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Travel of pregnant women in emergency situations to hospital and maternal mortality in Lagos, Nigeria: A retrospective cohort study

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Introduction Prompt access to emergency obstetrical care (EmOC) reduces the risk of maternal mortality. We assessed institutional maternal mortality by distance and travel time for pregnant women with obstetrical emergencies in Lagos State, Nigeria. Methods We conducted a facility-based retrospective cohort study across 24 public hospitals in Lagos. Reviewing case notes of the pregnant women presenting between 1 November 2018 and 30 October 2019, we extracted socio-demographic, travel and obstetrical data. The extracted travel data were exported to Google Maps, where driving distance and travel time data were extracted. Multivariable logistic regression was conducted to determine the relative influence of distance and travel time on maternal death. Findings Of 4181 pregnant women with obstetrical emergencies, 182 (4.4%) resulted in maternal deaths. Among those who died, 60.3% travelled ≤10 km directly from home, and 61.9% arrived at the hospital ≤30 mins. The median distance and travel time to EmOC was 7.6 km (IQR 3.4–18.0) and 26 mins (IQR 12–50). For all women, travelling 10–15 km (2.53, 95% CI 1.27 to 5.03) was significantly associated with maternal death. Stratified by referral, odds remained statistically significant for those travelling 10–15 km in the non-referred group (2.48, 95% CI 1.18 to 5.23) and for travel ≥120 min (7.05, 95% CI 1.10 to 45.32). For those referred, odds became statistically significant at 25–35 km (21.40, 95% CI 1.24 to 36.72) and for journeys requiring travel time from as little as 10–29 min (184.23, 95% CI 5.14 to 608.51). Odds were also significantly higher for women travelling to hospitals in suburban (3.60, 95% CI 1.59 to 8.18) or rural (2.51, 95% CI 1.01 to 6.29) areas. Conclusion Our evidence shows that distance and travel time influence maternal mortality differently for referred women and those who are not. Larger scale research that uses closer-to-reality travel time and distance estimates as we have done, rethinking of global guidelines, and bold actions addressing access gaps, including within the suburbs, will be critical in reducing maternal mortality by 2030.
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Banke- ThomasA, etal. BMJ Global Health 2022;7:e008604. doi:10.1136/bmjgh-2022-008604
Travel of pregnant women in emergency
situations to hospital and maternal
mortality in Lagos, Nigeria: a
retrospective cohort study
Aduragbemi Banke- Thomas ,1,2,3 Cephas Ke- on Avoka ,4
Uchenna Gwacham- Anisiobi ,5 Olufemi Omololu,6 Mobolanle Balogun ,7
Kikelomo Wright,8 Tolulope Temitayo Fasesin,9 Adedotun Olusi,10
Bosede Bukola Afolabi ,3,9 Charles Ameh 11
Original research
To cite: Banke- ThomasA,
AvokaCK, Gwacham- AnisiobiU,
etal. Travel of pregnant women
in emergency situations to
hospital and maternal mortality
in Lagos, Nigeria: a retrospective
cohort study. BMJ Global Health
2022;7:e008604. doi:10.1136/
bmjgh-2022-008604
Handling editor Seye Abimbola
Additional supplemental
material is published online only.
To view, please visit the journal
online (http:// dx. doi. org/ 10.
1136/ bmjgh- 2022- 008604).
Received 21 January 2022
Accepted 19 April 2022
For numbered afliations see
end of article.
Correspondence to
Dr Aduragbemi Banke- Thomas;
a. bankethomas@ gre. ac. uk
© Author(s) (or their
employer(s)) 2022. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published by
BMJ.
ABSTRACT
Introduction Prompt access to emergency obstetrical
care (EmOC) reduces the risk of maternal mortality. We
assessed institutional maternal mortality by distance
and travel time for pregnant women with obstetrical
emergencies in Lagos State, Nigeria.
Methods We conducted a facility- based retrospective
cohort study across 24 public hospitals in Lagos.
Reviewing case notes of the pregnant women presenting
between 1 November 2018 and 30 October 2019, we
extracted socio- demographic, travel and obstetrical
data. The extracted travel data were exported to Google
Maps, where driving distance and travel time data were
extracted. Multivariable logistic regression was conducted
to determine the relative inuence of distance and travel
time on maternal death.
Findings Of 4181 pregnant women with obstetrical
emergencies, 182 (4.4%) resulted in maternal deaths.
Among those who died, 60.3% travelled≤10 km directly
from home, and 61.9% arrived at the hospital≤30 mins.
The median distance and travel time to EmOC was 7.6 km
(IQR 3.4–18.0) and 26 mins (IQR 12–50). For all women,
travelling 10–15 km (2.53, 95% CI 1.27 to 5.03) was
signicantly associated with maternal death. Stratied
by referral, odds remained statistically signicant for
those travelling 10–15 km in the non- referred group
(2.48, 95% CI 1.18 to 5.23) and for travel≥120 min (7.05,
95% CI 1.10 to 45.32). For those referred, odds became
statistically signicant at 25–35 km (21.40, 95% CI 1.24 to
36.72) and for journeys requiring travel time from as little
as 10–29 min (184.23, 95% CI 5.14 to 608.51). Odds were
also signicantly higher for women travelling to hospitals
in suburban (3.60, 95% CI 1.59 to 8.18) or rural (2.51,
95% CI 1.01 to 6.29) areas.
Conclusion Our evidence shows that distance and travel
time inuence maternal mortality differently for referred
women and those who are not. Larger scale research that
uses closer- to- reality travel time and distance estimates
as we have done, rethinking of global guidelines, and
bold actions addressing access gaps, including within the
suburbs, will be critical in reducing maternal mortality by
2030.
INTRODUCTION
Globally, 295 000 maternal deaths occur
every year due to complications related to
WHAT IS ALREADY KNOWN ON THIS TOPIC
In sub- Saharan Africa, limited and conicting ev-
idence exists on the effect of travel time and dis-
tance on maternal mortality. The available evidence
is based on research conducted in rural areas which
used straight- line distances, assuming the women
went to the nearest facility or health worker ‘guesti-
mation’ of travel time.
WHAT THIS STUDY ADDS
Our ndings in this study using closer- to- reality
distance and travel time estimates showed that in
Lagos, almost two- thirds of maternal deaths oc-
cur among pregnant women who travelled≤10 km
directly from home and arrived at the hospital in
30 mins.
There was a higher likelihood of maternal deaths
with a 10–15 km distance and travel to a hospital
that principally served suburban and rural areas. The
odds of maternal death were statistically signicant
even when travel of 10–29 min was required for
those referred.
HOW THIS STUDY MIGHT AFFECT RESEARCH,
PRACTICE AND/OR POLICY
Functional health facilities must be available with-
in 10 km of every woman, with particular attention
placed in the suburbs. Across board, robust systems
are needed to support travel of pregnant women
from the community to a health facility with capac-
ity for emergency obstetrical care, with or without
referral.
The 2- hour benchmark recommended by the WHO
warrants a careful review with consideration given
to more symptom- specic thresholds, urbanicity
and recognition for the other delays that women
may experience, including at referring facilities.
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pregnancy and childbirth, including bleeding, hyper-
tension, infection and abortion.1 These complica-
tions also lead to some pregnant women delivering
babies in their third trimester who are born dead,
otherwise referred to as stillbirths. It is estimated that
approximately 2 million stillbirths occur annually.2
Approximately 70% of maternal deaths occur in sub-
Saharan Africa (SSA), with Nigeria alone accounting
for more than two- fifths of the global burden of
maternal deaths.1 For stillbirths, over 40% occurs in
SSA, with those occurring in Nigeria being about 10%
of the worldwide burden.2 At present, the consensus
strategy to reduce deaths related to pregnancy and
childbirth, re- emphasised by the Sustainable Devel-
opment Goals (SDGs), has predominantly focused
on increasing access to prompt emergency obstetrical
care (EmOC) provided by skilled health personnel
(online supplemental tables S1 and S2A,B).3 4 Avail-
able research evidence indicates that prompt access
to EmOC can lead to as much as a 50% reduction in
intra- facility maternal deaths and a 45%–75% reduc-
tion in stillbirths occurring after the onset of labour
but before birth.5 However, before a pregnant woman
arrives at a health facility, delays in the decision to
seek care (first delay), travel to reach appropriately
equipped health facilities (second delay) and delay
in receiving care (third delay) can increase the risk
of death of the pregnant woman or that of her yet
unborn child.6
In 2009, the WHO recommended that health facil-
ities be ‘available within 2–3 hours of travel for most
women’, highlighting that this is the time it takes
from onset of symptoms to death for a woman with
postpartum haemorrhage as a complication of child-
birth.4 Despite recognising the implication of travel
delays on maternal mortality, studies on association
of distance and travel time on maternal mortality are
limited. When such studies have been conducted in
SSA, they have been situated in rural settings and
focused on travel time or distance.7–10 It is critical for
studies attempting to understand the second delay
to assess both travel time and distance, as though
it is intuitive to expect both variables to increase or
decrease together, some pregnant women travel for
a long time despite living near a health facility.11
This is even more of an issue in urban and peri-
urban settings, where traffic, poor road conditions
and high population density are common features.11
Indeed, focus on these urban settings is crucial now
more than ever before as almost 40% of the projected
additional 2.5 billion urban residents globally are
expected to concentrate in Africa.12 Our objective
in this study was to assess the association between
distance, travel time and maternal mortality among
pregnant women who presented with obstetrical
emergencies at public hospitals in Nigeria’s most
urbanised state, Lagos.
