Maternal healthcare inequalities over time in lower and middle income countries Amos Channon 30 th October 2014 Oxford Institute of Population Ageing
Overview The importance of reducing maternal healthcare inequities How inequalities change over time in theory What has happened in practice? How to measure The results Will countries reach the target for reducing inequalities? Where now? Policy implications
Why are maternal inequalities important to study? Known to be a key indicator of general inequity in a population Inequalities have a large social and economic cost Poor maternal health affects families over generations Reduced GDP and other development indicators Large literature about inequalities Focused on a country level rather than a regional or local Has not been placed in the context of Universal Health Care
The Development Agenda Millennium Development Goals focused attention on a small range of health indicators Progress shown in all goals but with some indicators lagging Groundswell to place Universal Health Care as a central pillar in the post-2015 development agenda All people receiving quality health services without financial hardship Maternal health central to UHC as there are defined services and population to serve
Equity and UHC Vital that equity is hard-wired into the post-2015 agenda Acknowledged that the MDGs did not take this into account New focus is to leave no-one behind Recent framework (WHO and World Bank) for monitoring progress towards UHC stated that: By 2030, all populations, independent of household income, expenditure or wealth, place of residence or gender, have at least 80% essential health services coverage. 5
Monitoring Equity for UHC Initial drafts of the monitoring framework indicated that the target would be: 80% of the poorest 40% of the population should be able to obtain services by 2030 This has been changed to be: By 2030, all populations,, have at least 80% essential health services coverage. all measures should be disaggregated by socioeconomic and demographic strata in order to allow assessment of the equitable distribution of service and financial protection coverage
How to measure progress? Currently monitoring looks at wealth, place of residence and gender separately Contention is whether this should be done in this way Can you separate wealth from place of residence? Should you separate wealth from place of residence? This paper will look at combining these dimensions to get a different view of inequality Understand the way equity evolves over time
Services and wealth Clear relationship between service use and wealth The rich have the means to access healthcare Services set up nearer the more affluent areas (private and public, at least to start with) Education is closely related to wealth Previous research has shown disparities that are increasing over time in service use by wealth
Inverse Equity Hypothesis Proposed by Victora (2000) based on Tudor-Hart s inverse care law Postulates that new public health interventions initially reach those of higher socioeconomic status and only later affect the poor Inequality initially increases Only after the rich reach a certain point then do the poor start to benefit
Services and location Urban advantage an established concept Urban dwellers have better health in general Have better service use statistics Why? Services are located in towns and cities Wealth in urban areas higher to purchase the services BUT Many poor slum areas where few services and little money to access Poor urban dwellers do not experience this advantage
% using service Hypothesised Changes over time 100 80 60 40 Urban Rural 20 0 Poorest Poor Average Rich Richest Wealth
How to study change over time Simple percentage of women: Giving birth in a hospital Having sufficient antenatal care (4 or more visits) Skilled attendance at birth By urban/rural residence By wealth quintile
