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Cited by 4,057Healthcare Quality and ManagementHealthcare Systems and ReformsHealthcare Systems and Practices

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High-quality health systems in the Sustainable Development Goals era: time for a revolution

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Kruk, Margaret E, Gage, Anna D, Arsenault, Catherine, Jordan, Keely, Leslie, Hannah H, Roder-DeWan, Sanam, Adeyi, Olusoji, Barker, Pierre, Daelmans, Bernadette, Doubova, Svetlana V, English, Mike, García-Elorrio, Ezequiel, Guanais, Frederico, Gureje, Oye, Hirschhorn, Lisa R, Jiang, Lixin, Kelley, Edward, Lemango, Ephrem Tekle, Liljestrand, Jerker, Malata, Address, Marchant, Tanya, Matsoso, Malebona Precious, Meara, John G, Mohanan, Manoj, Ndiaye, Youssoupha, Norheim, Ole F, Reddy, K Srinath, Rowe, Alexander K, Salomon, Joshua A, Thapa, Gagan, Twum-Danso, Nana A Y, Pate, Muhammad·2018·The Lancet Global Health
DOI 10.1016/s2214-109x(18)30386-3PMC PMC7734391
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Abstract

deaths from cardio vascular disease, 1 million newborn deaths, 900 000 deaths from tubercu losis, and half of all maternal deaths each year.Quality of care will become an even larger driver of population health as utilisation of health systems increases and as the burden of disease shifts to more complex conditions.The high mortality rates in LMICs for treatable causes, such as injuries and surgical con ditions, maternal and newborn complications, cardio vascular disease, and vaccine preventable diseases, illustrate the breadth and depth of the healthcare quality challenge.Poor-quality care can lead to other adverse outcomes, including un necessary health-related suffering, persistent symptoms, loss of function, and a lack of trust and confidence in health systems.Waste of resources and catastrophic expenditures are economic side effects of poor-quality health systems.As a result of this, only one-quarter of people in LMICs believe that their health systems work well. Health systems should measure and report what matters most to people, such as competent care, user experience, health outcomes, and confidence in the systemMeasurement is key to accountability and improvement, but available measures do not capture many of the processes and outcomes that matter most to people.At the same time, data systems generate many metrics that produce inadequate insight at a substantial cost in funds and health workers' time.For example, although inputs such as medicines and equipment are commonly counted in surveys, these are weakly related to the quality of care that people receive.Indicators such as proportion of births with skilled attendants do not reflect quality of childbirth care and might lead to false complacency about progress in maternal and newborn health.This Commission calls for fewer, but better, measures of health system quality to be generated and used at national and subnational levels.Countries should report health system performance to the public annually by use of a dashboard of key metrics (eg, health outcomes, people's confidence in the system, system competence, and user experience) along with measures of financial protection and equity.Robust vital registries and trust worthy routine health information systems are prerequi sites for good performance assessment.Countries need agile new surveys and real-time measures of health facilities and populations that reflect the health systems of today and not those of the past.To generate and interpret data, countries need to invest in national institutions and professionals with strong quantitative and analytical skills.Global develop ment partners can support the generation and testing of public goods for health system measurement (civil and vital registries, routine data systems, and routine health system surveys) and promote national and regional institutions and the training and mentoring of scientists. New research is crucial for the transformation of low-quality health systems to high-quality onesData on care quality in LMICs do not reflect the current disease burden.In many of these countries, we know little about quality of care for respiratory diseases, cancer, mental health, injuries, and surgery, as well as the care of adolescents and elderly people.There are vast blind spots in areas such as user experience, system competence, confidence in the system, and the wellbeing of people, including patient-reported outcomes.Measuring the quality of the health system as a whole and across the care continuum is essential, but not