METHODS
Study design
Our study was a retrospective cohort study of pregnant
women who presented as obstetrical emergencies at 1 of
the 24 public hospitals (20 non- apex referral and 4 apex
referral hospitals) in Lagos State (online supplemental
tables S3 and S4).4
Setting
Lagos State, located in the southwestern part of Nigeria,
has various geographical terrains (including land and
water) and settlement types (including a central metrop-
olis, suburbs, towns, slums and informal settlements)
(online supplemental table S3). While primarily urban,
the state has some rural parts in its extreme east and
west. The state has 20 local government areas (LGAs)
with population ranging from 117 542 (Ibeju- Lekki LGA)
to 11 456 783 (Alimosho LGA). Population across the
state was estimated to be about 26 million in 2019, with
researchers projecting the state’s population will triple by
the year 2050.13
The most recent national estimate of maternal
mortality ratio (MMR) in Nigeria is 917 per 100 000 live
births.1 However, there is no recent state- level MMR esti-
mate. In Lagos State, a ratio as high as 1050 (95% CI 894
to 1215) per 100 000 live births has been reported in one
of its urban slums.14 Institutional MMR between 987 to
2111 per 100 000 live births have also been estimated in
Lagos public hospitals. More than one- third of maternal
deaths are associated with delayed presentation of preg-
nant women at facilities.15
In Lagos, the most typical mode of transport is by
road. However, in many parts of the state, the road infra-
structure is poorly maintained, as evidenced by several
potholes that sometimes make some roads impassable.
Severe traffic congestions are common, with flooding
during the rainy season making conditions worse. Road
repair works are at best stopgaps and sometimes lead to
more travel disruptions.16–18
Public health facilities manage more than two- fifths of
all births in the state.19 However, many pregnant women
use and indeed prefer public hospitals for many reasons,
including the availability of 24/7 care, greater concen-
tration of highly skilled health personnel and equipment
and sometimes ‘free’ or reduced hospital cost.20 In emer-
gencies, many pregnant women travel to the hospitals
without health personnel support.11 If they require a
referral, the Lagos State Ambulance Service occasionally
help to transfer pregnant women between public hospi-
tals.11 19 However, its effectiveness for patient transfer is
limited by the traffic congestion and lack of willingness
among other commuters to give way to ambulances.21
Data sources
Data were extracted from patient records over 6 months
by the in- country research team, all of whom were quali-
fied medical doctors, including consultant obstetricians,
resident doctors and medical officers who had clinical
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BMJ Global Health
experience working in the obstetrical units of the hospi-
tals and were familiar with the patient records system in
Lagos public health facilities. All team members were
trained on using the pretested online data collection tool
and ethical procedures guiding the research.
Participants
In each hospital, we identified and included all preg-
nant women who presented with an obstetrical emer-
gency, because of themselves or their babies, between
1 November 2018 and 30 October 2019. Women who
had an obstetrical emergency while on admission in the
hospital were excluded, as their hospital journeys were
not deemed critical to the pregnancy outcomes of women
or their babies.
Variables
From the case notes, we extracted routinely reported
data on socio- demographic characteristics, obstetrical
history, travel to the hospital (including the day and
period- of- day of travel, street name of women’s self-
reported address, referring points of care, if any, and the
final facility of care), obstetrical complication, mode of
birth and pregnancy outcome. These data were collected
because they helped us understand key characteristics of
each included woman, allowed us to be able to map their
journeys in an emergency and establish the outcome of
care. All data apart from the pregnancy outcome were
treated as dependent variables.
We categorised obstetrical complications in the case
notes following WHO’s Monitoring EmOC guidelines,
which highlights five major complications: obstetrical
haemorrhage (antepartum or postpartum haemorrhage),
hypertensive disorders in pregnancy (pre- eclampsia
or eclampsia), pregnancy- related infections (sepsis),
pregnancy with an abortive outcome and prolonged/
obstructed labour (online supplemental table S1).4 We
categorised pregnancy complications outside these broad
categories, including premature rupture of membranes,
oligohydramnios, polyhydramnios, ectopic pregnancy,
footling breech, and previous surgical scar, as ‘other
complications’.
Additional data gathering involving the estimation
of driving distance and travel time using Google Maps
(Google, Mountain View, California, USA), which offers
closer- to- reality estimates compared with other commonly
used methods,22 were required to characterise travel of
pregnant women to the hospital fully. To achieve this,
we geo- located the place of residence, referral points
and destination facility for each woman in the applica-
tion. For undiscoverable addresses on Google Maps, we
contacted persons acquainted with the localities to check
for spelling errors and re- attempted to locate the street.
In cases where it was impossible to find specific travel
points of the women, we labelled the case as untrace-
able (4% of cases). For those with traceable journeys, we
extracted distance (in kilometres (km)) and travel time
(in minutes (mins)) from Google Map using its ‘typical
time of travel’ feature for the period- of- day of travel. We
used specific time slots to collect travel time estimates for
each period (09:00, 15:00, 18:00 and 21:00 for morning,
afternoon, evening, or night journeys, respectively). In
cases in which we could not tell the period- of- day of travel
(33% of cases), travel time was extracted for the after-
noon (15:00), as it was a mid- point estimate between the
two known travel peak periods in Lagos (06:30 and 11:30
(morning peak period) and 15:00 and 19:30 (evening
peak period)).23 We assumed that all used four- wheeled
motor vehicles for travel since these are widely used by
pregnant women in emergencies in SSA,24 25 and alterna-
tives like motorcycles and tricycles had been banned in
Lagos at the time of this study.11 26
For the dependent variable of maternal death, we
aligned with the 10th edition of the International Clas-
sification of Diseases which defines maternal mortality as
‘the death of a woman while pregnant or within 42 days
of termination of pregnancy, irrespective of the duration
and site of the pregnancy, from any cause related to or
aggravated by the pregnancy or its management, but not
from accidental or incidental causes’.27
Data analysis
Following data cleaning and validation, we calculated
causespecific case fatality rates and conducted descrip-
tive analysis for pertinent demographic, obstetrical, travel
and facility- related variables. In addition, we conducted a
comparative analysis of median distances and travel times
for pregnant women who travelled directly to the hospital
and those referred. We prioritised median values, as
these are known to be robust to outliers. We compared
median distances and travel times of actual paths to care
for referred pregnant women with an assumed scenario if
they travelled directly to the hospital. We also compared
travel distance and time for various obstetrical complica-
tions and types of referral institutions by outcome.
After converting age, travel time and distance into
categorical variables, we conducted bivariate logistic
regression to test the null hypothesis that there is no
association between independent variables and maternal
death, presenting crude ORs. By including statistically
significant variables and others that have been shown as
potential predictors of maternal death but not statistically
significant in our analysis, we conducted multivariable
logistic regression to determine the relative influence
of the independent variables on maternal death while
controlling for other variables. We used the Wald test
to check if the independent variables in the model
were significant. Model 1 incorporated relevant socio-
demographic, travel- related and facility- related variables.
Model 2A and model 2B are subgroup analyses that
stratified model 1 by referral status for non- referred and
referred women, respectively, as travel paths to care for
both vary (online supplemental table S5). We reported
both p values and 95% CIs of adjusted ORs derived from
regression coefficients to show the strength of evidence
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and considered differences observed as statistically signif-
icant when p<0.05. Missing data were excluded from the
analysis.
We mapped the location of public hospitals and
maternal deaths disaggregated by referral status, using
ArcGIS V.10.6 (Esri, Redlands, California, USA). All
other analyses were done in Stata SE V.16.1 (StataCorp,
College Station, Texas, USA).
Patient and public involvement
Patients and the public were not involved in the design,
conduct, reporting or dissemination of this research.
RESULTS
A total of 4181 pregnant women who presented with
obstetrical emergencies in Lagos public hospitals were
included in the study. Of the total sample, 182 (4.4%)
were maternal deaths. Of the maternal deaths, 140
(76.9%) were women who travelled directly to the
hospital, the other 40 (23.1%) were referred. Of the
maternal deaths who were referred, 17 (40.5%), 10
(23.8%) and 8 (19.1%) were women who first travelled
to primary health centres (PHCs), private hospitals and
traditional birth attendants (TBAs), respectively. Most
maternal deaths occurred in public hospitals based in
the suburbs (129 (71.0%)), followed by rural areas (40
(22.0%)). Disaggregated by referral, deaths in both
hospitals in the suburbs (94 (72.9%)) and rural areas (35
(87.5%)) were mostly women who travelled directly to a
hospital. Cause- specific case fatality rates were 3.3% (pre-
eclampsia or eclampsia), 3.5% (haemorrhage), 3.5%
(sepsis), 8.8% (ectopic pregnancy), 12.2% (abortion)
and 2.6% (other complications).
Maternal deaths involved women who lived across the
entire state, with 8 of 182 (4.4%) coming from neigh-
bouring Ogun State. Most maternal deaths occurred in
the suburbs of Ajeromi- Ifelodun, Alimosho, Ifako- Ijaiye,
Ikorodu, and in rural Badagry. Disaggregated by referral
status, most maternal deaths among pregnant women
who were referred occurred in suburban Ikorodu. All
maternal deaths from rural Epe were referred, all of
whom lived within 10 km of the hospital (figure 1).
