternal and reproductive health. In rural areas home births remain the most common practice, with only 29% of the deliveries taking place in a health facility . ResultsOver half (59%) of the pregnant women in India are anemic according to estimations based on data from the NFHS 3 . FurtherGender as a structural determinant of health operates through different intermediary determinants that influence the maternal and reproductive health of women and their access to care. In 2010 India was ranked as number 112 of 134 countries on the global gender gap index . Since tIn India, there is an association between the use of adequate prenatal, delivery and postnatal care and women's autonomy . The quaDomestic violence is a kind of gender-based violence. Several associations between domestic violence and women's reproductive health can be found in the context of India. One study, based on a follow-up of the NFHS 2 conducted in rural parts of Bihar, Jharkhand, Maharashtra, and Tamil Nadu, showed that women who experienced physical violence during pregnancy were less likely to receive ANC compared to women who did not experience physical violence . Women hGender norms have also been shown to influence attitudes toward the use of contraceptives and women's ability to make decisions on family planning. A qualitative study conducted in the western parts of India showed how women publicly were reluctant to acknowledge awareness and use of modern contraceptives and described the use of contraceptives, other than sterilization, as socially unacceptable . A studyResults from a qualitative study conducted on women seeking emergency obstetric care in Karnataka showed that maternal deaths are underreported, and not reviewed, and that ANC and institutional delivery are not linked to postpartum or emergency obstetric care . The resSeventy-four percent of the Indian population is literate. Increased literacy rates are highlighted by the Indian Government as key in reducing inequities in India. Literacy among females rose from 53.7% in 2001 to 65% in 2011, a more rapid progress than the progress seen among men, where the literacy rate increased from 75.3 to 82.1% . Female The literature indicates a strong association between education and use of reproductive health services such as family planning and ANC. The NFHS 3  indicateOther studies have found similar associations. A study from Madhya Pradesh showed that the odds of using ANC, skilled birth attendants, and postnatal care are significantly higher for women with secondary education and above compared to illiterate women . A largeAge can also be a source of health inequity. In India, children and adolescents (defined by the WHO as a person between 10 and 19 years of age) seem to have inequitable access to reproductive health. Pre-marital sexual relationships are widely discouraged in the Indian context and adolescent reproductive health remains taboo. There are few studies on the topic of adolescent reproductive health and national health surveys do not include non-married women when collecting data on women's maternal and reproductive health and utilization of health care. Some insights can be found in the subnational survey Youth in India: Situation and Needs conducted in 2006–2020 . The surChild marriage remains a common practice in India, despite its illegality. Data from the NFHS 3 conducted in 2006–2007 show that among women in the age group of 20–24, 18% married before the age of 15, and 47% were married before the legal age of 18 . MoreoveEven though officially abandoned, the caste system is still apparent in the Indian society. In India the stratification of social class (caste) is one of the strongest social determinants of health . FurtherBeing a member of an OBC in India is associated with lower use of reproductive health services and poorer maternal health outcomes. According to the NFHS 3, the likelihood of receiving any type of ANC is lowest among women belonging to SC or ST. Only 18% of the births among these women are conducted at a health facility, compared to 51% among women who do not belong to SC, ST, or any OBC. These results are supported by state-level studies. For example, a study from Jharkhand showed that women who belong to a tribal caste are less likely to have received ANC, more likely to deliver at home, and less likely to have received a postnatal check-up . A studyThere are also several studies showing that contraceptive use is low among women belonging to tribal and SC. A study conducted on family planning among two ST in West Bengal showed that awareness of contraceptives methods other than sterilization was low and that the among women using contraceptives 97.6% belonging to the Lodhas tribe and 73% belonging to the Santal tribes were using sterilization . SimilarThe largest minority religion in India is Islam and the Muslim population is estimated to be around 138 million. A large proportion of the Muslim population is found in rural areas of West Bengal, Bihar, Maharashtra, and Uttar Pradesh. Religion in India overlaps with the caste system and 40.7% of the Muslim community belong to the group ‘other backwards castes’ . Forty-tA study based on data from the NFHS 3 showed that the relative odds that a Muslim couple had concordant desired waiting time were only 42% of the odds that a Hindu couple had concordant desires . A studyIn the last few decades, Indian society has experienced rapid economic growth. However, large proportions of the population have not benefited from this progress. Balarajan et al.  describeSocial power is a central concept in the CSDH framework in explaining the dynamics of social stratification. The framework is influenced by previous research, especially a model developed by Diderichsen . This moProgress toward reaching MDG 5 in India is unevenly distributed between wealth quintiles, with women belonging to the lowest wealth quintiles falling behind , 12. EcoIn addition to economic status, the stratification of caste is one of the strongest social determinants of health in the setting of India . Caste aIndian society is largely stratified by gender and patrilineal descent and women's autonomy in terms of decision-making, mobility and access to and control over resources is constrained . Gender In India, one in three women is illiterate . The assOne of the major findings that emerged from this review is that structural determinants of inequity relevant for understanding maternal and reproductive health in the setting of India are closely interlinked and difficult to separate from each other. This linkage is supported by the CSDH framework. The framework emphasizes the close relationship between the context and structural determinants, and between the different structural determinants, to determine inequity. When targeting inequity in terms of access to maternal and reproductive health care, economic status, caste and gender need to be considered jointly. This conclusion is supported by findings from a large systematic review looking at the impact of interventions on maternal and reproductive health in poor settings . The revThis review has several limitations. The search strategy was carefully developed but it is possible that articles relevant for the