done.Filling in these gaps will require not only better routine health information systems for monitoring, but also new research, as proposed in the research agenda of this Commission.For example, research will be needed to rigorously evaluate the effects and costs of recommended improvement approaches on health, patient experience, and financial protection.Implementation science studies can help discern the contextual factors that promote or hinder reform.New data collection and research should be explicitly designed to build national and regional research capacity. Improving quality of care will require system-wide actionTo address the scale and range of quality deficits we documented in this Commission, reforming the foundations of the health system is required.Because health systems are complex adaptive systems that function at multiple interconnected levels, fixes at the micro-level (ie, health-care provider or clinic) alone are unlikely to alter the underlying performance of the whole system.However, we found that interventions aimed at changing provider behaviour dominate the improvement field, even though many of these interventions have a modest effect on provider performance and are difficult to scale and sustain over time.Achieving high-quality health systems requires expanding the space for improvement to structural reforms that act on the foundations of the system.This Commission endorses four universal actions to

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Figures

Figure 1 High-quality health system framework
Figure 1 High-quality health system frameworkCC BY · PubMed Central
Figure 2 Adherence to evidence-based guidelines and diagnostic accuracy Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. Indicator definitions are shown in appendix 1, and country specific means are shown in appendix 2. (A) Data are from Service Provision Assessment (SPA) surveys done in ten countries (Ethiopia 2014, Haiti 2013, Kenya 2010, Malawi 2013, Namibia 2009, Nepal 2015, Rwanda 2007, Senegal 2015–16, Tanzania 2015, and Uganda 2007) and baseline facility surveys of Results-based Financing impact evaluations (RBF) in eight countries (Burkina Faso 2013, Central African Republic 2012, Cameroon 2011, Republic of the Congo 2014, Democratic Republic of the Congo 2015, Kyrgyzstan 2012–13, Nigeria 2013, and Tajikistan 2014–15). (B) Data are from clinical vignettes from the Service Delivery Indicators surveys done by the World Bank, in cooperation with the African Economic Research Consortium and the African Development Bank in Kenya (2012), Nigeria (2013), Tanzania (2014), Togo (2013), and Uganda (2013) and from the Service Provision Assessment survey in Ethiopia (2014).
Figure 2 Adherence to evidence-based guidelines and diagnostic accuracy Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. Indicator definitions are shown in appendix 1, and country specific means are shown in appendix 2. (A) Data are from Service Provision Assessment (SPA) surveys done in ten countries (Ethiopia 2014, Haiti 2013, Kenya 2010, Malawi 2013, Namibia 2009, Nepal 2015, Rwanda 2007, Senegal 2015–16, Tanzania 2015, and Uganda 2007) and baseline facility surveys of Results-based Financing impact evaluations (RBF) in eight countries (Burkina Faso 2013, Central African Republic 2012, Cameroon 2011, Republic of the Congo 2014, Democratic Republic of the Congo 2015, Kyrgyzstan 2012–13, Nigeria 2013, and Tajikistan 2014–15). (B) Data are from clinical vignettes from the Service Delivery Indicators surveys done by the World Bank, in cooperation with the African Economic Research Consortium and the African Development Bank in Kenya (2012), Nigeria (2013), Tanzania (2014), Togo (2013), and Uganda (2013) and from the Service Provision Assessment survey in Ethiopia (2014).CC BY · PubMed Central
Figure 3 Proportion of individuals receiving appropriate treatments among those who seek care in 112 low-income and middle-income countries Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. Data sources for tetanus injections and iron during antenatal care were Demographic and Health surveys (DHS) and Multiple Indicator Cluster surveys in 75 countries; for oral rehydration therapy (ORT) were DHS in 54 countries; for antibiotics for pneumonia were DHS and Multiple Indicator Cluster surveys in 63 countries; for antiretroviral therapy among those aware of their HIV status were UNAIDS estimates in 78 countries; and for minimally adequate depression treatment were World Mental Health Surveys in 8 countries. Indicators are defined in appendix 1; country specific means are shown in appendix 2.