Data Countries with three or more DHS over a ten year period Only provides a snapshot of the transitions A number of different services possible to use Will concentrate on facility births Indicator: proportion of births where the mother reports that she gave birth in some form of facility
Country Year of Survey Bangladesh 1993-4, 1996-7, 1999-2000, 2004, 2007 Benin 1996, 2001, 2006 Bolivia 1994, 1998, 2003 Burkina Faso 1993, 1998-9, 2003 Cameroon 1991, 1998, 2004 Columbia 1990, 1995, 2000, 2005 Cote d Ivoire 1994, 1998-9, 2005 Dominican Republic 1991, 1996, 1999, 2002, 2007, Egypt 1992, 1995, 2000, 2003, 2005, 2008 Ghana 1993, 1998, 2003,,2008 Haiti 1994-5, 2000, 2005-6 India 1992-3, 1998-9, 2005-6 Indonesia 1991, 1994, 1997, 2002-3, 2007 Jordan 1990, 1997, 2002, 2007 Kenya 1993, 1998, 2003 Madagascar 1992, 1997, 2003, 2008 Malawi 1992, 2000, 2004 Namibia 1992, 2000, 2006-7 Nepal 1996, 2001, 2006 Niger 1992, 1998, 2006 Peru 1991-2, 1996, 2000, 2004-8 Philippines 1993, 1998, 2003, 2008 Tanzania 1991-2, 1996, 1999, 2004-5 Uganda 1995, 2000, 2006 Zambia 1992, 1996, 2001-2, 2007 Zimbabwe 1994, 1999, 2005 Data
Wealth Quintiles DHS have standard asset quintiles as a proxy for wealth Conducted over the whole country BUT are the same assets important in urban and rural areas? Most asset quintiles are simply a proxy for urban/rural New quintiles constructed for each survey through running a separate PCA for urban and rural Gives relative wealth in both urban and rural areas
Wealth and Place for Kenya National Wealth Quintiles Urban Only Wealth Poorest Below Average Average Above Average Wealthiest Poorest 2.3 5.8 8.7 51.9 31.2 Below Average 0.0 0.0 3.4 26.6 70.0 Average 0.0 0.0 0.0 3.5 96.5 Above Average 0.0 0.0 0.0 0.0 100.0 Wealthiest 0.0 0.0 0.0 0.0 100.0 Overall 0.6 1.5 3.0 19.7 75.3
Bangladesh
Indonesia
India
Benin
Columbia
Dominican Republic
Tanzania going backwards?
Current progress? The common pathway can inform about progress towards UHC Highlighting the dimensions of wealth and place of residence Focus here on skilled attendance at birth Look at the progress needed to achieve 80% coverage by 2030 for each population group: How feasible is achieving this UHC goal equitably based on past performance? Which groups are most likely not to achieve 80% coverage? 25
Countries included 35 countries were analysed All have at least three Demographic and Health Surveys, with the most recent since 2005
Country Survey 1 Survey 2 Survey 3 (Most Recent) Armenia 2000 2005 2010 Bangladesh 1999 2007 2011 Benin 1996 2006 2011 Bolivia 1994 2003 2008 Burkina Faso 1998 2003 2011 Cambodia 2000 2005 2010 Cameroon 1998 2004 2011 Columbia 2000 2005 2010 Cote d Ivoire 1998 2005 2011 Dominican Republic 1996 2002 2007 Egypt 1995 2005 2008 Ethiopia 2000 2005 2011 Ghana 1998 2003 2008 Guinea 1999 2005 2012 Haiti 2000 2005 2012 India 1992 1998 2005 Indonesia 1997 2007 2012 Jordan 1997 2007 2012 Kenya 1998 2003 2008 Country Survey 1 Survey 2 Survey 3 (Most Recent) Madagascar 1997 2003 2008 Malawi 2000 2004 2010 Mali 1995 2001 2006 Mozambique 1997 2003 2011 Namibia 1992 2000 2006 Nepal 2001 2006 2011 Niger 1998 2006 2012 Nigeria 1999 2004 2008 Peru 2000 2004 2012 Philippines 1998 2003 2008 Rwanda 2000 2007 2010 Senegal 1997 2005 2012 Tanzania 1999 2004 2010 Uganda 2000 2006 2011 Zambia 1996 2001 2007 Zimbabwe 1999 2005 2010
Countries and Groups 35 countries were analysed All have at least three Demographic and Health Surveys, with the most recent since 2005 Countries grouped into five categories based on overall SBA coverage in latest survey Very low (<30) Low (30%-49%) Moderate (50%-64%) High (65%-79%) Very high (80% or above)
% women receiving skilled attendance at birth Current gap between rich and poor 100 90 80 70 60 50 40 30 20 Poorest Poorer Average Richer Richest 10 0 Very low (<30%) Low (30-49%) Moderate (50-64%) Coverage Groupings High (71-85%) Very high (>80%)
Very low (<30%) Low (30-49%) Moderate (50-64%) Current gap by country Senegal Philippines Ghana Cote divoire Uganda Mozambique Cameroon Guinea Tanzania India Zambia Kenya Madagascar Nepal Nigeria Haiti Niger Bangladesh Mali Ethiopia Richest quintile Poorest quintile 0 20 40 60 80 100 % women receiving skilled attendance at birth
