Final

Even the smartest students need writing assistance at some point during their academic career. Should you lock yourself in a room and spend the entire weekend trying to write a paper? We promise you that the paper that you pay for won’t be resold or submitted elsewhere. It will also be written according to the instructions that you and your professor provide. Our excellent essays stand out among the rest for a reason. Don’t just take our word, check them out by yourself.


Order a Similar Paper Order a Different Paper

What must be done to intervene and ensure that history does not repeat itself for future populations? This week, you examine the impact of the historical roots of social disparities on health of populations in low-income countries. As you go through this week’s Learning Resources, think about what we can learn from history. This week, you consider developing a policy in a country you selected and think about various issues in practicing population health.

For your Final Project, share some of your ideas on how you can use the knowledge and insights gained in this course to promote positive social change in a community/country and the world. It is advisable to select a community/country other than the one where you live.

To prepare for the Final Project, review all the week’s Learning Resources and consider possible issues you might encounter when implementing a policy.

Final Project (7–10 pages), not including the cover and the references:

In developing a policy in the country you selected, consider the following:

  • Explain the rationale for selecting the country.
  • Describe the social determinants of health in the country that you would need to address. Explain why you need to address these determinants.
  • Explain the possible public issues you might encounter in health literacy and cultural awareness in this country.
  • Describe the relationship between health inequality/inequities and life expectancy for the population in your selected country.
  • Describe two current efforts in this country (you selected) to reduce health inequities.
  • Explain how you might develop a health policy so that it gets the support of the country you selected. Note:Take into account the culture of the country.

Use APA formatting for your Final Project and to cite your resources. Expand on your insights utilizing the Learning Resources.

Comment

www.thelancet.com/lancetgh Vol 5 June 2017 e557

Smoking status and HIV in low-income and middle-income
countries

In high-income settings, the prevalence of tobacco use
has been shown to be significantly higher in people
living with HIV than among HIV-negative individuals of
the same age and sex distribution. This at-risk pattern is
one of the biggest threats to the number of years of life
saved with antiretroviral therapy (ART).1,2 Extrapolation
of these findings to low-income and middle-income
countries (LMICs) is risky because social, cultural, and
behavioural factors influencing tobacco use differ
widely across different regions. The epidemiology of
tobacco use in HIV-positive individuals in LMICs has
been sparsely reported, with limited representativeness
and no or biased control populations.3–5 In The Lancet
Global Health, Noreen Mdege and colleagues6 report
an unprecedented estimation of tobacco use in people
living with HIV, using nationally representative samples
extracted from the Demographic and Health Surveys
(DHS) from 28 countries on three continents. In addition
to depicting the burden and diversity of tobacco
use, the authors show significantly higher figures of
tobacco use in people living with HIV compared with
their HIV-negative counterparts, regardless of gender.
These results confirm what has already been reported
in high-income settings, and emphasise the need for
adapted preventive measures and tobacco cessation
programmes in LMICs.

Countries highly affected by the HIV epidemic
usually have underfunded health-care systems and
are overburdened with other major epidemics such
as malaria and tuberculosis, and are therefore less
inclined to invest in preventive measures against non-
communicable diseases and their determinants. In this
context, smoking-targeted preventive and cessation
programmes are often limited or nonexistent. HIV
care programmes represent by far the largest chronic care
programmes rolled out in LMICs, potentially paving the
way for an integrated panel of services targeting non-
communicable diseases. Measures directed towards
smoking avoidance and cessation can then be introduced
and piloted before their extension and adaption to a
larger set of health facilities.

Although Mdege and colleagues’ analysis6 of publicly
available data provides a comprehensive presentation

of prevalence estimates of tobacco use in HIV-positive
individuals in LMICs, the number of people living with
HIV in the study represents less than 0·001% of the
estimated 34 million people living with HIV in 2014
in these parts of the world; this limited size might lead
to imprecision and potential bias in the prevalence
estimates of tobacco use, especially outside of Africa.7
Although the data were fairly representative of the
African region, data for southeast Asia were only
available for India, leaving important uncertainties
concerning the association between tobacco use and
HIV infection in countries particularly affected by
tobacco smoking—especially China. This report6 comes
at a time when LMICs represent a major target for the
tobacco industry.8 Southeast Asia is the widest market
for the tobacco industry, and the Chinese tobacco
market represents more cigarettes than all other LMICs
combined.9

Additional data sources on tobacco use are needed for
people living with HIV in LMICs. Achievements made
by the international community to enable universal
access to ART were accompanied by initiatives providing
worldwide data on the follow-up of patients initiating
ART. The International Epidemiology Databases to
Evaluate AIDS (IeDEA), funded by the US National
Institutes of Health, is a unique platform that has so
far gathered data on more than 1 700 000 people living
with HIV on ART, most of whom live in LMICs. This
platform has successfully collected core information
on ART exposure, and harmonisation is underway to
standardise the collection of basic behavioural risk
factors such as tobacco use. Data from observational
cohorts participating in IeDEA have already provided
regional estimates on tobacco use from west Africa,4
and in the future could contribute to a more robust and
complementary estimation of tobacco use in people
living with HIV, especially in the context of universal
ART.10

Nevertheless, the DHS offer a good opportunity to
access a somewhat representative control group of HIV-
uninfected people and can be repeated over time using
the same methodological approach. This use of DHS
data is therefore a unique framework to conduct sound

For more on IeDEA see

See Articles page e578

Comment

e558 www.thelancet.com/lancetgh Vol 5 June 2017

analyses for identification of trends in tobacco use and to
measure the effect of smoking prevention and cessation
programmes according to HIV infection status. To expand
their analysis, Mdege and colleagues could also consider
prevalence estimates of tobacco use in younger age
groups because these groups are the most susceptible to
smoking initiation. Additionally, the low prevalence of
tobacco smoking reported in women compared with men
in LMICs makes women—along with young people—a
particular target for the tobacco industry, whether they
live with HIV or not.8

Antoine Jaquet, *François Dabis
Institut de Santé Publique, d’Epidémiologie et de Développement,
University of Bordeaux, and Inserm, Bordeaux Population Health
Research Center, UMR 1219, F-33000 Bordeaux, France
[email protected]

We are investigators of the West Africa IeDEA collaboration, and declare no
competing interests.

Copyright © The Author(s). Published by Elsevier Ltd. This is an Open Access
article under the CC BY 4.0 license.

1 Reddy KP, Parker RA, Losina E, et al. Impact of cigarette smoking and
smoking cessation on life expectancy among people with HIV: A US-based
modeling study. J Infect Dis 2016; 214: 1672–81.

2 Mdodo R, Frazier EL, Dube SR, et al. Cigarette smoking prevalence among
adults with HIV compared with the general adult population in the United
States: cross-sectional surveys. Ann Intern Med 2015; 162: 335–44.

3 Iliyasu Z, Gajida AU, Abubakar IS, Shittu O, Babashani M, Aliyu MH.
Patterns and predictors of cigarette smoking among HIV-infected patients
in northern Nigeria. Int J STD AIDS 2012; 23: 849–52.

4 Jaquet A, Ekouevi DK, Aboubakrine M, et al. Tobacco use and its
determinants in HIV-infected patients on antiretroviral therapy in West
African countries. Int J Tuberc Lung Dis 2009; 13: 1433–39.

5 Mwiru RS, Nagu TJ, Kaduri P, Mugusi F, Fawzi W. Prevalence and patterns of
cigarette smoking among patients co-infected with human
immunodeficiency virus and tuberculosis in Tanzania. Drug Alcohol Depend
2017; 170: 128–32.

6 Mdege ND, Shah S, Ayo-Yusuf OA, Hakim J, Siddiqi K. Tobacco use among
people living with HIV: analysis of data from Demographic and Health
Surveys from 28 low-income and middle-income countries.
Lancet Glob Health 2017; 5: e578–92.

7 UNAIDS. Global AIDS Update 2016. Geneva: UNAIDS, 2016.
8 Gilmore AB, Fooks G, Drope J, Bialous SA, Jackson RR. Exposing and

addressing tobacco industry conduct in low-income and middle-income
countries. Lancet 2015; 385: 1029–43.

9 Eriksen M, Mackay J, Schluger N, Islami F, Drope J. The Tobacco Atlas,
5th edn. Atlanta, GA: American Cancer Society, 2015.

10 WHO. Consolidated guidelines on the use of antiretroviral drugs for
treating and preventing HIV infection: recommendations for a public
health approach, 2nd edn. Geneva: World Health Organization, 2016.

  • Smoking status and HIV in low-income and middle-income countries
    • References

RESEARCH ARTICLE Open Access

Prevalence of arthritis according to age, sex
and socioeconomic status in six low and
middle income countries: analysis of data
from the World Health Organization study
on global AGEing and adult health (SAGE)
Wave 1
Sharon L. Brennan-Olsen1,2,3,4* , S. Cook1, M. T. Leech5, S. J. Bowe1, P. Kowal6,7, N. Naidoo6, I. N. Ackerman8,
R. S. Page1,9, S. M. Hosking1, J. A. Pasco1,3 and M. Mohebbi1

Abstract

Background: In higher income countries, social disadvantage is associated with higher arthritis prevalence; however,
less is known about arthritis prevalence or determinants in low to middle income countries (LMICs). We assessed
arthritis prevalence by age and sex, and marital status and occupation, as two key parameters of socioeconomic
position (SEP), using data from the World Health Organization Study on global AGEing and adult health (SAGE).

Methods: SAGE Wave 1 (2007–10) includes nationally-representative samples of older adults (≥50 yrs), plus smaller
samples of adults aged 18-49 yrs., from China, Ghana, India, Mexico, Russia and South Africa (n = 44,747). Arthritis was
defined by self-reported healthcare professional diagnosis, and a symptom-based algorithm. Marital status and
education were self-reported. Arthritis prevalence data were extracted for each country by 10-year age strata, sex and
SEP. Country-specific survey weightings were applied and weighted prevalences calculated.

Results: Self-reported (lifetime) diagnosed arthritis was reported by 5003 women and 2664 men (19.9% and 14.1%,
respectively), whilst 1220 women and 594 men had current symptom-based arthritis (4.8% and 3.1%, respectively). For
men, standardised arthritis rates were approximately two- to three-fold greater than for women. The highest rates were
observed in Russia: 38% (95% CI 36%–39%) for men, and 17% (95% CI 14%–20%) for women. For both sexes and in all
LMICs, arthritis was more prevalent among those with least education, and in separated/divorced/widowed women.

Conclusions: High arthritis prevalence in LMICs is concerning and may worsen poverty by impacting the ability to
work and fulfil community roles. These findings have implications for national efforts to prioritise arthritis prevention
and management, and improve healthcare access in LMICs.

Keywords: Arthritis, Epidemiology, Prevalence, Socio-demographic characteristics, Low and middle income countries

* Correspondence: [email protected]
1Deakin University, Geelong, Australia
2Australian Institute for Musculoskeletal Science (AIMSS), The University of
Melbourne-Western Precinct, Level 3, Western Centre for Health Research
and Education (WCHRE) Building, C/- Sunshine Hospital, Furlong Road, St
Albans, Melbourne, VIC 3021, Australia
Full list of author information is available at the end of the article

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271
DOI 10.1186/s12891-017-1624-z

Background
Worldwide, musculoskeletal disorders represent a global
threat to healthy ageing [1], and are ranked as the sec-
ond most common cause of disability, measured by years
lived with disability (YLDs) [2]. Lower and middle in-
come countries (LMICs) are not immune to the burden
of musculoskeletal diseases, indeed the prevalence of this
non-communicable disease (NCD) group is dramatically
increasing in LMICs [3]. The 2010 Global Burden of
Disease (GBD) study reported that musculoskeletal dis-
eases accounted for 19.2% of all YLDs in LMICs [3].
Despite this, the majority of the global NCD initiatives
do not include musculoskeletal diseases [3]. Significantly
contributing to the global disability burden associated
with the musculoskeletal system are arthritis diseases.
Arthritis is an umbrella term that encompasses in ex-
cess of 100 different arthritic conditions which are a
chronic, painful, and debilitating group of diseases.
Arthritis, specifically osteoarthritis, is a significant
contributor to global disability burden, and the YLDs
attributable to osteoarthritis have increased by 75%
from 1990 to 2013 [2], indicating this disease as a
growing problem internationally. In combination with
an increasing trajectory of arthritis prevalence [2, 4],
growth in YLDs attributable to arthritis is due pri-
marily to increased life expectancy worldwide, and
prolonged exposure to arthritis risk factors [5].
Compared to higher income countries, many LMICs

[6], where two-thirds of the world’s population resides,
have a much lower capacity to pay for adequate health-
care. Indeed, LMICs have 90% of the global burden of
disease but only 12% of global health spending [7]. In
higher income countries, arthritis is associated with re-
duced workplace productivity [8, 9]; however, for resi-
dents of LMICs, arthritis imposes a potential additional
burden by creating a vicious cycle that subsequently
worsens poverty [10]. For example, compared to higher
income countries, and in context of scarce medical
and social support systems, residents of LMICs with
arthritis also experience reduced ability to access,
afford or utilize treatments including analgesic and
anti-inflammatory pharmacotherapies [11, 12], or
arthroplasty for advanced disease [13, 14]. They also
have, in context of workforce capacity limitations, less
flexibility regarding working conditions or hours [15],
and few if any options for early retirement, or social
security ‘safety nets’ pertaining to minimum income,
including financial and/or material goods.
Whilst the majority of research regarding arthritis

prevalence has been undertaken in higher income coun-
tries, recent data from the 2010 GBD Study provides
some evidence that LMICs may have greater arthritis
prevalence than higher income countries [16]. Yet, while
valuable population level estimates, extrapolation from

these GBD estimates is difficult given that they are based
on published prevalence and incidence data from a small
number of heterogeneous studies spanning different
time periods in a limited number of LMIC [17]. Further-
more, data from multi-country studies of LMICs that
have examined prevalence of arthritis across sociodemo-
graphic factors are typically not readily available [18, 19],
with the exception of a recent publication, which
showed that more years of schooling and greater levels
of wealth decreased the odds of having an undiagnosed
NCD, including arthritis [20]. Understanding the preva-
lence of arthritis across different parameters of socioeco-
nomic position (SEP) data would augment our global
understanding of global arthritis prevalence, social deter-
minants and burden.
To date, country-specific arthritis prevalence across

parameters of SEP has not been systematically evalu-
ated in large, nationally representative samples of
populations from LMICs. This information is crucial
for planning future healthcare delivery for high bur-
den chronic conditions and to ensure sufficient
health workforce capacity – both significant concerns
in an ageing world [21]. Comprehensive data have
been collected in the World Health Organization
(WHO) Study on global AGEing and adult health
(SAGE) [20, 22, 23], thus providing an important re-
source to investigate disease prevalence in large
population samples from six LMICs. Using SAGE
Wave 1, these analyses were undertaken to determine
the prevalence of arthritis in LMICs according to
age, sex, and socioeconomic position (SEP).

Methods
Study population and design
SAGE Wave 1 (2007–10) is a longitudinal study with na-
tionally representative samples of persons aged 50+ years
and a smaller sample of adults aged 18–49 years that in-
cludes 44,747 adults aged ≥18 years from China, Ghana,
India, Mexico, Russian Federation and South Africa [23].
Multistage cluster sampling strategies were used with
households as sampling units. Households were classi-
fied into one of two mutually exclusive categories: i) all
persons aged 50 years and older were selected from
“older” households, and ii) one person aged 18–49 years
was selected from each “younger” household. An older
or younger household was defined by the age of the re-
spondent targeted for individual interview. Household-
level and person-level analysis weights were calculated
for each country. This research was performed in ac-
cordance with the Declaration of Helsinki. The WHO
and the respective implementing agency in each country
provided ethics approvals. Written, informed consent
was obtained from all participants.

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 2 of 12

Data collection in WHO SAGE
Using a standardized survey instrument to ensure
consistency, and based on standardized methods, inter-
viewer training and translation protocols, face-to-face in-
terviews were conducted in China (2008–10; response
93%), Ghana (2008–09; response 81%), India (2007–08;
response 68%), Mexico (2009–10; response rate 53%),
the Russian Federation (2007–10; response 83%) and
South Africa (2007–08; response 75%), as previously
published [23]. Full details regarding the probability
sampling design, cluster sampling strategies and
country-specific areas included in SAGE have been pub-
lished elsewhere [23]. Briefly, the SAGE questionnaire
consisted of household, individual and proxy question-
naires, a verbal autopsy, and appendices: the domains of
which are summarised in Table 1 [23].

Arthritis status: self-reported and symptom-based
For the current analyses, self-reported diagnosis of arth-
ritis (lifetime) was based on participant responses to the
question; “Have you ever been diagnosed with/told by a
health care professional you have arthritis (a disease of the
joints; or by other names rheumatism or osteoarthritis)?”
As a secondary endpoint, a symptom-based determination
of arthritis (yes/no for current within the previous
12 months) was also employed, by applying an algorithm
developed by the WHO SAGE study team [23]; questions
and the algorithm are presented in Table 2.

Socioeconomic position
SEP was measured using two key parameters of marital
status and educational attainment: the latter used due to
the inextricable link between education and skilled vs. un-
skilled labour, and thus financial remuneration for work.
Self-reported marital status was categorised for analyses
into three groups of: (i) never married, (ii) currently mar-
ried or cohabitating, and (iii) separated/divorced or

widowed. Participants were asked if they had ever been to
school; for those that indicated ‘yes’, they were also asked
to identify the highest level of education completed. Educa-
tion was categorised as (i) ‘no formal schooling’, (ii) less
than primary school, or primary school completed, (iii) sec-
ondary school completed, or high school (or equivalent)
completed, or (iv) college, pre-university or university com-
pleted, or post-graduate degree completed. Education
levels were mapped to an international standard [24].

Statistical analyses
Arthritis (self-reported and symptom-based) prevalence
and 95% confidence intervals (95%CI) were calculated
by implementing household level analysis weights separ-
ately for each of the six countries across 10-year age
strata (the 20–29 year age group was expanded to also
include those aged 18–19 years), sex, marital status and
education. Country-specific survey weightings were
applied, and weighted prevalence calculated for each
country. Adjustment of prevalence estimates for differ-
ences in the age structure across countries was accom-
plished by age-standardisation, using the direct method
of standardisation [25] and the WHO World Standard
Population distribution (%) as standard population [26].
Ten-year intervals were used for age categorisation.

Results
Country-specific numbers and proportions of the total
44,747 participants (total 57.1% women), were; China
n = 15,050 (33.6%), Ghana n = 5573 (12.5%), India
n = 12,198 (27.3%), Mexico n = 2752 (6.1%), the Russian
Federation n = 4947 (11.1%), and South Africa n = 4227
(9.5%). Across the entire study population, 5003 women
and 2664 men had (lifetime) self-reported arthritis (19.9%
and 14.1%, respectively), whilst 1220 women and 594 men

Table 1 Questionnaire sections included in the SAGE Wave 1
standardized survey instrument [23]

Questionnaire
section

Household roster Questions regarding the dwelling, income, transfers
[of family members] in and out of the household,
assets and expenditures

Individual
questionnaire

Questions regarding health and its determinants,
disability, work history, risk factors, chronic
conditions, caregiving, subjective well-being, health
care utilization and health systems responsiveness

Proxy
questionnaire

Questions regarding health, functioning, chronic
conditions, and health care utilization

Verbal autopsy Performed to ascertain the probable cause of death
for deaths in the household in the 24 months prior
to interview or between interview waves

Appendices Includes show-cards to assist with the interviews

Table 2 Symptom-based questions and the related algorithm
to ascertain prevalent arthritis, developed as part of the World
Health Organization SAGE Wave 1 [23]

Question number Question text and algorithm

1 During the last 12 months, have you experienced
pain, aching, stiffness or swelling in or around the
joints (like arms, hands, legs or feet) which were
not related to an injury and lasted for more than
a month?

2 During the last 12 months, have your experienced
stiffness in the joint in the morning after getting up
from bed, or after a long rest of the joint without
movement?