Figure 3 Proportion of individuals receiving appropriate treatments among those who seek care in 112 low-income and middle-income countries Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. Data sources for tetanus injections and iron during antenatal care were Demographic and Health surveys (DHS) and Multiple Indicator Cluster surveys in 75 countries; for oral rehydration therapy (ORT) were DHS in 54 countries; for antibiotics for pneumonia were DHS and Multiple Indicator Cluster surveys in 63 countries; for antiretroviral therapy among those aware of their HIV status were UNAIDS estimates in 78 countries; and for minimally adequate depression treatment were World Mental Health Surveys in 8 countries. Indicators are defined in appendix 1; country specific means are shown in appendix 2.CC BY · PubMed Central
Figure 4 User experience in 49 low-income and middle-income countries (LMICs) and 11 high-income countries Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. High-income countries do not contribute to the illustrated medians. Data are from the surveys indicated. AFRO=Afrobarometer survey done in 34 African LMICs (2011–13). HQSS=Commission-led internet survey done in 12 LMICs (2017). IDB=nationally representative phone survey on primary care access, use, and quality done by the Inter-American Development Bank in six Latin-American and Caribbean LMICs (2013). SPA=Service Provision assessment surveys done in ten LMICs (2007–16). CWF=International Health Policy Survey done by the Commonwealth Fund in 11 high-income countries (2013). Indicators are defined in appendix 1; country specific means are shown in appendix 2.
Figure 4 User experience in 49 low-income and middle-income countries (LMICs) and 11 high-income countries Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. High-income countries do not contribute to the illustrated medians. Data are from the surveys indicated. AFRO=Afrobarometer survey done in 34 African LMICs (2011–13). HQSS=Commission-led internet survey done in 12 LMICs (2017). IDB=nationally representative phone survey on primary care access, use, and quality done by the Inter-American Development Bank in six Latin-American and Caribbean LMICs (2013). SPA=Service Provision assessment surveys done in ten LMICs (2007–16). CWF=International Health Policy Survey done by the Commonwealth Fund in 11 high-income countries (2013). Indicators are defined in appendix 1; country specific means are shown in appendix 2.CC BY · PubMed Central
Figure 5 Deaths from Sustainable Development Goal conditions due to poor-quality care and non-utilisation in 137 low-income and middle-income countries External factor deaths are those due to poisonings and adverse medical events. Other infectious diseases deaths are those due to diarrhoeal diseases, intestinal infections, malaria, and upper and lower respiratory infections.
Figure 5 Deaths from Sustainable Development Goal conditions due to poor-quality care and non-utilisation in 137 low-income and middle-income countries External factor deaths are those due to poisonings and adverse medical events. Other infectious diseases deaths are those due to diarrhoeal diseases, intestinal infections, malaria, and upper and lower respiratory infections.CC BY · PubMed Central
Figure 6 Differences in maternal and neonatal mortality rates across low-income and middle-income countries with 80–90% skilled birth attendance coverage Mortality estimates are from WHO, using 2015 modelled estimates. Skilled birth attendance is from the World Bank World Development Indicators, using the most recent data available in the past 10 years. Horizontal lines indicate Sustainable Development Goal targets. Few deaths in these countries are recorded in complete vital registration systems; global estimates must account for missing and unreliable data. Mortality estimates should be interpreted with caution because of uncertainty from measurement error. References can be found in appendix 1.
Figure 6 Differences in maternal and neonatal mortality rates across low-income and middle-income countries with 80–90% skilled birth attendance coverage Mortality estimates are from WHO, using 2015 modelled estimates. Skilled birth attendance is from the World Bank World Development Indicators, using the most recent data available in the past 10 years. Horizontal lines indicate Sustainable Development Goal targets. Few deaths in these countries are recorded in complete vital registration systems; global estimates must account for missing and unreliable data. Mortality estimates should be interpreted with caution because of uncertainty from measurement error. References can be found in appendix 1.CC BY · PubMed Central
Figure 7 Confidence and trust in health systems in 45 low-income and middle-income countries (LMICs) and 11 high-income countries Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. High-income countries do not contribute to the illustrated medians. Data are from the surveys indicated. AFRO=Afrobarometer survey done in 34 African countries (2011–13). HQSS=Commission-led internet survey done in 12 LMICs (2017). IDB=nationally representative phone survey on primary care access, use, and quality done by the Inter-American Development Bank in six Latin-American and Caribbean LMICs (2013). CWF=International Health Policy Survey done by the Commonwealth Fund in 11 high-income countries (2013). Indicators are defined in appendix 1; country specific means are shown in appendix 2.
