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Bull World Health Organ 202098394ndash405 | doi httpdxdoiorg102471BLT19249078

Research

394

IntroductionReaching all women and children with essential reproduc-tive maternal newborn and child health interventions is a critical part of universal health coverage and is represented by the Global Strategy for Womenrsquos Childrenrsquos and Adoles-centsrsquo Health 2016ndash20301 The millennium development goals focused on national improvements and not within-country inequalities even though initiatives such as the Countdown to 2015 Collaboration began tracking inequalities in coverage2 However the sustainable development goals and the associated global and country-specific strategies continue to emphasize the importance of womenrsquos childrenrsquos and adolescentsrsquo health with a focus on reducing all dimensions of inequality3

Globally sub-Saharan Africa has the highest mortality rates for women during pregnancy and childbirth for children and for adolescents as well as the lowest coverage for many maternal newborn and child health interventions4 Economic status measured by wealth of the household is often one of the

most critical factors affecting coverage of such interventions with large gaps between the poorest and wealthiest households A study published in 2011 based on 28 sub-Saharan African countries quantified the impact of wealth on inequalities in in-tervention coverage in 26 of the 28 countries within-country wealth-related inequality accounted for more than one quarter of the intervention coverage gap (the difference between full coverage ie 100 and the actual coverage)5 These findings illustrate the importance of the analysis of coverage trends by wealth to improve the targeting of health programmes

We investigated the extent to which sub-Saharan African countries have succeeded in reducing wealth-related inequali-ties in maternal newborn and child health intervention cov-erage We calculated a composite coverage index measured its changes over time and performed between-country comparisons to highlight subregional patterns and identify countries that have shown remarkable progress in reducing wealth-related inequalities These analyses were conducted as part of four workshops organized and funded by the Count-

a International Center for Equity in Health Federal University of Pelotas Rua Marechal Deodoro 1160 3 piso Pelotas Brazilb African Population and Health Research Centre West Africa Regional Office Dakar Senegalc Department of International Health Johns Hopkins Bloomberg School of Public Health Baltimore United States of America (USA)d Centre for Global Public Health University of Manitoba Canadae African Population and Health Research Center Central Office Nairobi Kenyaf Health Information System West African Health Organization Bobo-Dioulasso Burkina Fasog Department of Data and Analytics World Health Organization Geneva Switzerlandh Department of Maternal Newborn Child and Adolescent Health and Aging World Health Organization Geneva Switzerlandi Division of Data Analytics Planning and Monitoring United Nations Childrenrsquos Fund New York USACorrespondence to Fernando C Wehrmeister (email fwehrmeisterequidadeorg)(Submitted 13 December 2019 ndash Revised version received 6 March 2020 ndash Accepted 9 March 2020 ndash Published online 8 April 2020 )

Wealth-related inequalities in the coverage of reproductive maternal newborn and child health interventions in 36 countries in the African RegionFernando C Wehrmeistera Cheikh Mbackeacute Fayeacuteb Inaacutecio Crochemore M da Silvaa Agbessi Amouzouc Leonardo Z Ferreiraa Safia S Jiwanic Dessalegn Y Melessed Martin Mutuae Abdoulaye Maiumlgac Tome Caf Estelle Sidzee Chelsea Taylorg Kathleen Strongh Liliana Carvajal-Aguirrei Tyler Porthi Ahmad Reza Hosseinpoorg Aluisio J D Barrosa amp Ties Boermad on the behalf of the Countdown to 2030 for Womenrsquos Childrenrsquos and Adolescentsrsquo Health regional collaboration in sub-Saharan Africa

Objective To investigate whether sub-Saharan African countries have succeeded in reducing wealth-related inequalities in the coverage of reproductive maternal newborn and child health interventionsMethods We analysed survey data from 36 countries grouped into Central East Southern and West Africa subregions in which at least two surveys had been conducted since 1995 We calculated the composite coverage index a function of essential maternal and child health intervention parameters We adopted the wealth index divided into quintiles from poorest to wealthiest to investigate wealth-related inequalities in coverage We quantified trends with time by calculating average annual change in index using a least-squares weighted regression We calculated population attributable risk to measure the contribution of wealth to the coverage indexFindings We noted large differences between the four regions with a median composite coverage index ranging from 508 for West Africa to 753 for Southern Africa Wealth-related inequalities were prevalent in all subregions and were highest for West Africa and lowest for Southern Africa Absolute income was not a predictor of coverage as we observed a higher coverage in Southern (around 70) compared with Central and West (around 40) subregions for the same income Wealth-related inequalities in coverage were reduced by the greatest amount in Southern Africa and we found no evidence of inequality reduction in Central AfricaConclusion Our data show that most countries in sub-Saharan Africa have succeeded in reducing wealth-related inequalities in the coverage of essential health services even in the presence of conflict economic hardship or political instability

395Bull World Health Organ 202098394ndash405| doi httpdxdoiorg102471BLT19249078

ResearchHealth intervention coverage inequalities African RegionFernando C Wehrmeister et al

down to 2030 for Womenrsquos Childrenrsquos and Adolescentsrsquo Health programme and attended by teams of analysts from public health institutions and ministries of health from 38 African countries in 2018

MethodsSurvey data

We performed our analysis on second-ary data acquired from 127 national surveys conducted in 36 countries in which at least 2 surveys had been con-ducted since 1995 Surveys were either Demographic and Health Surveys or Multiple Indicator Cluster Surveys which allowed us to compare standard indicators with time We grouped the countries into four subregions accord-ing to the United Nations Population Division classification6 Central Africa (6 countries 18 surveys) East Africa (11 countries 41 surveys) Southern Africa (5 countries 15 surveys) and West Af-rica (14 countries 54 surveys Table 1)

Composite coverage index

We calculated the composite coverage index (percentage) of maternal new-born and child health interventions a weighted function of essential maternal and child health intervention indicators representing the four-stage continuum of care (family planning antenatal care and delivery child immunization and disease management) defined as7ndash9

(1)

where the variables represent the pro-portion of women aged 15ndash49 years of age in need of contraception and had access to modern methods to modern contraceptive methods (FPmo) at least four antenatal care visits (A) and a skilled birth attendant (S) children aged 12ndash23 months who received tuberculo-sis immunization by Bacillus CalmettendashGueacuterin (B) measles immunization (M) and three doses of diphtheriandashtetanusndashpertussis immunization (or pentavalent vaccine) (D) and children younger than 5 years of age who received oral rehydration salts for diarrhoea treat-ment (O) and care for suspected acute respiratory infection (C) The index is a robust single measure of the coverage

Table 1 Survey data used to calculate composite coverage index of reproductive maternal newborn and child health interventions in sub-Saharan African countries 1996ndash2016

Country Source Year Composite coverage index (SE)

Central AfricaCameroon DHS 1998 397 (17)Cameroon DHS 2004 471 (11)Cameroon DHS 2011 476 (11)Cameroon MICS 2014 515 (12)Chad DHS 1996 186 (11)Chad DHS 2004 159 (12)Chad MICS 2010 197 (09)Chad DHS 2014 280 (09)Congo DHS 2005 516 (12)Congo DHS 2011 579 (10)Congo MICS 2014 561 (09)Democratic Republic of the Congo

DHS 2007 417 (12)

Democratic Republic of the Congo

MICS 2010 439 (09)


DHS 2013 471 (08)

Gabon DHS 2000 463 (09)Gabon DHS 2012 581 (08)Sao Tome and Principe

DHS 2008 681 (12)

Sao Tome and Principe

MICS 2014 736 (09)