Among the pregnant women who died, a greater
proportion were aged 20–34 years (68.1%), married
(86.3%) and self- employed petty traders (44.0%). Most
women (93.4%) had no obstetrical complications in
previous pregnancies. With the index pregnancy, most
were multiparous (42.9%), had a singleton pregnancy
(98.9%), were un- booked (ie, not registered for antenatal
care (ANC) at the hospital they presented) (94.0%) and
presented with abortion requiring evacuation (40.1%).
For travel, most of those who died travelled on a weekday
(80.8%), travelled afternoon or evening (58.6%) and trav-
elled directly to the facility (76.9%). For those referred,
most maternal deaths followed referrals from PHCs
(40.5%). Majority presented in non- apex referral hospi-
tals (90.1%), hospitals principally serving suburban areas
(69.8%) and non- slum populations (75.8%) (table 1).
For all women, the median distance covered was 7.6 km
(IQR 3.4–18.0), and the median travel time was 26 min
(IQR 12–50). Pregnant women who travelled from home
directly to a hospital travelled a median distance of 6.5 km
(IQR 3.0–15.0) and required a median time of 22 min
(IQR 11–45). Those who travelled from home to an
initial facility before being referred to another travelled
a total median distance of 15.6 km (IQR 7.6–30.0) and
Figure 1 Map of Lagos showing points of origin of referred and non- referred maternal deaths in relation to the location of
public hospitals in Lagos.
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Table 1 Socio- demographics, obstetrical history and characteristics of the index pregnancy
Characteristics
Number of pregnant
women ((%)N=4181)
Number of deaths
((%)n=182)
Number of those
alive ((%)n=3999)
Age
12–19 123 (2.9) 10 (5.5) 113 (2.8)
20–34 3094 (74.0) 124 (68.1) 2970 (74.3)
35–60 964 (23.1) 48 (26.4) 916 (22.9)
Marital status
Single 304 (7.3) 25 (13.7) 279 (7.0)
Married 3877 (92.7) 157 (86.3) 3720 (93.0)
Employment status
Unemployed/housewife 721 (17.2) 33 (18.1) 688 (17.2)
Student 274 (6.6) 17 (9.3) 257 (6.4)
Self- employed (petty- trader) 1877 (44.9) 80 (44.0) 1797 (44.9)
Self- employed (mid- high business) 442 (10.6) 17 (9.3) 425 (10.6)
Employed 867 (20.7) 35 (19.2) 832 (20.8)
Obstetric complications in a previous pregnancy
Yes 733 (17.5) 12 (6.6) 721 (18.0)
No 3448 (82.5) 170 (93.4) 3278 (82.0)
Parity
Nulliparous (0) 1495 (35.8) 59 (32.4) 1436 (35.9)
Primiparous (1) 1066 (25.5) 40 (22.0) 1026 (25.7)
Multiparous (2–4) 1515 (36.2) 78 (42.9) 1437 (35.9)
Grand- multiparous (5 or more) 105 (2.5) 5 (2.8) 100 (2.5)
Number of gestations
Singleton 4000 (95.7) 180 (98.9) 3820 (95.5)
Multiple 181 (4.3) 2 (1.1) 179 (4.5)
Booking status
Booked 1502 (35.9) 11 (6.0) 1491 (37.3)
Unbooked 2679 (64.1) 171 (94.0) 2508 (62.7)
Fetal complications
No fetal complication, only maternal complication 2748 (65.7) 24 (13.2) 2724 (68.1)
Reduced/absent fetal movement 428 (10.2) 53 (29.1) 375 (9.4)
Intrauterine fetal death 159 (3.8) 10 (5.5) 149 (3.7)
Aborted 846 (20.2) 95 (52.2) 751 (18.8)
Obstetrical complications
No maternal complication, only fetal complication 145 (3.7) 6 (3.3) 139 (3.5)
Obstructed labour 996 (23.8) 9 (5.0) 987 (24.7)
Haemorrhage 737 (17.6) 26 (14.3) 711 (17.8)
Pre- eclampsia/eclampsia 942 (22.5) 31 (17.0) 911 (22.8)
Sepsis 173 (4.1) 6 (3.3) 167 (4.2)
Abortion 597 (14.3) 73 (40.1) 524 (13.1)
Ectopic pregnancy 249 (6.0) 22 (12.1) 227 (5.7)
Others 342 (8.2) 9 (5.0) 333 (8.3)
Weekend travel to a health facility
Yes 948 (22.7) 35 (19.2) 913 (22.8)
No 3233 (77.3) 147 (80.8) 3086 (77.2)
Continued
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a median time of 54 min (IQR 28–92). Assuming these
women had travelled directly to the final facility without
being referred, they would have travelled a total median
distance of 8.3 km (IQR 3.8–17.2) with a median time of
28 min (IQR 14–55) (figure 2). Women who presented as
an emergency with a possible fetal complication and died
travelled a longer median distance (16.2 km (IQR 6.8–
33.7)) and for a longer time to the hospital (44 min (IQR
28–67)) compared with other complications (online
supplemental tables S6a,b). Additionally, women who
were referred from PHCs covered the shortest distance
from home through the referral to a hospital (13.2 (IQR
6.3–23.8)) while women who were referred from TBAs
used the least travel time from home through the referral
to reach a hospital (41 mins (IQR 28–58)) (online supple-
mental table S6c).
Among all the women who died, a larger proportion of
them travelled 10 km if they came directly from home
(60.3%) and arrived at to the hospital 30 min (61.9%).
For women who were referred and died, 71.4% travelled
10 km to the initial facility with 71.5% getting there
in 30 min, while 65.5% travelled 10 km from initial
facility to final facility with 72.4% getting there 30 min
(table 2).
Age, marital status, a complication in a previous preg-
nancy, number of gestations, booking status, maternal
complications, distance and time from home directly to a
hospital, total travel time, mode of delivery and principal
Characteristics
Number of pregnant
women ((%)N=4181)
Number of deaths
((%)n=182)
Number of those
alive ((%)n=3999)
Period of the day of travel to the facility (n=2813)
Morning 1021 (36.3) 32 (25.0) 989 (36.8)
Afternoon 751 (26.7) 38 (29.7) 713 (26.6)
Evening 644 (22.9) 37 (28.9) 607 (22.6)
Night 397 (14.1) 21 (16.4) 376 (14.0)
Referral
Not referred 3143 (75.2) 140 (76.9) 3003 (75.1)
Referred 1037 (24.8) 42 (23.1) 996 (24.9)
The initial point of care for those referred (N=1037)
Another hospital (public) 164 (15.8) 4 (9.5) 160 (16.1)
Another hospital (private 238 (23.0) 10 (23.8) 228 (22.9)
Clinic (public or private) 79 (7.6) 2 (4.8) 77 (7.7)
Primary health centre 425 (41.0) 17 (40.5) 408 (41.0)
Traditional birth attendant 103 (9.9) 8 (19.1) 95 (9.6)
Nursing/maternity home 6 (0.6) 0 (0.0) 6 (0.6)
Non- formal referral 22 (2.1) 1 (2.4) 21 (2.1)
Mode of birth
Spontaneous vaginal birth 1240 (29.7) 46 (25.3) 1194 (29.9)
Assisted vaginal birth 151 (3.6) 20 (11.0) 131 (3.3)
Caesarean birth 1944 (46.5) 21 (11.5) 1923 (48.1)
Uterine evacuation 846 (20.2) 95 (52.2) 751 (18.8)
Facility type
Non- apex referral 3450 (82.5) 164 (90.1) 3286 (82.2)
Apex referral 731 (17.5) 18 (9.9) 713 (17.8)
Principal settlement type served by the hospital
Urban 858 (20.5) 13 (7.1) 845 (21.1)
Suburban 2332 (55.8) 127 (69.8) 2205 (55.1)
Rural 991 (23.7) 42 (23.1) 949 (23.8)
The majority population type served in the hospital
catchment area
Non- slum 3400 (81.3) 138 (75.8) 3262 (81.6)
Slum 781 (18.7) 44 (24.2) 737 (18.4)
Table 1 Continued
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settlement served by the hospital were statistically signifi-
cant from the bivariate analysis (table 3).
In Model 1, factors that were significantly associated
with maternal death were having an obstetrical complica-
tion in a previous pregnancy (0.41, 95% CI 0.22 to 0.77),
being booked (0.21, 95% CI 0.11 to 0.42), travelling
10–15 km (2.53, 95% CI 1.27 to 5.03) and travelling to a
hospital that principally serves suburban (3.60, 95% CI
1.59 to 8.18) and rural areas (2.51, 95% CI 1.01 to 6.29)
and delivering via assisted vaginal birth (3.37, 95% CI
1.76 to 6.46) or by caesarean section (0.39, 95% CI 0.22
to 0.71) (table 4).
In Model 2A (subgroup analysis), odds remained
statistically significant for those travelling 10–15 km in
the non- referred group (2.48, 95% CI 1.18 to 5.23). In
addition, odds became statistically significant for those
travelling 120–480 min (7.05, 95% CI 1.10 to 45.32). For
the referred group (Model 2B), odds became statistically
significant for those travelling 25–35 km (21.40, 95% CI
1.24 to 36.72) and >35 km (49.63, 95% CI 2.39 to 103.05).
Odds also became statistically significant for those travel-
ling 10–29 min (184.23, 95% CI 5.14 to 608.51), 30–59 min
(74.82, 95% CI 3.42 to 163.79) and 60–119 min (13.83,
95% CI 1.11 to 171.51) (online supplemental table S7).