3 Did this stiffness last for more than 30 min?

4 Did this stiffness go away after exercise or
movement in the joint?

Algorithm If a participant responded with ‘yes’ to questions
1 and/or 2, and responded with ‘yes’ to question
3 and ‘no’ to question 4, then the participant was
categorised as having arthritis

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 3 of 12

were identified as having (within previous 12 months)
symptom-based arthritis (4.8% and 3.1%, respectively).
Table 3 presents the country-specific proportional

responses (non-weighted) to the four symptom-based
questions (see Table 2), that were included in the
algorithm to determine symptom-based arthritis. For
women, proportions that reported ‘any pain during the
last 12 months’ or ‘any stiffness during the last 12
months’ were lowest for Mexico (28.4% [95% CI 26.3%–
30.9%] and 23.3% [95% CI 20.9%–26.0%], respectively)
and highest for the Russian Federation (48.4% [95% CI
46.4%–50.4%] and 50.5% [95% CI 48.8%–52.1%], respect-
ively). For men, the proportions that reported ‘any pain
during the last 12 months’ or ‘any stiffness during the
last 12 months’ were lowest for Mexico (20.1% [95% CI
17.5%–23.0%] and 16.1% [95% CI%CI 14.1%–18.3%], re-
spectively) and highest for the Russian Federation (32.9%
[95% CI 30.5%–35.5%] and 34.6% [95% CI 32.4%–
36.9%], respectively).
Table 4 presents the country-specific and sex-stratified

prevalence of self-reported arthritis (weighted), across
age strata, educational attainment and marital status.
For both sexes in each country, arthritis prevalence
increased proportionally with advancing age; with the
exception of women from China and men and women
from South Africa who had the greatest prevalence in
the age group of 60–69 years, all other groups showed a
peak in arthritis prevalence in the oldest age group
≥70 years. For women, the prevalence by country ranged
from 22.9% (95% CI 11.2%–41.1%) in Mexico to 45.7%
(95% CI 39.1%–52.3%) in the Russian Federation. For
men, prevalence ranged from 9.7% (95% CI 6.3%–14.5%)
in Mexico to 37.8% (95% CI 30.3%–46.0%) in the
Russian Federation. In each country, women who had
never been formally schooled or had completed less than
primary school had the highest prevalence of arthritis
compared to those with a greater level of educational at-
tainment. Higher arthritis prevalence was consistently
observed for women that were separated, divorced or
widowed (range: Russian Federation 36.4% [95% CI
29.1%–44.4%] to Ghana 11.7% [95% CI 8.9%–15.1%])
compared to those that were never married or currently
married (range: China 0.9% [95% CI 0.3%–3.0%] to
South Africa 12.1% [95% CI 5.5%–24.7%]). Similar to
women, men that had never been formally schooled had
the highest arthritis prevalence, with the exception of
men from the Russian Federation, for whom the greatest
prevalence was observed in those that had completed all
or some primary school level education (39.6% [95% CI
21.3%–61.4%]), however these numbers were small.
Compared to other categories, men that were never
married had the lowest arthritis prevalence (range:
Mexico 0.1% [95% CI 0.0%–0.5%] to India 3.9% [95% CI
1.5%–9.5%]). In China and India, men that were

currently married had the highest prevalence (11.9%
[95% CI 9.4%–14.8%], and 8.8% [95% CI 7.2%–10.7%],
respectively), whilst for all other countries, men that
were separated, divorced or widowed were observed to
have the highest arthritis prevalence (highest: Russian
Federation 33.5% [95% CI 13.3%–62.3%]).
Table 5 presents the country-specific and sex-stratified

prevalence of symptom-based arthritis prevalence
(weighted), across age strata, educational attainment and
marital status, for each LMIC. Patterns of symptom-
based arthritis prevalence were similar to self-reported
arthritis for both sexes; however, prevalence was lower
than observed for self-reported arthritis.
Figure 1 presents a box plot of the age-standardised

rates of self-reported arthritis, stratified by sex, across each
country (crude and age-standardised rates are presented
in Additional file 1: Online Table S1). For five of the six
LMICs, the standardised rates of arthritis for men were
approximately twice that observed for women; the excep-
tion was Ghana, where men had rates three times greater
than those observed for women (12% [95% CI 11%–13%]
vs. 4% [95% CI 3%–5%]). The highest rates of arthritis
were observed in the Russian Federation: for men the rate
was 38% (95% CI 36%–39%) and for women it was 17%
(95% CI 14%–20%).

Discussion
We present the prevalence of arthritis across age, sex
and different parameters of SEP in a large population-
based study spanning six LMICs. Across the countries
and for both sexes, higher arthritis prevalence was con-
sistently associated with older age and lower educational
attainment, whilst higher prevalence was also observed
in women, but not men, that were separated, divorced,
or widowed.
The pattern between advancing age and increasing

arthritis prevalence in LMICs appears similar to the pat-
tern observed in higher income countries [27]. However,
after age-standardisation, we observed in our current
study that the rates of arthritis in LMICs were greater
than those reported in higher income countries, specific-
ally for men from China, India, the Russian Federation
and South Africa. Compared to higher income countries,
higher age-standardised rates of arthritis were also ob-
served for women from the Russian Federation; however,
for the remaining five LMICs, rates appeared to be simi-
lar to those observed from higher income countries. Our
results indicate the importance of age-standardisation
when reporting prevalence data, in order that fair com-
parisons can be applied when discussing whether any
disparities in diseases exist between countries. In
addition to the peak of arthritis prevalence observed in
older age groups, we observed a sizeable proportion of
arthritis in younger age groups; prevalence that would

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 4 of 12

T
a
b
le

3
Re
sp
o
n
se
s
to

th
e
fo
u
r
q
u
es
ti
o
n
sa
in
cl
u
d
ed

in
th
e
al
g
o
rit
h
m

fo
r
sy
m
p
to
m
-b
as
ed

ar
th
rit
is
,s
tr
at
ifi
ed

b
y
co
u
n
tr
y
an
d
se
xb

(n
o
n
-w

ei
g
h
te
d
)

W
o
m
en

(n
=
25
,1
80
)

C
h
in
a
(n

=
80
16
)

G
h
an
a
(n

=
27
49
)

In
d
ia
(n

=
74
89
)

M
ex
ic
o
b
(n

=
16
92
)

Ru
ss
ia
n
Fe
d
er
at
io
n
(n

=
28
06
)

So
u
th

A
fr
ic
a
(n

=
24
28
)

c A
n
y
p
ai
n
d
u
rin

g
la
st
12

m
o
n
th
s?

(Y
es
)

29
.1
%

(2
8.
0%


30
.2
%
)

38
.2
%

(3
6.
4%


40
.0
%
)

29
.2
%

(2
8.
0%


30
.4
%
)

28
.4
%

(2
6.
3%


30
.9
%
)

48
.4
%

(4
6.
4%


50
.4
%
)

36
.5
%

(3
4.
6%


38
.4
%
)

c A
n
y
st
iff
n
es
s
d
u
rin

g
la
st
12

m
o
n
th
s?

(Y
es
)

24
.2
%

(2
3.
2%


25
.2
%
)

43
.5
%

(4
1.
5%


45
.6
%
)

29
.7
%

(2
8.
5%


30
.8
%
)

23
.3
%

(2
0.
9%


26
.0
%
)

50
.5
%

(4
8.
8%


52
.1
%
)

33
.2
%

(3
1.
2%


35
.3
%
)

d
D
id

st
iff
n
es
s
la
st
fo
r
>
30

m
in
?
(Y
es
)

24
.7
%

(2
2.
4%


27
.1
%
)

38
.1
%

(3
5.
6%


40
.7
%
)

33
.3
%

(3
0.
9%


35
.2
%
)

26
.1
%

(2
1.
8%


31
.0
%
)

45
.3
%

(4
2.
8%


47
.9
%
)

36
.3
%

(3
3.
3%


39
.4
%
)

d
D
id

st
iff
n
es
s
g
o
aw

ay
af
te
r
m
o
ve
m
en

t?
(N
o
)

19
.2
%

(1
7.
4%


21
.0
%
)

31
.5
%

(2
8.
9%


34
.2
%
)

25
.4
%

(2
3.
7%


27
.3
%
)

15
.3
%

(1
2.
3%


18
.9
%
)

33
.1
%

(3
0.
5%


35
.9
%
)

19
.8
%

(1
7.
2%


22
.7
%
)

M
en

(n
=
18
,9
14
)

C
h
in
a
(n

=
69
93
)

G
h
an
a
(n

=
28
16
)

In
d
ia
(n

=
47
09
)

M
ex
ic
o
(n

=
10
50
)

Ru
ss
ia
n
Fe
d
er
at
io
n
b
(n

=
15
49
)

So
u
th

A
fr
ic
a
(n

=
17
97
)

c A
n
y
p
ai
n
d
u
rin

g
la
st
12

m
o
n
th
s?

(Y
es
)

20
.4
%

(1
9.
6%


21
.3
%
)

25
.2
%

(2
3.
5%


26
.9
%
)

23
.4
%

(2
2.
0%


24
.7
%
)

20
.1
%

(1
7.
5%


23
.0
%
)

32
.9
%

(3
0.
5%


35
.5
%
)

25
.3
%

(2
3.
3%


27
.5
%
)

c A
n
y
st
iff
n
es
s
d
u
rin

g
la
st
12

m
o
n
th
s?

(Y
es
)

17
.2
%

(1
6.
4%


17
.9
%
)

29
.8
%

(2
8.
2%


31
.5
%
)

25
.4
%

(2
4.
1%


26
.7
%
)

16
.1
%

(1
4.
1%


18
.3
%
)

34
.6
%

(3
2.
4%


36
.9
%
)

23
.7
%

(2
1.
9%


25
.5
%
)

d
D
id

st
iff
n
es
s
la
st
fo
r
>
30

m
in
?
(Y
es
)

26
.5
%

(2
4.
4%


28
.8
%
)

29
.2
%

(2
5.
6%


33
.1
%
)

29
.0
%

(2
6.
5%


31
.6
%
)

25
.9
%

(1
9.
7%


33
.3
%
)

40
.0
%

(3
5.
0%


45
.1
%
)

30
.1
%

(2
5.
6%


35
.0
%
)

d
D
id

st
iff
n
es
s
g
o
aw

ay
af
te
r
m
o
ve
m
en

t?
(N
o
)

20
.4
%

(1
8.
1%


22
.9
%
)

25
.2
%

(2
2.
3%


28
.3
%
)

22
.5
%

(1
9.
8%


25
.4
%
)

17
.9
%

(1
2.
6%


24
.8
%
)

29
.4
%

(2
5.
5%


33
.7
%
)

16
.4
%

(1
3.
0%


20
.6
%
)

D
at
a
p
re
se
n
te
d
as

p
ro
p
o
rt
io
n
s
w
it
h
9
5
%

co
n
fi
d
en

ce
in
te
rv
al
s
(9
5
%

C
I)

a
C
o
m
p
le
te

w
o
rd
in
g
o
f
th
e
sy
m
p
to
m
-b
as
ed

q
u
es
ti
o
n
s
ar
e
p
re
se
n
te
d
in

Ta
b
le

2
b
A
p
p
ro
xi
m
at
el
y
1
2
%

o
f
th
e
sa
m
p
le

fr
o
m

th
e
R
u
ss
ia
n
Fe
d
er
at
io
n
h
ad

n
o
in
fo
rm

at
io
n
re
g
ar
d
in
g
se
x
o
f
re
sp
o
n
d
en

ts
c P
ro
p
o
rt
io
n
s
(9
5
%

co
n
fi
d
en

ce
in
te
rv
al
s)
ar
e
b
as
ed

o
n
th
e
to
ta
l
st
u
d
y
p
o
p
u
la
ti
o
n
fr
o
m

ea
ch

LM
IC

d
P
ro
p
o
rt
io
n
s
(9
5
%

co
n
fi
d
en

ce
in
te
rv
al
s)
ar
e
b
as
ed

o
n
th
o
se

th
at

re
sp
o
n
d
ed

‘y
es

to

ei
th
er

o
n
e
o
r
b
o
th

o
f
th
e
fi
rs
t
tw

o
sy
m
p
to
m
-b
as
ed

q
u
es
ti
o
n
s

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 5 of 12

T
a
b
le

4
C
o
u
n
tr
y-
sp
ec
ifi
c
se
lf-
re
p
o
rt
ed

ar
th
rit
is
p
re
va
le
n
ce

(w
ei
g
h
te
d
),
ac
ro
ss

ag
e
st
ra
ta
,e
d
u
ca
ti
o
n
al
at
ta
in
m
en

t
an
d
m
ar
it
al
st
at
u
s,
st
ra
ti
fie
d
b
y
se
x

W
o
m
en

w
it
h
se
lf-
re
p
o
rt
ed

ar
th
rit
is
(n

=
50
03
)

C
h
in
a

n
=
18
51

G
h
an
a

n
=
35
0

In
d
ia

n
=
94
6

M
ex
ic
o

n
=
20
6

Ru
ss
ia
n
Fe
d
er
at
io
n

n
=
10
49

So
u
th

A
fr
ic
a

n
=
60
1

A
g
e
(y
ea
rs
)

18

29

3.
7%

(0
.9
%

14
.5
%
)

4.
4%

(1
.3
%

13
.8
%
)

2.
9%

(1
.9
%

4.
2%

)
0.
4%

(0
.1
%

2.
8%

)
4.
0%

(0
.6
%

22
.1
%
)

8.
9%

(1
.8
%

34
.2
%
)

30

39

6.
0%

(3
.8
%

9.
5%

)
3.
0%

(0
.9
%

9.
2%

)
8.
5%

(6
.7
%

10
.7
%
)

1.
8%

(0
.5
%

6.
0%

)
14
.7
%

(7
.0
%

28
.3
%
)

0.
2%

(0
.0
%

1.
6%

)

40

49

15
.1
%

(1
1.
2%


20
.0
%
)

3.
6%

(1
.6
%

8.
1%

)
12
.2
%

(9
.6
%

15
.3
%
)

7.
9%

(2
.2
%

24
.5
%
)

21
.4
%

(1
0.
5%


38
.6
%
)

11
.3
%

(5
.6
%

21
.4
%
)

50

59

22
.1
%

(2
0.
0%


24
.4
%
)

11
.5
%

(9
.1
%

14
.5
%
)

19
.8
%

(1
6.
7%


23
.2
%
)

6.
6%

(2
.3
%

17
.5
%
)

21
.1
%

(1
5.
6%


27
.9
%
)

29
.2
%

(2
4.
6%


34
.2
%
)

60

69

29
.7
%

(2
7.
1%


32
.6
%
)

15
.4
%

(1
2.
1%


19
.5
%
)

21
.4
%

(1
6.
7%


26
.9
%
)

13
.0
%

(8
.8
%

18
.7
%
)

36
.4
%

(2
9.
6%


43
.8
%
)

31
.5
%

(2
5.
7%


38
.0
%
)

70
+

29
.2
%

(2
6.
7%


31
.9
%
)

22
.8
%

(1
8.
6%


27
.6
%
)

23
.5
%

(1
8.
8%


29
.0
%
)

22
.9
%

(1
1.
2%


41
.1
%
)

45
.7
%

(3
9.
1%


52
.3
%
)

26
.5
%

(2
0.
7%


33
.2
%
)

Fo
rm

al
ed

u
ca
ti
o
n
a

N
ev
er

sc
h
o
o
le
d

24
.1
%

(1
9.
9%


28
.8
%
)

9.
5%

(7
.0
%

12
.7
%
)

12
.6
%

(1
0.
9%


14
.6
%
)

11
.0
%

(4
.7
%

23
.5
%
)

51
.8
%

(3
1.
0%


72
.1
%
)

17
.5
%

(1
2.
8%


23
.5
%
)


Pr
im

ar
y
sc
h
o
o
l

18
.1
%

(1
3.
7%


23
.6
%
)

5.
2%

(2
.9
%

9.
3%

)
12
.7
%

(1
0.
5%


15
.3
%
)

7.
4%

(3
.7
%

14
.4
%
)

42
.4
%

(3
3.
0%


52
.4
%
)

31
.1
%

(2
1.
0%


43
.5
%
)

Se
co
n
d
ar
y
sc
h
o
o
l

13
.0
%

(1
0.
1%


16
.5
%
)

4.
6%

(2
.4
%

8.
9%

)
5.
5%

(4
.0
%

7.
5%

)
3.
1%

(1
.3
%

7.
4%

)
25
.0
%

(2
0.
0%


30
.8
%
)

8.
4%

(4
.8
%

14
.3
%
)

C
o
lle
g
e

4.
7%

(1
.6
%

13
.1
%
)

1.
6%

(0
.7
%

4.
0%

)
6.
7%

(2
.7
%

15
.6
%
)

1.
6%

(0
.7
%

3.
6%

)
15
.1
%

(1
0.
0%


22
.2
%
)

1.
5%

(0
.6
%

3.
6%

)

M
ar
it
al
st
at
u
sb

N
ev
er

m
ar
rie
d

0.
9%

(0
.3
%

3.
0%

)
7.
8%

(2
.3
%

23
.2
%
)

1.
1%

(0
.4
%

3.
0%

)
1.
3%

(0
.7
%

2.
4%

)
7.
8%

(4
.4
%

13
.4
%
)

12
.1
%

(5
.5
%

24
.7
%
)

M
ar
rie
d

14
.7
%

(1
2.
6%


17
.2
%
)

3.
5%

(2
.1
%

6.
0%

)
10
.3
%

(9
.1
%

11
.7
%
)

4.
3%

(2
.5
%

7.
3%

)
17
.4
%

(1
2.
4%


24
.0
%
)

9.
2%

(5
.5
%

14
.9
%
)

D
iv
o
rc
ed

/w
id
o
w
ed

25
.2
%

(1
9.
9%


31
.5
%
)

11
.7
%

(8
.9
%

15
.1
%
)

19
.1
%

(1
5.
9%


22
.7
%
)

19
.0
%

(8
.1
%

38
.4
%
)

36
.4
%

(2
9.
1%


44
.4
%
)

19
.3
%

(1
2.
8%


28
.1
%
)

M
en

w
it
h
se
lf-
re
p
o
rt
ed

ar
th
rit
is
(n

=
26
64
)

C
h
in
a

n
=
11
45

G
h
an
a

n
=
23
0

In
d
ia

n
=
57
8

M
ex
ic
o

n
=
77

Ru
ss
ia
n
Fe
d
er
at
io
n

n
=
36
3

So
u
th

A
fr
ic
a

n
=
27
1

A
g
e
st
ra
ta

(y
ea
rs
)

18

29

1.
3%

(0
.2
%

8.
8%

)

2.
1%

(1
.0
%

4.
7%

)


0.
7%

(0
.1
%

3.
4%

)

30

39

5.
5%

(2
.4
%

12
.1
%
)

0.
2%

(0
.0
%

1.
4%

)
6.
1%

(3
.8
%

9.
8%

)

14
.6
%

(5
.4
%

34
.1
%
)

1.
3%

(0
.3
%

5.
8%

)

40

49

12
.0
%

(7
.9
%

18
.0
%
)

3.
7%

(1
.5
%

8.
7%

)
7.
9%

(5
.1
%

12
.1
%
)

2.
9%

(0
.6
%

13
.2
%
)

4.
7%

(1
.3
%

15
.9
%
)

0.
9%

(0
.3
%

3.
0%

)

50

59

13
.7
%

(1
1.
8%


15
.8
%
)

7.
4%

(5
.4
%

10
.1
%
)

13
.7
%

(1
1.
3%


16
.5
%
)

0.
9%

(0
.3
%

2.
6%

)
21
.6
%

(9
.5
%

42
.2
%
)

12
.6
%

(9
.3
%

16
.8
%
)

60

69

20
.0
%

(1
7.
7%


22
.5
%
)

11
.6
%

(8
.6
%

15
.4
%
)

16
.9
%

(1
3.
8%


20
.6
%
)

8.
0%

(4
.7
%

13
.3
%
)

21
.3
%

(1
5.
2%


29
.0
%
)

28
.2
%

(2
2.
1%


35
.2
%
)

70
+

22
.9
%

(2
0.
7%


25
.2
%
)

16
.7
%

(1
2.
6%


21
.7
%
)

17
.8
%

(1
4.
5%


21
.7
%
)

9.
7%

(6
.3
%

14
.5
%
)

37
.8
5
(3
0.
3%


46
.0
%
)

20
.9
%

(1
3.
5%


30
.9
%
)

Fo
rm

al
ed

u
ca
ti
o
n
a

N
ev
er

sc
h
o
o
le
d

22
.3
%

(1
3.
2%


35
.2
%
)

6.
3%

(4
.5
%

8.
7%

)
9.
0%

(6
.7
%

12
.1
%
)

7.
7%

(3
.2
%

17
.3
%
)

4.
4%

(0
.6
%

26
.8
%
)

10
.4
%

(6
.0
%

17
.6
%
)


Pr
im

ar
y
sc
h
o
o
l

14
.8
%

(1
0.
3%


20
.7
%
)

2.
9%

(1
.6
%

4.
9%

)
9.
0%

(6
.6
%

12
.1
%
)

3.
7%

(1
.8
%

7.
7%

)
39
.6
%

(2
1.
3%


61
.4
%
)

7.
1%

(4
.4
%

11
.2
%
)

Se
co
n
d
ar
y
sc
h
o
o
l

9.
2%

(7
.3
%

11
.5
%
)

4.
6%

(2
.3
%

8.
9%

)
8.
5%

(6
.4
%

11
.3
%
)

0.
3%

(0
.1
%

0.
7%

)
11
.9
%

(7
.2
%

19
.0
%
)

2.
2%

(1
.1
%

4.
6%

)

C
o
lle
g
e

7.
4%

(3
.8
%
-1
3.
95
)

2.
3%

(1
.1
%

4.
9%

)
4.
1%

(2
.0
%

8.
0%

)
0.
2%

(0
.0
%

1.
1%

)
9.
4%

(3
.1
%

25
.1
%
)