East AfricaBurundi DHS 2010 561 (06)Burundi DHS 2016 626 (05)Comoros DHS 1996 460 (15)Comoros DHS 2012 517 (13)Ethiopia DHS 2000 163 (09)Ethiopia DHS 2005 247 (09)Ethiopia DHS 2011 351 (12)Ethiopia DHS 2016 451 (12)Kenya DHS 1998 579 (09)Kenya DHS 2003 524 (10)Kenya DHS 2008 591 (09)Kenya DHS 2014 704 (05)Madagascar DHS 1997 368 (13)Madagascar DHS 2003 435 (17)Madagascar DHS 2008 498 (10)Malawi DHS 2000 554 (07)Malawi DHS 2004 585 (06)Malawi DHS 2010 702 (04)Malawi MICS 2013 752 (04)Malawi DHS 2015 770 (04)Mozambique DHS 1997 408 (25)Mozambique DHS 2003 568 (10)Mozambique DHS 2011 546 (11)Mozambique DHS 2015 612 (14)Rwanda DHS 2000 332 (05)Rwanda DHS 2005 386 (05)Rwanda DHS 2010 635 (07)

(continues )

396 Bull World Health Organ 202098394ndash405| doi httpdxdoiorg102471BLT19249078

ResearchHealth intervention coverage inequalities African Region Fernando C Wehrmeister et al

of such interventions and is particularly suitable for examining broad patterns of inequality it has also been reported to correlate well with health-related indi-cators such as the mortality of children younger than 5 years of age and stunting prevalence9

Wealth-related inequalities

We adopted the wealth index to examine inequalities which is based on a prin-cipal component analysis of dwelling and household assets The wealth index is weighted according to the assets in urban and rural places of residence and then divided into quintiles the first quintile represents the poorest 20 in the population and the fifth quintile represents the wealthiest 201011 We then calculated the predicted absolute income attributed to each within-country wealth distribution quintile12 using data from the International Center for Equity in Health database13 acquired from surveys conducted in low- and middle-income countries gross domestic product data adjusted for purchasing parity (extracted from the World Bank)14 and income inequality data from the World Income Inequality Database15 By using absolute income data we expand the capability of the wealth index to explore inequalities within countries and over time12

We used the software Stata version 15 (StatCorp College Station Texas) to describe wealth-related inequalities according to the most recent survey for each country We provide the calculated composite coverage index and its stan-dard error based on a binomial distri-bution for the entire population and for the poorest and wealthiest quintiles within each country For the relationship between absolute income and composite coverage index we considered each quintile as independent and performed a locally weighted scatterplot smoothing regression

Time trends

We analysed time trends in the compos-ite coverage index for the entire popula-tion and for the poorest and wealthiest groups within each country and subre-gion by calculating the average annual absolute change (percentage points) in the composite coverage index We used a least-squares regression weighted by the standard error of the composite coverage index estimate for each year in country-specific analyses We used


Rwanda DHS 2014 677 (05)United Republic of Tanzania

DHS 1996 586 (11)

United Republic of Tanzania

DHS 1999 607 (22)


DHS 2004 588 (10)


DHS 2010 596 (09)


DHS 2015 623 (09)

Uganda DHS 2000 445 (09)Uganda DHS 2006 505 (08)Uganda DHS 2011 583 (07)Uganda DHS 2016 651 (05)Zambia DHS 1996 590 (09)Zambia DHS 2001 598 (09)Zambia DHS 2007 624 (10)Zambia DHS 2013 695 (07)Southern AfricaEswatini DHS 2006 781 (07)Eswatini MICS 2010 782 (07)Eswatini MICS 2014 833 (10)Lesotho DHS 2004 628 (09)Lesotho DHS 2009 686 (10)Lesotho DHS 2014 753 (09)Namibia DHS 2000 690 (11)Namibia DHS 2006 751 (10)Namibia DHS 2013 770 (07)South Africa DHS 1998 757 (06)South Africa DHS 2016 752 (11)Zimbabwe DHS 2005 575 (08)Zimbabwe DHS 2010 636 (09)Zimbabwe MICS 2014 759 (06)Zimbabwe DHS 2015 731 (11)West AfricaBenin DHS 1996 417 (13)Benin DHS 2001 471 (10)Benin DHS 2006 458 (07)Benin DHS 2011 513 (07)Benin MICS 2014 481 (07)Burkina Faso DHS 1998 272 (12)Burkina Faso DHS 2003 368 (11)Burkina Faso DHS 2010 546 (08)Cocircte drsquoIvoire DHS 1998 390 (20)Cocircte drsquoIvoire DHS 2011 436 (12)Cocircte drsquoIvoire MICS 2016 479 (10)Gambia MICS 2010 598 (07)Gambia DHS 2013 615 (07)Ghana DHS 1998 453 (10)Ghana DHS 2003 535 (10)Ghana DHS 2008 588 (10)Ghana MICS 2011 625 (08)Ghana DHS 2014 653 (09)

( continued)

(continues )



a multilevel approach for subregional analysis and considered the country as the level-two regression variable

Contribution of wealth

To investigate the contribution of wealth to composite coverage index or to quantify the level of health interven-tion coverage that would be achieved if wealth-related inequalities were eliminated we calculated the popula-tion attributable risk The World Health Organization (WHO) Handbook on Health Inequality Monitoring defines population attributable risk (in percent-age points) or absolute inequality as the coverage gap in the wealthiest quintile subtracted from the coverage gap in the

entire population16 Alternatively we define population attributable risk in terms of coverage that is the coverage in the entire population subtracted from the coverage in the wealthiest quintile Mathematically equivalent to the WHO definition we believe our definition in terms of coverage (instead of coverage gap) is simpler

Relative inequality or population attributable risk percentage can be cal-culated as population attributable risk expressed as a percentage of the com-posite coverage index within the entire population We calculated the popula-tion attributable risk and its percentage for each country for two different time periods using the oldest survey up until

2010 and the most recent survey from 2011 onwards

Ethics

All survey data are publicly available and all ethical aspects were the respon-sibility of the relevant agencies and countries

ResultsCoverage and inequalities

We observed a median composite coverage index (median year 2014) of 608 from the most recent surveys of sub-Saharan African countries un-til 2016 We noted large differences between the four regions with a com-posite coverage index ranging from 508 for West Africa to 753 for Southern Africa Between countries we calculated the lowest value of the index of 280 for Chad and the highest of 833 for Eswatini (Table 1)

In terms of within-country in-equalities Fig 1 highlights the higher levels of coverage among the wealthier groups in all countries We note that the highest levels of coverage as well as the lowest within-country inequali-ties are observed for Southern African countries We observed much higher inequalities between rich and poor within the other three subregions es-pecially in West Africa however such subregional inequalities vary greatly within each subregion ranging from for instance very high inequality in Nigeria (West) and Ethiopia (East) to virtually no difference in intervention coverage between rich and poor in Ghana (West) and Malawi (East)

We also note how the association between predicted absolute income and composite coverage index differs by subregion (Fig 2) at comparable income levels coverage is considerably higher in East and Southern Africa than in Central and West Africa For instance at an income level of 1000 United States dollars coverage is about 40 in Central and West Africa around 60 in East Africa and over 70 in Southern Africa We also observed marked differences within the subregions the coverage index in Southern Africa is relatively constant with absolute income while health intervention coverage increases almost linearly with predicted absolute income in both West and Central Africa subregions (Fig 2)


Guinea DHS 1999 372 (12)Guinea DHS 2005 394 (14)Guinea DHS 2012 399 (12)Liberia DHS 2007 494 (15)Liberia DHS 2013 603 (10)Mali DHS 2001 293 (11)Mali DHS 2006 393 (09)Mali MICS 2009 399 (06)Mali DHS 2012 452 (11)Mali MICS 2015 396 (09)Mauritania MICS 2011 487 (08)Mauritania MICS 2015 494 (11)Niger DHS 1998 255 (13)Niger DHS 2006 289 (12)Niger DHS 2012 454 (10)Nigeria DHS 1999 374 (15)Nigeria DHS 2003 320 (15)Nigeria MICS 2007 351 (11)Nigeria DHS 2008 365 (09)Nigeria MICS 2011 419 (10)Nigeria DHS 2013 377 (10)Nigeria MICS 2016 359 (07)Senegal DHS 2005 455 (08)Senegal DHS 2010 515 (09)Senegal DHS 2012 513 (14)Senegal DHS 2014 541 (12)Senegal DHS 2015 552 (12)Senegal DHS 2016 565 (13)Senegal DHS 2017 619 (09)Sierra Leone DHS 2008 477 (11)Sierra Leone MICS 2010 621 (07)Sierra Leone DHS 2013 664 (09)Sierra Leone DHS 2017 710 (07)Togo DHS 1998 334 (11)Togo MICS 2010 451 (10)Togo DHS 2013 521 (11)