DISCUSSION
This study set out to explore associations between
maternal mortality and travel distance and time in Lagos,
Nigeria—a geographical area that is Nigeria’s most
urbanised state and includes SSA’s largest megacity. Our
findings showed that in such a principally urban SSA
setting, the odds of maternal death were significantly
higher for all pregnant women with an obstetrical emer-
gency who travelled 10–15 km to care. Disaggregated
by referral status, similar to all women, those who trav-
elled directly had a higher likelihood of death if they
travelled 10–15 km to care. As no other study has been
conducted in a principally urban SSA setting, there is no
comparator to our finding. When a woman is referred,
we found that the odds of maternal death significantly
increased when 25 km was travelled. This aligns with
results from studies conducted in rural Guinea- Bissau
and Tanzania.9 10 However, our findings contrast with a
previous rural Burkina- Faso study, which concluded that
there was no association between maternal mortality and
distance.7 The discord might relate to the fact that this
study focused on pregnancy- related deaths and not direct
causes of maternal mortality.
Figure 2 Box and whisker plot displaying distance and travel time to the hospital for pregnant women with traceable
journeys. All pregnant women: Complete travel for all women. Non- referred direct: Travel from home directly to a hospital that
could provide care. Referred A- B: Travel from home to initial point of care that then referred. Referred B- C: Travel from the initial
point of care that then referred to the nal facility that could provide care. Referred A- B- C: Total travel for referred women from
home through the initial point of care to the nal facility. Referred if direct: Total travel for referred women if the journey was
tracked from home direct to nal facility.
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Table 2 Description of distance and time to a health facility, by maternal outcome
Characteristics Total (n (%)) Dead (n (%)) Alive (n (%))
Distance of travel from home directly to a hospital (N=2978*)
Within 5km 1225 (41.1) 37 (28.2) 1188 (41.7)
5–10 km 704 (23.6) 42 (32.1) 662 (23.2)
>10–15 km 319 (10.7) 24 (18.3) 295 (10.4)
>15–25 km 312 (10.5) 13 (9.9) 299 (10.5)
>25–35 km 192 (6.5) 7 (5.3) 185 (6.5)
>35 km 226 (7.6) 8 (6.1) 218 (7.7)
Distance of travel from home to an initial point of care that then referred (N=611†)
Within 5 km 283 (46.3) 11 (39.3) 272 (46.7)
5–10 km 120 (19.6) 9 (32.1) 111 (19.0)
>10–15 km 72 (11.8) 4 (14.3) 68 (11.7)
>15–25 km 77 (12.6) 3 (10.7) 74 (12.7)
>25–35 km 29 (4.8) 1 (3.6) 28 (4.8)
>35 km 30 (4.9) 0 (0.0) 30 (5.2)
Distance of travel from an initial point of care to the nal facility of care for referred women (N=611†)
Within 5 km 224 (36.7) 13 (44.8) 211 (36.3)
5–10 km 146 (23.9) 6 (20.7) 140 (24.1)
>10–15 km 76 (12.4) 3 (10.3) 73 (12.5)
>15–25 km 90 (14.7) 3 (10.3) 87 (14.9)
>25–35 km 32 (5.3) 2 (6.9) 30 (5.2)
>35 km 43 (7.0) 2 (6.9) 41 (7.0)
Total traceable distance for all women whose journeys were traceable (N=3590*†‡)
Within 5 km 1316 (36.7) 41 (25.6) 1275 (37.2)
5–10 km 832 (23.2) 49 (30.6) 783 (22.8)
>10–15 km 395 (11.0) 28 (17.5) 367 (10.7)
>15–25 km 442 (12.3) 19 (11.9) 423 (12.4)
>25–35 km 258 (7.2) 11 (6.9) 247 (7.2)
>35 km 347 (9.7) 12 (7.5) 335 (9.8)
Time of travel from home directly to a hospital (N=2978*)
0–9 min 617 (20.7) 15 (11.5) 602 (21.2)
10–29 min 1201 (40.3) 66 (50.4) 1135 (39.9)
30–59 min 667 (22.4) 32 (24.4) 635 (22.3)
60–119 min 423 (14.2) 12 (9.2) 411 (14.4)
120–480 min 70 (2.4) 6 (4.6) 64 (2.3)
Time of travel from home to initial facility for referred women (N=611†)
0–9 min 134 (21.9) 5 (17.9) 129 (22.1)
10–29 min 235 (38.5) 15 (53.6) 220 (37.7)
30–59 min 147 (24.1) 6 (21.4) 141 (24.2)
60–119 min 73 (12.0) 2 (7.1) 71 (12.2)
120–480 min 22 (3.6) 0 (0.0) 22 (3.8)
Time of travel from initial facility to the nal facility of care for referred women (N=611†)
0–9 min 112 (18.4) 4 (13.8) 108 (18.6)
10–29 min 224 (36.5) 17 (58.6) 207 (35.5)
30–59 min 169 (27.6) 5 (17.2) 164 (28.2)
60–119 min 93 (15.2) 3 (10.3) 90 (15.4)
Continued
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Disaggregated by referral status, women who trav-
elled directly to the hospital of care in our study had
significantly higher odds of maternal death if they trav-
elled 120 min. This travel time of statistical signifi-
cance is lower than findings from a previous rural Mali
study which reported a statistically significant associa-
tion between institutional MMR and travel time only
after women had travelled 240 min to a hospital.8 The
observed difference might relate to the rural popula-
tion selected or the method of determining travel time
which involved guestimates from hospital workers. In our
study, for those referred, odds of maternal death, which
were statistically significant, increased appreciably when
travel of 10 min was required. Keeping in mind that for
women who died, over 60% travelled 10 km or 30 min
for both referred and non- referred groups, this signifi-
cantly increased odds of maternal deaths for women who
were referred and travelled 10 min. This finding may
suggest that they experienced a significant delay in the
initial facility before being referred, at which point, so
little could be done to improve their odds of survival. In
SSA, care delays at referral points have been attributed
to long waiting times, lack of skill and shortage of equip-
ment and supplies in health facilities.6 Also, poor case
management involving wrong assessment of risk, diag-
nosis or treatment has been reported, especially with
private facilities and traditional birth attendants.15
Though our results support that there is an urban–rural
divide, it also shows that even within hospitals situated
in suburban areas, the odds of maternal death are even
higher than those in the rural areas for all women, irre-
spective of their referral status. This suggests that there
are issues even in the relatively urban–suburban areas. In
Lagos, most pregnant women with obstetrical emergen-
cies living in the peripheral rural areas usually travelled
to public hospitals around them.28 In a separate study, we
found that travel to care in this settlement type is typically
prolonged in the suburbs.28 This blurring of the so- called
‘urban advantage’, or at least the ‘suburb advantage’,
may explain the higher odds of maternal death in hospi-
tals situated in the suburbs.
For other factors before and after travel to the hospital,
our results showed a maternal death odds- reducing effect
of having a previous obstetrical complication, booking
and caesarean birth. It might be the case that knowl-
edge and experience gained from previous pregnancies
or following booking resulted in comprehensive birth
preparedness plans for the index pregnancy, including
timely decision to seek care, invariably reducing travel
time to a facility.11 Caesarean birth was ‘protective’ from
maternal death in our study. Contrarily, assisted vaginal
birth led to significantly increased odds of maternal
death. A clinical audit of these deaths is warranted to
understand this association better.
To the best of our knowledge, this is the first study
conducted in a principally urban SSA setting that explic-
itly and comprehensively explored association between
travel time, distance and maternal mortality. Our study
used driving distance and travel time estimates from
Google Maps, which are more reflective of reality,
compared with other estimation methods.22 Previous
similar studies used Euclidean (straight- line) distances,
assuming the women went to the nearest facility or health
worker guestimates of travel time.7 9 10 However, evidence
in the literature queries the realism and accuracy of both
approaches to travel time and distance estimates. Women
do not always go to the nearest facility, and if they do, their
journeys are typically a lot more convoluted than travel in
a straight line. Furthermore, since health workers did not
make the journeys, they are not likely to make accurate
travel time and distance estimates. Even if women them-
selves were asked, issues of recall bias have been raised by
researchers.28–30 In addition, our study included women
with emergencies across the entire pregnancy period and
disaggregates by referral status, reflecting the different
journeys that women follow to care.31 These are some of
the key strengths of our study.
However, there are some limitations to keep in mind in
interpreting our findings. First, while we mapped women’s
journeys to health facilities based on data reported in
their case notes, we cannot be sure that the women took
the routes mapped in Google Maps or to other points
Characteristics Total (n (%)) Dead (n (%)) Alive (n (%))
120–480 min 13 (2.3) 0 (0.0) 13 (2.4)
Total traceable time for all women whose journeys were traceable (N=3590*†‡)
0–9 min 639 (17.8) 15 (9.4) 624 (18.2)
10–29 min 1343 (37.4) 75 (46.9) 1268 (37.0)
30–59 min 844 (23.5) 43 (26.9) 801 (23.4)
60–119 min 609 (17.0) 20 (12.5) 589 (17.2)
120–480 min 155 (4.3) 7 (4.4) 148 (4.3)
*Excludes women whose journey to the facility could not be determined (n=166).
†Excludes women whose referral journey could not be traced (n=415).
‡Excludes 10 missing values.