Figure 7 Confidence and trust in health systems in 45 low-income and middle-income countries (LMICs) and 11 high-income countries Dots represent country-specific means, vertical bars indicate median performance across countries, and boxes delineate the IQR. High-income countries do not contribute to the illustrated medians. Data are from the surveys indicated. AFRO=Afrobarometer survey done in 34 African countries (2011–13). HQSS=Commission-led internet survey done in 12 LMICs (2017). IDB=nationally representative phone survey on primary care access, use, and quality done by the Inter-American Development Bank in six Latin-American and Caribbean LMICs (2013). CWF=International Health Policy Survey done by the Commonwealth Fund in 11 high-income countries (2013). Indicators are defined in appendix 1; country specific means are shown in appendix 2.CC BY · PubMed Central
Figure 8 Proportion of households that report quality concerns as reason for bypassing public facilities in districts in India Data are from the fourth cycle of the District Level Household and Facility Survey done by the International Institute of Population Sciences from 2012 to 2014, in 21 states of India. A quality concern was defined as mentioning any of the following as a reason for bypassing government facilities: inadequate infrastructure, doctor not available, absent health workers, poor quality, drugs not available, inconvenient hours, long wait time, or distrust. In darker coloured districts, a higher proportion of households cited quality concerns.
Figure 8 Proportion of households that report quality concerns as reason for bypassing public facilities in districts in India Data are from the fourth cycle of the District Level Household and Facility Survey done by the International Institute of Population Sciences from 2012 to 2014, in 21 states of India. A quality concern was defined as mentioning any of the following as a reason for bypassing government facilities: inadequate infrastructure, doctor not available, absent health workers, poor quality, drugs not available, inconvenient hours, long wait time, or distrust. In darker coloured districts, a higher proportion of households cited quality concerns.CC BY · PubMed Central
Figure 9 Quality of care across health system platforms in low-income and middle-income countries (LMICs) DALYs=disability-adjusted life-years. HDI=Human Development Index. References can be found in appendix 1.
Figure 9 Quality of care across health system platforms in low-income and middle-income countries (LMICs) DALYs=disability-adjusted life-years. HDI=Human Development Index. References can be found in appendix 1.CC BY · PubMed Central
Figure 10 Dimensions of vulnerability to poor-quality care
Figure 10 Dimensions of vulnerability to poor-quality careCC BY · PubMed Central
Figure 11 Equity in maternal and child health-care quality and in user experience in low-income and middle-income countries (LMICs) (A) Data are from Demographic and Health Surveys and Multiple Indicator Cluster Surveys done in 90 LMICs (2007–16); wealth quintiles are pooled across countries and sampling weights are adjusted to weigh countries equally. (B) Data are from Demographic and Health Surveys and Multiple Indicator Cluster Surveys done in 91 LMICs (2007–16) and are weighted using individual-level survey weights. (C) Data from Commission-led internet survey in 12 LMICs (2017); proportion of respondents who classified their experience for each indicator as “good”, “very good”, or “excellent” (vs “fair” or “poor”) for their last outpatient visit within the prior 12 months; education levels are pooled across country. Indicators are defined in appendix 1. ORT=oral rehydration therapy. DTP=diphtheria tetanus pertussis vaccine.