DHS Demographic and Health Survey MICS Multiple Indicator Cluster Survey SE standard error

( continued)



Time trends

We depict the evolution of the com-posite coverage index in the poorest and wealthiest groups by subregion in Fig 3 (see also Table 2) In Southern Africa the average annual increase in coverage among the poorest (186 95 confidence interval (CI) 111 to 260) was higher than among the wealthiest (002 95 CI minus086 to 089) reduc-ing the inequality between rich and poor Inequalities by income have been statistically not significant since 2013 in Southern Africa Inequalities have also been reducing in the last two decades in East Africa as the average annual increase in coverage among the poorest (121 95 CI 077 to 164) was higher than among the wealthiest (056 95 CI 014 to 097) however a substantive gap between the two groups remains If the average annual increase over the last two decades continues we predict that the discrepancy in

coverage between rich and poor will be eliminated before 2030 Inequalities in West Africa has been substantially reduced but by 2015 the estimated gap was still around 25 percentage points Finally we observed no evidence of a reduction in wealth-related inequalities in Central Africa

We provide the annual average change in composite coverage index during 1995ndash2016 for each country in Table 2 Calculated for the entire popu-lation of each country we observed sta-tistically significant (P lt 005) increases in health intervention coverage in all 36 countries except Gambia Guinea Mauritania Nigeria and South Africa We observed the largest average annual increases in coverage index in Burkina Faso (23 percentage points) Rwanda (27 percentage points) and Sierra Leone (19 percentage points)

In examining data for the poorest and wealthiest quintiles we note that the coverage index statistically significantly

increased among the poorest in 30 coun-tries among the wealthiest coverage index increased in only 20 countries The annual average annual change was greater in the poorest quintile than in the wealthiest quintile in 31 countries although the 95 CIs overlapped for all countries We note there were five coun-tries in which wealth-related inequalities increased (Cameroon Congo Ethiopia Guinea and Mauritania) We observed either very small increases or else de-creases in the coverage index among the poorest in four of these countries In Ethiopia the average annual increase in coverage index among the poorest (13 percentage points) was outpaced by that among the wealthiest (18 percentage points)

Impact of wealth

We calculated the population attribut-able risk that is the contribution of wealth to the composite coverage index in Table 3 for two separate periods The

Fig 1 Current levels of wealth-related inequalities in health intervention coverage sub-Saharan Africa 1996ndash2016

0 20 40 60 80 100

Cameroon

Congo

Chad

Democratic Republicof the Congo

Gabon


Central Africa

Composite coverage index ()0 20 40 60 80 100

EthiopiaMadagascar

MozambiqueKenya

United Republic of TanzaniaZambia

ComorosUgandaRwandaBurundiMalawi

East Africa

Composite coverage index ()

0 20 40 60 80 100

Zimbabwe

Namibia

Lesotho

South Africa

Eswatini

Southern Africa


NigeriaMali

GuineaMauritania

NigerSenegal

Cocircte drsquoIvoireBurkina Faso

BeninTogo

LiberiaSierra Leone

GambiaGhana

West Africa

Composite coverage index ()Q1 (poorest) Q2 Q3 Q4 Q5 (wealthiest)



Fig 2 Composite coverage index as a function of absolute income sub-Saharan Africa 1996ndash2016

50 150 400 1000 3000 8000 22000 80000

Central Africa East Africa

Southern Africa West Africa

Q1 (poorest) Q2 Q3 Q4 Q5 (wealthiest)

Com

posit

e cov

erag

e ind

ex (

) 100

80

60

40

20

0

Absolute income PPP (US$) log scale50 150 400 1000 3000 8000 22000 80000

Com

posit

e cov

erag

e ind

ex (

)

Absolute income PPP (US$) log scale

50 150 400 1000 3000 8000 22000 80000

50 150 400 1000 3000 8000 22000 80000

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

PPP purchasing power parity US$ United States dollars

Fig 3 Changes in composite coverage index with time for the poorest (Q1) and wealthiest (Q5) groups sub-Saharan Africa 1996ndash2016

1995 2000 2005 2010 2015

Central Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

East Africa 100

80

60

40

20

0

Southern Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

West Africa 100

80

60

40

20

0

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015

95 CI Q1(poorest) 95 CI Q5 (wealthiest)

Year Year

CI confidence interval



population attributable risk declined considerably from the first to the second period we note that the median popula-tion attributable risk and population at-tributable risk percentage declined from 165 to 109 percentage points and from 360 to 169 respectively

We show the potential for improve-ment in national coverage of repro-ductive maternal newborn and child health interventions in Fig 4 by plot-ting the coverage that could be achieved if the entire population experienced the same level of coverage as the wealthi-est subgroup That is we extended the composite coverage index as measured for the total population from the most recent survey by the population attrib-utable risk calculated for the second period (Table 3) The four countries with the largest values of population attributable risk as calculated for the second period (Ethiopia Guinea Mali and Nigeria Table 3) all had very low levels of national coverage (Table 1) but could achieve an improvement of 20 percentage points or more in national coverage if within-country wealth-re-lated inequality was eliminated In con-trast countries with a national coverage index of more than 70 typically have a small population attributable risk however some countries in this group (eg Kenya and Zimbabwe) would still see an improvement of approximately 10 percentage points

DiscussionWe obtained encouraging results from our investigation not only were many countries successful in improving cover-age of essential reproductive maternal newborn and child health interventions but they also succeeded in significantly reducing wealth-related inequalities in coverage during the last two decades This progress was achieved without explicit focus on within-country in-equalities Even though our study was not designed to quantify the effects of context policies or specific programmes on inequalities in coverage we can pro-vide several insights that are relevant to current efforts to achieve 100 coverage by 2030

First our data reveal very distinct subregional patterns in sub-Saharan Africa Southern Africa has already eliminated much inequality and if trends in East Africa continue this region will also eliminate much of the

remaining inequality in the coming decade The Central and West African subregions were characterized by large wealth-related inequalities in coverage and slow progress to reduce these Most countries in these subregions are cur-rently not predicted to reduce wealth-related inequalities by 203048

Second large differences in income or wealth do not necessarily translate

into large differences in health inter-vention coverage Income inequalities measured by the Gini index are con-siderably larger in Southern African countries than elsewhere in sub-Saharan Africa17 The higher levels of health ser-vice utilization in Southern Africa could be a result of higher levels of education compared with countries in other sub-Saharan Africa regions18

Table 2 Annual average change in composite coverage index of reproductive maternal newborn and child health interventions in sub-Saharan African countries 1996ndash2016

Country Average annual change percentage points (95 CI)