Table 2 Continued
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Table 3 Bivariate analysis for maternal outcomes
Characteristics Number of deaths ((%)n=182)
Number of those alive ((%)
n=3999) P value
Age
12–19 10 (8.1) 113 (91.9) 0.050
20–34 124 (4.0) 2970 (96.0)
35–60 48 (5.0) 916 (95.0)
Marital status
Single 25 (8.2) 279 (91.8) 0.001
Married 157 (4.1) 3, (95.9)
Employment status
Unemployed/housewife 33 (4.6) 688 (95.4) 0.581
Student 17 (6.2) 257 (93.8)
Self- employed (petty- trader) 80 (4.3) 1797 (95.7)
Self- employed (mid- high business) 17 (3.9) 425 (96.1)
Employed 35 (4.0) 832 (96.0)
Obstetric complications in a previous pregnancy
Ye s 12 (1.6) 721 (98.4) <0.001
No 170 (4.9) 3278 (95.1)
Parity
Nulliparous (0) 59 (4.0) 1436 (96.0) 0.277
Primiparous (1) 40 (3.8) 1026 (96.2)
Multiparous (2–4) 78 (5.2) 1437 (94.2)
Grand- multiparous (5 or more) 5 (4.8) 100 (95.2)
Number of gestations
Singleton 180 (4.5) 3820 (95.5) 0.029
Multiple 2 (1.1) 179 (98.9)
Booking status
Booked 11 (0.7) 1491 (99.3) <0.001
Un- booked 171 (6.4) 2508 (93.6)
Maternal complications
No maternal complication, only fetal complication 6 (4.1) 139 (95.9) <0.001
Obstructed labour 9 (0.9) 987 (99.1)
Haemorrhage 26 (3.5) 711 (96.5)
Pre- eclampsia/eclampsia 31 (3.3) 911 (96.7)
Sepsis 6 (3.5) 167 (96.5)
Abortion 73 (12.2) 524 (87.8)
Ectopic pregnancy 22 (8.8) 227 (91.2)
Others 9 (2.6) 333 (97.4)
Weekend travel to a facility
Ye s 35 (3.7) 913 (96.3) 0.257
No 147 (4.5) 3086 (95.5)
Period of the day of travel to the facility (n=2813)
Morning 32 (3.1) 989 (96.9) 0.063
Afternoon 38 (5.1) 713 (94.9)
Evening 37 (5.8) 607 (94.2)
Night 21 (5.3) 376 (94.7)
Referral
Continued
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Characteristics Number of deaths ((%)n=182)
Number of those alive ((%)
n=3999) P value
Not referred 140 (4.5) 3003 (95.5) 0.576
Referred 42 (4.1) 996 (95.9)
Distance of travel from home directly to a hospital (N=2978*)
Within 5 km 37 (3.0) 1188 (97.0) 0.003
5–10 km 42 (6.0) 662 (94.0)
>10–15 km 24 (7.5) 295 (92.5)
>15–25 km 13 (4.2) 299 (95.8)
>25–35 km 7 (3.7) 185 (96.3)
>35 km 8 (3.5) 218 (96.5)
Distance of travel from home to initial facility for referred women (N=611†)
Within 5 km 11 (3.9) 272 (96.1) 0.491
5–10 km 9 (7.5) 111 (92.5)
>10–15 km 4 (5.6) 68 (94.4)
>15–25 km 3 (3.9) 74 (96.1)
>25–35 km 1 (3.5) 28 (96.5)
>35 km 0 (0.0) 30 (100.0)
Distance of travel from initial facility to the nal facility of care for referred women (N=611†)
Within 5 km 13 (5.8) 211 (94.2) 0.933
5–10 km 6 (4.1) 140 (95.9)
>10–15 km 3 (3.9) 73 (96.1)
>15–25 km 3 (3.3) 87 (96.7)
>25–35 km 2 (6.1) 30 (93.9)
>35 km 2 (4.6) 41 (95.4)
Total traceable distance for all women whose journeys were traceable (N=3590*†‡)
Within 5 km 41 (3.1) 1275 (96.9) 0.005
5–10 km 49 (5.9) 782 (94.1)
>10–15 km 28 (7.1) 367 (95.7)
>15–25 km 19 (4.3) 423 (95.7)
>25–35 km 11 (4.3) 247 (95.7)
>35 km 12 (3.5) 335 (96.5)
Time of travel from home directly to a hospital (N=2978*)
0–9 min 15 (2.4) 602 (97.6) 0.005
10–29 min 66 (5.5) 1135 (94.5)
30–59 min 32 (4.8) 635 (95.2)
60–119 min 12 (2.8) 411 (97.2)
120–480 min 6 (8.6) 64 (91.4)
Time of travel from home to initial facility for referred women (N=611†)
0–9 min 5 (3.7) 129 (96.3) 0.452
10–29 min 15 (6.4) 220 (93.6)
30–59 min 6 (4.1) 141 (95.9)
60–119 min 2 (2.7) 71 (97.3)
120–480 min 0 (0.0) 22 (100.0)
Time of travel from initial facility to the nal facility of care for referred women (N=611†)
0–9 min 4 (3.6) 108 (96.4) 0.151
10–29 min 17 (7.6) 207 (92.4)
30–59 min 5 (2.9) 164 (97.1)
Table 3 Continued
Continued
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of care not reported. Second, though Google Maps has
been shown to provide closer- to- reality estimates of travel
time and distance, it is still not perfect reality, especially in
rural and remote areas.22 However, as already established,
our setting for this research is principally urban, where
networks of tarred roads allow for improved accuracy
with Google Maps.32 33 Third, while we collected data for
a year and accounted for diurnal variations using Google
Maps, we could not account for seasonal variations, as
this is not a functionality that is presently available on the
application. Also, we have not captured the time women
could have spent deciding to seek care (delay I). In addi-
tion, we did not have data reflecting the time between
presentation at the hospital and the initiation of care or
referral, as needed (delay III). Though these delays all
contribute to maternal death,6 they would have occurred
for both referred and non- referred women. As such, it
does not significantly influence our findings. Addition-
ally, we have not included private hospitals as endpoints
of care, although public hospitals are recognised as the
cornerstone for EmOC in SSA.34 Moreover, we could not
fully capture women’s socio- economic and educational
characteristics, which are also essential factors that influ-
ence access to care. However, both data are not routinely
reported inpatient records in many SSA health systems.35
Furthermore, being a facility- based study, our study does
not include pregnant women who died in transit. Finally,
we observed wide CIs around the travel time and distance
estimates for referred women. However, these wide inter-
vals do not alter the validity of our findings, as it relates
more to the absolute number of maternal deaths than
the sample size.36 In any case, our conclusion regarding
the association between travel time and distance and
maternal death remains true across the interval. Consid-
ering this limitation, while recognising that maternal
deaths remain a rare event in Africa,37 larger- scale
research that includes more maternal death events, espe-
cially among women referred to care, is warranted for
future research.
There are some implications for policy and practice
for SSA health systems, as our study was conducted in
the largest metropolis of the subregion. Before travel is
even warranted for an emergency, engaging with ANC
early in the pregnancy remains pivotal for optimising
Characteristics Number of deaths ((%)n=182)
Number of those alive ((%)
n=3999) P value
60–119 min 3 (3.2) 90 (96.8)
120–480 min 0 (0.0) 13 (100.0)
Total travel time for all women whose journeys were traceable (N=3590*†‡)
0–9 min 15 (2.3) 624 (97.7) 0.009
10–29 min 75 (5.6) 1268 (94.4)
30–59 min 43 (5.1) 801 (94.9)
60–119 min 20 (3.3) 589 (96.7)
120–480 min 7 (4.5) 148 (95.5)
Mode of birth
Spontaneous vaginal birth 46 (3.7) 1194 (96.3) <0.001
Assisted vaginal birth 20 (13.2) 131 (86.8)
Caesarean birth 21 (1.1) 1923 (98.9)
Uterine evacuation 95 (11.2) 751 (88.8)
Facility type
Non- apex referral 164 (4.8) 3286 (95.2) 0.006
Apex referral 18 (2.5) 713 (97.5)
Principal settlement type served by the hospital
Urban 13 (1.5) 845 (98.5) <0.001
Suburban 127 (5.5) 2205 (94.5)
Rural 42 (4.2) 949 (95.8)
The majority population type served in the hospital catchment area
Non- slum 138 (4.1) 3262 (95.9) 0.052
Slum 44 (5.6) 737 (94.4)
Educational level attained was excluded as the level of missingness was too high for computation in the model. Fetal complication was excluded as
it does not inuence the outcome of this study. Type of referral facility was also excluded, as this variable was not relevant to the entire sample.
*Excludes women whose journey to the facility could not be determined (n=166).
†Excludes women whose referral journey could not be traced (n=415).
‡Excludes 10 missing values.