2.
3%

(0
.9
%

6.
1%

)

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 6 of 12

T
a
b
le

4
C
o
u
n
tr
y-
sp
ec
ifi
c
se
lf-
re
p
o
rt
ed

ar
th
rit
is
p
re
va
le
n
ce

(w
ei
g
h
te
d
),
ac
ro
ss

ag
e
st
ra
ta
,e
d
u
ca
ti
o
n
al
at
ta
in
m
en

t
an
d
m
ar
it
al
st
at
u
s,
st
ra
ti
fie
d
b
y
se
x
(C
o
n
tin
u
ed
)

M
ar
it
al
st
at
u
sb

N
ev
er

m
ar
rie
d

3.
0%

(1
.5
%

5.
9%

)
0.
3%

(0
.1
%

1.
0%

)
3.
9%

(1
.5
%

9.
5%

)
0.
1%

(0
.0
%

0.
5%

)
0.
9%

(0
.3
%

3.
1%

)
2.
0%

(0
.8
%

4.
7%

)

M
ar
rie
d

11
.9
%

(9
.4
%

14
.8
%
)

4.
5%

(3
.1
%

6.
5%

)
8.
8%

(7
.2
%

10
.7
%
)

2.
2%

(1
.1
%

4.
4%

)
11
.3
%

(7
.5
%

16
.7
%
)

5.
6%

(4
.1
%

7.
6%

)

D
iv
o
rc
ed

/w
id
o
w
ed

11
.8
%

(8
.7
%

15
.7
%
)

8.
5%

(5
.4
%

13
.2
%
)

6.
5%

(3
.8
%

10
.7
%
)

6.
6%

(3
.3
%

12
.6
%
)

33
.5
%

(1
3.
3%


62
.3
%
)

13
.2
%

(5
.6
%

27
.9
%
)

D
at
a
p
re
se
n
te
d
as

p
ro
p
o
rt
io
n
s
w
it
h
9
5
%

co
n
fi
d
en

ce
in
te
rv
al
s
(9
5
%

C
I)

A
b
b
re
vi
a
ti
o
n
s:
LM

IC
lo
w

an
d
m
id
d
le

in
co
m
e
co
u
n
tr
ie
s,
W
H
O
W
o
rl
d
H
ea
lt
h
O
rg
an

iz
at
io
n

a
C
at
eg

o
ri
es

o
f
fo
rm

al
ed

u
ca
ti
o
n
ar
e;


p
ri
m
ar
y
sc
h
o
o
l
(l
es
s
th
an

p
ri
m
ar
y
sc
h
o
o
l,
o
r
p
ri
m
ar
y
sc
h
o
o
l
co
m
p
le
te
d
);
se
co
n
d
ar
y
sc
h
o
o
l
(s
ec
o
n
d
ar
y
sc
h
o
o
l
co
m
p
le
te
d
,
o
r
h
ig
h
sc
h
o
o
l
o
r
it
s
eq

u
iv
al
en

t
co
m
p
le
te
d
);
co
lle
g
e

(c
o
lle
g
e
o
r
p
re
-u
n
iv
er
si
ty

co
m
p
le
te
d
,
o
r
p
o
st
-g
ra
d
u
at
e
d
eg

re
e
co
m
p
le
te
d
)

b
C
at
eg

o
ri
es

o
f
m
ar
it
al

st
at
u
s
ar
e;
m
ar
ri
ed

(c
u
rr
en

tl
y
m
ar
ri
ed

o
r
co
h
ab

it
in
g
);
d
iv
o
rc
ed

/w
id
o
w
ed

(s
ep

ar
at
ed

o
r
d
iv
o
rc
ed

,o
r
w
id
o
w
ed

)

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 7 of 12

have significant impacts on work capacity and social
roles. Indeed, whilst contextually different and focused
upon osteoarthritis, similar findings have been reported
in higher income countries [28, 29].

Ours are the first prevalence figures of arthritis to be
presented across different socioeconomic parameters for
residents of LMICs. Whilst the overall arthritis preva-
lence has been reported for some countries included the

Table 5 Country-specific symptom-related arthritis prevalence (weighted) across age strata, educational attainment and marital sta-
tus, stratified by sex

Women With symptom-related arthritis (n = 1220)

China
n = 201

Ghana
n = 290

India
n = 238

Mexico
n = 29

Russian Federation
n = 358

South Africa
n = 104

Age (years)

18–29 − − 0.9% (0.4%–1.8%) − − −

30–39 − 1.6% (0.4%–6.6%) 1.5% (0.7%–3.2%) − 12.5% (4.4%–30.7%) 0.2% (0.0%–1.7%)

40–49 0.3% (0.1%–1.3%) 3.3% (1.3%–7.9%) 2.8% (1.7%–4.4%) 1.2% (0.2%–8.0%) 2.3% (0.5%–9.5%) 2.4% (0.3%–15.7%)

50–59 4.1% (3.0%–5.7%) 11.5% (8.6%–15.2%) 5.9% (43%–8.0%) 0.7% (0.1%–4.1%) 4.3% (2.6%–7.1%) 6.2% (3.7%–10.2%)

60–69 4.0% (2.8%–5.8%) 16.5% (12.3%–21.9%) 5.6% (4.0%–7.9%) 1.5% (0.7%–3.2%) 10.0% (7.0%–14.2%) 5.5% (3.0%–9.8%)

70+ 5.6% (3.9%–7.9%) 18.6% (14.9%–23.0%) 6.7% (4.7%–9.7%) 2.1% (0.9%–4.7%) 20.1% (14.4%–27.4%) 5.6% (3.3%–9.2%)

Formal educationa

Never schooled 4.1% (3.3%–5.1%) 9.4% (6.9%–12.7%) 3.7% (2.9%–4.7%) 1.5% (0.5%–4.1%) 41.8% (16.6%–72.2%) 3.5% (1.7%–6.9%)

≤ Primary school 2.0% (1.3%–3.1%) 2.2% (1.4%–3.6%) 2.2% (1.5%–3.4%) 1.0% (0.3%–3.3%) 22.6% (14.3%–33.7%) 5.9% (2.3%–14.3%)

Secondary school 0.5% (0.3%–0.8%) 3.0% (1.3%–6.5%) 1.2% (0.6%–2.5%) 0.0% (0.0%–0.3%) 8.9% (4.9%–15.4%) 1.0% (0.4%–2.6%)

College 0.0% (0.0%–0.2%) − 1.1% (0.2%–6.4%) 0.0% (0.0%–0.3%) 4.3% (2.4%–7.5%) 0.3% (0.1%–1.2%)

Marital statusb

Never married − 1.9% (0.4%–9.0%) 1.1% (0.3%–3.7%) 0.3% (0.1%–0.9%) 1.7% (0.7%–4.2%) 2.6% (0.6%–11.2%)

Married 1.1% (0.9%–1.4%) 2.6% (1.7%–4.1%) 2.5% (1.9%–3.2%) 0.7% (0.2%–2.4%) 3.1% (1.9%–4.9%) 1.3% (0.6%–2.9%)

Divorced/widowed 4.2% (2.2%–7.9%) 10.8% (7.7%–15.0%) 4.8% (3.5%–6.7%) 0.6% (0.3%–1.4%) 18.0% (9.9%–30.5%) 3.4% (2.0%–5.6%)

Men With symptom-based arthritis (n = 594)

China
n = 138

Ghana
n = 170

India
n = 113

Mexico
n = 15

Russian Federation
n = 117

South Africa
n = 41

Age strata (years)

18–29 − 1.0% (0.1%–7.2%) 0.8% (0.1%–4.5%) − 2.3% (0.3%–16.2%) −

30–39 − 1.7% (0.5%–5.4%) 0.8% (0.2%–3.8%) − 5.2% (1.0%–22.2%) −

40–49 0.8% (0.2%–2.8%) 0.6% (0.1%–2.5%) 1.9% (0.8%–43%) − 1.9% (0.2%–13.4%) 1.7% (0.4%–6.6%)

50–59 2.3% (1.6%–3.1%) 3.8% (2.7%–5.4%) 2.6% (1.1%–6.2%) − 1.9% (0.9%–4.1%) 2.3% (1.0%–4.9%)

60–69 3.8% (3.3%–4.4%) 9.1% (6.7%–12.2%) 3.5% (2.0%–6.1%) 0.6% (0.2%–2.2%) 6.4% (3.3%–12.0%) 3.7% (1.8%–7.5%)

70+ 4.3% (3.5%–5.2%) 9.2% (6.9%–12.3%) 4.8% (3.0%–7.5%) 3.0% (1.5%–5.9%) 10.7% (6.9%–16.4%) 6.0% (2.4%–14.3%)

Formal educationa

None 4.8% (3.3%–6.8%) 5.6% (4.0%–7.7%) 2.7% (1.3%–5.5%) 1.5% (0.5%–3.8%) 3.4% (0.3%–27.7%) 4.2% (1.5%–10.8%)

≤ Primary school 1.2% (1.0%–1.6%) 1.9% (1.1%–3.2%) 1.2% (0.7%–1.9%) 0.2% (0.1%–0.6%) 10.4% (5.0%–20.4%) 1.6% (0.8%–3.4%)

Secondary school 1.0% (0.4%–2.3%) 1.4% (0.6%–3.1%) 2.0% (1.0%–4.2%) 0.1% (0.0%–0.5%) 3.9% (1.7%–9.0%) 0.9% (0.2%–5.4%)

College 0.1% (0.0%–0.4%) 0.7% (0.2%–2.2%) 0.4% (0.1%–1.6%) 0.2% (0.0%–1.4%) 1.4% (0.4%–5.3%) −

Marital statusb

Never married 0.9% (0.2%–5.0%) 1.5% (0.2%–8.8%) 1.6% (0.3%–8.2%) − 0.2% (0.0%–0.9%) 0.8% (0.2%–2.8%)

Married 1.1% (0.6%–1.8%) 2.5% (1.8%–3.4%) 1.8% (1.1%–2.7%) 0.3% (0.1%–0.5%) 3.7% (1.7%–8.0%) 1.4% (0.6%–3.2%)

Divorced/widowed 2.9% (1.7%–4.9%) 6.5% (3.8%–10.9%) 1.9% (0.6%–5.5%) 0.6% (0.1%–2.6%) 7.9% (2.9%–19.6%) 2.9% (0.7%–11.4%)

Data presented as proportions with 95% confidence intervals (95% CI)
Abbreviations: LMIC low and middle income countries, WHO World Health Organization
aCategories of formal education are; ≤primary school (less than primary school, or primary school completed); secondary school (secondary school completed, or
high school or its equivalent completed); college (college or pre-university completed, or post-graduate degree completed)
bCategories of marital status are; married (currently married or cohabiting); divorced/widowed (separated or divorced, or widowed)

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 8 of 12

SAGE, specifically India [30] and China [31], we now
present age-standardised prevalence across all six coun-
tries (Additional file 1: Table S1). Higher prevalence of
arthritis among individuals with lower educational at-
tainment in LMICs, may be indicative of the inextricable
link between lower education and lower-skilled, highly
manual labour. Furthermore, these findings also
replicate the association observed in higher income
countries. For instance, lower educational attainment
has been associated with the prevalence of many chronic
diseases, including self-reported arthritis (non-specific)
[32], osteoarthritis [33] and rheumatoid arthritis [34].
Our observation of higher prevalence of arthritis in indi-
viduals that were divorced, widowed or separated, may
be related to those persons also more likely to be older.
However, and whilst speculative, it may plausibly be due
to having a greater workload that cannot shared with a
partner. Should these individuals also have lower educa-
tional attainment, any job-related exposures will likely
be manual and thus with greater biomechanical stress
on the joints due to increased exposure to heavy lifting,
repetitive movements and/or squatting [35, 36].
The prevalence of musculoskeletal diseases per se in

LMICs [3] will potentially have a greater impact than in
high income countries due to the reduced capacity of
LMICs to avoid and/or alleviate the impact at individual
and national levels. This is especially pertinent given that
global NCD initiatives do not list musculoskeletal
diseases within the ‘top four’ [3]. In LMICs where pain
management is less than optimal [37], the burden of
chronic, and possibly untreated, pain will be com-
pounded by social and environmental stressors that
require individuals work and fulfil community roles re-
gardless of pain. Indeed, data from a WHO collaboration
reported that between 5 and 33% of individuals in
LMICs experience chronic pain on a daily basis [38].
Similarly, we observed a sizeable proportion of

respondents to have stiffness lasting longer than 30 min
and which did not alleviate with movement; these char-
acteristics are indicative of chronic pain, and potentially
suggest inflammatory arthropathy. In addition, diseases
such as fibromyalgia are likely to cause joint pain,
however, we are unable to determine if this, and similar
issues, may have biased responses to symptomatology-
related questions. Any ‘treatment gap’ is at odds with
the WHO Constitution, which recognises “…the highest
attainable standard of health as a fundamental right of
every human being” [39], however, LMICs experience a
disproportionately lower likelihood of achieving that
standard. We speculate that resource-poor populations,
where ‘informal workers’ are central to community
structure, are most at risk of worsening poverty levels
due to increased YLD attributable to highly prevalent,
and potentially undertreated, arthritis. It is important to
note that whilst the burden of non-communicable
diseases is increasing, there is a concurrent decline in
the burden of infectious diseases [2]. Given this, more
attention must be given to the management of diseases
such as arthritis in LMICs: action on musculoskeletal
diseases per se in LMICs present opportunities for such
action [3]. Models of care (MoC) for musculoskeletal
diseases have been developed and implemented in the
LMICs of The Philippines, Malaysia, Bangladesh and
Myanmar [40]. Despite mixed results, a four-step
process was designed to inform future development of
musculoskeletal-related MoC for implementation in
LMICs; (i) identify the scale of the problem, (ii) identify
the need, (iii) develop the action plan (including com-
munity engagement and addressing workforce capacity),
and (iv) employ a coordinated approach to implement-
ing the intervention program/MoC [40].
Despite advances in diagnosis and treatment of

arthritis during the last few decades in higher income
countries [16], these advances have not impacted on

a b

Fig. 1 Box plot presenting the direct age-standardised prevalence estimates (%) and 95% confidence intervals of self-reported arthritis diagnosis
for each of the lower to middle income countries, for women (a) and men (b)

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 9 of 12

LMICs, which are primarily resource-poor. Gross
domestic product and health care expenditures per
capita are strongly correlated [14, 41]. Governments in
LMICs are constrained by competitive social, economic,
health- and poverty-related issues [7]; this frequently re-
sults in chronic diseases such as arthritis achieving lower
priority when urgent health needs are considered in an
environment with poor education, scarce resources, and
rapid population growth [7, 42]. Not only is suboptimal
access to healthcare a concern, but the cost of healthcare
may be many-fold the gross domestic product, and thus
unattainable for the majority of the population of LMICs
[5]. For many individuals and households in LMIC, there
are inadequate financial resources to manage the cost of
chronic disease, with an impoverishing effect of paying
for healthcare services out-of-pocket [43]. In order to
address the problem of out-of-pocket healthcare ex-
penses, the WHO is encouraging countries to provide
universal health coverage [7]. For LMICs the provision
of universal health coverage may be in the form of
community-based health insurance schemes, whereby
the community voluntarily raises, pools, allocates,
purchases and supervises the health financing arrange-
ment [7, 44]. Whilst there are some national efforts to
prioritise healthcare resources and achieve universal
health coverage, these schemes are likely to focus on
supporting healthcare for diseases that cause early
mortality rather than those that result in disability.
Our study has a number of strengths. The SAGE study

consists of a large multi-national cohort, and our
population for this analysis encompassed almost 45,000
participants. The integrity and coordination of these data
is overseen by WHO, in close collaboration with leading
research institutions in each of the countries, and with a
level of involvement from national health authorities
[23]. The use of a standardized survey instrument and
methods for SAGE Wave 1, the recruitment of represen-
tative samples, and the application of country-specific
weightings to calculate our prevalence estimates have
enabled comparison with similar surveys conducted in
higher income countries. In addition, the use of stan-
dardized tools to measure SEP in each of the countries
in SAGE enables us to undertake between-country com-
parisons. Our findings build on the prevalence data re-
ported by the GBD Study, whereby estimates were based
on systematic reviews of published data on incidence,
prevalence, and severity; however, for some LMICs only
limited data were available [45]. Our study also builds
on previous analyses using the SAGE dataset, as no
study to date has presented arthritis prevalence figures
across parameters of SEP.
This study also has some limitations. We acknow-

ledge that SAGE chronic disease data are self-
reported, and thus may be subject to recall bias and

potential inaccuracy with a subsequent uncertainty of
estimates. However, the self-reported arthritis ques-
tion is similar to that used for other large population
level studies, including those reported by the Centers
for Disease Control Arthritis Program in the United
States [46], and self-reported arthritis has also been
reported as a sensitive measure for public health sur-
veillance [47]. It is possible that limited access to
healthcare professionals in LMICs may lead to an
underestimation of arthritis prevalence, and those
who have arthritis but have not yet sought care may
have been missed. In addition, it may be possible that
in many countries diagnoses of arthritis may be made
by a non-medical healthcare provider, thus introdu-
cing some ambiguity in responses to the diagnosis
question. Yet here, the symptom-reported prevalence,
where access to healthcare professionals would be re-
moved from the equation, indicated an even lower
burden of arthritis than by self-reported diagnosis; an
issue that may also be related to diagnosed arthritis
being across the lifetime, whilst symptom-based arth-
ritis was within the previous 12 months. Our study
does not link prevalence data with disability; however
it should be noted that arthritis has highly variable
impacts on the person. A high proportion of SAGE
Wave 1 participants indicated that they had no formal
education (~50%); this may explain the level of miss-
ing data pertaining to the ‘highest level of educational
attainment’ variable. However, missing data may also
be attributable to the WHO data collection ‘Individual
Questionnaire’ tool, which did not include a category
for those that had completed primary school but who
had not completed secondary school. It has been re-
ported that, in several countries, urban dwellers were
more likely to refuse to participate in SAGE [23],
which may present a bias toward rural-based participants;
however, the high proportion of rural residents may
conversely be considered a key strength of the SAGE
dataset as non-metropolitan groups are commonly
under-represented in population-based surveys. We
acknowledge that the six countries differ substantially
in terms of culture, society, and healthcare system,
and thus our pooled estimates should be considered
in this light. Finally, the response rates were relatively
low for Mexico (53%), due to a short time-frame for
data collection, although response rates for all other
countries in SAGE Wave 1 were 75% or greater, with
the exception of India at 68% [23].

Conclusions
In conclusion, we have identified a high prevalence of
arthritis in LMICs. For people living in LMIC, functional
ability and mobility is imperative to survival, and our
findings therefore have implications for prioritising

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 10 of 12

healthcare resources toward arthritis prevention and
treatment in relatively resource-poor countries. It is
plausible that, especially for residents of LMICs, the high
prevalence of arthritis may limit their ability to finan-
cially and/or materially support themselves. Similarly,
poverty and lower educational attainment may predis-
pose populations to manual labour, and subsequent pre-
disposition to diseases such as osteoarthritis. Future
work will focus on occupational types and occupational
activities as risk factors for arthritis and related symp-
tomatology. Our current findings have implications for
national efforts to achieve universal health coverage and
to prioritise healthcare resources toward preventing and/
or treating arthritis.

Additional file

Additional file 1: Online Table S1. Crude and direct age-standardised
prevalence estimates (95%CI) of arthritis, stratified by sex. (DOCX 12 kb)

Abbreviations
GBD: Global burden of disease study; LMICs: Lower and middle income
countries; MoC: Models of care; NCD: Non-communicable disease;
SAGE: Study on global AGEing and adult health; SEP: Socioeconomic
position; WHO: World Health Organization; YLD: Years lived with disability

Acknowledgements
We thank the participants in each country for their contribution to the SAGE,
and acknowledge the contributions and expertise of the country-specific
investigators and their respective survey teams.

Funding
SLB-O is supported by a Career Development Fellowship from the National
Health and Medical Research Council (NHMRC) of Australia (1107510). SAGE is
supported by WHO and the Division of Behavioral and Social Research (BSR) at
the US National Institute on Aging (NIA) through Interagency Agreements
(OGHA 04034785; YA1323–08-CN-0020; Y1-AG-1005-01) with WHO and a
Research Project Grant R01AG034479. In addition, the governments of China
and South Africa provided financial or other support for Wave 1 of their national
studies. USAID provided additional funds in support of SAGE India to increase
the sample of women aged 15–49 years as a nested study examining health in
younger women. All collaborating institutions provided substantial resources to
conduct the studies.

Availability of data and materials
The datasets used and analysed during the current study are available from
Professor Nirmala Naidoo (World Health Organization) on reasonable request.
Permission was granted to access and analyse the data in SAGE Wave 1.