Entire population Q1 Q5

Central Africa 06 (04 to 09) 08 (minus02 to 17) 06 (02 to 10)Cameroon 06 (06 to 08) 00 (minus03 to 03) 05 (03 to 08)Chad 05 (04 to 07) 06 (04 to 08) 04 (02 to 06)Congo 05 (02 to 08) minus002 (minus06 to 05) 08 (01 to 14)Democratic Republic of Congo 09 (05 to 14) 09 (02 to 16) 03 (minus04 to 11)Gabon 10 (08 to 12) 12 (09 to 16) 05 (00 to 10)Sao Tome and Principe 09 (04 to 14) 03 (minus07 to 12) 01 (minus15 to 17)East Africa 10 (07 to 13) 12 (08 to 16) 06 (01 to 10)Burundi 11 (08 to 13) 13 (08 to 18) 07 (02 to 12)Comoros 04 (01 to 06) 06 (02 to 09) minus04 (minus08 to 00)Ethiopia 18 (16 to 20) 13 (11 to 15) 18 (15 to 22)Kenya 10 (09 to 11) 09 (07 to 11) 07 (05 to 09)Madagascar 12 (09 to 15) 11 (07 to 15) 05 (02 to 09)Malawi 16 (15 to 17) 19 (17 to 20) 08 (06 to 09)Mozambique 05 (03 to 07) 11 (08 to 1 5) 01 (minus01 to 03)Rwanda 27 (26 to 28) 26 (24 to 27) 22 (20 to 24)United Republic of Tanzania 02 (01 to 03) 03 (00 to 05) minus01 (minus03 to 01)Uganda 13 (12 to 15) 14 (12 to 16) 06 (04 to 08)Zambia 07 (05 to 08) 09 (07 to 11) 0 3 (01 to 05)Southern Africa 06 (01 to 10) 19 (11 to 26) 00 (minus09 to 09)Eswatini 06 (03 to 09) 13 (09 to 18) 02 (minus06 to 10)Lesotho 13 (10 to 15) 21 (16 to 25) 05 (minus01 to 11)Namibia 06 (04 to 08) 10 (06 to 15) 00 (minus04 to 04)South Africa 00 (minus02 to 01) 05 (02 to 08) minus02 (minus05 to 02)Zimbabwe 19 (17 to 21) 25 (21 to 30) 16 (11 to 22)West Africa 08 (06 to 11) 10 (09 to 12) 05 (03 to 06)Benin 03 (02 to 05) 03 (01 to 06) minus01 (minus03 to 00)Burkina Faso 23 (21 to 26) 20 (17 to 23) 14 (11 to 18)Cocircte drsquoIvoire 05 (03 to 08) 08 (03 to 13) minus01 (minus05 to 02)Gambia 06 (minus01 to 12) 14 (04 to 24) minus04 (minus18 to 10)Ghana 12 (11 to 14) 17 (15 to 20) 04 (01 to 08)Guinea 02 (minus01 to 05) 01 (minus02 to 05) 02 (minus01 to 06)Liberia 18 (12 to 24) 24 (15 to 33) minus05 (minus15 to 05)Mali 07 (05 to 09) 04 (02 to 07) 02 (minus00 to 04)Mauritania 02 (minus05 to 09) minus03 (minus12 to 07) 13 (02 to 24)Niger 15 (13 to 17) 12 (09 to 15) 07 (04 to 10)Nigeria 01 (minus01 to 02) 00 (minus01 to 02) minus05 (minus07 to minus03)Senegal 12 (10 to 14) 11 (09 to 14) 10 (08 to 13)Sierra Leone 19 (17 to 22) 22 (18 to 26) 15 (10 to 20)Togo 12 (10 to 14) 14 (11 to 16) 08 (08 to 11)

CI confidence interval Q1 poorest quintile Q5 wealthiest quintile



Third our data show that cover-age appears to stagnate at about 80 irrespective of wealth this stagnation may be partly the result of measure-ment challenges associated with some coverage indicators for which the exact denominator is difficult to measure

(eg women in need of family plan-ning and sick children in need of treatment) Greater efforts are also needed to identify and understand the populations that do not or cannot access these health interventions for example tracking the poorest 10 or

ethnic minorities by targeted popula-tion surveys1920

Fourth we observe considerable heterogeneity within the subregions in terms of both national levels of cover-age and wealth-related inequalities In East Africa Malawi and Rwanda have

Table 3 Changing contribution of wealth to composite coverage index of reproductive maternal newborn and child health interventions in sub-Saharan African countries 1996ndash2016

Country First period coverage (up until 2010) Second period coverage (from 2011 onwards)

Entire population Q5 PARa PARb Entire population Q5 PARa PARb

Central AfricaCameroon 397 580 183 460 515 690 175 340Chad 186 385 199 1069 280 453 173 620Congo 516 629 114 220 561 686 125 223Democratic Republic of Congo

417 571 155 371 471 591 121 256

Gabon 463 554 92 198 581 616 35 61Sao Tome and Principe 681 744 63 92 736 750 14 19East AfricaBurundi 561 620 59 105 626 660 34 55Comoros 460 631 171 372 517 569 51 99Ethiopia 163 381 218 1333 451 653 202 448Kenya 579 729 150 259 704 800 96 137Madagascar 368 624 256 695 498 683 186 373Malawi 554 691 137 248 770 786 17 22Mozambique 408 660 252 618 612 745 132 216Rwanda 332 446 115 346 677 714 37 55United Republic of Tanzania 586 742 156 266 623 729 106 170Uganda 445 636 190 427 651 717 66 101Zambia 590 758 168 285 695 812 117 168Southern AfricaEswatini 781 829 48 61 833 852 18 22Lesotho 628 750 122 195 753 798 44 59Namibia 690 820 130 189 770 811 42 54South Africa 757 829 72 95 752 799 47 63Zimbabwe 575 708 133 231 731 817 86 117West AfricaBenin 417 624 207 496 481 592 111 231Burkina Faso 272 500 228 837 546 683 137 251Cocircte drsquoIvoire 390 632 242 620 479 642 163 341Gambia 598 684 86 143 615 671 56 91Ghana 453 621 168 371 653 698 45 70Guinea 372 586 214 575 399 616 218 547Liberia 494 675 180 365 603 646 43 71Mali 293 575 282 961 396 596 201 507Mauritania 487 619 131 269 494 672 177 359Niger 255 542 287 1127 454 634 180 397Nigeria 320 627 307 960 359 603 244 680Senegal 455 616 161 354 619 757 138 223Sierra Leone 477 617 140 293 710 774 64 90Togo 334 539 205 615 521 657 136 206

PAR population attributable risk Q5 wealthiest quintilea PAR = health intervention coverage in wealthiest quintile less that in total populationb PAR = PAR as a percentage of composite coverage index for the entire population



succeeded in reducing wealth-related inequalities however although Ethio-pia has made progress in coverage we observed no reduction in inequality In West Africa while Sierra Leone was one of the best performers in terms of both increased coverage and reduced inequality we do not observe progress in either indicator in Guinea and Nigeria Studies have identified several success factors equitable policies for essential services increased health expenditure per capita and strong implementation of national programmes in Malawi2122 and policies on human resources health service delivery health information systems and financing in Rwanda23ndash25 A full analysis of factors contributing to reduced wealth-related inequalities re-quires an in-depth assessment of drivers of change and stagnation over a broader set of countries

Fifth our macro-level analyses provide insights into the effects of hu-manitarian emergencies on the coverage of essential health interventions We observed rapid improvements in cov-erage in Liberia and Sierra Leone after prolonged periods of armed conflicts until the Ebola epidemic struck during 2013ndash2016 However our analysis of the 2017 Demographic and Health Survey results in Sierra Leone showed that the composite coverage index continued to increase and wealth-related inequalities continued to decrease Our preliminary analysis of health intervention coverage using the 2016 Malaria Indicator Sur-vey (MIS) in Liberia26 (which does not have data on family planning) and the 2018 Demographic and Health Survey in neighbouring Guinea also provide evidence of continued improvement (except in immunization) We therefore

suggest that improvements in essential health intervention coverage are still possible during or shortly after epidemic situations Other analysis has shown that strong and fast recovery in coverage is possible following conflicts27 but large wealth-related inequalities persist in conflict-affected countries28 Our analy-sis for Central Africa where protracted political instability is greatest (Camer-oon Chad and Democratic Republic of Congo) shows that the wealthiest and poorest groups experienced increases in health intervention coverage at the same pace that is no reduction in wealth-re-lated inequality Poor access to primary health-care services for women and children and forced displacement are likely factors in Central Africa and may also contribute to the large inequalities observed in other subregions For ex-ample the current conflict in northern Nigeria may be exacerbating existing wealth-related inequalities16