Table 3 Continued
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Table 4 Logistic regression showing association between maternal death and the selected independent variables
Factor Unadjusted (95% CI) Adjusted—Model 1 (95% CI)
Age
12–19 2.12 (1.08 to 4.15)* 1.50 (0.62 to 3.63)
20–34 1.00 1.00
35–60 1.25 (0.89 to 1.76) 1.30 (0.88 to 1.92)
Marital status
Single 2.12 (1.37 to 3.29)** 0.89 (0.49 to 1.61)
Married 1.00 1.00
Obstetrical complications in a previous pregnancy
No 1.00 1.00
Ye s 0.32 (0.18 to 0.58)*** 0.41 (0.22 to 0.77)**
Booking status at the hospital of care
Un- booked 1.00 1.00
Booked 0.11 (0.06 to 0.20)*** 0.21 (0.11 to 0.42)***
Maternal complications
No maternal complication, only fetal complication 1.00 1.00
Obstructed labour 0.21 (0.74 to 0.60)** 0.28 (0.09 to 0.89)*
Haemorrhage 0.85 (0.34 to 2.09) 0.68 (0.26 to 1.79)
Hypertension 0.79 (0.32 to 1.92) 0.76 (0.29 to 1.98)
Sepsis 0.83 (0.26 to 2.64) 0.79 (0.22 to 2.79)
Abortion 3.23 (1.38 to 7.57)** 1.90 (0.74 to 4.87)
Ectopic pregnancy 2.25 (0.89 to 5.67) 1.30 (0.47 to 3.60)
Others 0.63 (0.22 to 1.79) 0.84 (0.27 to 2.60)
Mode of birth
Spontaneous vaginal birth 1.00 1.00
Assisted vaginal birth 3.96 (2.27 to 6.90)*** 3.37 (1.76 to 6.46)***
Caesarean birth 0.28 (0.17 to 0.47)*** 0.39 (0.22 to 0.71)**
Uterine evacuation 3.28 (2.28 to 4.72)*** (omitted)
Referral
Travel from home/other location initial facility 1.00 1.00
Referred from initial facility to nal facility 0.90 (0.64 to 1.29) 1.09 (0.67 to 1.77)
Total traceable distance for all women whose journeys were
traceable (N=3590†‡§)
Within 5 km 1.00 1.00
5–10 km 1.95 (1.27 to 2.98)** 1.56 (0.93 to 2.62)
>10–15 km 2.37 (1.45 to 3.89)** 2.53 (1.27 to 5.03)**
>15–25 km 1.39 (0.80 to 2.43) 1.74 (0.74 to 4.09)
>25–35 km 1.38 (0.70 to 2.73) 1.99 (0.67 to 5.91)
>35 km 1.11 (0.58 to 2.14) 1.58 (0.42 to 5.85)
Total traceable time for all women whose journeys were traceable
(N=3590†‡*)
0–9 min 1.00 1.00
10–29 min 2.46 (1.40 to 4.32)** 1.58 (0.81 to 3.09)
30–59 min 2.23 (1.23 to 4.06)** 1.10 (0.46 to 2.63)
60–119 min 1.41 (0.72 to 2.79) 0.79 (0.25 to 2.50)
120–480 min 1.97 (0.79 to 4.91) 1.39 (0.28 to 6.85)
Facility type
Non- apex referral 1.98 (1.21 to 3.24)** 0.79 (0.38 to 1.65)
Apex referral 1.00 1.00
Continued
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14 Banke- ThomasA, etal. BMJ Global Health 2022;7:e008604. doi:10.1136/bmjgh-2022-008604
BMJ Global Health
pregnancy outcomes. Skilled health personnel need
to discuss danger signs, hospitals with the capacity to
manage specific emergencies should they arise and travel
plans with couples as part of birth preparedness. In addi-
tion to other points, this discussion should highlight
the urgency of travel straight to a hospital for women
even if they are only concerned about the health of
their unborn child. Furthermore, negative experiences
of health facility delivery that cause women to bypass
nearer facilities in emergency to travel further need
to be minimised.11 38 Efforts also need to be geared to
reduce intrafacility delays at referring points of care to
minimise maternal deaths among referred women. For
governments, it is not enough to say health facilities have
been ‘strategically placed’,39 facility location needs to be
evidence- based. Planning geographical distribution of
health facilities needs to ensure a functional health facility
within 10 km of every woman and a robust referral system
supported by patient transfer services to public hospitals.
Some authors have suggested that the way forward to
achieve the SDGs is to focus on hospital- level care instead
of PHCs.40 Recognising concerns with costs associated
with building new hospitals and the need for such invest-
ments to demonstrate value for money and be sustain-
able,41–43 we argue that close- to- community PHCs need
to remain the fulcrum for SSA health systems. Sufficient
training should be conducted with PHC health workers
to help them better recognise complications, resuscitate
and refer promptly.44 Any such capacity- building inter-
vention for PHCs and private sector facilities needs to
particularly focus management of abortion, which had
the highest cause- specific case fatality rate in our study.
As an alternative, governments in principally urban
SSA settings should establish partnerships with quality-
assured private providers who can be integrated into the
EmOC referral network. This will be particularly crucial
as private providers manage between 5% (Lusaka) and
64% (Lagos) of facility births in urban SSA settings.45 In
addition, as every minute counts for the mother and her
unborn child,46 ambulance services within the referral
network need to be fully optimised to transfer women
with obstetrical emergencies efficiently and effectively to
hospitals that can manage them. This transfer should also
be done at no cost to them, as many women already find
the cost of care too high.47 For those travelling from their
homes, access should not be a choice between ‘too far’
or ‘too poor’ to afford travel to an appropriate facility.48
In responding, governments also need to recognise that
suburban might be the new rural and should therefore
address access issues in the suburbs.
At a global level, our finding of statistical significance
at a travel time of 120 min for pregnant women travel-
ling directly to a hospital with the capacity to provide the
care needed partly supports the applicability of the glob-
ally agreed benchmark of 2- hour travel.49 However, with
many maternal deaths involving women who travelled
less than an hour, there is a need to expand these guide-
lines to reflect delays permissible at referring facilities,
recognising that women still face a median additional
time of an hour even if they make it to hospitals that can
provide the care needed.50
CONCLUSION
In conclusion, distance and travel time influence maternal
outcomes following pregnancy and childbirth in different
ways for women who are referred and those not. Leaving
no one behind in achieving global targets of 2- hour travel
to a hospital with the capacity to provide essential anaes-
thesia and surgical services, including caesarean for 80%
of the population by 2030,3 49 will require more research
like ours replicated in the many sprawling urban areas
of SSA, rethinking of global EmOC geographical access
guidelines and bold actions to get women closer and
quicker to functional health facilities.
Author afliations
1LSE Health, London School of Economics and Political Science, London, UK
2School of Human Sciences, University of Greenwich, Greenwich, London, UK
Factor Unadjusted (95% CI) Adjusted—Model 1 (95% CI)
Principal settlement type served by the hospital
Urban 1.00 1.00
Suburban 3.74 (2.10 to 6.66)*** 3.60 (1.59 to 8.18)**
Rural 2.88 (1.54 to 5.40)*** 2.51 (1.01 to 6.29)*
The majority population type served in the hospital catchment area
Non- slum 1.00 1.00
Slum 1.41 (1.00 to 2.00)* 1.53 (0.99 to 2.34)
Model description: Model 1: adjusted for all variables in the model including socio- demographic, distance and time of travel as well as facility
characteristics for all women whose journey from home to the hospital could be traced, Model 2A: subgroup analysis for all women who travelled
from home directly to a health facility, Model 2B: subgroup analysis for all women who were referred in their journey from home to a health facility.
***p≤0.001; **p≤0.010; *p≤0.050.
†Excludes 10 missing values.
‡Excludes women whose referral journey could not be traced (n=415).
§Excludes women whose journey to a hospital could not be determined (n=166).
Table 4 Continued
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Banke- ThomasA, etal. BMJ Global Health 2022;7:e008604. doi:10.1136/bmjgh-2022-008604 15
BMJ Global Health
3Maternal and Reproductive Health Research Collective, Lagos, Nigeria
4Faculty of Public Health, Ghana College of Physicians and Surgeons, Accra, Greater
Accra, Ghana
5Nufeld Department of Population Health, University of Oxford, Oxford, UK
6Department of Obstetrics and Gynaecology, Lagos Island Maternity Hospital,
Lagos, Nigeria
7Department of Community Health and Primary Care, College of Medicine,
University of Lagos, Idi- Araba, Lagos, Nigeria
8Department of Community Health and Primary Health Care, Lagos State University
College of Medicine, Ikeja, Lagos, Nigeria
9Department of Obstetrics and Gynaecology, College of Medicine, University of
Lagos, Idi- Araba, Lagos, Nigeria
10Department of Obstetrics and Gynaecology, Federal Medical Centre Ebute- Metta,
Ebute- Metta, Lagos, Nigeria
11Department of International Public Health, Liverpool School of Tropical Medicine,
Liverpool, Merseyside, UK
Twitter Aduragbemi Banke- Thomas @abankethomas, Cephas Ke- on Avoka
@AvokaKeon, Mobolanle Balogun @drmbalogun, Bosede Bukola Afolabi
@Coolgynae and Charles Ameh @acameh
Acknowledgements We are grateful to the Lagos State Government, in particular,
the Lagos State Ministry of Health and the Lagos State Health Service Commission,
for their support in gaining access to all state- owned facilities. We also thank
the leadership of Federal government- owned facilities (Federal Medical Centre
Ebute- Metta and Lagos University Teaching Hospital) included in our study for their
support. We are indebted to the medical ofcers and residents who helped with
collating case notes of pregnant women to be included in the study. We also thank
the AXA Research Fund for supporting this research. The funder of the study had no
role in study design, data collection, data interpretation or writing of the report.
Contributors AB- T conceived the study. AB- T, CK- oA and CA designed the study.
AB- T, KW, MB, TTF, AO and BBA coordinated data collection across the public
hospitals. AB- T, CK- oA and UG- A did the statistical analyses with support from KW,
MB and CA. AB- T, CK- oA, UG- A and CA wrote the rst draft of the article. All authors
critically reviewed the article, contributed to the interpretation of ndings, saw and
approved the nal version of the article. AB- T is the guarantor.
Funding This research was funded by a grant provided by AXA Research Fund.
Map disclaimer The inclusion of any map (including the depiction of any
boundaries therein), or of any geographical or locational reference, does not imply
the expression of any opinion whatsoever on the part of BMJ concerning the legal
status of any country, territory, jurisdiction or area or of its authorities. Any such
expression remains solely that of the relevant source and is not endorsed by BMJ.