Authors’ contributions
Data collection and harmonization between countries: NN, PK, MM.
Conceived and designed the project: SLB-O, SC, MTL, SJB, PK, NN, INA, RSP,
SMH, JAP, MM. Analyzed the data: MM, SC, SJB. Interpreted the results: SLB-O,
SC, MTL, SJB, PK, NN, INA, RSP, SMH, JAP, MM. Wrote, edited, and approved
the final version of this manuscript: SLB-O, SC, MTL, SJB, PK, NN, INA, RSP,
SMH, JAP, MM.

Competing interests
None of the authors have any relevant conflicts of interest related to the
work under consideration for publication. SLB-O has received speaker fees
from Amgen. RSP has received institutional support from De Puy-Synthesis
for educational/training purposes.

Consent for publication
Not applicable.

Ethics approval and consent to participate
This research was performed in accordance with the Declaration of Helsinki.
The WHO and the respective implementing agency in each country
provided ethics approvals. Written, informed consent was obtained from all
participants.

Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.

Author details
1Deakin University, Geelong, Australia. 2Australian Institute for
Musculoskeletal Science (AIMSS), The University of Melbourne-Western
Precinct, Level 3, Western Centre for Health Research and Education
(WCHRE) Building, C/- Sunshine Hospital, Furlong Road, St Albans,
Melbourne, VIC 3021, Australia. 3Department of Medicine-Western Health,
Melbourne Medical School, The University of Melbourne, Melbourne,
Australia. 4Institute of Health and Ageing, Australian Catholic University,
Melbourne, Australia. 5Faculty of Medicine, Nursing and Health Sciences,
Monash University, Melbourne, Australia. 6Department of Health Statistics
and Information Systems, World Health Organization, Geneva, Switzerland.
7Research Centre for Generational Health and Ageing, University of
Newcastle, Newcastle, Australia. 8Department of Epidemiology and
Preventive Medicine, Monash University, Melbourne, Australia. 9Barwon
Centre for Orthopaedic Research and Education, Barwon Health, Geelong,
Australia.

Received: 31 March 2017 Accepted: 9 June 2017

References
1. Briggs AM, Cross MJ, Hoy DG, et al. Musculoskeletal health conditions

represent a global threat to healthy aging: a report for the 2015 World
Health Organization world report on Ageing and health. Gerontologist.
2016;56(S2):S234–55.

2. Global Burden of Disease Collaborators. Global, regional, and national
incidence, prevalence, and years lived with disability for 301 acute and
chronic diseases and injuries in 188 countries, 1990–2013: a systematic
analysis for the Global Burden of Disease Study 2013. Lancet. 2015;
386(9995):743–800.

3. Hoy D, Geere JA, Davatchi F, et al. A time for action: opportunities for
preventing the growing burden and disability from musucloskeletal
conditions in low- and middle-income countries. Best Pract Res Clin
Rheumatol. 2014;28(3):377–93.

4. Vos T, Flaxman AD, Naghavi M, et al. Years lived with disability (YLDs) for
1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis
for the global burden of disease study. Lancet. 2012;380:2163–96.

5. Woolf AD, Brooks P, Akesson K, et al. Prevention of musculoskeletal
conditions in the developing world. Best Pract Res Clin Rheumatol. 2008;
22(4):759–72.

6. Rudan I, Sidhu S, Papana A, et al. Prevalence of rheumatoid arthritis in low-
and middle-income countries: a systematic review and analysis. J Glob
Health. 2015;5(1):010409.

7. Adebayo EF, Uthman OA, Wiysonge CS, et al. A systematic review of factors
that affect uptake of community-based health insurance in low-income and
middle-income countries. BMC Health Serv Res. 2015;15:543.

8. Lenssinck M-LB, Burdorf A, Boonen A, et al. Consequences of inflammatory
arthritis for workplace productivity loss and sick leave: a systematic review.
Ann Rheum Dis. 2013;72:493–505.

9. Agoliotis M, Fransen M, Bridgett L, et al. Risk factors associated with
reduced work productivity among people with chronic knee pain.
Osteoarthr Cart. 2013;21(9):1160–9.

10. United Nations. Prevention and control of non-communicable diseases: report
of the secretary-general. New York, United Nations: United Nations; 2011.

11. Sokka T, Kautiainen H, Pincus R, et al. Disparities in rheumatoid arthritis
disease activity according to gross domestic product in 25 countries in the
QUEST-RA database. Ann Rheum Dis. 2009;68:1666–72.

12. Jonsson B, Kobelt G, Smolen J. The burden of rheumatoid arthritis and
access to treatment: uptake of new therapies. Eur J Health Econ. 2008;
(Suppl 2):S61–86.

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 11 of 12

13. Woolf AD, Pfleger B. Burden of major musculoskeletal conditions. Bull World
Health Organ. 2003;81:646–56.

14. Pabinger C, Geissler A. Utilization rates of hip arthroplasty in OECD
countries. Osteoarthr Cart. 2014;22(6):734–41.

15. Sokka T, Kautiainen H, Pincus R, et al. Work disability remains a major
problem in rheumatoid arthritis in the 2000s: data from 32 countries in the
QUEST-RA study. Arthrit Res Ther. 2010;12:R42.

16. Storheim K, Zwart J-A. Musculoskeletal disorders and the global burden of
disease study. Ann Rheum Dis. 2014;73:949–50.

17. Chopra A, Abdel-Nasser A. Epidemiology of rheumatoid musculoskeletal disorders
in the developing world. Best Pract Res Clin Rheumatol. 2008;22:583–604.

18. Hosseinpoor AR, Bergen N, Mendis S, et al. Socioeconomic inequality in the
prevalence of noncommunicable diseases in low- and middle-income
countries: results from the world health survey. BMC Public Health. 2012;12:474.

19. Kumar B. Global health inequities in rheumatology. Rheumatol. 2016; Online
First March 24.

20. Arokiasamy P, Uttamacharya, Kowal P, et al. Chronic noncommunicable
diseases in 6 low- and middle-income countries: findings from wave 1 of
the World Health Organization’s study on global Ageing and adult health
(SAGE). Am J Epidemiol. 2017; 185(6):414-428.

21. Bloom DE, Chatterji S, Kowal P, et al. Macroeconomic implications of population
ageing and selected policy responses. Lancet. 2015;385(9968):649–57.

22. Chatterji S. World Health Organization’s (WHO) study global AGEing and
adult health (SAGE). BMC Proc. 2013;7(Suppl 4):S1.

23. Kowal P, Chatterji S, Naidoo N, et al. Data resource profile: the World Health
Organization study on global AGEing and adult health (SAGE). Int J
Epidemiol. 2012;41(6):1639–49.

24. UNESCO. International Standard Classification of Education: ISCED. 1997:
2006.

25. Esteve J, Benhamou E, Raymond L. Statistical methods in cancer research.
Volume IV. Descriptive epidemiology. IARC Sci Publ. 1994;128:1–302.

26. Ahmad OB, Boschi-Pinto C, Lopez AD, et al. Age standardization of rates: a
new WHO standard. Geneva, Switzerland: WHO; 2001.

27. Cross M, Smith E, Hoy D, et al. The global burden of rheumatoid arthritis:
estimates from the Global Burden of Disease 2010 study. Ann Rheum Dis.
2014; Online First(Feb 18).

28. Ackerman IN, Bucknill A, Page RS, et al. The substantial personal burden
experienced by younger people with hip or knee osteoarthritis. Osteoarthr
Cart. 2015;23(8):1276–84.

29. Ackerman I, Kemp JL, Crossley KM, et al. Hip and knee osteoarthritis affects
younger people too. J Orthop Sports Phys Ther. 2017;47(2):67–79.

30. Basu S, King AC. Disability and chronic disease among older adults in India:
detecting vulnerable populations through the WHO SAGE study. Amer J
Epidemiol. 2013;178(11):1620–8.

31. Wu F, Guo Y, Kowal P, et al. Prevalence of major chronic conditions among
older Chinese adults: the study on global AGEing and adult health (SAGE)
wave 1. PLoS One. 2013;8(9):e74176.

32. Brennan SL, Turrell G. Neighborhood disadvantage, individual-level
socioeconomic position, and self-reported chronic arthritis: a cross-sectional
multilevel study. Arthrit Care Res. 2012;64(5):721–8.

33. Hannan MT, Anderson JJ, Pincus T, et al. Educational attainment and
osteoarthritis: differential associations with radiographic changes and
symptom reporting. J Clin Epidemiol. 1992;45(2):139–47.

34. Bengtsson C, Nordmark B, Klareskog L, et al. Socioeconomic status and the
risk of developing rheumatoid arthritis: results from the Swedish EIRA study.
Ann Rheum Dis. 2005;64:1588–94.

35. Kirkhorn S, Greenlee RT, Reeser JC. The epidemiology of agriculture-related
osteoarthritis and its impact on occupational disability. Wisconsin Med J.
2003;102(7):38–44.

36. Andersen S, Caspar-Thygesen L, Davidsen M, et al. Cumulative years in
occupation and the risk of hip or knee osteoarthritis in men and women: a
register-based follow-up study. Occup Environ Med. 2012;69:325–30.

37. Bond M. Pain education issues in developing countries and responses to
them by the International Association for the Study of Pain. Pain Res
Manage. 2011;16(6):404–6.

38. Gureje O, Von Korff M, Simon GE, et al. Persistent pain and well-being: a
World Health Organization study in primary care. JAMA. 1998;280:147–51.

39. WHO. Health and human rights. Geneva, Switzerland: WHo; 2015.
40. Lim KK, Chan M, Navarra S, et al. Development and implementation of

models of care for musculoskeletal conditions in middle-income and low-
income Asian countries. Best Pract Res Clin Rheumatol. 2016;30:398–419.

41. Putrik P, Ramiro S, Keszei AP, et al. Lower education and living in countries
with lower wealth are associated with higher disease activity in rheumatoid
arthritis: results from the multinational COMORA study. Ann Rheum Dis.
2016; 75(3):540–46.

42. Mody GM, Cardiel MH. Challenges in the management of rheumatoid arthritis
in developing countries. Best Pract Res Clin Rheumatol. 2008;22(4):621–41.

43. Xu K, Evans DB, Kawabata K, et al. Household catastrophic health
expenditure: a multicountry analysis. Lancet. 2003;362:111–7.

44. Hsiao WC. Experience of community health financing in the Asian Region.
In: Preker AS, Currin G, editors. Health financing for poor people: resource
mobilization and risk sharing. Washington: World Bank; 2004. p. 119.

45. WHO. World Report on Disability 2011; Technical Appendix D, Global
Burden of Disease Methodology. Geneva: World Health Organization; 2011.

46. Helmick CG, Felson DT, Lawrence RC, et al. Estimates of the prevalence of
arthritis and other rheumatic conditions in the United States. Arthrit Rheum.
2008;58(1):15–25.

47. Sacks JJ, Harrold LR, Hemick CG, et al. Validation of a surveillance case
definition for arthritis. J Rheumatol. 2005;32:340–7.

• We accept pre-submission inquiries
• Our selector tool helps you to find the most relevant journal
• We provide round the clock customer support
• Convenient online submission
• Thorough peer review
• Inclusion in PubMed and all major indexing services
• Maximum visibility for your research

Submit your manuscript at
www.biomedcentral.com/submit

Submit your next manuscript to BioMed Central
and we will help you at every step:

Brennan-Olsen et al. BMC Musculoskeletal Disorders (2017) 18:271 Page 12 of 12

BioMed Central publishes under the Creative Commons Attribution License (CCAL). Under
the CCAL, authors retain copyright to the article but users are allowed to download, reprint,
distribute and /or copy articles in BioMed Central journals, as long as the original work is
properly cited.

SYNTHESIS

Saving Mothers, Giving Life: It Takes a System to
Save a Mother
Claudia Morrissey Conlon,a Florina Serbanescu,b Lawrence Marum,c Jessica Healey,d Jonathan LaBrecque,a

Reeti Hobson,e Marta Levitt,f Adeodata Kekitiinwa,g Brenda Picho,h Fatma Soud,i Lauren Spigel,j

Mona Steffen,e Jorge Velasco,k Robert Cohen,a William Weiss,a on behalf of the Saving Mothers, Giving
Life Working Group

A multi-partner effort in Uganda and Zambia employed a districtwide health systems strengthening approach,
with supply- and demand-side interventions, to address timely use of appropriate, quality maternity care.
Between 2012 and 2016, maternal mortality declined by approximately 40% in both partnership-supported
facilities and districts in each country. This experience has useful lessons for other low-resource settings.

ABSTRACT
Background: Ending preventable maternal and newborn deaths remains a global health imperative under United Nations Sustainable
Development Goal targets 3.1 and 3.2. Saving Mothers, Giving Life (SMGL) was designed in 2011 within the Global Health Initiative as
a public–private partnership between the U.S. government, Merck for Mothers, Every Mother Counts, the American College of
Obstetricians and Gynecologists, the government of Norway, and Project C.U.R.E. SMGL’s initial aim was to dramatically reduce mater-
nal mortality in low-resource, high-burden sub-Saharan African countries. SMGL used a district health systems strengthening approach
combining both supply- and demand-side interventions to address the 3 key delays to accessing effective maternity care in a timely
manner: delays in seeking, reaching, and receiving quality obstetric services.
Implementation: The SMGL approach was piloted from June 2012 to December 2013 in 8 rural districts (4 each) in Uganda and
Zambia with high levels of maternal deaths. Over the next 4 years, SMGL expanded to a total of 13 districts in Uganda and 18 in
Zambia. SMGL built on existing host government and private maternal and child health platforms, and was aligned with and guided
by Ugandan and Zambian maternal and newborn health policies and programs. A 35% reduction in the maternal mortality ratio (MMR)
was achieved in SMGL-designated facilities in both countries during the first 12 months of implementation.
Results: Maternal health outcomes achieved after 5 years of implementation in the SMGL-designated pilot districts were substantial: a 44% reduc-
tion in both facility and districtwide MMR in Uganda, and a 38% decrease in facility and a 41% decline in districtwide MMR in Zambia. Facility
deliveries increased by 47% (from 46% to 67%) in Uganda and by 44% (from 62% to 90%) in Zambia. Cesarean delivery rates also increased:
by 71% in Uganda (from 5.3% to 9.0%) and by 79% in Zambia (from 2.7% to 4.8%). The average annual rate of reduction for maternal deaths
in the SMGL-supported districts exceeded that found countrywide: 11.5% versus 3.5% in Uganda and 10.5% versus 2.8% in Zambia. The
changes in stillbirth rates were significant (�13% in Uganda and �36% in Zambia) but those for pre-discharge neonatal mortality rates were
not significant in either Uganda or Zambia.

Conclusion: A district health systems strengthening approach to
addressing the 3 delays to accessing timely, appropriate, high-
quality care for pregnant women can save women’s lives from
preventable causes and reduce stillbirths. The approach appears
not to significantly impact pre-discharge neonatal mortality.

INTRODUCTION

Despite a 45% drop in global maternal deathsbetween 1990 and 2015,1 maternal mortality
remains an intractable public health problem in many
low-resource settings. Only 1 sub-Saharan African
country, Rwanda, achieved the target for Millennium
Development Goal 5 (reduce by three-quarters, between
1990 and 2015, the maternal mortality ratio).1,2

Attempts have been made to bring high-level

a Bureau for Global Health, U.S. Agency for International Development,
Washington, DC, USA.
b Division of Reproductive Health, U.S. Centers for Disease Control and
Prevention, Atlanta, GA, USA.
cCenters for Disease Control and Prevention, Lusaka, Zambia. Now retired.
d U.S. Agency for International Development, Lusaka, Zambia. Now based in
Monrovia, Liberia.
e Bureau for Global Health, U.S. Agency for International Development,
Washington, DC. Now with ICF, Rockville, MD, USA.
f Bureau for Global Health, U.S. Agency for International Development and RTI,
Washington, DC, USA. Now with Palladium, Abuja, Nigeria.
g Baylor College of Medicine Children’s Foundation-Uganda, Kampala, Uganda.
h Infectious Diseases Institute, College of Health Sciences, Makerere University,
Kampala, Uganda.
i Centers for Disease Control and Prevention, Lusaka, Zambia. Now an inde-
pendent consultant, Gainesville, FL, USA.
j ICF, Fairfax, VA, USA. Now with Ariadne Labs, Boston, MA, USA.
k U.S. Agency for International Development, Papua, New Guinea.
Correspondence to Claudia Morrissey Conlon ([email protected]).

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S6

visibility to the cause, but many countries have
not directed sustained political attention or
sufficient resources to eliminate preventable
maternal mortality3—despite solid evidence of
the profound effects a mother’s death has on her
family, her community, and on development in
general.4,5 The situation is particularly dire in
sub-Saharan African countries where 60% of
global maternal deaths occur.1,5,6 In these coun-
tries, obstetrical risk is compounded by high fertil-
ity rates, raising the lifetime risk of death due to
childbirth to 1 in 36, compared with 1 in 8,400 in
the European Union.7–9

Newborns fare no better. Globally, the reduc-
tion in newborn deaths has not kept pace with the
reduction of deaths in children under age 5, with
newborn deaths now contributing to nearly half of
child mortality.1 The average neonatal mortality
rate is 27 deaths per 1,000 live births in low-
income countries compared with 3 deaths per
1,000 live births in high-income countries. Eight
of the 10 most dangerous places to be born are in
sub-Saharan Africa.10

In 2011 the Office of the Global Health
Initiative (GHI) within the U.S. Department of
State was tasked with designing an endeavor that
would bring public and private investment to-
gether with committed Ministry of Health (MOH),
national, and district leaders to address maternal
mortality in sub-Saharan Africa.11,12 It was felt
that a highly visible, well-financed, bold initiative
similar to the U.S. President’s Emergency Plan for
AIDS Relief (PEPFAR), the President’s Malaria
Initiative, and Feed the Future was needed to
inspire and recruit new public and private actors to
the cause, while energizing and mobilizing the
global health and development communities. The
resulting initiative was Saving Mothers, Giving
Life (SMGL), a public–private partnership. SMGL
was composed of 6 U.S. agencies: GHI; the United
States Agency for International Development
(USAID) (which took over oversight of the partner-
ship from GHI in July 2012 and responsibility as
Secretariat from Merck for Mothers in 2014); the
U.S. Centers for Disease Control and Prevention
(CDC); the Office of the Global AIDS Coordinator
(OGAC); Peace Corps; and the Department of
Defense. It also included the Governments of
Norway (became inactive in 2014), Uganda,
Zambia, and Nigeria (joining in 2015 as the
third SMGL country and slated to end in October
2019); Merck for Mothers; Every Mother
Counts; the American College of Obstetricians and
Gynecologists; and Project C.U.R.E (joined the

partnership in 2013). SMGL’s initial goal was to
decrease maternal mortality by 50% in 1 year in
SMGL-designated districts in Uganda and Zambia,
building on existing national public health plat-
forms and systems, and aligning with country
maternal health strategies and aspirations.13,14 At
the end of the first phase of the partnership, the
time frame for the goal was extended to the close
of the initiative in 2017. An additional goal of
reducing the neonatal mortality rate by 30% was
added in 2013.

The Saving Mothers, Giving Life journal sup-
plement consists of 11 articles on the SMGL initia-
tive. The articles describe the formation and
function of the partnership, the SMGL theory of
change, programming approach and costs, and
the results achieved in Uganda and Zambia where
implementation ended in October 2017 (Table 1).
It aims to answer key questions about the initia-
tive and identify outstanding implementation
issues. Results from Nigeria will be reported in
2019 after implementation in that country has
ended.

THEORY OF CHANGE
The SMGL theory of change model was built on a
district health systems strengthening approach. It
was designed to surmount the critical demand-
and supply-side delays that prevent women and
newborns from receiving lifesaving care in a
timely manner, while strengthening the capacity
and resilience of the health care system
(Figure 1).15

The governments of Uganda and Zambia, their
public health systems, the PEPFAR- and USAID-
supported maternal and child health platforms,
and private for-profit and nonprofit providers
were critical inputs and served as the foundation
for SMGL’s contributions to the district maternity
care system. Evidence-based interventions were
designed to address all key delays, be context-
specific, and strengthen the capacity of the district
health system. Four outcomes were anticipated:
(1) increased use of services and improved self-
care, (2) timelier access to appropriate care, (3) im-
proved quality and experience of care, and (4) a
more robust and resilient district health system. It
was hypothesized that if these 4 outcomes were
achieved together, SMGL-designated populations
would see a substantial decrease in maternal and
perinatal mortality.

Implementation of the SMGL theory of change
followed 7 organizing principles:

SMGL’s initial goal
was to decrease
maternal
mortality by 50%
in 1 year in
selected districts in
Uganda and
Zambia.

The SMGL theory
of change was
built on a district
health systems
strengthening
approach.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S7

1. Reap system-level synergies by addressing
all 3 delays to obtaining lifesaving maternal
and newborn care concurrently: delays in
seeking appropriate care, delays in reaching
services in a timely manner, and delays in
receiving quality care at a health facility
with the capacity to perform 9 signal emer-
gency obstetric and newborn care (EmONC)
functions.16–22

2. Recognize the district health system, which
extends from community health workers to
district hospitals (and to higher levels of care
through referrals), as the primary unit for
strengthening capacity.23–25 Potential inter-
ventions should be assessed in terms of their
contributions to improving the functioning of
the entire district-level system.