Sixth although the poorest quintile experienced lower health intervention coverage than the wealthiest quintile in every country the patterns of inequal-ity are different In Chad Ethiopia and Niger the wealthiest quintile experi-ences much higher coverage than the other four quintiles a pattern called top inequality29 However in countries such as Congo Gabon and Kenya the poorest quintile is far behind the other four quintiles referred to as bottom inequality These patterns should guide policy-makers in their prioritization of targeted or general health intervention approaches

Our analysis should be interpreted in the light of several limitations First we used data from 36 countries in which at least two national surveys had been conducted since 1995 representing 75 (3648) of the African countries and 894 (964 006 0291 078 306 520) of the total population in sub-Saharan Africa Several countries had not conducted a survey in the last 5 years limiting our ability to assess subregional develop-ments since 20156 Second we used the composite coverage index as a measure of health intervention coverage but that may have concealed differential inequalities in specific indicators such as family planning immunization or delivery care however multiple stand-alone indicators would complicate the comparative assessment at country and

Fig 4 Composite coverage index achievable if wealth-related inequalities were eliminated sub-Saharan Africa 1996ndash2016

0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho

South AfricaSao Tome and Principe

ZimbabweSierra Leone

KenyaZambia

RwandaGhana

UgandaBurundi

United Republic of TanzaniaSenegalGambia

MozambiqueLiberiaGabonCongo

Burkina FasoTogo

ComorosCameroon

MadagascarMauritania

BeninCocircte drsquoIvoire


NigerEthiopia

GuineaMali

NigeriaChad

Percentage ()National composite coverage index Population attributable risk



regional levels Third a limitation is our use of a relative measure of socioeco-nomic position that is wealth index This index is influenced by the choice of variables included as most of the wealthiest individuals live in urban ar-eas the index could be reflecting urbanndashrural inequalities11 Between-country comparisons using the wealth index are also misleading Fourth our results are

not weighted by country population even though we aimed to identify subre-gional patterns However we believe the unweighted results allow us to highlight the large between-country differences

Addressing the wealth-related in-equalities in coverage of essential health interventions remains a high-priority public health issue A thorough analy-sis of factors contributing to wealth-

related inequalities as well as health intervention programmes targeting specific groups within a population are required However our analyses demonstrate that countries can reduce wealth-related inequalities even in the presence of conflict economic hardship or political instability

Competing interests None declared

摘要非洲地区 36 个国家在生育孕产妇新生儿和儿童健康干预措施覆盖方面呈现与财富有关的不平等现象目的旨在调查撒哈拉以南的非洲国家是否成功减少了在生育孕产妇新生儿和儿童健康干预措施覆盖方面呈现出的与财富有关的不平等现象方法我们分析了来自中非东非南非和西非地区的 36 个国家的调查数据这些国家自 1995 年以来已至少进行了两次调查我们计算了综合覆盖指数这是一种妇幼健康干预基本参数函数我们采用财富指数从最贫困到最富有共划分了五个等级以调查在覆盖范围方面与财富相关的不平等现象我们使用最小二乘加权回归法计算指数的年平均变化从而量化其随时间的变化趋势我们计算了人群归因风险度来衡量财富对覆盖指数的贡献结果我们发现这四个地区之间存在巨大差异综合覆盖指数中位数从西非的 508到南非的 753与财富有关的不平等现象普遍存在于非洲各个次区域其中西非的不平等程度最高南非的不平等程度最低

我们观察到在相同的收入水平下南部地区（约 70）的覆盖率高于中部和西部地区（约 40）因此绝对收入并非覆盖率的预测指标与财富相关的不平等现象在非洲南部减少最多而在中非我们没有发现不平等现象减少的迹象结论我们的数据显示即使存在冲突经济困难或政治动荡问题大多数撒哈拉以南的非洲国家在基本卫生服务覆盖方面成功地减少了与财富相关的不平等现象

ملخصحاالت عدم املساواة املتعلقة بالثروة يف تغطية التدخالت الصحية اخلاصة بالصحة اإلنجابية وصحة األم واملواليد

واألطفال يف 36 دولة يف املنطقة األفريقيةالغرض التحقق مما إذا كانت الدول األفريقية يف جنوب الصحراء املتعلقة املساواة عدم حاالت من احلد يف نجحت قد الكربى اإلنجابية بالصحة املتعلقة الصحية التدخالت تغطية يف بالثروة

وصحة األم وصحة املواليد وصحة األطفالثم جتميعها 36 دولة املسح من بيانات بتحليل قمنا الطريقة والتي الفرعية إفريقيا وغرب وجنوب ورشق وسط مناطق يف تم فيها إجراء مسحني عىل األقل منذ عام 1995 وقمنا بحساب الصحي التدخل ملعامالت وظيفة وهي املركبة التغطية مؤرش وقسمناه الثروة مؤرش بانتهاج قمنا والطفل لألم األساسية يف للتحقيق غنى األكثر إىل فقرا األشد من اخلمسية الفئات إىل بقياس وقمنا التغطية يف بالثروة املتعلقة املساواة عدم حاالت التغيري متوسط حساب طريق عن الوقت مرور مع االجتاهات املرجح الصغرى املربعات انحدار باستخدام املؤرش يف السنوي قمنا بحساب املخاطر التي تعزى إىل السكان لقياس مسامهة الثروة

يف مؤرش التغطية

النتائج الحظنا وجود اختالفات كبرية بني املناطق األربع مع متوسط للمؤرش املركب للتغطية من 508 يف غرب إفريقيا إىل املتعلقة املساواة عدم حاالت كانت أفريقيا جنوب يف 753الفرعية وكانت يف أعىل درجاهتا يف املناطق بالثروة سائدة يف كل الدخل يكن مل أفريقيا جنوب يف درجاهتا وأدنى أفريقيا غرب املطلق عامال للتنبؤ بالتغطية حيث الحظنا تغطية أعىل يف املناطق اجلنوبية (حوايل 70) مقارنة باملناطق الفرعية الوسطى والغربية يف املساواة عدم حاالت تقلصت الدخل لنفس (40 (حوايل التغطية املتعلقة بالثروة بأكرب قدر ممكن يف جنوب أفريقيا بينام مل

نجد دليال عىل احلد من عدم املساواة يف وسط أفريقياجنوب يف األفريقية الدول معظم أن بياناتنا تظهر االستنتاج املساواة عدم حاالت من احلد يف نجحت قد الكربى الصحراء يف حتى األساسية الصحية اخلدمات تغطية يف بالثروة املتعلقة االستقرار عدم أو االقتصادية الصعوبات أو الرصاع وجود

السيايس



Reacutesumeacute

Limpact des ineacutegaliteacutes de richesse sur la prise en charge des interventions de santeacute reproductives maternelles et infantiles dans 36 pays de la reacutegion AfriqueObjectif Deacuteterminer si les pays dAfrique subsaharienne sont parvenus agrave reacuteduire limpact des ineacutegaliteacutes de richesse sur la prise en charge des interventions de santeacute reproductives maternelles et infantilesMeacutethodes Nous avons analyseacute des donneacutees denquecirctes meneacutees dans 36 pays regroupeacute en sous-reacutegions (Afrique centrale Afrique de lEst Afrique australe et Afrique de lOuest) Au moins deux enquecirctes devaient dater dapregraves 1995 Nous avons calculeacute lindice de prise en charge composite en fonction de plusieurs paramegravetres essentiels dinterventions de santeacute maternelle et infantile Nous avons eacutegalement adopteacute lindice de richesse diviseacute en quintiles du plus pauvre au plus nanti afin deacutetudier limpact des ineacutegaliteacutes de richesse dans cette prise en charge Nous avons quantifieacute les tendances par rapport au temps en calculant leacutevolution annuelle moyenne de lindice agrave laide de la reacutegression des moindres carreacutes pondeacutereacutes Enfin nous avons eacutevalueacute le risque attribuable agrave la population pour mesurer la contribution de la richesse agrave lindice de prise en charge