Figure 11 Equity in maternal and child health-care quality and in user experience in low-income and middle-income countries (LMICs) (A) Data are from Demographic and Health Surveys and Multiple Indicator Cluster Surveys done in 90 LMICs (2007–16); wealth quintiles are pooled across countries and sampling weights are adjusted to weigh countries equally. (B) Data are from Demographic and Health Surveys and Multiple Indicator Cluster Surveys done in 91 LMICs (2007–16) and are weighted using individual-level survey weights. (C) Data from Commission-led internet survey in 12 LMICs (2017); proportion of respondents who classified their experience for each indicator as “good”, “very good”, or “excellent” (vs “fair” or “poor”) for their last outpatient visit within the prior 12 months; education levels are pooled across country. Indicators are defined in appendix 1. ORT=oral rehydration therapy. DTP=diphtheria tetanus pertussis vaccine.CC BY · PubMed Central
Figure 12 Representation of quality subdomains in global, crossnational, and national measurement sets We mapped indicators against domains of the high-quality health systems framework (figure 1), identifying the single domain most relevant for each indicator. We additionally classified indicators as patient-reported if the data were collected with individual self-reports. Full methods are detailed in appendix 1. Cells are coloured by greatest number of indicators per row (source), with red indicating 0, orange and yellow the midrange, and green the maximum number observed for that measurement set. DHIS2=District Health Information System 2. DHS=Demographic and Health Surveys. HIS=Health Information System. HMIS=Health Management Information System. IMSS=Instituto Mexicano del Seguro Social. IPCHS=Integrated People-Centred Health System. ISSSTE=Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado. OECD=Organisation for Economic Co-operation and Development. SARA=Service Availability and Readiness Assessment. SDG=Sustainable Development Goals. SDI=Service Delivery Indicator Survey—health. SPA=Service Provision Assessment. * Population, governance, platforms, workforce, and tools.
Figure 12 Representation of quality subdomains in global, crossnational, and national measurement sets We mapped indicators against domains of the high-quality health systems framework (figure 1), identifying the single domain most relevant for each indicator. We additionally classified indicators as patient-reported if the data were collected with individual self-reports. Full methods are detailed in appendix 1. Cells are coloured by greatest number of indicators per row (source), with red indicating 0, orange and yellow the midrange, and green the maximum number observed for that measurement set. DHIS2=District Health Information System 2. DHS=Demographic and Health Surveys. HIS=Health Information System. HMIS=Health Management Information System. IMSS=Instituto Mexicano del Seguro Social. IPCHS=Integrated People-Centred Health System. ISSSTE=Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado. OECD=Organisation for Economic Co-operation and Development. SARA=Service Availability and Readiness Assessment. SDG=Sustainable Development Goals. SDI=Service Delivery Indicator Survey—health. SPA=Service Provision Assessment. * Population, governance, platforms, workforce, and tools.CC BY · PubMed Central
Figure 13 Illustrative indicators for advancing Sustainable Development Goal (SDG) monitoring from coverage towards effective coverage Tier 1=priority action is implementation (routine or targeted, as for immunisation). Tier 2=priority action is determining efficiency in indicators and data collection. Tier 3=priority action is development of valid indicators for use at scale. IMCI=Integrated Management of Childhood Illness. *Excludes health indicators focusing on population outcomes alone. †Six indicators not shown: two primarily measuring determinants outside the health system (tobacco use and access to basic household sanitation) and four service capacity and access indicators. References can be found in appendix 1.