3. Apply a “whole market approach,” which
requires identifying and including both public
and private inputs (e.g., providers, delivery
systems, stakeholders) in planning, execu-
tion, and evaluation in a designated district.
Together they form the district maternity
safety net.

4. Focus on improving services during the most
vulnerable period for mothers and newborns—

labor, delivery, and early postpartum. Inter-
ventions at this time have the possibility of
saving the lives of mothers and newborns
and preventing fresh stillbirths. The level of
fresh stillbirths is often seen as an indicator
of the quality of care during labor and
delivery.

5. Strengthen the capacity of the health care
system to provide comprehensive emergency
obstetricand newborn care (CEmONC) within
2 hours of travel time from home or a deliv-
ery site for all pregnant women, approxi-
mately 15% of whom will experience a
life-threatening complication, many with-
out clear predictors.26,27

6. Integrate maternal and newborn health
(MNH) services with other reproductive
health services, including (1) HIV counseling
and testing services to maximize identification
and treatment of seropositive pregnant
women and prevent mother-to-child trans-
mission, and (2) postpartum family planning
for women wishing to delay their next
pregnancy.

7. Count, analyze, and report all maternal and
perinatal deaths along with the cause of

TABLE 1. Saving Mothers, Giving Life Supplement Articles

Article
No. Article Title

1 Saving Mothers, Giving Life: it takes a system to save a mother

2 Impact of the Saving Mothers, Giving Life approach on decreasing maternal and perinatal deaths in Uganda and
Zambia

3 Addressing the first delay in Saving Mothers, Giving Life districts in Uganda and Zambia: approaches and results
for increasing demand for facility delivery services

4 Addressing the second delay in Saving Mothers, Giving Life districts in Uganda and Zambia: reaching
appropriate maternal care in a timely manner

5 Addressing the third delay in Saving Mothers, Giving Life districts in Uganda and Zambia: ensuring adequate
and appropriate facility-based maternal and perinatal health care

6 The costs and cost-effectiveness of a district-strengthening strategy to mitigate the 3 delays to quality maternal
health care: results from Uganda and Zambia

7 Saving lives together: a qualitative evaluation of the Saving Mothers, Giving Life public-private partnership

8 Community perceptions of a 3-delays model intervention: a qualitative evaluation of Saving Mothers, Giving Life
in Zambia

9 Did the Saving Mothers, Giving Life initiative expand timely access to lifesaving care in Uganda? A spatial
district-level analysis of travel time to emergency obstetric and newborn care

10 Saving Mothers, Giving Life approach for strengthening health systems to reduce maternal and newborn deaths
in 7 scale-up districts in northern Uganda

11 Sustainability and scale of the Saving Mothers, Giving Life approach in Uganda and Zambia

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S8

death; improve completion of facility records
and registries; institutionalize maternal and
perinatal death surveillance and response
(MPDSR) in each district and foster high-
level awareness of these reviews among tradi-
tional, religious, and political leadership to
learn from each preventable death and pro-
mote necessary health system and cultural
changes.

COUNTRY CONTEXT
In 2011, Uganda and Zambia were chosen as
the first SMGL-supported countries based on
(1) their interest to the Global Health Initiative;
(2) high levels of maternal mortality—MMR of
420 in Uganda and 262 in Zambia in 20101;
(3) solid MOH commitment to decreasing mater-
nal and newborn mortality, as evidenced by their
Roadmap to Accelerate Reduction of Maternal
and Neonatal Mortality and Morbidity and

Campaign to Accelerate the Reduction of
Maternal, Newborn, and Child Mortality in
Africa plans; and (4) the existence of robust
PEPFAR- and USAID-supported maternal and
child health platforms.28–30 Direct causes of
maternal deaths were similar in both countries,
with postpartum hemorrhage being the leading
cause followed by preeclampsia/eclampsia, sepsis,
obstructed labor/ruptured uterus, and complica-
tions of unsafe abortions.1 The most deadly indi-
rect causes were malaria and HIV.29,31

Inadequate skilled human resources for
health were a major constraint to providing effec-
tive coverage in both countries.29,31 When SMGL
began, the human resources vacancy rate at
health facilities in SMGL-supported districts was
40% in both Uganda and Zambia.11,12,32–34

Uganda and Zambia also shared high HIV rates
(7% and 12% among adults ages 15 to 49, respec-
tively) and their total fertility rates were among
the highest in the world (6.2 for both countries)

FIGURE 1. Saving Mothers, Giving Life Theory of Change Model

Abbreviations: EmONC, emergency obstetric and newborn care; MCH, maternal and child health; MPDSR, maternal and perinatal death surveillance and
response; MMR, maternal mortality ratio; NMR, neonatal mortality rate; PEPFAR, U.S. President’s Emergency Plan for AIDS Relief; SMGL, Saving Mothers,
Giving Life; USG, U.S. Government.

Source: Adapted from Saving Mothers, Giving Life.57

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S9

(Table 2). Less than half of births in Zambia, and
57% in Uganda, were attended by skilled birth
attendants and the cesarean delivery rates were
low at 5% in Uganda and 3% in Zambia.
Neonatal mortality rates were 27 and 34 per
1,000 live births in Uganda and Zambia, respec-
tively (Table 2).

PROJECT DESIGN, IMPLEMENTATION,
AND ASSESSMENT

SMGL Learning Districts
Four districts each in Uganda and Zambia were
selected for SMGL support by their MOH based
on the large numbers of deliveries and maternal
deaths, the availability of existing implementing
partners working in the district, and national pri-
orities. The 8 districts in total, designated as the
SMGL learning districts, were mostly rural and
poor.8,11,12,30,31 Figure 2 shows the learning dis-
tricts and the scale-up districts. Over the life of
the initiative, the 4 learning districts in each coun-
try were administratively split further to total
6 learning districts in each country.

In Zambia, the 4 initial learning districts were
spread across the country with 2 in Eastern
Province (Nyimba and Lundazi), 1 in Southern

Province (Kalomo), and 1 in Luapula Province
(Mansa). The 4-district population was 880,000
with 46,157 deliveries in 2011. Throughout the
initiative, 110 health facilities were engaged,
94% public and 6% private, including 16 health
posts, 88 health centers, and 6 hospitals.11,35

Uganda’s SMGL-supported districts (Kyenjojo,
Kamwenge, Kabarole, and Kibaale, aka “the
4Ks”) were contiguous and located in Western
Uganda. The population in the 4Ks was 1.75 mil-
lion with 78,400 deliveries in 2011. Throughout
the initiative, 105 delivering facilities, 61% public
and 39% private (18 health centers II, 70 health
centers III, 11 health centers IV, and 6 hospitals),
were supported by SMGL.12,36

SMGL Phases
The SMGL initiative was divided into 3 phases:
Phase 0—design and startup (June 2011 to May
2012), Phase 1—proof of concept (June 2012 to
December 2013), and Phase 2—scale-up and
scale-out (January 2014 to October 2017).

Phase 0: Design and Startup
Initiative design. Design of the SMGL district
health systems strengthening approach began in
mid-2011 under the aegis of the Global Health

TABLE 2. Uganda and Zambia National-Level Indicators at the Start of the SMGL Initiative

Indicator Uganda Zambia

Maternal mortality ratio (per 100,000 live births) 420a 262a

Deliveries in facilities 57%b 48%c

Births by cesarean delivery 5%b 3%c

Birth attended by skilled birth attendant 57%b 47%c

Antenatal care coverage: at least 4 visits 48%b 60%c

HIV prevalence among adults 15–49 7%d 12%d

Pregnant women with HIV receiving antiretroviral therapy 61%d 93%d

Total fertility rate 6.2b 6.2c

Modern contraceptive prevalence rate among all women 15–49 21%b 25%c

Neonatal mortality rate (per 1,000 live births) 27b 34c

Abbreviation: SMGL, Saving Mothers, Giving Life.
a 2010 data from Trends in Maternal Mortality: 1990 to 2015. Estimates by WHO, UNICEF, UNFPA, World Bank Group and the
United Nations Population Division (https://www.who.int/reproductivehealth/publications/monitoring/maternal-mortality-2015/
en/).
b 2011 data from Uganda Demographic and Health Survey 2011 (https://dhsprogram.com/pubs/pdf/FR264/FR264.pdf).
c 2007 data from Zambia Demographic and Health Survey 2007 (https://www.dhsprogram.com/pubs/pdf/FR211/FR211
[revised-05-12-2009].pdf).
d 2011 data from UNAIDS AIDSinfo (http://aidsinfo.unaids.org/).

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S10

Initiative. The Global Health Initiative convened a
design team of MNH and HIV technical experts in
project development, implementation, costing,
policy formulation, and monitoring and evalua-
tion. The aim was to create a highly visible, bold
initiative that would galvanize global action and
financial support. A draft SMGL model was devel-
oped, guided by GHI principles and informed by
extensive examination of the evidence base and
modeling from the Lives Saved Tool (LiST).
(Supplement 1) A goal was established to reduce
maternal mortality in SMGL-supported facilities
in Uganda and Zambia by 50% in 1 year and an
implementation plan was formulated. A notable
feature of the plan was that partner funding for
SMGL implementation was only guaranteed for
an initial 12-month period; if performance was
deemed subpar, funding for SMGL could end.

After country and district selection, the U.S.
ambassadors for Uganda and Zambia assigned
coordination roles to U.S. agency heads (USAID
mission director, CDC director, PEPFAR coordina-
tor, Peace Corps lead, and Department of Defense
liaison), and interagency working groups were
formed. The working groups collaborated with
national, provincial, and district MOH-designated
SMGL leads (usually district health officers) and
implementing partners, forming SMGL country
teams. The country teams initially met weekly
and then monthly to develop plans and lever-
age existing partner programs and capabilities.
Country teams then created intensive 1-year
workplans for the pilot districts in Uganda and
Zambia based on addressing the 3 delays and
strengthening the system.

The rapid design and execution of the initial
SMGL 1-year plan required the participation of
existing implementing partners working in
SMGL-selected districts. Between Uganda and
Zambia, 39 implementing partners were identi-
fied, most with set workplans and deliverables
(Supplement 2). Under the leadership and super-
vision of MOH district health management teams
and district health and medical officers, extant
implementing partner workplans were adapted to
support SMGL country and district plans.

Evaluation design. The ability to assess and
report health outcomes resulting from SMGL
efforts required robust evaluation. The headquar-
ters monitoring and evaluation (M&E) commit-
tee, composed of specialists from CDC and
USAID, developed an ambitious evaluation plan
for Phase 1 that was endorsed by the ministries of
health and implementing partner representatives
in both countries.37 The plan included ongoing

FIGURE 2. Saving Mothers, Giving Life-Designated Learning and Scale-Up
Districts in Uganda and Zambia

Source: Adapted from Saving Mothers, Giving Life.57

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S11

enumeration of all maternal deaths with verbal
autopsies to ascertain cause of death. (See the arti-
cle by Serbanescu and colleagues from the SMGL
supplement.38)

Thirty-one indicators were selected for moni-
toring care at all delivering facilities through quar-
terly record and registry reviews in SMGL-
supported districts in Uganda and Zambia
(Supplement 3). In Uganda, these data were col-
lected through Pregnancy Outcomes Monitoring
Studies; data were also gathered and displayed
monthly at selected SMGL facilities in Uganda
using a simple matrix referred to as “BABIES”
(Birthweight by Age-at-Death Boxes for Inter-
vention and Evaluation System), which provided
short-loop feedback to improve newborn care.
Formative special studies37 included a qualitative
study of women’s and communities’ perceptions
of childbirth in Zambia and a 2-hour travel-time
mapping study in Uganda.39 (See the article
by Schmitz and colleagues from the SMGL
supplement.40)

Baseline assessment. During Phase 0, base-
line studies were undertaken in the 8 learning dis-
tricts. MMRs were measured through a census
with verbal autopsies of deaths among women of
reproductive age in Zambia and a Reproductive
Age Mortality Survey (RAMOS) in Uganda.
(RAMOS uses a variety of sources to identify all
deaths of women of reproductive age and decide
which of these are maternal- or pregnancy-
related.) Health facility assessments (HFAs) of
capacity and readiness of the system to provide
9 lifesaving signal functions were undertaken in
all public and private delivering facilities in the
SMGL-supported districts (Table 3). This enabled
planners and implementers to take stock of the
existing availability of basic and comprehensive

emergency obstetric and newborn care. HFAs
were carried out at 3 time points during SMGL:
(1) at baseline, to inform SMGL planning and
design and to identify needed investments; (2) at
the end of the pilot year in 2013 to gauge progress
and inform funding and operational decisions
during subsequent years; and (3) at endline in
2017 to assess outcomes.

Common gaps identified from the baseline
HFA included the following:

� Delay 1: Demand. ThenumberofGovernment-
established community health workers, village
health teams (VHTs) in Uganda and Safe
Motherhood Action Groups (SMAGs) in Zambia,
was inadequate. Women booked late for antenatal
care visits and attendance of 4 or more antenatal
care visits was low (46% in Uganda).41

� Delay 2: Access. Women had limited access
to comprehensive CEmONC facilities within
2 hours (only 51% to 55% of women were
able to reach CEmONC within 2 hours using
motorized vehicles) due to few operating thea-
ters and blood banks, and lack of transport
vehicles and referral protocols. Maternity wait-
ing homes were often dilapidated and deserted.

� Delay 3: Quality. Many maternity blocks in
hospitals and health centers were run-down
and overcrowded, and they lacked water, elec-
tricity, and functioning toilets. Equipment was
missing, inoperative, or insufficient for the client
load. Facilities lacked 24-hour staffing of skilled
birth attendants, anesthetists, and surgeons.

� Health Systems Strengthening. In the face
of limited quality improvement activities, facili-
ties experienced frequent drug and supply
stock-outs and weak capture, analysis, and
reporting of health outcome data.

TABLE 3. Emergency Obstetric and Newborn Care 9 Signal Functions

Basic Services Comprehensive Services

1. Administer parenteral antibiotics Perform signal functions 1 through 7 plus:

2. Administer uterotonic drugs (i.e., parenteral oxytocin, misoprostol) 8. Surgery (cesarean delivery)

3. Administer parenteral anticonvulsants for preeclampsia (i.e., magnesium sulfate) 9. Blood transfusion

4. Manually remove the placenta

5. Remove retained products of conception (e.g., manual vacuum extraction, misoprostol, dilation
and curettage)

6. Perform assisted vaginal delivery (e.g., vacuum extraction, forceps delivery)

7. Perform basic neonatal resuscitation (e.g., bag and mask)

Source: WHO, UNFPA, UNICEF, and Mailman School of Public Health.27

SMGL developed
a robust
evaluation plan
that included
ongoing
enumeration of all
maternal deaths
with verbal
autopsies to
ascertain cause of
death.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S12

These gaps and other district-specific challenges
were addressed in SMGL district workplans.

Startup. Startup activities began early in
2012. At the national level in Uganda and
Zambia, routine meetings were held with the
interagency working groups, MOH representa-
tives, and implementing partners. Preparations
for work with private providers through the
Programme for Accessible Health Communication
and Education (PACE) project and Marie Stopes
International were initiated in Uganda. In Zambia,
where the SMGL learning districts were spread out
across the country, SMGL district coordinators—
often retired midwives—were hired to harmonize
all SMGL activities in their district with district
health officers and district health management
teams, and to serve as a link with implementing
partners. During this phase, training commenced
for providers and existing government-sponsored
community health workers—SMAGs and VHTs.
These health workers were recruited from the local
community. Groups were a mix of men and
women and often included former traditional birth
attendants. SMGL provided these volunteers with
resources such as gumboots, flashlights, T-shirts,
and bicycles. In Zambia, Peace Corps volunteers
were recruited and trained as community mobiliz-
ers to work with SMAGs to increase demand and
organize community transport systems. By the
end of the initiative, SMGL-dedicated Peace Corps
volunteers were in all 18 SMGL-supported districts.

Phase 1: Proof of Concept
Results for Phase 1 are based on data for the
12-month period from June 2012 through May
2013. Analysis and write-up of lessons, however,
continued through December 2013.

Interventions. District-level MOH staff led
the implementation process working with imple-
menting partners funded by PEPFAR, CDC,
USAID, and Merck. In the learning districts, the
following interventions were carried out to
address the health system gaps identified in the
Phase 0 HFAs, by delay, in accord with the SMGL
theory of change.

� Delay 1: Demand. Tackling this delay
required not only effecting change in individual
behaviors but also influencing community
norms. SMAGs and VHTs identified pregnant
women and initiated antenatal home visits cov-
ering all villages across the 8 learning districts.
They provided childbirth education and antici-
patory guidance. Specific topics included: self-
care and a healthy diet, attending antenatal

and postnatal care visits and delivering in a fa-
cility, family planning, recognition of maternal
and newborn danger signs, being tested for
HIV, and undertaking birth planning and sav-
ing to cover the costs of transport and medical
care. Messages given during these family visits
were reinforced by including husbands and
household members, holding community sen-
sitization meetings, and training traditional
leaders to be “change champions.” Multimedia
campaigns, which included community sensiti-
zation skits, radio announcements, community
documentary screenings, and billboards, were
also fielded in both countries. In Zambia, Peace
Corps volunteers trained SMAGs on a home-
visit protocol using the national SMAG curricu-
lum. (See the article by Serbanescu and
colleagues from the SMGL supplement.41)

� Delay 2: Access. A travel-time study in
Uganda and HFA results from both countries
confirmed that timely access to care was a
major problem in all 8 SMGL-supported dis-
tricts. SMGL programming addressed this prob-
lem in 3 ways: bringing lifesaving care closer to
women, decreasing travel time to appropriate
care, and bringing women closer to emergency
services. Select maternity wards and surgical
theaters were refurbished to upgrade facility
capacity and optimize 2-hour access to
CEmONC care. In Uganda, subsidized motor-
cycle transport vouchers and private-care
vouchers, distributed by VHTs, were rapidly
scaled up during Phase 1. In Zambia, where
long distances to care are the norm, maternity
waiting homes were refurbished or built next
to EmONC facilities by the Department of
Defense and the Merck-led Maternity Waiting
Home Alliance. In both countries, SMGL
ensured appropriate communication tools,
such as cell phones and radios, and district-
specific protocols to facilitate transfers. (See
the article by Ngoma et al. from the SMGL
supplement.42)

� Delay 3: Quality. Baseline HFA results had
revealed the need for significant improvements
in the quality of services provided if women
were to receive lifesaving care for complica-
tions; many aspects of the health system would
need to be strengthened. Efforts to improve the
quality of services engaged frontline health care
providers and facility managers. SMGL hired
strategically placed midwives, nurses, anesthe-
tists, and doctors (147 providers in Uganda and
19 in Zambia). In both countries, many of the

To address
barriers to timely
access to care,
SMGL brought
lifesaving care
closer to women,
decreased travel
time to
appropriate care,
and brought
women closer to
emergency
services.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S13

midwives hired were retired, seasoned health
professionals. In Uganda, staff was hired with
the understanding that their positions would
be picked up by the MOH when SMGL funding
ended. SMGL doctors received increases in
their salaries to work in rural health center IVs
rather than hospitals, an incentive that was
subsequently adopted nationally by the MOH.
Quality improvement committees were
formed and the BABIES matrix was intro-
duced into all EmONC facilities. Quality
improvement committees were trained to sen-
sitize providers on the importance of respect-
ful care. Merck for Mothers worked through
the PACE project to provide technical assis-
tance to private providers in order to upgrade
their skills.