Reacutesultats Nous avons remarqueacute des diffeacuterences consideacuterables entre les quatre reacutegions avec un indice meacutedian de prise en charge composite allant de 508 en Afrique de lOuest agrave 753 en Afrique australe Les ineacutegaliteacutes lieacutees agrave la richesse eacutetaient tregraves reacutepandues dans toutes les sous-reacutegions les plus fortes ont eacuteteacute observeacutees en Afrique de lOuest les plus faibles en Afrique australe Le revenu absolu nest pas un indicateur valable car nous avons constateacute une meilleure prise en charge dans la sous-reacutegion dAfrique australe (environ 70) que dans celles dAfrique centrale et dAfrique de lOuest (environ 40) pour des revenus eacutequivalents La majoriteacute des ineacutegaliteacutes de prise en charge lieacutees agrave la richesse ont diminueacute en Afrique australe et nous navons trouveacute aucune preuve de reacuteduction des ineacutegaliteacutes en Afrique centraleConclusion Nos donneacutees montrent que la plupart des pays dAfrique subsaharienne ont reacuteussi agrave atteacutenuer les ineacutegaliteacutes de prise en charge lieacutees agrave la richesse pour les services de santeacute essentiels mecircme en cas de conflit de difficulteacutes eacuteconomiques ou dinstabiliteacute politique

Резюме

Неравенство в охвате мероприятиями по охране репродуктивного здоровья материнства здоровья новорожденных и детского здравоохранения связанное с уровнем доходов в 36 странах Африканского регионаЦель Изучить удалось ли странам Африки расположенным южнее Сахары успешно сократить неравенство в охвате мероприятиями по охране репродуктивного здоровья материнства здоровья новорожденных и детского здравоохранения связанное с уровнем доходовМетоды Авторы проанализировали данные опросов в 36 странах сгруппированных в субрегионы Восточной Западной Центральной и Южной Африки в которых в период с 1995 года было проведено по меньшей мере два опроса Авторы рассчитали составной индекс охвата как функцию существенных параметров мероприятий по охране здоровья матери и ребенка За основу был взят показатель благосостояния разбитый на квинтили от самых бедных до самых богатых позволяющий изучить неравенство в охвате мероприятиями связанное с различиями в уровне доходов Авторы выполнили количественную оценку тенденций по времени посредством расчета среднегодового изменения показателя с использованием средневзвешенной регрессии по методу наименьших квадратов Авторы рассчитали популяционный риск чтобы измерить зависимость показателя охвата мероприятиями от уровня дохода

Результаты Было отмечено значительное различие между четырьмя регионами средний составной индекс охвата составил от 508 в Западной Африке до 753 в Южной Африке Неравенство связанное с уровнем доходов присутствовало во всех субрегионах и было сильнее всего выражено в странах Западной Африки и слабее всего выражено в странах Южной Африки Абсолютный доход не позволял прогнозировать уровень охвата мероприятиями по охране здоровья поскольку авторы наблюдали более высокий уровень охвата (около 70) в Южной Африке по сравнению с уровнем охвата в Центральном и Западном субрегионах (около 40) при том же уровне дохода Неравенство связанное с различиями в уровне дохода значительно уменьшилось в Южной Африке тогда как в Центральной Африке снижения неравенства не отмечаетсяВывод Данные показывают что большинству стран Африки расположенных южнее Сахары удалось сократить неравенство в охвате основными видами медицинских услуг связанное с уровнем доходов несмотря на наличие конфликтов тяжелой экономической ситуации или политической нестабильности

Resumen

Desigualdades relacionadas con la riqueza en la cobertura de las intervenciones sanitarias relativas a la salud reproductiva materna neonatal e infantil en 36 paiacuteses de la Regioacuten de AacutefricaObjetivo Investigar si los paiacuteses del Aacutefrica subsahariana han logrado reducir las desigualdades relacionadas con la riqueza en la cobertura de las intervenciones sanitarias relativas a la salud reproductiva materna neonatal e infantilMeacutetodos Se analizaron los datos de las encuestas de 36 paiacuteses agrupados en las subregiones de Aacutefrica central oriental meridional y occidental en los que se habiacutean realizado por lo menos dos encuestas desde 1995 Se calculoacute el iacutendice compuesto de cobertura en funcioacuten de los paraacutemetros esenciales de las intervenciones sanitarias relativas a la salud maternoinfantil Se adoptoacute el iacutendice de riqueza dividido en

quintiles de maacutes pobres a maacutes ricos para investigar las desigualdades de cobertura relacionadas con la riqueza Se cuantificaron las tendencias con el tiempo al calcular el cambio anual medio del iacutendice por medio de una regresioacuten por miacutenimos cuadrados ponderados Tambieacuten se calculoacute el riesgo atribuible a la poblacioacuten para medir la contribucioacuten de la riqueza al iacutendice de coberturaResultados Se observaron grandes diferencias entre las cuatro regiones con un iacutendice compuesto medio de cobertura que oscilaba entre el 508 para el Aacutefrica occidental y el 753 para el Aacutefrica meridional Las desigualdades relacionadas con la riqueza prevaleciacutean en todas las



subregiones y eran mayores en el Aacutefrica occidental y menores en el Aacutefrica meridional Los ingresos absolutos no eran un factor de prediccioacuten de la cobertura ya que se observoacute una mayor cobertura en las subregiones del sur (alrededor del 70 ) en comparacioacuten con las subregiones central y occidental (alrededor del 40 ) para los mismos ingresos Las desigualdades de cobertura relacionadas con la riqueza se redujeron

en mayor medida en el Aacutefrica meridional y no encontramos pruebas de reduccioacuten de la desigualdad en el Aacutefrica centralConclusioacuten Estos datos muestran que la mayoriacutea de los paiacuteses del Aacutefrica subsahariana han logrado reducir las desigualdades relacionadas con la riqueza en la cobertura de los servicios sanitarios esenciales incluso en presencia de conflictos dificultades econoacutemicas o inestabilidad poliacutetica

References1 Global Strategy for Womenrsquos Childrenrsquos and Adolescentsrsquo Health

(2016-2030) Geneva World Health Organization 2016 Available from httpswwwwhointlife-coursepartnersglobal-strategyglobal-strategy-2016-2030en [cited 2020 Mar 17]

2 The Countdown to 2015 Collaboration A decade of tracking progress for maternal newborn and child survival the 2015 report Geneva and New York World Health Organization and UNICEF 2015 Available from httpwwwcountdown2015mnchorgdocuments2015ReportCountdown_to_2015-A_Decade_of_Tracking_Progress_for_Maternal_Newborn_and_Child_Survival-The2015Report-Conference_Draftpdf [cited 2020 Mar 17]

3 Sustainable development goals New York United Nations 2016 Available from httpssustainabledevelopmentunorgtopicssustainabledevelopmentgoals [cited 2020 Mar 17]

4 Boerma T Requejo J Victora CG Amouzou A George A Agyepong I et al Countdown to 2030 Collaboration Countdown to 2030 tracking progress towards universal coverage for reproductive maternal newborn and child health Lancet 2018 04 14391(10129)1538ndash48 doi httpdxdoiorg101016S0140-6736(18)30104-1 PMID 29395268

5 Hosseinpoor AR Victora CG Bergen N Barros AJ Boerma T Towards universal health coverage the role of within-country wealth-related inequality in 28 countries in sub-Saharan Africa Bull World Health Organ 2011 Dec 189(12)881ndash90 doi httpdxdoiorg102471BLT11087536 PMID 22271945

6 Population databases New York United Nations Department of Economics and Social Affairs Population 2019 Available from httpswwwunorgendevelopmentdesapopulationpublicationsdatabaseindexasp [cited 2020 Mar 17]

7 Countdown 2008 Equity Analysis Group Boerma JT Bryce J Kinfu Y Axelson H Victora CG Mind the gap equity and trends in coverage of maternal newborn and child health services in 54 Countdown countries Lancet 2008 Apr 12371(9620)1259ndash67 doi httpdxdoiorg101016S0140-6736(08)60560-7 PMID 18406860