objective of this study were not identified using the search terms for this study. The objective of this review was to describe the current situation and did not include intervention studies. The intervention studies may have provided information relevant when describing structural determinants of equity in regards to maternal and reproductive health in India. We are currently conducting a systematic review of interventions proven to be effective in reducing inequity in maternal and child health globally, including in India. It is also possible that there are other structural determinants of equity relevant to maternal and reproductive health in India but these have not been studied and/or published in peer-reviewed journals. Moreover, some of the themes we identified in the literature are clearly well-studied in India (such as economic status and gender), whereas others are less so . In fact, the studies on education, adolescence and to some degree social class are often nested within studies of economic status and/or gender. This points to a general lack of published research on these topics that limit our understanding. Most studies identified were based on quantitative data. Such studies do not provide explanations, for example, for inequity based on economic disparities. More qualitative research is needed on this topic in India.This review has shown that it is not sufficient to speak of achieving MDG 5 in India as a whole. The differences between states, places of residence, age groups, social status and economic background are simply too great in India to allow such generalizations. A very focused campaign of action delivery services to the most disadvantaged, to reduce inequities in health, particularly in maternal and reproductive health, will be necessary at the national, state, and district levels to have a significant impact on the most disadvantaged populations. In addition, due to the wide disparities between and within states, which are often caused by varying policies and programs, health infrastructural shortcomings and governance challenges, analysis of inequities in maternal health care need to be undertaken at the state and district level. This also calls for additional research focusing on specific geographical areas or a specific group within the population. Especially qualitative research is needed to answer questions on how social determinants influence access to and use of maternal and reproductive health in a specific setting or among a specific community. Disparities between regions and subpopulations also emphasize the need for context sensitive policies and programs. It is clear that one-size will not fit all and especially the poor and vulnerable will be left out. How social determinants interplay in a specific context influencing access to and use of maternal and reproductive health needs to be carefully considered when designing and implementing policy and interventions.Our review identified several key populations that are disadvantaged in terms of access to and use of maternal and reproductive health services that need to be addressed by policymakers and programmers across India. Some of these populations are well-known but still under-served, such as the poor and the socially backward castes. Although the Indian government has attempted to rectify some of these inequities with, for example, conditional cash transfers to encourage women to give birth in institutions with skilled birth attendants under Janani Suraksha Yojana (JSY), the measures are not sufficient to reach the most disadvantaged population . Also, tThis review shows that the cost for maternal health care is a significant burden on households among the poor and socially backwards castes. Interventions targeting the cost for transportation and loss of income are needed, as well as intervention reducing the burden of out-of-pocket payment for care. The mechanisms through which economic status causes barriers to care may differ between geographical areas and it is likely that different strategies are needed to address inequity due to poverty depending on residency. There is also a need for strengthening the health system capacity to provide care to this fragment of the population, both in terms of referral systems and the quality of care.This study also highlights a disadvantaged population in terms of maternal and reproductive health that has not received sufficient attention: adolescents. Adolescents represent 20% of the population in India , and witThis review shows that there is an overlap in how economic status; gender and social status interact when influencing use of and access to maternal and reproductive health care. This makes it important to consider all these in relation to each other when designing policies and programs. For example, a program addressing economic barriers should also consider social status and gender since it likely that intermediary determinants caused by theses are closely linked to the intermediary determinants that the program is targeting.The government of India and state governments should consider following specific steps to reduce the inequity gap. There has to be substantial improvement of services in terms of capacity and quality in the public system where the poor and vulnerable live. Maternal and health care needs to be provided close to their homes, thus reducing the distance barrier. The Indian Government should develop and annually publish special rates of infant mortality rate, maternal mortality rate, and still birth rate for marginalized groups, such as the poor and tribals, through an expansion of the Sample Registration System already implemented. One of the reasons that services are poor in remote rural and tribal areas is difficulties in recruiting doctor or health workers to these areas, thus creating barriers to service delivery or health education. This can be reduced by creating a special cadre or force of doctors, nurses, and health staff to run the health centers in remote and poor areas. The staff recruited will have to be specially selected based on commitment and attitude for serving the poor and marginalized. Furthermore, incentives, in the form of high salaries and other benefits such as better housing, need to be provided to recruited staff. A special strategy in regards to services during delivery will be needed to reach out the most vulnerable and marginalized.India is making progress toward reduced maternal mortality and improved access to reproductive health care. However, evidence shows that the progress made is uneven and inequitable. The objective of this review was to describe the evidence in terms of structural determinants of maternal and reproductive health in India, and how these causes affect access to care. The collective picture that is presented in this paper is that structural determinants prevent reduced maternal mortality and increased access to reproductive health for women belonging to disadvantaged populations. Interventions that target maternal mortality and increased access to reproductive health care need to take into account how these structural determinants operate in the Indian society and how this may influence access to health care for certain groups of women.