Health care providers in both countries were
trained by MOH trainers and routinely men-
tored on EmONC, Helping Babies Breathe,
essential newborn care, uterine balloon tam-
ponade (Zambia), maternal and perinatal death
reviews, syphilis screening, prevention of
mother-to-child transmission of HIV, infection
prevention, and operative skills. Obstetricians
and gynecologists associations in both countries
provided clinical mentoring to district medical
officers and district health officers in SMGL-
designated districts and the professional soci-
eties were in turn strengthened with technical
assistance from the American College of
Obstetrics and Gynecology. Project C.U.R.E.
supplied donated facility-specific, essential
equipment and commodities (including hospi-
tal and delivery beds, surgical tables and lights,
resuscitation supplies, sterilization equipment,
sutures, and gloves), shipping 16 containers to
Uganda and 20 to Zambia over the life of the
initiative. (See the article by Morof et al. from
the SMGL supplement.43)

� Health systems strengthening. Activities to
strengthen the health system included provid-
ing HIV-related diagnostics and treatment and
family planning services at the same location
and times as MNH services to create “one-stop”
shops. Both countries followed the Option Bþ
HIV treatment guidance, which supports HIV
testing and counseling during antenatal care
and offering women found to have HIV infec-
tion lifelong antiretroviral therapy. This facili-
tated the SMGL HIV testing and treatment
approach: pregnant women were tested for
HIV during antenatal care visits, and if seropos-
itive, midwives were empowered to place them
on antiretroviral therapy to protect the life of

the mother and prevent mother-to-child trans-
mission. In select SMGL-supported districts,
providers were trained to provide postpartum
family planning. District medical and health
officers and in-charges received instruction on
drug logistics and forecasting to prevent
chronic stock-outs of essential medicines.
Facilities were equipped with rainwater catch-
ment systems, solar panels, and functioning
toilets. Maternal and perinatal deaths were
reviewed through routine maternal and peri-
natal death surveillance and response efforts in
facilities. The CDC provided capacity strength-
ening of district-level teams on monitoring and
evaluation. SMGL staff supported monthly
district-led data reviews of MNH indicators,
quarterly provincial-level reviews, and strength-
ening of the District Health Information
Management System (DHIS2), a free and open-
source health management data platform. (See
the article by Serbanescu and colleagues from
the SMGL supplement.38)

� Data collection activities. After 12 months
of Phase 1 implementation (June 2012 to May
2013), endline Phase 1 studies were conducted
in the 8 learning districts to assess the status of
the SMGL indicators and thus gauge progress at
the end of year 1.17 In addition, a mixed-
methods external implementation evaluation
of Phase 1 was undertaken by Columbia
University.29 This evaluation examined the
reach, extent, fidelity, and dynamic effects of
the initiative in order to identify best practices
and remaining barriers to reducing maternal
mortality. Data from these evaluations were
analyzed and results were reported at an
SMGL global dissemination meeting in
January 2014.44,45 (See Supplement 4.)

Phase 2: Scale-Up and Scale-Out
Early in 2014, the partners met to examine SMGL
performance and to modify SMGL’s approach,
governance, assessment, and implementation for
Phase 2. These adjustments are described in the
following sections.

Initiative. The partners decided to maximize
the return on initial investments in Uganda and
Zambia by committing to operate in both coun-
tries until October 2017. SMGL would aim to
achieve near-national coverage of the SMGL
approach in Uganda and Zambia, defined by the
partners as ≥70% population coverage, and would
select 1 additional country for SMGL implementa-
tion. In 2015, Nigeria became the third and final

Health systems
strengthening
activities included
providing HIV-
related
diagnostics and
treatment and
integrated family
planning services.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S14

SMGL country.46 There, the SMGL systems
approach was rolled out across Cross River State
(population 3.7 million) and will be supported
until October 2019. The governing partners for
SMGL Nigeria are USAID Washington, USAID
Nigeria, Merck for Mothers, and Project C.U.R.E.

Governance. MOH representatives were
invited to join the Leadership Council, SMGL’s
global governing body, and partners agreed to re-
examine their resource pledges and submit quar-
terly contribution reports.

Scale-up district assessment. The SMGL
partners agreed that due to the high cost and man-
agement burden of undertaking detailed informa-
tion gathering, a limited number of M&E activities
would take place in the scale-up districts of
both countries. The focus of these efforts would
be to guide program adjustments for quality
improvement: HFAs at baseline to inform initial
programming, quarterly record and registry data
gathering at CEmONC facilities only, and Health
Management Information System reporting on
indicators of interest for all facilities on a quarterly
basis. (See the article by Isabirye et al. from the
SMGL supplement.47)

Implementation. Interventions introduced
in Phase 1 were largely maintained with a few
exceptions: Mama Pack distribution in Zambia
ended based on concerns about sustainability;
repair or replacement of 2-way radios became
unnecessary as the availability of cell phones
increased; and ongoing enumeration of maternal
deaths by Zambia SMAGs was discontinued after
problems with data gathering during the proof-
of-concept phase. VHTs in Uganda continued
ongoing enumeration. The partners endorsed sev-
eral context-specific programmatic changes for
the learning districts and the scale-up districts.
The SMGL time frame of interest was lengthened
from intrapartum through 24 hours postpartum
to 48 hours postpartum in Uganda and 72 hours
postpartum in Zambia to conform to host country
guidelines and enable greater focus on postpartum
family planning and postnatal care. In the face of
nonsignificant reductions in pre-discharge neona-
tal mortality in Uganda during Phase 1, SMGL
increased programming for newborns. Additional
interventions included: ensuring availability of
newborn corners (flat surfaces for newborn resus-
citation) in each delivery room; opening neonatal
special care units, and Kangaroo Mother Care
units in 8 health center IVs and 3 hospitals where
stable low birth weight and premature newborns
could be cared for; upgrading the existing neona-
tal intensive care unit at Fort Portal Regional

Referral Hospital; increasing training and drilling
on newborn resuscitation and essential newborn
care; and implementing the BABIES matrix in
additional facilities. (See the article by Morof and
colleagues from the SMGL supplement.43)

In Zambia, where postpartum hemorrhage
was the leading cause of maternal death and
in the context of long distances to delivery care,
3 interventions were prioritized for Phase 2:
(1) constructing and refurbishing maternity wait-
ing homes, (2) introducing and institutionalizing
uterine balloon tamponade, and (3) strengthening
the national blood transfusion system. Maternity
homes, located next to SMGL-supported EmONC
facilities, were built or refurbished by the U.S.
Department of Defense or under the Maternity
Waiting Home Alliance. (See the article by
Ngoma and colleagues from the SMGL supple-
ment.42) SMGL helped drive policy changes that
allowed uterine balloons to be placed by nurses
and midwives and for uterine balloon tamponade
to be included in the national EmONC curriculum.
Across the 18 Zambian SMGL districts, providers
were trained in the assembly and use of uterine
balloon tamponade. With funds from SMGL and
the government of Zambia, several district blood
bank hubs were established to provide 24-hour
blood testing and availability of fresh-frozen blood
and plasma.

Contextual changes. Important contextual
changes occurred during Phase 2 at the district
level. The 4 original learning districts in Uganda
were divided into 6 districts—Kabarole, Kibaale,
Kamwenge, Kyenjojo, Kakumiro, and Kagadi—
and 7 new scale-up districts were added in the
north—Nwoya, Gulu, Omolo, Pader, Lira, Apac,
and Dokolo. All were selected by the MOH. Due to
a change in the implementing partner for the new
SMGL Uganda northern districts, full execution of
the SMGL approach did not begin until 2015 and
ended 2 years later. (Project description and results
can be found in the article by Isabirye and col-
leagues from the SMGL supplement.47) In Zambia,
the 4 learning districts were divided into 6 districts
through an administrative re-districting process—
Nyimba, Lundazi, Kalomo, Zimba, Mansa, and
Chembe—and 12 additional districts were added
across the country—Samfya, Lunga, Kabwe,
Choma, Pemba, Chipata, Petauke, Sinda, Vubwi,
Mumbwa, Livingstone, and Luangwa (Figure 2).

Endline evaluation studies. After the Phase 1
endline studies showed a 35% reduction in facility
maternal mortality and positive results for process
and quality indicators in the SMGL-supported
learning districts in both countries, a summative

SMGL increased
programming for
newborns during
the second phase,
including opening
neonatal special
care units and
increasing
training on
newborn
resuscitation.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S15

evaluation plan was developed by the M&E com-
mittee and the SMGL Secretariat. The plan was
endorsed by the SMGL Leadership Council mem-
bers who also pledged funding for executing the
plan. Using 2016 as the index year for SMGL final
results, end-of-initiative studies were undertaken
in 2017 to establish outcomes in the learning dis-
tricts: (1) a census in Zambia and a RAMOS in
Uganda,38,48 (2) repeat HFAs in all delivering facili-
ties in the learning districts,38 (3) a cost-
effectiveness study addressing the 3 delays,7 (4)
a secondary analysis comparing SMGL district
outcomes with findings from the Uganda
Demographic and Health Survey (DHS) in compar-
ison districts and nationally,31 (5) a follow-on qual-
itative study of community perspectives on
childbearing in Zambia,49 and (6) a repeat travel-
time mapping study in Uganda to gauge if the
SMGL initiative resulted in greater access to care.50

RESULTS
Key Health Facility and Population-Based
Assessment Results
Select results from Phase 1 have previously been
reported.7,17,29,39,51–53 What follows is an over-
view of key results at baseline and 2016 endline
for the SMGL-supported learning districts. Table
4 compares selected baseline and endline indica-
tors by type. A description of data collection meth-
ods, indicators, and baseline and endline results
are included in the article by Serbanescu and col-
leagues from the SMGL supplement.38 A compar-
ison of SMGL outcomes with those from DHS and
UN maternal mortality estimates is presented in
Supplement 5.

Demand
The chances of surviving childbirth are improved
when a woman gives birth in a facility, attended
by a skilled birth attendant.54–56 Over the life of
SMGL, the institutional delivery rate, or the propor-
tion of births occurring in delivery facilities, increased
from 46% to 67% in Uganda (a 47% increase) and
from 63% to 90% (a 44% increase) in Zambia
SMGL-supported facilities.

Timely Access
SMGL prioritized bolstering the system’s capacity
to provide timely lifesaving emergency care. The
number of facilities that performed all 7 signal
functions that constitute basic emergency obste-
tric and newborn care (BEmONC) increased from
3 to 9 in Uganda (200%) and from 3 to 8 in Zambia

(167%). Similarly, the number of CEmONC facili-
ties increased from 7 to 17 (143%) in Uganda and
from 4 to 5 (25%) in Zambia.

In 3 SMGL-supported districts in Uganda,
transportation vouchers enhanced women’s
access to essential and emergency health ser-
vices by covering the cost of motorcycle rides to
facilities for delivery, 4 antenatal care visits, and
1 postnatal care visit. In 2016, almost 1 out of
4 women who delivered in SMGL facilities used
transportation vouchers to reach care. In Zambia
where motorcycle transport is not generally
available, maternity waiting homes were built
or upgraded to provide mothers a safe place to
stay near an EmONC facility during the last
weeks of pregnancy. The proportion of SMGL
facilities that reported having an associated ma-
ternity waiting home increased significantly
from 29% at baseline to 49% at endline (a
69% increase).

Quality
The range of interventions that SMGL imple-
mented to enhance quality of care largely proved
effective:

� Population-based cesarean delivery rates in-
creased by 71% (from 5.3% to 9.0%) in Uganda
and 79% (from 2.7% to 4.8%) in Zambia in
SMGL-supported districts. The rates achieved
are still below the World Health Organiza-
tion (WHO) recommended rates of 10% to
15%. (Regardless of the rate, cesarean deliv-
eries should be performed only when medi-
cally indicated).

� The percentage of facilities reporting having
performed newborn resuscitation in the last
3 months increased by 155% (from 34% to
88%) in Uganda and by 173% (from 27% to
75%) in Zambia.

� The percentage of all SMGL-supported facilities in
Uganda that reported active management of
the third stage of labor increased by 28% (from
75% to 96%). In Zambia, the change from base-
line was 33% (72% to 96%).

� Having at least 1 long-acting reversible family
planning method in SMGL-supported facilities
increased in both counties. In Uganda, avail-
ability increased by 51% (from 63% to
94%) of facilities. In Zambia, it improved by
50% (from 50% to 75%) of facilities.

� The percentage of hospitals conducting mater-
nal death audits tripled in Uganda (from

The institutional
delivery rate
increased by 47%
in SMGL-
supported
facilities in
Uganda and by
44% in Zambia.

Cesarean delivery
rates increased by
71% in SMGL-
supported districts
in Uganda and by
79% in Zambia.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S16

TABLE 4. Key Results at Baseline and Phase 2 Endline in the SMGL Learning Districts

Uganda Zambia

SMGL Indicator
2012
Baseline

2016
Phase 2
Endline

% Change
Baseline to
Phase 2 Significancea

2012
Baseline

2016
Phase 2
Endline

% Change
Baseline to
Phase 2 Significancea

GOAL

Institutional MMR (per 100,000 live births) 534 300 �44 *** 370 231 �37.6 ***
Community MMR (per 100,000 live births) 452 255 �44 *** 480 284 �40.8 ***
Pre-discharge neonatal mortality rate (per 1,000 live births) 8.4 7.6 �10 NS 7.7 8.7 þ14 NS
Institutional perinatal mortality rate (per 1,000 births) 39.3 34.4 �13 *** 37.9 28.2 �26 ***
Institutional total stillbirth rate (per 1,000 births) 31.2 27.0 �13 *** 30.5 19.6 �36 ***
DEMAND

Health facilities that report having a VHT (Uganda) or SMAG
(Zambia) (%)

18 92 þ400 *** 64 93 þ46 ***

Institutional delivery rate (%) 46 67 þ47 *** 63 90 þ44 ***
Deliveries in EmONC facilities (%) 28 41 þ45 *** 26 29 þ12 ***
Deliveries in lower-level facilities (health center II, III) (%) 17 26 þ48 *** 37 61 þ67 ***
ACCESS

Facilities that report having an associated mother’s shelter (%) 0 4 NA NA 29 49 þ69 ***
Institutional deliveries supported by transport vouchers (%) 6 24 þ277 *** Vouchers not provided in Zambia
Number of BEmONC facilities where the 7 signal functions
were performed in last 3 months

3 9 þ200 NA 3 8 þ167 NA

Number of CEmONC facilities where the 9 signal functions
were performed in last 3 months

7 17 þ143 NA 4 5 þ25 NA

24/7 services at health centers (%) 75 89 þ18 NS 65 96 þ41 ***
QUALITY OF CARE

Facilities reporting having performed newborn resuscitation in
the previous 3 months (%)

34 88 þ155 *** 27 75 þ173 ***

Facilities providing active management of the third stage of
labor (%)

75 96 þ28 *** 72 96 þ33 ***

Population-based cesarean delivery rate (%) 5.3 9.0 þ71 *** 2.7 4.8 þ79 ***
Hospitals that currently have at least 1 long-acting family
planning method (%)

63 94 þ51 ** 50 75 þ50 NS

Number of women receiving PMTCT treatment 1262 2155 þ71 NA 930 1036 þ11 NA
HEALTH SYSTEMS STRENGTHENING

Hospitals conducting maternal death audits or reviews (%) 31 94 þ201 *** 50 100 þ100 NA
Health facilities that did not experience stock-outs of oxytocin in
the last 12 months (%)

56 82 þ46 *** 75 75 �0.4 NS

Health facilities that did not experience stock-outs of
magnesium sulfate in the last 12 months (%)

48 64 þ34 *** 20 43 þ115 ***

Abbreviations: EmONC, emergency obstetric and newborn care; BEmONC, basic emergency obstetric and newborn care; CEmONC, comprehensive emer-
gency obstetric and newborn care; MMR, maternal mortality ratio; NA, not applicable; NS, nonsignificant; SMAG, Safe Motherhood Action Group; VHT, Village
Health Team; PMTCT, prevention of mother-to-child transmission of HIV.
a *** P <.01; ** P <.05; * P <.10. NA in cases where significance testing was not warranted.
Source: Serbanescu et al.38

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S17

31% to 94%) and doubled in Zambia (from
50% to 100%).

� The number of HIV-seropositive women who
received prophylaxis or treatment for the preven-
tion of mother-to-child transmission increased by
71% in Uganda, from 1,262 to 2,155 women, and
by 11% in Zambia, from 930 to 1,036 women
(denominators not available).

Health Systems Strengthening
Access to medications was positive but uneven.
While SMGL funds were not used to procure med-
icines in Phase 2, providers were trained in supply
chain management. The proportion of all health
facilities that did not experience stock-outs of
oxytocin in the last 12 months increased by
46% (from 56% to 82%) in Uganda but did not
change in Zambia (from 75% to 75%). The pro-
portion of all health facilities that did not experi-
ence stock-outs of magnesium sulfate in the last
12 months increased significantly in both coun-
tries, by 34% (from 48% to 64%) in Uganda and
by 115% in Zambia (from 20% to 43%).

Impact
From baseline to endline (2012–2016), the MMR
declined by 44% in both facilities and districtwide
in Uganda (from 534 to 300 per 100,000 live births
in facilities and from 452 to 255 in the commu-
nity). MMR declined by 38% in SMGL-supported
facilities in Zambia (from 370 to 231) and by
41% districtwide (from 480 to 284). All declines
were statistically significant.

In Uganda, the perinatal mortality rate declined
by 13% in SMGL-supported facilities (from 39.3 to
34.4 perinatal deaths per 1,000 births). The

total institutional stillbirth rate also declined by
13% (from 31.2 to 27.0 per 1,000 births). Both val-
ues are statistically significant. The pre-discharge
neonatal mortality rate fell by 10% (from 8.4 to
7.6 per 1,000 live births); however, this was
a nonsignificant change. In Zambia, the insti-
tutional perinatal mortality rate declined by
26% in SMGL-supported facilities (from 37.9 to
28.2) and the institutional stillbirth rate declined
by 36% (from 30.5 to 19.6). Both declines were
significant. The change in the pre-discharge
neonatal mortality rate was not significant at
þ14% (from 7.7 to 8.7).

Public and Private Health Care Facilities
In Uganda, where 40% of facilities receiving
SMGL support were private, the endline evalua-
tion explored in a separate analysis whether any
differences existed in the impact indicators by the
type of sector providing delivery care (Table 5).
The majority of SMGL facility deliveries
occurred in public facilities (83.4% public vs.
16.6% private). The proportion of women who
delivered by cesarean delivery was slightly
lower in public-sector facilities compared with
the private sector (13.0% vs. 15.7%, respec-
tively) (data not shown). Generally, no signifi-
cant differences existed in the occurrence of
adverse pregnancy outcomes among women
delivering in the private and public sectors in
2016 in Uganda, with the exception of the intra-
partum stillbirth rate, which was higher in pri-
vate facilities than in public facilities (17.0 vs.
13.8 per 1,000 births, respectively). See
Supplement 6 for more information about
private-sector activities in Uganda.

TABLE 5. Select Indicators by Delivery Care Service Sector in Uganda, 2016

Indicator Public-Sector Facilities Private-Sector Facilities Significancea

Maternal mortality ratio (per 100,000 live births) 301 295 NS

Direct case fatality rate 1.8 1.5 NS

Perinatal mortality rate (per 1,000 births) 34.0 36.4 NS

Intrapartum stillbirth rate (per 1,000 births) 13.8 17.0 **

Total stillbirth rate (per 1,000 births) 26.6 28.7 NS

Pre-discharge neonatal mortality rate (per 1,000 live births) 7.6 7.9 NS

Abbreviation: NS, nonsignificant.
a ** P<.05.
Source: Serbanescu et al.38

The MMR declined
significantly in
both SMGL-
supported
facilities and
districts in Uganda
and Zambia.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S18

DISCUSSION
The positive results from the SMGL Phase 2 end-
line evaluation studies (2016 data) in the learn-
ing districts in Uganda and Zambia are
substantial. However, SMGL’s non-randomized,
before-and-after design makes it challenging to
attribute the outcomes documented after nearly
5 years of implementation solely to the SMGL
health systems strengthening approach. The
Columbia University implementation evaluation
of SMGL’s proof-of-concept year did include
comparison districts, but there was no random-
ization. Still, the MMR declined significantly
faster in the SMGL-supported learning districts
compared with national-level declines. Over a
5-year span the average annual rate of reduction
in Uganda learning districts was 11.5% compared
with the national rate of 3.5% using DHS values.
The difference-in-differences between the drop
in MMR in SMGL areas compared with the drop
in the MMR nationally is statistically significant
(P = .02) (Supplement 5).

The findings for Zambia are similar although
the timing of the DHS did not allow use of DHS
data for comparison. Instead, the UN maternal
mortality estimates for Zambia for the period
2011–2015 were used. The average annual rate
of reduction in SMGL districts in Zambia was
10.5% vs. a national rate of 2.8%.1 These more
rapid declines in MMR in SMGL program areas
compared with national levels in both countries
over a 5-year period suggest that SMGL outcomes
are not solely due to secular trends (Supplement 5).

The results of the SMGL evaluation provide
answers to some questions that are critical to end-
ing preventable maternal and newborn deaths,
while leaving other questions unresolved.

Why Does the SMGL Theory of Change Focus
on All Pregnant Women Rather Than Only
Those Experiencing a Complication?
The 3-delays model, introduced by Thaddeus and
Maine in 1994 in their seminal article,16 provided a
conceptual framework for programming to sur-
mount the key barriers faced by women with obstet-
ric complications. In the SMGL theory of change,
we focused on all pregnant women within the
SMGL-supported districts because many maternal
complications are difficult to predict and prevent,
can arise quickly, and can result in a maternal death
in a short period of time. The SMGL systems
approach aimed to provide access to emergency
care within 2 hours from home or a lower-level

health facility for all pregnant women in SMGL-sup-
ported districts.