8 The Countdown to 2030 Collaboration Tracking progress towards universal coverage for womenrsquos childrenrsquos and adolescentsrsquo health The 2017 Report New York United Nations Childrenrsquos Fund 2017 Available from httpsdatauniceforgwp-contentuploads201801Countdown-2030pdf [cited 2020 Mar 17]

9 Wehrmeister FC Restrepo-Mendez MC Franca GV Victora CG Barros AJ Summary indices for monitoring universal coverage in maternal and child health care Bull World Health Organ 2016 Dec 194(12)903ndash12 doi httpdxdoiorg102471BLT16173138 PMID 27994283

10 Harper S Lynch J Methods for measuring cancer disparities using data relevant to Healthy People 2010 cancer-related objectives Ann Arbor Center for Social Epidemiology and Population Health 2005 Available from httpsseercancergovarchivepublicationsdisparitiesmeasuring_disparitiespdf [cited 2020 Mar 17]

11 Rutstein SO Rojas G Guide to DHS statistics the Demographic and Health Survey methodology Calverton USAID 2006

12 Fink G Victora CG Harttgen K Vollmer S Vidaletti LP Barros AJ Measuring socioeconomic inequalities with predicted absolute incomes rather than wealth quintiles a comparative assessment using child stunting data from national surveys Am J Public Health 2017 04107(4)550ndash5 doi httpdxdoiorg102105AJPH2017303657 PMID 28207339

13 International Centre for Equity in Health [internet] Pelotas International Centre for Equity in Health 2016 Available from wwwequidadeorg [cited 2020 Mar 17]

14 GDP PPP (current international $) The World Bank Available from httpsdataworldbankorgindicatorNYGDPMKTPPPCD [cited 2020 Mar 23]

15 World income inequality database (WIID3c) [internet] New York United Nations 2019 Available from httpswwwwiderunueduprojectwiid-world-income-inequality-database [cited 2020 Mar 17]

16 Handbook on health inequality monitoring with a special focus on low-and middle-income countries Geneva World Health Organization 2013 Available from httpswwwwhointghohealth_equityhandbooken [cited 2020 Mar 17]

17 Gini index (World Bank estimate) Washington DC World Bank 2019 Available from httpsdataworldbankorgindicatorsipovgini [cited 2020 Mar 2017]

18 World inequality database on education Paris United Nations Educational - Scientific and Cultural Organization 2019 Available from httpswwweducation-inequalitiesorg [cited 2020 Mar 17]

19 Barros AJD Wehrmeister FC Ferreira LZ Vidaletti LP Hosseinpoor AR Victora CG Are the poorest poor being left behind Estimating global inequalities in reproductive maternal newborn and child health BMJ Glob Health 2020 01 265(1)e002229 doi httpdxdoiorg101136bmjgh-2019-002229 PMID 32133180

20 Victora CG Barros AJD Blumenberg C Costa JC Vidaletti LP Wehrmeister FC et al Association between ethnicity and under-5 mortality analysis of data from demographic surveys from 36 low-income and middle-income countries Lancet Glob Health 2020 Mar8(3)e352ndash61 doi httpdxdoiorg101016S2214-109X(20)30025-5 PMID 32087172

21 Achieving MDGS 4 amp 5 Malawirsquos Progress on Maternal and Child Health Washington DC The World Bank 2014 Available from httpdocumentsworldbankorgcurateden709591468774674336pdf925480BRI0Box30August0201400PUBLIC0pdf [cited 2020 Mar 17]

22 Yaya S Bishwajit G Shah V Wealth education and urban-rural inequality and maternal healthcare service usage in Malawi BMJ Glob Health 2016 08 161(2)e000085 doi httpdxdoiorg101136bmjgh-2016-000085 PMID 28588940

23 Iyer HS Chukwuma A Mugunga JC Manzi A Ndayizigiye M Anand S A comparison of health achievements in Rwanda and Burundi Health Hum Rights 2018 Jun20(1)199ndash211 PMID 30008563

24 Rwandarsquos progress in health leadership performance and insurance London Overseas Development Institute 2011 Available from httpswwwodiorgpublications5483-rwandas-progress-health-leadership-performance-and-health-insurance [cited 2020 March 17]

25 Sayinzoga F Bijlmakers L Drivers of improved health sector performance in Rwanda a qualitative view from within BMC Health Serv Res 2016 04 816(1)123 doi httpdxdoiorg101186s12913-016-1351-4 PMID 27059319

26 Liberia malaria indicator survey 2016 Available from httpsdhsprogramcompubspdfMIS27MIS27pdf [cited 2020 Mar 23]

27 Boerma T Tappis H Saad-Haddad G Das J Melesse DY DeJong J et al Armed conflicts and national trends in reproductive maternal newborn and child health in sub-Saharan Africa what can national health surveys tell us BMJ Glob Health 2019 06 244 Suppl 4e001300 doi httpdxdoiorg101136bmjgh-2018-001300 PMID 31297253

28 Akseer N Wright J Tasic H Everett K Scudder E Amsalu R et al Women children and adolescents in conflict countries an assessment of inequalities in intervention coverage and survival BMJ Glob Health 2020 01 265(1)e002214 doi httpdxdoiorg101136bmjgh-2019-002214 PMID 32133179

29 Victora CG Joseph G Silva ICM Maia FS Vaughan JP Barros FC et al The inverse equity hypothesis analyses of institutional deliveries in 286 national surveys Am J Public Health 2018 04108(4)464ndash71 doi httpdxdoiorg102105AJPH2017304277 PMID 29470118

Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4



down to 2030 for Womenrsquos Childrenrsquos and Adolescentsrsquo Health programme and attended by teams of analysts from public health institutions and ministries of health from 38 African countries in 2018

MethodsSurvey data

We performed our analysis on second-ary data acquired from 127 national surveys conducted in 36 countries in which at least 2 surveys had been con-ducted since 1995 Surveys were either Demographic and Health Surveys or Multiple Indicator Cluster Surveys which allowed us to compare standard indicators with time We grouped the countries into four subregions accord-ing to the United Nations Population Division classification6 Central Africa (6 countries 18 surveys) East Africa (11 countries 41 surveys) Southern Africa (5 countries 15 surveys) and West Af-rica (14 countries 54 surveys Table 1)

Composite coverage index

We calculated the composite coverage index (percentage) of maternal new-born and child health interventions a weighted function of essential maternal and child health intervention indicators representing the four-stage continuum of care (family planning antenatal care and delivery child immunization and disease management) defined as7ndash9

(1)

where the variables represent the pro-portion of women aged 15ndash49 years of age in need of contraception and had access to modern methods to modern contraceptive methods (FPmo) at least four antenatal care visits (A) and a skilled birth attendant (S) children aged 12ndash23 months who received tuberculo-sis immunization by Bacillus CalmettendashGueacuterin (B) measles immunization (M) and three doses of diphtheriandashtetanusndashpertussis immunization (or pentavalent vaccine) (D) and children younger than 5 years of age who received oral rehydration salts for diarrhoea treat-ment (O) and care for suspected acute respiratory infection (C) The index is a robust single measure of the coverage

Table 1 Survey data used to calculate composite coverage index of reproductive maternal newborn and child health interventions in sub-Saharan African countries 1996ndash2016


Central AfricaCameroon DHS 1998 397 (17)Cameroon DHS 2004 471 (11)Cameroon DHS 2011 476 (11)Cameroon MICS 2014 515 (12)Chad DHS 1996 186 (11)Chad DHS 2004 159 (12)Chad MICS 2010 197 (09)Chad DHS 2014 280 (09)Congo DHS 2005 516 (12)Congo DHS 2011 579 (10)Congo MICS 2014 561 (09)Democratic Republic of the Congo

DHS 2007 417 (12)


MICS 2010 439 (09)


DHS 2013 471 (08)

Gabon DHS 2000 463 (09)Gabon DHS 2012 581 (08)Sao Tome and Principe

DHS 2008 681 (12)