Figure 13 Illustrative indicators for advancing Sustainable Development Goal (SDG) monitoring from coverage towards effective coverage Tier 1=priority action is implementation (routine or targeted, as for immunisation). Tier 2=priority action is determining efficiency in indicators and data collection. Tier 3=priority action is development of valid indicators for use at scale. IMCI=Integrated Management of Childhood Illness. *Excludes health indicators focusing on population outcomes alone. †Six indicators not shown: two primarily measuring determinants outside the health system (tobacco use and access to basic household sanitation) and four service capacity and access indicators. References can be found in appendix 1.CC BY · PubMed Central
Figure 14 Sample high-quality health system dashboard with illustrative indicators
Figure 14 Sample high-quality health system dashboard with illustrative indicatorsCC BY · PubMed Central
Figure 15 Types of interventions and levels targeted to improve quality of primary health care according to published literature from 2008 to 2017
Figure 15 Types of interventions and levels targeted to improve quality of primary health care according to published literature from 2008 to 2017CC BY · PubMed Central
Figure 16 Universal actions for improving quality of care
Figure 16 Universal actions for improving quality of careCC BY · PubMed Central

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  1. Cisneros-González, 2016, Índice de amputaciones de extremidades inferiores en pacientes con diabetes, Rev Med Inst Mex Seguro Soc, 54, 472
  2. Jordan K, Marten R, Gureje O, Daelmans B, Kruk ME. Where is quality in health systems policy? A commentary on global policy documents. Lancet Glob Health (in press).
  3. Akhtar, 2011, Health care regulation in low-and middle income countries: a review of the literature, Health Policy and Health Finance Knowledge Hub: Working Paper Series

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Authors
Kruk, M. E., Gage, A. D., Arsenault, C., Jordan, K., Leslie, H. H., Roder-DeWan, S., Adeyi, O., Barker, P., Daelmans, B., Doubova, S. V., English, M., García-Elorrio, E., Guanais, F., Gureje, O., Hirschhorn, L. R., Jiang, L., Kelley, E., Lemango, E. T., Liljestrand, J., Malata, A., Marchant, T., Matsoso, M. P., Meara, J. G., Mohanan, M., Ndiaye, Y., Norheim, O. F., Reddy, K. S., Rowe, A. K., Salomon, J. A., Thapa, G., Twum-Danso, N. A. Y., & Pate, M.
Affiliations
  • Harvard University (US)
  • New York University (US)
  • World Bank (US)
  • Institute for Healthcare Improvement (US)
  • World Health Organization (CH)
  • Mexican Social Security Institute (MX)
  • Kenya Medical Research Institute (KE)
  • Inter-American Development Bank (US)
  • University of Ibadan (NG)
  • Northwestern University (US)
  • Federal Ministry of Health (ET)
  • Gates Foundation (US)
  • Malawi University of Science and Technology (MW)
  • London School of Hygiene & Tropical Medicine (GB)
  • Department of Health (ZA)
  • Duke University (US)
  • Ministère de la Santé Publique et Action Sociale (SN)
  • University of Bergen (NO)
  • Public Health Foundation of India (IN)
  • Centers for Disease Control and Prevention (US)
  • Stanford Medicine (US)
  • Government of Nepal (NP)
Corresponding author
Kruk, Margaret E
ORCID
Venue
The Lancet Global Health
Publisher
Elsevier BV
Published
2018-11
Volume
6
Issue
11
Pages
e1196-e1252
ISSN
2214-109X (ISSN-L)
DOI
10.1016/s2214-109x(18)30386-3
PMC
PMC7734391
OpenAlex
W2891125414
Semantic Scholar
13a6473d422ee5503822377a9c9072bceafe7270
Cited by
4,057
References
274
Citations by year
2026: 704 · 2025: 696 · 2024: 682 · 2023: 600 · 2022: 408 · 2021: 394 · 2020: 329 · 2019: 213 · 2018: 28
FWCI
44.62
Type
journal article
Language
en
Update notices
Open access
Licence
Locations
  • The Lancet Global Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • CONICET Digital (CONICET) (submittedVersion, cc-by)
  • Greater South Information System
Funders
  • Wellcome Trust
  • Medical Research Council
  • Bill and Melinda Gates Foundation
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Quality and Management
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Systems and Reforms
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Systems and Practices
Keywords
health system quality, Sustainable Development Goals, low- and middle-income countries, health indicators, health outcomes, user experience, health system accountability, health information systems, health facility survey, healthcare workers, health care financing, health equity, health system transformation, health professions education, health workforce recruitment, health workforce distribution
MeSH terms
Sustainable Development, Delivery of Health Care, Developing Countries, Goals, Humans, Quality of Health Care

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