Maps are provided without any warranty of any kind, either express or implied.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in
the design, or conduct, or reporting, or dissemination plans of this research.
Patient consent for publication Not applicable.
Ethics approval We obtained ethical approval from the Research and Ethics
Committees of the Lagos University Teaching Hospital (ADM/DCST/HREC/APP/2880)
and Lagos State University Teaching Hospital (LREC/06/10/1226). The risk of
identifying pregnant women in the study was substantially reduced by not collecting
identiers such as names and specic street numbers.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon reasonable request.
Supplemental material This content has been supplied by the author(s). It has
not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been
peer- reviewed. Any opinions or recommendations discussed are solely those
of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and
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of the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the
use is non- commercial. See:http://creativecommons.org/licenses/by-nc/4.0/.
ORCID iDs
AduragbemiBanke- Thomas http://orcid.org/0000-0002-4449-0131
Cephas Ke- onAvoka http://orcid.org/0000-0002-7298-3670
UchennaGwacham- Anisiobi http://orcid.org/0000-0001-5459-309X
MobolanleBalogun http://orcid.org/0000-0001-8147-2111
Bosede BukolaAfolabi http://orcid.org/0000-0002-7511-7567
CharlesAmeh http://orcid.org/0000-0002-2341-7605
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... 6 Travel time and distance to care may lead to maternal or perinatal deaths. [7][8][9][10] There is a global consensus that understanding the reasons underpinning the death of a pregnant woman or her unborn child is an important first step in forestalling future similar deaths. To reach this understanding, in addition to being able to label the obstetric complication that led to the death(s), it is crucial to capture the pregnant woman's personal story to care and the precise circumstances around her death or that of her unborn child. ...
Article
Background: The Maternal and Perinatal Death Surveillance and Response (MPDSR) proposed by the World Health Organization recognises the importance for health systems to understand the reasons underpinning the death of a pregnant woman or her newborn as an essential first step in preventing future similar deaths. Data for the surveillance component of the MPDSR process are typically collected from health facility sources and post-mortem interviews with affected families, though it may be traumatising to them. This brief report aimed to assess the potential utility of an augmented data collection method for mapping journeys of maternal and perinatal deaths, which does not require sourcing additional information from grieving family members. Methods: A descriptive analysis of maternal and perinatal deaths that occurred across 24 public hospitals in Lagos State, Nigeria, between 1 st November 2018 and 30 th October 2019 was conducted. Data on their demographic, obstetric history and complication at presentation, travel to the hospital, and mode of birth were extracted from their hospital records. The extracted travel data was exported to Google Maps, where driving distance and travel time to the hospital for the period of the day of travel were also extracted. Results: Of the 182 maternal deaths, most presented during the week (80.8%), travelled 5-10 km (30.6%) and 10-29 minutes (46.9%), and travelled to the nearest hospital to their places of residence (70.9%). Of the 442 pregnant women who had perinatal deaths, most presented during the week (78.5%), travelled <5 km (26.9%) and 10-29 minutes (38.0%). For both, the least reported travel data was the mode of travel used to care (>90.0%) and the period of the day they travelled (approximately 30.0%). Conclusion: An augmented data collection approach that includes accurate and complete travel data and closer-to-reality estimates of travel time and distance can be beneficial for MPDSR purposes.
... 6 Travel time and distance to care may lead to maternal or perinatal deaths. [7][8][9][10] There is a global consensus that understanding the reasons underpinning the death of a pregnant woman or her unborn child is an important first step in forestalling future similar deaths. To reach this understanding, in addition to being able to label the obstetric complication that led to the death(s), it is crucial to capture the pregnant woman's personal story to care and the precise circumstances around her death or that of her unborn child. ...
Article
Background: The Maternal and Perinatal Death Surveillance and Response (MPDSR) proposed by the World Health Organization recognises the importance for health systems to understand the reasons underpinning the death of a pregnant woman or her newborn as an essential first step in preventing future similar deaths. Data for the surveillance component of the MPDSR process are typically collected from health facility sources and post-mortem interviews with affected families, though it may be traumatising to them. This brief report aimed to assess the potential utility of an augmented data collection method for mapping journeys of maternal and perinatal deaths, which does not require sourcing additional information from grieving family members. Methods: A descriptive analysis of maternal and perinatal deaths that occurred across all 24 public hospitals in Lagos State, Nigeria, between 1 st November 2018 and 30 th October 2019 was conducted. Data on their demographic, obstetric history and complication at presentation, travel to the hospital, and mode of birth were extracted from their hospital records. The extracted travel data was exported to Google Maps, where driving distance and travel time to the hospital for the period of the day of travel were also extracted. Results: Of the 182 maternal deaths, most presented during the week (80.8%), travelled 5-10 km (30.6%) and 10-29 minutes (46.9%), and travelled to the nearest hospital to their places of residence (70.9%). Of the 442 pregnant women who had perinatal deaths, most presented during the week (78.5%), travelled <5 km (26.9%) and 10-29 minutes (38.0%). For both, the least reported travel data was the mode of travel used to care (>90.0%) and the period of the day they travelled (approximately 30.0%). Conclusion: An augmented data collection approach that includes accurate and complete travel data and closer-to-reality estimates of travel time and distance can be beneficial for MPDSR purposes.
... Women living in urban areas have been assumed to have better physical access EmOC compared to their rural counterparts due to relatively shorter travel distances to health facilities (11). However, emerging evidence shows that this so called "urban advantage" is shrinking and, in some LMIC settings, almost non-existent partly because while travel distances might be shorter, travel time can get longer (9,12,13). In urban LMIC settings, typically characterized by poor spatial planning, haphazardly built environments, growing informal settlements, poor road infrastructure, and extreme traffic congestion prolong travel time, delay care-seeking, and aggravate the risk of long-term morbidity and mortality for women and their babies. ...
Article
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Maternal and perinatal mortality remain huge challenges globally, particularly in low- and middle-income countries (LMICs) where >98% of these deaths occur. Emergency obstetric care (EmOC) provided by skilled health personnel is an evidence-based package of interventions effective in reducing these deaths associated with pregnancy and childbirth. Until recently, pregnant women residing in urban areas have been considered to have good access to care, including EmOC. However, emerging evidence shows that due to rapid urbanization, this so called “urban advantage” is shrinking and in some LMIC settings, it is almost non-existent. This poses a complex challenge for structuring an effective health service delivery system, which tend to have poor spatial planning especially in LMIC settings. To optimize access to EmOC and ultimately reduce preventable maternal deaths within the context of urbanization, it is imperative to accurately locate areas and population groups that are geographically marginalized. Underpinning such assessments is accurately estimating travel time to health facilities that provide EmOC. In this perspective, we discuss strengths and weaknesses of approaches commonly used to estimate travel times to EmOC in LMICs, broadly grouped as reported and modeled approaches, while contextualizing our discussion in urban areas. We then introduce the novel OnTIME project, which seeks to address some of the key limitations in these commonly used approaches by leveraging big data. The perspective concludes with a discussion on anticipated outcomes and potential policy applications of the OnTIME project.
... 6 Travel time and distance to care may lead to maternal or perinatal deaths. [7][8][9][10] There is a global consensus that understanding the reasons underpinning the death of a pregnant woman or her unborn child is an important first step in forestalling future similar deaths. To reach this understanding, in addition to being able to label the obstetric complication that led to the death(s), it is crucial to capture the pregnant woman's personal story to care and the precise circumstances around her death or that of her unborn child. ...
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Objective: To review the evidence on interventions to improve obstetric emergency referral decision making, communication, and feedback between health facilities in sub-Saharan Africa (SSA). Methods: A systematic search of PubMed, Embase, Cochrane Register and CINAHL Plus was conducted to identify studies on obstetric emergency referral in SSA. Studies were included based on pre-defined eligibility criteria. Details of reported referral interventions were extracted and categorized. The Joanna Biggs Institute Critical Appraisal checklists were used for quality assessment of included studies. A formal narrative synthesis approach was used to summarise findings guided by WHO's referral systems flow. Results: 14 studies were included, with seven deemed high quality. Overall, 7 studies reported referral decision-making interventions including training programmes for health facility and community health workers, use of a triage checklist and focused obstetric ultrasound, which resulted in improved knowledge and practice of recognising danger signs for referral. 9 studies reported on referral communication using mobile phones and referral letters/notes, resulting in increased communication between facilities, despite telecommunication network failures. Referral decision-making and communication interventions achieved a perceived reduction in maternal mortality. 2 studies focused on referral feedback, which improved collaboration between health facilities. Conclusion: There is limited evidence on how well referral interventions work in sub-Saharan Africa, and limited consensus regarding the framework underpinning the expected change. This review has led to the proposition of a logic model that can serve as the base for future evaluations which robustly expose the (in)efficiency of referral interventions.