MICS 2014 736 (09)

East AfricaBurundi DHS 2010 561 (06)Burundi DHS 2016 626 (05)Comoros DHS 1996 460 (15)Comoros DHS 2012 517 (13)Ethiopia DHS 2000 163 (09)Ethiopia DHS 2005 247 (09)Ethiopia DHS 2011 351 (12)Ethiopia DHS 2016 451 (12)Kenya DHS 1998 579 (09)Kenya DHS 2003 524 (10)Kenya DHS 2008 591 (09)Kenya DHS 2014 704 (05)Madagascar DHS 1997 368 (13)Madagascar DHS 2003 435 (17)Madagascar DHS 2008 498 (10)Malawi DHS 2000 554 (07)Malawi DHS 2004 585 (06)Malawi DHS 2010 702 (04)Malawi MICS 2013 752 (04)Malawi DHS 2015 770 (04)Mozambique DHS 1997 408 (25)Mozambique DHS 2003 568 (10)Mozambique DHS 2011 546 (11)Mozambique DHS 2015 612 (14)Rwanda DHS 2000 332 (05)Rwanda DHS 2005 386 (05)Rwanda DHS 2010 635 (07)

(continues )







Time trends




DHS 1996 586 (11)


DHS 1999 607 (22)


DHS 2004 588 (10)


DHS 2010 596 (09)


DHS 2015 623 (09)


( continued)

(continues )









Ethics









( continued)



Time trends






Impact of wealth



0 20 40 60 80 100

Cameroon

Congo

Chad


Gabon


Central Africa


EthiopiaMadagascar

MozambiqueKenya



East Africa


0 20 40 60 80 100

Zimbabwe

Namibia

Lesotho

South Africa

Eswatini

Southern Africa


NigeriaMali

GuineaMauritania

NigerSenegal


BeninTogo

LiberiaSierra Leone

GambiaGhana

West Africa





50 150 400 1000 3000 8000 22000 80000




Com

posit

e cov

erag

e ind

ex (

) 100

80

60

40

20

0


Com

posit

e cov

erag

e ind

ex (

)


50 150 400 1000 3000 8000 22000 80000

50 150 400 1000 3000 8000 22000 80000

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0



1995 2000 2005 2010 2015

Central Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

East Africa 100

80

60

40

20

0

Southern Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

West Africa 100

80

60

40

20

0

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015


Year Year


























417 571 155 371 471 591 121 256











0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho



KenyaZambia

RwandaGhana

UgandaBurundi



Burkina FasoTogo

ComorosCameroon




NigerEthiopia

GuineaMali

NigeriaChad

















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4







Time trends




DHS 1996 586 (11)


DHS 1999 607 (22)


DHS 2004 588 (10)


DHS 2010 596 (09)


DHS 2015 623 (09)


( continued)

(continues )









Ethics









( continued)



Time trends






Impact of wealth



0 20 40 60 80 100

Cameroon

Congo

Chad


Gabon


Central Africa


EthiopiaMadagascar

MozambiqueKenya



East Africa


0 20 40 60 80 100

Zimbabwe

Namibia

Lesotho

South Africa

Eswatini

Southern Africa


NigeriaMali

GuineaMauritania

NigerSenegal


BeninTogo

LiberiaSierra Leone

GambiaGhana

West Africa





50 150 400 1000 3000 8000 22000 80000




Com

posit

e cov

erag

e ind

ex (

) 100

80

60

40

20

0


Com

posit

e cov

erag

e ind

ex (

)


50 150 400 1000 3000 8000 22000 80000

50 150 400 1000 3000 8000 22000 80000

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0



1995 2000 2005 2010 2015

Central Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

East Africa 100

80

60

40

20

0

Southern Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

West Africa 100

80

60

40

20

0

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015


Year Year


























417 571 155 371 471 591 121 256











0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho



KenyaZambia

RwandaGhana

UgandaBurundi



Burkina FasoTogo

ComorosCameroon




NigerEthiopia

GuineaMali

NigeriaChad

















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4









Ethics









( continued)



Time trends






Impact of wealth



0 20 40 60 80 100

Cameroon

Congo

Chad


Gabon


Central Africa


EthiopiaMadagascar

MozambiqueKenya



East Africa


0 20 40 60 80 100

Zimbabwe

Namibia

Lesotho

South Africa

Eswatini

Southern Africa


NigeriaMali

GuineaMauritania

NigerSenegal


BeninTogo

LiberiaSierra Leone

GambiaGhana

West Africa





50 150 400 1000 3000 8000 22000 80000




Com

posit

e cov

erag

e ind

ex (

) 100

80

60

40

20

0


Com

posit

e cov

erag

e ind

ex (

)


50 150 400 1000 3000 8000 22000 80000

50 150 400 1000 3000 8000 22000 80000

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0



1995 2000 2005 2010 2015

Central Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

East Africa 100

80

60

40

20

0

Southern Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

West Africa 100

80

60

40

20

0

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015


Year Year


























417 571 155 371 471 591 121 256











0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho



KenyaZambia

RwandaGhana

UgandaBurundi



Burkina FasoTogo

ComorosCameroon




NigerEthiopia

GuineaMali

NigeriaChad

















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4



Time trends






Impact of wealth



0 20 40 60 80 100

Cameroon

Congo

Chad


Gabon


Central Africa


EthiopiaMadagascar

MozambiqueKenya



East Africa


0 20 40 60 80 100

Zimbabwe

Namibia

Lesotho

South Africa

Eswatini

Southern Africa


NigeriaMali

GuineaMauritania

NigerSenegal


BeninTogo

LiberiaSierra Leone

GambiaGhana

West Africa





50 150 400 1000 3000 8000 22000 80000




Com

posit

e cov

erag

e ind

ex (

) 100

80

60

40

20

0


Com

posit

e cov

erag

e ind

ex (

)


50 150 400 1000 3000 8000 22000 80000

50 150 400 1000 3000 8000 22000 80000

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0



1995 2000 2005 2010 2015

Central Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

East Africa 100

80

60

40

20

0

Southern Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

West Africa 100

80

60

40

20

0

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015


Year Year


























417 571 155 371 471 591 121 256











0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho



KenyaZambia

RwandaGhana

UgandaBurundi



Burkina FasoTogo

ComorosCameroon




NigerEthiopia

GuineaMali

NigeriaChad

















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4




50 150 400 1000 3000 8000 22000 80000




Com

posit

e cov

erag

e ind

ex (

) 100

80

60

40

20

0


Com

posit

e cov

erag

e ind

ex (

)


50 150 400 1000 3000 8000 22000 80000

50 150 400 1000 3000 8000 22000 80000

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0



1995 2000 2005 2010 2015

Central Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

East Africa 100

80

60

40

20

0

Southern Africa

Com

posit

e cov

erag

e ind

ex (

)

100

80

60

40

20

0

West Africa 100

80

60

40

20

0

1995 2000 2005 2010 2015

1995 2000 2005 2010 2015 1995 2000 2005 2010 2015


Year Year


























417 571 155 371 471 591 121 256











0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho



KenyaZambia

RwandaGhana

UgandaBurundi



Burkina FasoTogo

ComorosCameroon




NigerEthiopia

GuineaMali

NigeriaChad

















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4

























417 571 155 371 471 591 121 256











0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho



KenyaZambia

RwandaGhana

UgandaBurundi



Burkina FasoTogo

ComorosCameroon




NigerEthiopia

GuineaMali

NigeriaChad

















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4









0 20 40 60 80 100

EswatiniMalawi

NamibiaLesotho



KenyaZambia

RwandaGhana

UgandaBurundi



Burkina FasoTogo

ComorosCameroon




NigerEthiopia

GuineaMali

NigeriaChad

















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4
















السيايس



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4



Reacutesumeacute



Резюме



Resumen





































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4



































Table 1

Figure 1

Figure 2

Figure 3

Table 2

Table 3

Figure 4

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