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Review
. 2018 Nov;6(11):e1196-e1252.
doi: 10.1016/S2214-109X(18)30386-3. Epub 2018 Sep 5.

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

Margaret E Kruk  1 , Anna D Gage  2 , Catherine Arsenault  2 , Keely Jordan  3 , Hannah H Leslie  2 , Sanam Roder-DeWan  2 , Olusoji Adeyi  4 , Pierre Barker  5 , Bernadette Daelmans  6 , Svetlana V Doubova  7 , Mike English  8 , Ezequiel García-Elorrio  9 , Frederico Guanais  10 , Oye Gureje  11 , Lisa R Hirschhorn  12 , Lixin Jiang  13 , Edward Kelley  6 , Ephrem Tekle Lemango  14 , Jerker Liljestrand  15 , Address Malata  16 , Tanya Marchant  17 , Malebona Precious Matsoso  18 , John G Meara  19 , Manoj Mohanan  20 , Youssoupha Ndiaye  21 , Ole F Norheim  22 , K Srinath Reddy  23 , Alexander K Rowe  24 , Joshua A Salomon  25 , Gagan Thapa  26 , Nana A Y Twum-Danso  27 , Muhammad Pate  28
Affiliations
Review

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

Margaret E Kruk et al. Lancet Glob Health. 2018 Nov.

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Figures

Figure 1
Figure 1
High-quality health system framework
Figure 2
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 3
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 4
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 5
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 6
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 7
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 8
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 9
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 10
Figure 10
Dimensions of vulnerability to poor-quality care
Figure 11
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 12
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 13
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 14
Figure 14
Sample high-quality health system dashboard with illustrative indicators
Figure 15
Figure 15
Types of interventions and levels targeted to improve quality of primary health care according to published literature from 2008 to 2017
Figure 16
Figure 16
Universal actions for improving quality of care
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