Can a District Health Systems Strengthening
Approach Addressing the 3 Delays
Contribute to Maternal Mortality Reductions
in High-Burden, Low-Resource Countries?
The data show significant reductions in the MMR
in the learning districts in both Uganda and
Zambia after nearly 5 years of SMGL implementa-
tion. The contribution of SMGL to these changes is
plausible given the greater rate of reduction in pro-
gram areas compared with national rates in both
countries (Supplement 5). In Uganda, 70% of the
total MMR reduction, from baseline to endline
Phase 2, occurred during Phase 1, suggesting that
once inputs were in place, the systems approach
was successful in sustaining the reduced MMR.
This is particularly instructive as after Phase 1, in
the context of erratic funding flows, implementa-
tion was uneven. In spite of these lapses, reduc-
tions were sustained over the life of SMGL,
based on robust analysis of the SMGL routine
quarterly indicators and Pregnancy Outcomes
Monitoring Studies values.57

What About Newborn Deaths and Stillbirths?
Decreases in institutional perinatal deaths were
statistically significant in Uganda and Zambia at
13% (31.2 to 27.0) and 26% (37.9 to 28.2), respec-
tively. The declines in the total stillbirth rate (fresh
and macerated stillbirths) were also significant in
both countries (13% in Uganda and 36% in
Zambia).38 However, changes in pre-discharge
neonatal mortality rates were nonsignificant.
Further analysis is needed to understand why the
SMGL approach was able to decrease stillbirths but
not newborn deaths. We hypothesize that, in the
past, newborns who were not breathing at birth
were laid aside and categorized as stillbirths but
that after HBB training some were successfully
resuscitated. A portion of these now breathing
newborns potentially succumbed to fatal complica-
tions. It is also unclear if the public-private differen-
ces seen in intrapartum stillbirth rates in Uganda
reflect differences in health care provision or in
clinical risk factors. (See the article by Serbanescu
and colleagues from the SMGL supplement.38)

What Is the Minimum Package of
Interventions Needed to Reduce Maternal
and Neonatal Mortality?
The SMGL theory of change posits that an inte-
grated systems approach addressing both demand-

The MMR declined
significantly faster
in the SMGL-
supported districts
than nationally.

Results from a
modeling exercise
support the SMGL
theory of change
that an integrated
systems approach
addressing both
demand- and
supply-side
barriers is more
impactful than
individual
interventions.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S19

and supply-side barriers is more impactful than
individual and/or uncoordinated interventions,
especially for a complex and multifaceted problem
such as maternal mortality. The results of the
Qualitative Comparative Analysis (QCA) model-
ing from Uganda support this hypothesis.58 The
QCA examined the relative power of varied bun-
dles of interventions to replicate the Phase 1, first-
year achievement of reducing community maternal
mortality in SMGL-supported districts in Uganda
by 30% (facility deaths were reduced by 35%)
The results suggest that the most powerful bundle
of interventions (most effective at lowest cost) was
comprised of 4 interventions: VHTs (demand);
transportation vouchers (access); availability of
staff (quality); and availability of medicines (health
systems strengthening). If run individually, none of
these interventions achieved the 30% MMR
reduction, and if the results from these individual
interventions were then added together, the sum
did not achieve the reduction of the optimal bun-
dle. It appears that it is not only these critical inter-
ventions but the synergy created by addressing
both supply- and demand-side barriers that accel-
erates change.60 It would be instructive to under-
take a QCA study in Zambia to see if similar results
are found.

What About Cost?
SMGL’s achievements are often tempered by
concerns that the SMGL approach was too ex-
pensive for replication. In order to rigorously
examine this critical consideration and establish
the relative value for money, it is necessary to com-
pare the cost of SMGL implementation with other
initiatives that have achieved equivalent health
outcomes. Unfortunately, few MNH projects
are comparable to SMGL in terms of complexity,
robust capture of both facility and districtwide
health outcomes (MMR, perinatal mortality
rate, neonatal mortality rates, cause of death),
and commitment to tallying expenditures.29,60

Even when examining the cost-effectiveness of
individual MNH interventions, there is a paucity
of high-quality cost-effectiveness studies.61–64

These features have left evaluators without ideal
counterfactuals.65–67

To better understand relevant SMGL cost out-
lays over the life of the initiative, 3 costing studies
were undertaken (Supplement 4). All 3 studies
projected that after investing in essential capital
improvements and streamlining operations, run-
ning costs would decrease substantially. Those
predictions proved accurate. By design, external

funding tranches for SMGL implementation in
Uganda and Zambia were decreased yearly while
the number of SMGL districts increased, resulting
in substantial reductions in funding per learning
district over Phase 2. During that same period,
maternal health outcomes in the learning districts
continued to show improvement.

The endline 3-delays costing study looked at
the cost in 2016 of addressing all 3 delays: demand
generation, accelerating access to appropriate care
including referral, and improving the quality of
care at the facility. The expenditure per maternal
and perinatal life-year gained was found to be
US$177 in Uganda and US$206 in Zambia. These
values are inclusive of startup and capital costs—
both expressed as annual equivalents. The authors
conclude that the SMGL approach is cost-
effective, with the cost per life-year gained in
Uganda at 25.6% of gross domestic product
(GDP) per capita and at 16.4% of GDP per capita
in Zambia. Both values are less than 50% of GDP
per capita, a benchmark for cost-effectiveness. In
terms of affordability, the additional (incremental)
costs associated with the SMGL approach would
add less than 0.5% to the health spending from
GDP in both countries (from 7.3% to 7.5% in
Uganda and from 5.4% to 5.8% in Zambia).
Recent models suggest that, at a minimum, an
additional US$11 per capita per year is necessary
to meet the full needs of MNH care in sub-
Saharan Africa.68 The incremental costs of the
SMGL initiative of US$1.36 per person per year in
Uganda and US$4.85 per person per year in Zambia
are far less than these modeled estimates, and
much less than that spent on antiretrovirals per
person treated per year, which stood at an average
of US$136.80 in 2015.69 (See the article by Johns
and colleagues from the SMGL supplement.70)

What About Sustainability?
In Uganda and Zambia there is both increased
MOH commitment to the health systems strength-
ening approach and heightened societal aware-
ness that most maternal and newborn deaths can
and should be prevented.29 Yet, it is likely that
ongoing donor funding and technical assistance
will be required in the short term to maintain the
positive results achieved during SMGL implemen-
tation. Below, we look at country capacity and
ownership as 2 important domains to gauge the
likelihood that key elements of the SMGL health
systems strengthening approach will be sustained.

Country capacity. Capacity building of
district-level medical and public health staff

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S20

included clinical training, monthly on-site mentor-
ing, and management; data gathering, analysis,
reporting, and response; quality improvement;
drug logistics; and budget development. Physicians
in both Uganda and Zambia were trained (for the
first time) on International Classification of
Diseases (ICD) 10 Maternal Mortality coding of
deaths, a prerequisite skill for a functioning mater-
nal death surveillance and response system and a
civil registration and vital statistics system. In
both countries, the initiative led to improvements
in tracking routine service delivery indicators as
part of the national health management and infor-
mation systems. In 2011, the governments of
Uganda and Zambia began using DHIS2 as an elec-
tronic platform for aggregated health service data.
In both countries, the SMGL-supported districts
piloted DHIS2 implementation to collect, store,
and analyze data on maternal and reproductive
health. The improvements were scaled up to the
national level by the end of 2012. Another impor-
tant activity in Zambia was training SMGL district
doctors and nurses in blood transfusion safety.
Hospital Transfusion Committees were estab-
lished to improve monitoring of blood supplies
through the use of short message services
(SMS or texts) for forecasting and planning to
avert shortages. When donor funding recently
decreased for blood-safety programs, the govern-
ment of Zambia increased its health budget to
ensure an adequate supply of blood for its citizens.
(See the article by Healey et al. from the SMGL
supplement.71)

Beyond training, SMGL country technical
leads were supported to assume leadership posi-
tions within SMGL and to provide technical assis-
tance to other SMGL countries. A team of Uganda
SMGL leads traveled to Nigeria to provide techni-
cal assistance to the Nigeria SMGL team to carry
out HFAs in Cross River State health care facilities,
public and private, and also to Zambia to support
HFAs in Phase 2 scale-up districts. A Zambia
SMGL lead traveled to Afghanistan and assisted
the USAID Mission to incorporate lessons learned
from the SMGL approach into their MNH strategic
plan. SMGL country staff prepared posters and
presented at the yearly SMGL team-building
meetings, and staff members were encouraged to
submit abstracts and present at global MNH
meetings.

Country ownership. District health leaders
in Uganda reported high levels of ownership of
SMGL and cited the addition of key inputs as stra-
tegic: filling human resource gaps; strengthening
referral systems; expanding the number of

CEmONC facilities; improving the supply of blood
for transfusion; mentoring health personnel; and
increasing demand and access through VHTs,
transportation vouchers, and community cham-
pions. SMGL also influenced national planning
and budgeting for maternal health: the Wage Bill
included allowances to support doctors working
at health center IVs located in rural areas based
on SMGL’s remuneration approach; nearly
75% of the midwives hired by SMGL were picked
up by the MOH; additional midwifery training was
provided for enrolled nurses; and the voucher
program laid the groundwork for a national pro-
gram.29 Lessons learned from the SMGL approach
were incorporated into the Global Financing
Facility Investment Case,72 the WHO Quality,
Equity, Dignity initiative country plan, and
USAID requests for assistance and contracts.
Between these initiatives, over half of the
Ugandan population will be covered by a district
health systems strengthening approach by 2020.
(See the articles by Healey et al.71 and Palaia et
al.73 from the SMGL supplement.)

In Zambia, preexisting CDC cooperative
agreements with provinces and district-support
from CDC and USAID implementing partners
enabled early leveraging of funds and increased
district ownership of SMGL. SMGL worked with
other donors, the Swedish International Devel-
opment Cooperation Agency (SIDA) and the UK
Department for International Development, to
carry out direct government-to-government
funding to provincial and district public health
systems through the Reproductive, Maternal,
Newborn, Adolescent Health and Nutrition
Continuum of Care Program, blanketing 6 of
10 provinces. With this partnership alone, over
50% of the Zambian population is covered by
projects informed by the SMGL systems
approach.17 (See the article by Healey et al. from
the SMGL supplement.71)

What Were the Main Challenges?
The initial 1-year time frame. Frustration was
generated when SMGL funding was guaranteed
for only 1 year with subsequent support based on
achievement of unprecedented reductions in
maternal mortality within a highly compressed
time frame. At the end of Phase 1 implementation
(June 2013) and before results from the
Phase 1 endline studies were available (December
2013), host countries and implementing partners
were without SMGL funds. Yet they were
expected to continue with interventions while a

In both Uganda
and Zambia,
SMGL led to
improvements in
tracking routine
service delivery
indicators as part
of the national
health
management and
information
systems.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S21

decision on continuation was made. This 6-month
period from July to December 2013 was chaotic.
Any future systems approach focused on maternal
and newborn mortality reduction should commit
to a minimum of 5 years of support from the out-
set.29 (See the article by Palaia et al. from the
SMGL supplement.73)

The heavy management burden. SMGL
was a partnership (all U.S. government) within a
partnership (countries, a global corporation, non-
governmental organizations, and a professional
society). Each partner had a different bottom line,
constituency, funding timeline, requirements,
and restrictions that all needed to be forged into a
dynamic force for change. The positive driver was
the ongoing commitment of all partners and
stakeholders to dramatically reduce maternal
deaths. When the SMGL Leadership Council was
recruiting additional countries for SMGL at the
end of Phase 1, “management burden” was cited
by USAID Mission directors and CDC country
office directors as their main concern and ration-
ale for not engaging. A simpler management
structure where partnerships provide direct-to-
government support with appropriate oversight
and ample technical assistance might produce
similar results; it might also accelerate country
self-sufficiency and increase value for money
by decreasing implementing partner overhead
charges. At the same time, the diversity of SMGL
partners encouraged innovation and enabled
access to a wide array of expertise and experience.

Erratic funding. Because of the complexity
of the partnership and its myriad resource
streams, funding to the implementing partners
in both countries was profoundly delayed for
several periods during Phase 2. These lapses in
funding were the result of prolonged U.S. gov-
ernment procurement processes, changes in
funding mechanisms, and delays in disburse-
ments from agency headquarters to country
offices. If public–private partnerships are in-
creasingly used to advance the goals of U.S. gov-
ernment agencies, streamlining funding for
these endeavors will be needed to increase flexi-
bility and responsiveness and to preserve mo-
mentum. Smaller amounts of reliable funding
are easier to manage than larger tranches of
unpredictable financial support.

What Were Some of the Unexpected Effects?
Having a range of stakeholders participating in
SMGL created a think-tank atmosphere that
brought together people with varied talents:

obstetricians, midwives, nurses, communications
specialists, epidemiologists, and district medical
and health officers. It also led to collective yearly
planning and country budget creation. In many
of the routine implementing partner meetings,
organizations would share tasks as well as ideas
that crossed bureaucratic and competitive bar-
riers. The bold goal of a rapid 50% reduction in
maternal mortality fostered a collaborative “all
hands on deck” spirit that inspired district leader-
ship and partners alike.

SMGL’s insistence on capturing, analyzing,
and reporting all maternal deaths resulted in
strengthened data gathering and interpretation
by district teams. District-level data were pre-
sented and critically reviewed by district M&E staff
at routine provincial and regional epidemiological
meetings. Results were compared within the prov-
inces and among the different project sites, and
served as a motivating factor for good performers
and as a call for improvement among less success-
ful districts. The heightened appreciation of the
need for quality mortality data accelerated the
rollout and practice of maternal and perinatal
death surveillance and response in both countries.
In Zambia, the district commissioner, as the chair
of the audit committee, was made responsible for
reporting surveillance and response results locally
and at the provincial level. This high-level owner-
ship of data was immediately replicated on a
national basis and had the effect of positioning
maternal mortality not just as a health concern
but also as a broader social issue, bringing in other
sectors of government and traditional leaders to
grapple with and be accountable for preventing
maternal mortality.

Better birth planning, involvement of men, and
increased community demand for facility deliveries
required leaders to raise awareness and address
community concerns in order to change cultural
norms. Involvement of chiefs and traditional lead-
ers in Zambia and local councils and religious lead-
ers in Uganda created “change champions” who
took on these challenges. However, qualitative
research by Greeson et al.74 identified punitive
actions by Zambian village chiefs and headmen,
such as fining a husband a goat if he did not provide
a sufficient reason for why his wife delivered at
home. Researchers suggested that negative unin-
tended consequences are possible by-products of a
“big push” endeavor where pressure to succeed is
high.74 These “disciplinary” actions were not
endorsed by SMGL or the MOH, but they do repre-
sent a traditional approach by cultural leaders to
induce social change in their communities.

Future systems
approaches
focused on
maternal and
newborn mortality
reduction should
commit to a
minimum of 5
years of support.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S22

What Are the Main Recommendations
Coming Out of the SMGL Experience?
Given the complexity of the SMGL initiative,
extracting lessons learned and turning them into
a few salient recommendations is challenging.
The following points are put forward in support
of SMGL’s theory of change and organizing
principles:

1. Create a culture of zero tolerance for preven-
table maternal and newborn deaths at all
strata of society including parliamentarians
and their constituents.

2. Follow key organizing principles by address-
ing all 3 delays with interventions that are
context-specific and time-bound (e.g., setting
a 2-hour ‘time-to-service’ limit for complica-
tions and focusing on labor, delivery, and
72 hours postpartum).

3. Assess the gaps in the existing maternity care
safety net, created by both public and private
providers, in the public health catchment
area of interest (e.g., district, woreda, county,
local government area).

4. Ensure district-level capacity building around
planning, execution, and evaluation; consider
working in contiguous areas to achieve
economies of scale, reduce management bur-
den, and facilitate greater coordination.

5. Support the local health system; work across
the district or relevant administrative units to
reinforce the system from communities to
health centers to hospitals in order to provide
equitable lifesaving care and support for
mothers and newborns, and by extension,
other community members.

6. Sensitize and mobilize community change
agents to accelerate normative change but be
aware of potential unintended consequences
of a “big push” effort.

7. Count, analyze, and report all maternal and
perinatal deaths and cause of death.

CONCLUSIONS
While a 50% reduction in maternal deaths was not
achieved during the initiative, the 44% decrease in
MMR in Ugandan SMGL-supported facilities and
districts, the 38% decrease in Zambian SMGL-
supported facilities, and the 41% decrease in
Zambian SMGL districts were substantial. There
was a marked increase in facility deliveries in both
countries and also in population cesarean delivery

rates: a 71% increase (5.3% to 9.0%) in Uganda
and a 79% increase (2.7% to 4.8%) in Zambia.
Perinatal health outcomes were small but signifi-
cant: the perinatal mortality rate was reduced by
13% in SMGL-supported facilities in Uganda and
by 26% in Zambia. The SMGL goal for reduction
of newborn deaths (30%) was not achieved in
Zambia or Uganda.

Still at question is whether the SMGL health
systems strengthening approach to addressing the
3 delays will be adopted or adapted to other coun-
try contexts and implemented by MOHs, donors,
and multilaterals. Clearly, the level of manage-
ment burden is high, and partners, especially
bilateral donors, are traditionally not structured
to be nimble, proactive, or inventive. Yet several
global endeavors could benefit from endorsing
the SMGL approach. For example, with expansion
of the number of Global Financing Facility coun-
tries and GFF emphasis on results-based financ-
ing, having a ready approach to improving
effective coverage (range plus quality) could
accelerate GFF impact. Similarly, the district
health systems strengthening approach dovetails
closely with the objectives and goals of the WHO
Quality, Equity, and Dignity initiative.

SMGL was a bold attempt to show that mater-
nal mortality could be reduced significantly in
developing countries over a few years of strategic,
synergistic programming. It was inspired by the
progress achieved by other U.S. government
global initiatives that showed how high-level po-
litical leadership, focused public attention,
evidence-based demand- and supply-side inter-
ventions, a broad coalition of stakeholders, and
strong M&E could achieve impressive results in a
short time. For many, it was an opportunity to
change the narrative around the serious prob-
lems pregnant women face in the developing
world.

Acknowledgments: Over the last 7 years, the Saving Mothers, Giving
Life initiative has been a labor of love for the SMGL Working Group
represented by many, many stakeholders across the globe. We are
grateful for the energy, commitment, and inspiration provided by the
SMGL Partners; the Governments of Uganda, Zambia, and Nigeria;
the central and provincial MOH leads and the district health teams
and providers; the community health workers (SMAGs and VHTs);
and the implementing partners. The driving force behind SMGL has
been the desire to end preventable maternal mortality, starting by
halving it in 5 years. The survival and well-being of the women and
babies of Uganda, Zambia, and Nigeria have been and remain our
motivation.

Funding: Saving Mothers, Giving Life implementation was primarily
funded by the Office of the Global AIDS Coordinator, the U.S. Agency
for International Development (USAID), Washington, DC, the Centers
for Disease Control and Prevention (CDC), Atlanta, Georgia, Merck for
Mothers, and Every Mother Counts. The funding agencies had no
influence or control over the content of this article.

Several global
endeavors, such
as the Global
Financing Facility
and the WHO
Quality, Equity,
and Dignity
initiative, could
benefit from
endorsing the
SMGL district
health systems
strengthening
approach.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S23

Disclaimer: The opinions expressed herein are those of the authors and
do not necessarily reflect the views of the United States Government.

Competing Interests: None declared.

REFERENCES
1. World Health Organization (WHO). Trends in Maternal Mortality:

1990 to 2015. Estimates by WHO, UNICEF, UNFPA, World Bank
Group and the United Nations Population Division. Geneva: WHO;
2015. http://apps.who.int/iris/bitstream/10665/194254/1/
9789241565141_eng.pdf?ua=1. Accessed December 13, 2018.

2. United Nations (UN). The Millennium Development Goals Report
2015. New York: UN; 2015. http://www.un.org/millenniumgoals/
2015_MDG_Report/pdf/MDG%202015%20rev%20(July%201).
pdf. Accessed December 13, 2018.

3. Shiffman J. Generating political priority for maternal mortality
reduction in 5 developing countries. Am J Public Health. 2007;97
(5):796–803. CrossRef. Medline

4. Organisation for Economic Co-operation and Development (OECD).
Gender Equality in Education, Employment and Entrepreneurship:
Final Report to the MCM 2012. Meeting of the OECD Council at
Ministerial Level, Paris, May 23–24, 2012. Paris: OECD; 2012.
http://www.oecd.org//employment/50423364.pdf. Accessed
April 9, 2018.

5. Miller S, Belizán JM. The true cost of maternal death: individual
tragedy impacts family, community and nations. Reprod Health.
2015;12(1):56. CrossRef. Medline

6. Bazile J, Rigodon J, Berman L, et al. Intergenerational impacts of
maternal mortality: qualitative findings from rural Malawi. Reprod
Health. 2015;12(suppl 1):S1. CrossRef. Medline

7. Saving Mothers, Giving Life (SMGL). Making Pregnancy and
Childbirth Safer in Uganda and Zambia. Washington, DC: SMGL;
2013. https://www.savingmothersgivinglife.org/docs/SMGL_
Annual_Report_2013.pdf. Accessed April 17, 2018.