What is current progress like? The average annual percentage increase in SBA for each wealth quintile by overall coverage level Average time period 11.9 years Coverage Poorest Poorer Average Richer Richest Overall <30% 0.3 0.3 0.4 0.5 1.0 0.4 30-49% 0.2 0.6 0.9 1.1 0.9 0.6 50-64% 0.7 1.2 1.1 0.7 0.6 0.8 65-79% 2.5 3.0 3.2 3.2 2.0 2.8 80% + 1.5 1.5 1.1 0.9 0.4 1.2
What progress is needed to achieve 80% Required annual percentage increase in coverage required to attain 80% coverage by 2030, by coverage group and wealth quintile SBA Poorest Poorer Average Richer Richest Overall <30% 3.6 3.4 3.2 2.7 0.9 2.9 30-49% 2.9 2.5 1.8 0.8-1.7 50-64% 2.6 1.6 0.9 0.1-1.1 65-79% 1.4 0.8 0.4 - - 0.4 80% + 0.1 - - - - - 32
% of women receiving skilled attendance at birth Does place matter? 100 90 80 70 60 50 40 30 Urban Rural 20 10 0 Very low (<30%) Low (30-49%) Moderate (50-64%) Coverage Groupings High (71-85%) Very high (>80%)
Current and required progress by place of residence Current Progress Required Progress per year Group Urban Rural Group Urban Rural <30% 0.7 0.3 <30% 0.9 3.3 30-49% 0.8 0.6 30-49% 0.2 2.2 50-64% 0.2 0.8 50-64% - 1.7 65-79% 2.0 2.5 65-79% - 0.8 80% + 0.6 1.5 80% + - -
Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural % women receiving skilled attendance at birth Current situation by wealth and place combined 100 80 60 40 Poorest 20% Overall Richest 20% 20 0 Ethiopia Nigeria Nepal Rwanda Zambia Malawi Uganda Indonesia Namibia Cambodia Columbia
Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Annual Percentage Change Required or Observed Progress needed by wealth and place combined - urban 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Progress Required -0.5 Very low Low Moderate High Very high Coverage Group and Wealth Level
Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Poorest Poorer Average Richer Richest Annual Percentage Change Required or Observed Progress needed by wealth and place combined - rural 4.0 3.5 3.0 2.5 2.0 1.5 1.0 Progress Required 0.5 0.0 Very low Low Moderate High Very high Coverage Group and Wealth Level
Reflections on the results UHC goal is an absolute rather than relative target Those with the lowest coverage will need to make most progress Infrastructure is the weakest and the workforce will need most strengthening Rate of progress needed for those with the lowest coverage is extremely challenging, but has been seen before: Cambodia has increased by 6.9% each year on average, from 2.2% to 71% in just 10 years Rwanda and Egypt increased by 2.4% on average per year
Progress for countries with lowest initial coverage
Can progress towards UHC be equitable? My contention is that equity during progress is not possible Inverse equity hypothesis Pro-poor interventions are known to reduce inequity Evidence for how this can be done for maternal health care sparse Little known about how maternal health care can be organised and delivered to promote equity
Thoughts Monitoring progress to UHC needs to be done simply, yet on a range of dimensions Interactions between wealth and place of residence important to ensure groups are not missed The rural poor for many countries with a good level of SBA coverage are not making enough progress These analyses don t take into account any aspect of quality which is vital Further evidence on pro-poor policies for maternal health care urgently required.
Policy implications Countries at different stages of the inequality transition need different interventions Those at the start have different needs than those at towards the end! Outreach or insurance systems for minority poor and marginalised families for those getting towards UHC Expanding service provision for all over a wide range of urban and rural groups for those that are a way off achieving the UHC goal
Maternal healthcare inequalities over time in lower and middle income countries Amos Channon 30 th October 2014 Oxford Institute of Population Ageing