8. Alkema L, Chou D, Hogan D, et al; United Nations Maternal
Mortality Estimation Inter-Agency Group collaborators and technical
advisory group. Global, regional, and national levels and trends in
maternal mortality between 1990 and 2015, with scenario-based
projections to 2030: a systematic analysis by the UN Maternal
Mortality Estimation Inter-Agency Group. Lancet. 2016;387
(10017):462–474. CrossRef. Medline

9. The World Bank. Lifetime risk of maternal death. The World Bank
DataBank website. https://data.worldbank.org/indicator/SH.
MMR.RISK. Published 2015. Accessed December 13, 2018.

10. Devine S, Taylor G. Every Child Alive: The Urgent Need to End
Newborn Deaths. Geneva: United Nations Children’s Fund; 2018.
https://www.unicef.org/eca/reports/every-child-alive. Accessed
December 13, 2018.

11. Saving Mothers, Giving Life (SMGL). Saving Mothers’ Lives Zambia:
Operational Plan October 2011–December 2012. Washington, DC:
SMGL; 2011.

12. Saving Mothers, Giving Life (SMGL). Saving Mothers, Giving Life
Uganda: Operational Plan January 2012–February 2013.
Washington, DC: SMGL; 2011.

13. Zambia Ministry of Health (MOH). Roadmap for Accelerating
Reduction of Maternal, Newborn and Child Mortality: 2013–2016.
Lusaka: MOH; 2013. https://extranet.who.int/nutrition/gina/
sites/default/files/ZMB%202013%20Reduction%20of%
20Maternal%20Newborn%20and%20Child%20Mortality.pdf.
Accessed January 4, 2019.

14. Uganda Ministry of Health (MOH). Roadmap for Accelerating the
Reduction of Maternal and Neonatal Mortality and Morbidity in
Uganda: 2007–2015. Kampala: MOH; 2008. http://www.
nationalplanningcycles.org/sites/default/files/country_docs/

Uganda/uganda_mnh_roadmap_2007-2015.pdf. Accessed
January 4, 2019.

15. Esamai F, Nangami M, Tabu J, Mwangi A, Ayuku D, Were E. A sys-
tem approach to improving maternal and child health care delivery
in Kenya: innovations at the community and primary care facilities
(a protocol). Reprod Health. 2017;14(1):105. CrossRef. Medline

16. Thaddeus S, Maine D. Too far to walk: maternal mortality in context.
Soc Sci Med. 1994;38(8):1091–1110. CrossRef. Medline

17. Serbanescu F, Goldberg HI, Danel I, et al. Rapid reduction of mater-
nal mortality in Uganda and Zambia through the saving mothers,
giving life initiative: results of year 1 evaluation. BMC Pregnancy
Childbirth. 2017;17(1):42. CrossRef. Medline

18. Awoonor-Williams JK, Sory EK, Nyonator FK, Phillips JF, Wang C,
Schmitt ML. Lessons learned from scaling up a community-based
health program in the Upper East Region of northern Ghana. Glob
Health Sci Pract. 2013;1(1):117–133. CrossRef. Medline

19. Combs Thorsen V, Sundby J, Malata A. Piecing together the maternal
death puzzle through narratives: the three delays model revisited.
PLoS One. 2012;7(12):e52090. CrossRef. Medline

20. Waiswa P, Kallander K, Peterson S, Tomson G, Pariyo GW. Using
the three delays model to understand why newborn babies die in
eastern Uganda. Trop Med Int Health. 2010;15(8):964–972.
CrossRef. Medline

21. Save the Children. Applying the Three Delays Model: Improving
Access to Care for Newborns with Danger Signs. Washington, DC:
Save the Children; 2013. https://www.healthynewbornnetwork.
org/hnn-content/uploads/Applying-the-three-delays-model_Final.
pdf. Accessed January 4, 2019.

22. Institute of Development Studies. Transport, the missing link? A
catalyst for achieving the MDG. id21 insights. 2006;(63). http://lib.
icimod.org/record/12551/files/4075.pdf. Accessed January 4,
2019.

23. Binagwaho A, Nutt CT, Uwaliraye P, Wagner CM, Nyemazi JP.
Taking health systems research to the district level: a new approach to
accelerate progress in global health. BMC Health Serv Res. 2013;13
(suppl 2):S11. CrossRef. Medline

24. Somanje H, Pathé S, Dramé BB, Mwikisa-Ngenda C. Health systems
strengthening: improving district health service delivery and commu-
nity ownership and participation. Afr Health Mon. 2012;15:48.
http://www.aho.afro.who.int/en/ahm/issue/15/reports/health-
systems-strengthening-improving-district-health-service-delivery-
and. Accessed December 14, 2018.

25. Bellagio District Public Health Workshop Participants. Public Health
Performance Strengthening at Districts: Rationale and Blueprint for
Action. Proceedings of a Bellagio Conference, November 21–24,
2016. http://www.who.int/alliance-hpsr/bellagiowhitepaper.pdf.
Published in 2017. Accessed July 10, 2018.

26. Hanson C, Cox J, Mbaruku G, et al. Maternal mortality and distance
to facility-based obstetric care in rural southern Tanzania: a second-
ary analysis of cross-sectional census data in 226 000 households.
Lancet Glob Health. 2015;3(7):e387–e395. CrossRef. Medline

27. World Health Organization (WHO); UNFPA; UNICEF; Mailman
School of Public Health. Monitoring Emergency Obstetric Care: A
Handbook. Geneva: WHO; 2009. https://www.who.int/
reproductivehealth/publications/monitoring/9789241547734/
en/. Accessed December 14, 2018.

28. World Health Organization (WHO). World Health Statistics 2010.
Geneva: WHO; 2010. https://www.who.int/whosis/whostat/
2010/en/. Accessed December 14, 2018.

29. Kruk M, Galea S, Grepin K, Rabkin M. External Evaluation of Saving
Mothers Giving Life: Final Report. New York: Columbia University
Mailman School of Public Health; 2013. http://pdf.usaid.gov/pdf_
docs/pbaaf149.pdf. Accessed June 18, 2018.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S24

30. Saving Mothers, Giving Life (SMGL). Saving Mothers, Giving Life:
Program Update. Washington, DC: SMGL; 2013. http://www.
savingmothersgivinglife.org/docs/SMGL_PrgmUpdate_Sept_
2013.pdf. Accessed April 17, 2018.

31. Uganda Bureau of Statistics (UBOS); ICF International. Uganda
Demographic and Health Survey 2011. Kampala, Uganda: UBOS
and ICF International; 2012. https://dhsprogram.com/
publications/publication-fr264-dhs-final-reports.cfm. Accessed
December 14, 2018.

32. Zambia Ministry of Health (MOH). Zambia National Health Strategic
Plan: 2011–2015. Lusaka: MOH; 2011. https://www.uhc2030.
org/fileadmin/uploads/ihp/Documents/Country_Pages/Zambia/
ZambiaNHSP2011to2015final.pdf. Accessed December 14, 2018.

33. Uganda Ministry of Health (MOH). Health Sector Strategic Plan III:
2010/11–2014/15. Kampala: MOH; 2010. http://www.health.
go.ug/docs/HSSP_III_2010.pdf. Accessed December 14, 2018.

34. Centers for Disease Control and Prevention (CDC). Saving Mothers,
Giving Life: Maternal and Perinatal Outcomes in Health Facilities.
Phase 1 Monitoring and Evaluation Report. Atlanta, GA: CDC;
2014. https://www.cdc.gov/reproductivehealth/global/
publications/pdfs/maternalandperinataloutcomes.pdf. Accessed
December 14, 2018.

35. Saving Mothers, Giving Life (SMGL). Zambia Health Facility
Assessment: Baseline To Endline Comparison. Washington, DC:
SMGL; 2018. http://www.savingmothersgivinglife.org/docs/
HFABaseEnd_Zambia_FinalVersionReport.pdf. Accessed July 10,
2018.

36. Saving Mothers, Giving Life (SMGL) Uganda Team. Uganda Health
Facility Assessment: Baseline to Endline Comparison. Washington,
DC: SMGL; 2018. http://www.savingmothersgivinglife.org/our-
work/uganda.aspx. Accessed January 17, 2019.

37. Lawn J, McCarthy B, Rae Ross S. The Heathy Newborn. Atlanta, GA:
CARE, Centers for Disease Control and Prevention; 2001. https://
www.k4health.org/toolkits/pc-mnh/healthy-newborn-reference-
manual-program-managers. Accessed December 14, 2018.

38. Serbanescu F, Clark T, Goodwin M, et al.; Saving Mothers, Giving
Life Working Group. Impact of the Saving Mothers, Giving Life
approach on decreasing maternal and perinatal deaths in Uganda
and Zambia. Glob Health Sci Pract. 2019;7(suppl 1):S27–S47.
CrossRef

39. Saving Mothers, Giving Life (SMGL). Zambia Ethnographic
Appraisal of Maternal Health Seeking Behavior. Lusaka: University
of Zambia School of Medicine; 2013. http://www.
savingmothersgivinglife.org/docs/Ethnographic-study-Exec-Summ-
1-6-14.pdf. Accessed July 11, 2018.

40. Schmitz MM, Serbanescu F, Kamara V, et al.; Saving Mothers,
Giving Life Working Group. Did Saving Mothers, Giving Life expand
timely access to lifesaving care in Uganda? A spatial district-level
analysis of travel time to emergency obstetric and newborn care.
Glob Health Sci Pract. 2019;7(suppl 1):S151–S167. CrossRef

41. Serbanescu F, Goodwin MM, Binzen S, et al. Addressing the first
delay through the Saving Mothers, Giving Life initiative in Uganda
and Zambia: approaches and results for increasing demand for fa-
cility delivery services. Glob Heal Sci Pr. 2019;7(suppl 1):S48–S67.
CrossRef

42. Ngoma T, Assimwe AR, Mukasa J, et al.; Saving Mothers, Giving Life
Working Group. Addressing the second delay in Saving Mothers,
Giving Life districts in Uganda and Zambia: reaching appropriate
maternal care in a timely manner. Glob Health Sci Pract. 2019;7
(suppl 1):S68–S84. CrossRef

43. Morof D, Serbanescu F, Goodwin M, et al; Saving Mothers, Giving
Life Working Group. Addressing the third delay in Saving Mothers,
Giving Life districts in Uganda and Zambia: ensuring adequate and
appropriate facility-based maternal and perinatal health care. Glob
Health Sci Pract. 2019;7(suppl 1):S85–S103. CrossRef

44. Saving Mothers, Giving Life: a year of results and lessons learned
. Washington, DC: Center for Strategic and International
Studies; 2014. https://www.csis.org/events/saving-mothers-
giving-life-year-results-and-lessons-learned. Accessed July 11, 2018.

45. Saving Mothers, Giving Life: how to optimize and expand .
Washington, DC: Center for Strategic and International Studies;
2014. https://www.csis.org/blogs/smart-global-health/saving-
mothers-giving-life-how-optimize-and-expand. Accessed July 11,
2018.

46. Saving Mothers, Giving Life (SMGL). 2015 Mid-Initiative Report:
Reducing Maternal Mortality in Sub-Saharan Africa, Delivering
Results. Washington, DC: SMGL; 2015. http://www.savingmothers
givinglife.org/docs/SMGL-mid-initiative-report.pdf. Accessed
July 11, 2018.

47. Sensalire S, Isabirye P, Karamagi E, Byabagambi J, Rahimzai M,
Calnan J; Saving Mothers, Giving Life Working Group. Saving
Mothers, Giving Life approach for strengthening health systems to
reduce maternal and newborn deaths in 7 scale-up districts in
Northern Uganda. Glob Health Sci Pract. 2019;7(suppl 1):S168–
S187. CrossRef

48. Zambia Central Statistical Office; University of Zambia Department
of Population Studies; ICF International. Saving Mothers, Giving Life:
2017 Zambia Maternal Mortality Endline Census in Selected
Districts. Rockville: ICF International; 2018. http://www.
savingmothersgivinglife.org/docs/2017-SMGL-Endline-Census-
Final-Report.pdf. Accessed July 11, 2018.

49. Ngoma-Hazemba A, Soud F, Hamomba L, Silumbwe A,
Munakampe MN, Spigel L; Saving Mothers, Giving Life Working
Group. Community perceptions of a 3-delays model intervention: a
qualitative evaluation of Saving Mothers, Giving Life in Zambia.
Glob Health Sci Pract. 2019;7(suppl 1):S139-S150. CrossRef

50. Sacks E, Vail D, Austin-Evelyn K, et al. Factors influencing modes of
transport and travel time for obstetric care: a mixed methods study in
Zambia and Uganda. Health Policy Plan. 2016;31(3):293–301.
CrossRef. Medline

51. Kruk ME, Rabkin M, Grépin KA, et al. “Big push” to reduce maternal
mortality in Uganda and Zambia enhanced health systems but
lacked a sustainability plan. Health Aff. 2014;33(6):1058–1066.
CrossRef. Medline

52. Centers for Disease Control and Prevention (CDC). Saving Mothers,
Giving Life: SMGL Phase I Monitoring and Evaluation Findings:
Executive Summary. Atlanta, GA: CDC, U.S. Department of Health
and Human Services; 2014. http://www.savingmothersgivinglife.
org/docs/SMGL_Executive_Summary.pdf. Accessed July 11, 2018.

53. Futures Group, Health Policy Project. Investments to Accelerate
Reductions in Maternal Mortality: Findings from Expenditure Studies
in Uganda and Zambia for the Saving Mothers, Giving Life
Partnership. Washington, DC: Futures Group; 2014. http://www.
savingmothersgivinglife.org/docs/USAID-SMGL-Expenditure-
Study-Executive-Summary.pdf. Accessed July 11, 2018.

54. Rosenstein MG, Romero M, Ramos S. Maternal mortality in
Argentina: a closer look at women who die outside of the health sys-
tem. Matern Child Health J. 2008;12(4):519–524. CrossRef.
Medline

55. Campbell OMR, Graham WJ; Lancet Maternal Survival Series steer-
ing group. Strategies for reducing maternal mortality: getting on with
what works. Lancet. 2006;368(9543):1284–1299. CrossRef.
Medline

56. US Institute of Medicine Committee on Improving Birth Outcomes;
Bale JR, Stoll BJ, Lucas AO, eds. Reducing maternal mortality and
morbidity. In: Improving Birth Outcomes: Meeting the Challenge in
the Developing World. Washington, DC: National Academies Press;
2003. Medline

57. Saving Mothers, Giving Life (SMGL). 2018 Final Report: Results of a
Five-Year Partnership to Reduce Maternal and Newborn Mortality.

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S25

Washington, DC: SMGL; 2018. http://www.savingmothersgiving
life.org/docs/smgl-final-report.pdf. Accessed July 11, 2018.

58. Ekirapa-Kiracho E, Tetui M, Bua J, et al. Maternal and neonatal
implementation for equitable systems. A study design paper.
Glob Health Action. 2017;10(suppl 4):1346925. CrossRef.
Medline

59. Saving Mothers, Giving Life Phase 1 in Uganda: cost effectiveness
analysis. USAID/Uganda Monitoring, Evaluation and Learning
Program. AID-617-c-13-00007. SoCha.IIc. 2016.

60. Colbourn T, Nambiar B, Bondo A, et al. Effects of quality improve-
ment in health facilities and community mobilization through wom-
en’s groups on maternal, neonatal and perinatal mortality in three
districts of Malawi: MaiKhanda, a cluster randomized controlled
effectiveness trial. Int Health. 2013;5(3):180–195. CrossRef.
Medline

61. Mangham-Jefferies L, Pitt C, Cousens S, Mills A, Schellenberg J. Cost-
effectiveness of strategies to improve the utilization and provision of
maternal and newborn health care in low-income and lower-middle-
income countries: a systematic review. BMC Pregnancy Childbirth.
2014;14(1):243. CrossRef. Medline

62. Bhutta ZA, Das JK, Bahl R, et al; Lancet Newborn Interventions
Review Group; Lancet Every Newborn Study Group; Lancet Every
Newborn Study Group. Can available interventions end preventable
deaths in mothers, newborn babies, and stillbirths, and at what cost?
Lancet. 2014;384(9940):347–370. CrossRef. Medline

63. Horton S, Wu DCN, Brouwer E, Levin C. Methods and results for
systematic search, cost and cost-effectiveness analysis. 2015. http://
dcp-3.org/resources/methods-and-results-systematic-search-cost-
and-cost-effectiveness-disease-control. Accessed December 14,
2018.

64. Horton S, Levin C. Cost-effectiveness of interventions for reproductive,
maternal, neonatal, and child health. In: Black RE, Laxminarayan R,
Temmerman M, Walker N, eds. Reproductive, Maternal, Newborn,
and Child Health: Disease Control Priorities. 3rd ed, vol 2.
Washington, DC: World Bank; 2016. CrossRef. Medline

65. Wilunda C, Tanaka S, Putoto G, Tsegaye A, Kawakami K. Evaluation
of a maternal health care project in South West Shoa Zone, Ethiopia:
before-and-after comparison. Reprod Health. 2016;13(1):95.
CrossRef. Medline

66. Rae Ross S, Uddin Ahmed J, McLellan I, Campbell W. USAID/
Bangladesh: Final Evaluation of the MaMoni Integrated Safe
Motherhood, Newborn Care and Family Planning Project.
Washington, DC: GH Tech Bridge III Project; 2013. https://pdf.
usaid.gov/pdf_docs/pdacy101.pdf. Accessed June 30, 2018.

67. Berhan Y, Berhan A. Commentary: Reasons for persistently high
maternal and perinatal mortalities in Ethiopia: Part III-Perspective of
the “three delays” model. Ethiop J Health Sci. 2014;24(suppl):137–
148. CrossRef. Medline

68. Darroch JE. Adding It Up: Investing in Contraception and Maternal
and Newborn Health, 2017—Estimation Methodology. New York:
Guttmacher Institute; 2018. https://www.guttmacher.org/sites/
default/files/report_pdf/adding-it-up-2017-estimation-
methodology.pdf. Accessed July 2, 2018.

69. Sagaon-Teyssier L, Singh S, Dongmo-Nguimfack B, Moatti J-P.
Affordability of adult HIV/AIDS treatment in developing countries:
modelling price determinants for a better insight of the market func-
tioning. J Int AIDS Soc. 2016;19(1):20619. CrossRef. Medline

70. Johns B, Hangoma P, Atuyambe L, et al.; Saving Mothers, Giving Life
Working Group. The costs and cost-effectiveness of a district-
strengthening strategy to mitigate the 3 delays to quality maternal
health care: results from Uganda and Zambia. Glob Health Sci Pract.
2019;7(suppl 1):S104–S122. CrossRef

71. Healey J, Conlon CM, Malama K, et al,; Saving Mothers, Giving Life
Working Group. Sustainability and scale of the Saving Mothers,
Giving Life approach in Uganda and Zambia. Glob Health Sci Pract.
2019;7(suppl 1):S188–S206. CrossRef

72. Uganda Ministry of Health (MOH). Investment Case for
Reproductive, Maternal, Newborn, Child, and Adolescent Health
Sharpened Plan for Uganda. Kampala: MOH; 2016. https://www.
globalfinancingfacility.org/sites/gff_new/files/documents/
Uganda-Investment-Case.pdf. Accessed May 25, 2018

73. Palaia A, Spigel L, Cunningham M, Yang A, Hooks T, Ross S;
Saving Mothers, Giving Life Working Group. Saving lives to-
gether: a qualitative evaluation of the Saving Mothers, Giving Life
public-private partnership. Glob Health Sci Pract. 2019;7
(suppl 1):S123–S138. CrossRef

74. Greeson D, Sacks E, Masvawure TB, et al. Local adaptations to a
global health initiative: penalties for home births in Zambia. Health
Policy Plan. 2016;31(9):1262–1269. CrossRef. Medline

Peer Reviewed

Received: October 31, 2018; Accepted: December 11, 2018

Cite this article as: Conlon CM, Serbanescu F, Marum L, Healey J, LaBrecque J, Hobson R, et al; Saving Mothers, Giving Life Working Group. Saving
Mothers, Giving Life: It takes a system to save a mother. Glob Health Sci Pract. 2019;7(suppl 1):S6-S26. https://doi.org/10.9745/GHSP-D-18-00427

© Conlon et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited. To view a
copy of the license, visit http://creativecommons.org/licenses/by/4.0/. When linking to this article, please use the following permanent link: https://
doi.org/10.9745/GHSP-D-18-00427

It Takes a System to Save a Mother www.ghspjournal.org

Global Health: Science and Practice 2019 | Volume 7 | Supplement 1 S26

  • fig1
  • fig2
Writerbay.net

Do you need help with this or a different assignment? In a world where academic success does not come without efforts, we do our best to provide the most proficient and capable essay writing service. After all, impressing professors shouldn’t be hard, we make that possible. If you decide to make your order on our website, you will get 15 % off your first order. You only need to indicate the discount code GET15.


Order a Similar Paper Order a Different Paper