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Introduction Globally, the majority of births happen in urban areas. Ensuring that women and their newborns benefit from a complete package of high-quality care during pregnancy, childbirth and the postnatal period present specific challenges in large cities. We examine health service utilisation and content of care along the maternal continuum of care (CoC) in 22 large African cities. Methods We analysed data from the most recent Demographic and Health Survey (DHS) since 2013 in any African country with at least one city of ≥ 1 million inhabitants in 2015. Women with live births from survey clusters in the most populous city per country were identified. We analysed 17 indicators capturing utilisation, sector and level of health facilities and content of three maternal care services: antenatal care (ANC), childbirth care and postnatal care (PNC), and a composite indicator capturing completion of the maternal CoC. We developed a categorisation of cities according to performance on utilisation and content within maternal CoC. Results The study sample included 25 326 live births reported by 19 217 women. Heterogeneity in the performance in the three services was observed across cities and across the three services within cities. ANC utilisation was high (>85%); facility-based childbirth and PNC ranged widely, 77%–99% and 29%–94%, respectively. Most cities showed inconsistent levels of utilisation and content across the maternal CoC, Cotonou and Accra showed relatively best and Nairobi and Ndjamena worst performance. Conclusion This exploratory analysis showed that many DHS can be analysed on the level of large African cities to provide actionable information about the utilisation and content of the three maternal health services. Our comparative analysis of 22 cities and proposed typology of best and worst-performing cities can provide a starting point for extracting lessons learnt and addressing critical gaps in maternal health in rapidly urbanising contexts.
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Introduction Access to skilled birth attendance has been prioritised as an intervention to minimise burden of maternal deaths in sub-Saharan Africa (SSA). However, poor experience of care (EoC) is impeding progress. We conducted a systematic review to holistically explore EoC patterns of facility-based childbirth in SSA. Methods PubMed, Embase and Scopus databases were searched to identify SSA EoC studies conducted between January 2000 and December 2019. Studies meeting our pre-defined inclusion criteria were quality assessed and relevant data extracted. We utilised the EoC quality standards (defined by the World Health Organization) to summarise and analyse findings while highlighting patterns. Results Twenty-two studies of varying quality from 11 SSA countries were included for review. Overall, at least one study from all included countries reported negative EoC in one or more domains of the WHO framework. Across SSA, ‘respect and preservation of dignity’ was the most reported domain of EoC. While most women deemed the pervasive disrespect as unacceptable, studies in West Africa suggest a “normalisation” of disrespect, if the intent is to save their lives. Women often experienced sub-optimal communication and emotional support with providers in public facilities compared to non-public ones in the region. These experiences had an influence on future institutional deliveries. Discussion Sub-optimal EoC is widespread in SSA, more so in public facilities. As SSA heath systems explore approaches make progress towards the Sustainable Development Goal 3, emphasis needs to be placed on ensuring women in the region have access to both high-quality provision and experience of care.
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Objective: to explore the role of transportation in seeking emergency obstetric care among women with obstetric complications. Methods: A mixed-methods design. The population for the study were women aged 15-49 years who had experience direct obstetric complications and were attending the health facility for care at the time of this study. Three-hundred and eighteen (318) women completed the questionnaires, whilst in-depth interviews were held for six women who were conveniently selected from a large (318) quantitative respondent. Both questionnaire and semi-structured interviews were used in collecting data for this study. Quantitative data were analysed using Statistical Package for Social Sciences (SPSS) using both inferential and bivariate analysis, whilst a qualitative content analysis was carried out on the qualitative data. Results: Of the 318 respondents, 91.2% accessed health facilities by motorised transport with 8.8% on-foot. Mode of transportation was (p=0.003) related to regularity at antenatal care, with those who came on-foot being regular at ANC than those that came on motorised transport. Conclusions: The study concludes that efficient and suitable transport system as well as distance are key factors influencing women role in decision-making to seek care.
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The criticality of referral makes it imperative to study its patterns and factors influencing it at a health systems level. This study of referral in Lagos, Nigeria is based on health records of 4181 pregnant women who presented with obstetric emergencies at one of the 24 comprehensive emergency obstetric care (EmOC) facilities in the state between November 2018 and October 2019 complemented with distance and time data extracted from Google Maps. Univariate, bivariate, and multivariate analyses were conducted. About a quarter of pregnant women who presented with obstetric emergencies were referred. Most referrals were from primary health centres (41.9 %), private (23.5 %) and public (16.2 %) hospitals. Apart from the expected low-level to high-level referral pattern, there were other patterns observed including non-formal, multiple, and post-delivery referrals. Travel time and distance to facilities that could provide needed care increased twofold on account of referrals compared to scenarios of going directly to the final facility, mostly travelling to these facilities by private cars/ taxis (72.8 %). Prolonged/obstructed labour was the commonest obstetric indication for referral, with majority of referred pregnant women delivered via caesarean section (52.9 %). After adjustment, being married, not being registered for antenatal care at facility of care, presenting at night or with a foetus in distress increased the odds of referral. However, parity, presentation in the months following the commissioning of a new comprehensive EmOC facility or with abortion reduced the likelihood of being referred. Our findings underscore the need for health systems strengthening interventions that support women during referral and the importance of antenatal care and early booking to aid identification of potential pregnancy complications whilst establishing robust birth preparedness plans that can minimise the need for referral in the event of emergencies. Indeed, there are context-specific influences that need to be addressed if effective referral systems are to be designed.
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Introduction Access to emergency obstetric care can lead to a 45%–75% reduction in stillbirths. However, before a pregnant woman can access this care, she needs to travel to a health facility. Our objective in this study was to assess the influence of distance and travel time to the actual hospital of care on stillbirth. Methods We conducted a retrospective cross-sectional study of pregnant women who presented with obstetric emergencies over a year across all 24 public hospitals in Lagos, Nigeria. Reviewing clinical records, we extracted sociodemographic, travel and obstetric data. Extracted travel data were exported to Google Maps, where typical distance and travel time for period-of-day they travelled were extracted. Multivariable logistic regression was conducted to determine the relative influence of distance and travel time on stillbirth. Results Of 3278 births, there were 408 stillbirths (12.5%). Women with livebirths travelled a median distance of 7.3 km (IQR 3.3–18.0) and over a median time of 24 min (IQR 12–51). Those with stillbirths travelled a median distance of 8.5 km (IQR 4.4–19.7) and over a median time of 30 min (IQR 16–60). Following adjustments, though no significant association with distance was found, odds of stillbirth were significantly higher for travel of 10–29 min (OR 2.25, 95% CI 1.40 to 3.63), 30–59 min (OR 2.30, 95% CI 1.22 to 4.34) and 60–119 min (OR 2.35, 95% CI 1.05 to 5.25). The adjusted OR of stillbirth was significantly lower following booking (OR 0.37, 95% CI 0.28 to 0.49), obstetric complications with mother (obstructed labour (OR 0.11, 95% CI 0.07 to 0.17) and haemorrhage (OR 0.30, 95%CI 0.20 to 0.46)). Odds were significantly higher with multiple gestations (OR 2.40, 95% CI 1.57 to 3.69) and referral (OR 1.55, 95% CI 1.13 to 2.12). Conclusion Travel time to a hospital was strongly associated with stillbirth. In addition to birth preparedness, efforts to get quality care quicker to women or women quicker to quality care will be critical for efforts to reduce stillbirths in a principally urban low-income and middle-income setting.
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Background The highest risk of maternal and perinatal deaths occurs during and shortly after childbirth and is preventable if functional referral systems enable women to reach appropriate health services when obstetric complications occur. Rising numbers of deliveries in health facilities, including in high mortality settings like Nigeria, require formalised coordination across the health system to ensure that women and newborns get to the right level of care, at the right time. This study describes and critically assesses the extent to which referral and its components can be captured using three different data sources from Nigeria, examining issues of data quality, validity, and usefulness for improving and monitoring obstetric care systems. Methods The study included three data sources on referral for childbirth care in Nigeria: a nationally representative household survey, patient records from multiple facilities in a state, and patient records from the apex referral facility in a city. We conducted descriptive analyses of the extent to which referral status and components were captured across the three sources. We also iteratively developed a visual conceptual framework to guide our critical comparative analysis. Results We found large differences in the proportion of women referred, and this reflected the different denominators and timings of the referral in each data source. Between 16 and 34% of referrals in the three sources originated in government hospitals, and lateral referrals (origin and destination facility of the same level) were observed in all three data sources. We found large gaps in the coverage of key components of referral as well as data gaps where this information was not routinely captured in facility-based sources. Conclusions Our analyses illustrated different perspectives from the national- to facility-level in the capture of the extent and components of obstetric referral. By triangulating across multiple data sources, we revealed the strengths and gaps within each approach in building a more complete picture of obstetric referral. We see our visual framework as assisting further research efforts to ensure all referral pathways are captured in order to better monitor and improve referral systems for women and newborns.
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Previous efforts to estimate the travel time to comprehensive emergency obstetric care (CEmOC) in low- and middle-income countries (LMICs) have either been based on spatial models or self-reported travel time, both with known inaccuracies. The study objectives were to estimate more realistic travel times for pregnant women in emergency situations using Google Maps, determine system-level factors that influence travel time and use these estimates to assess CEmOC geographical accessibility and coverage in Lagos state, Nigeria. Data on demographics, obstetric history and travel to CEmOC facilities of pregnant women with an obstetric emergency, who presented between 1st November 2018 and 31st December 2019 at a public CEmOC facility were collected from hospital records. Estimated travel times were individually extracted from Google Maps for the period of the day of travel. Bivariate and multivariate analyses were used to test associations between travel and health system-related factors with reaching the facility >60 minutes. Mean travel times were compared and geographical coverage mapped to identify ‘hotspots’ of predominantly >60 minutes travel to facilities. For the 4005 pregnant women with traceable journeys, travel time ranges were 2–240 minutes (without referral) and 7–320 minutes (with referral). Total travel time was within the 60 and 120 minute benchmark for 80 and 96% of women, respectively. The period of the day of travel and having been referred were significantly associated with travelling >60 minutes. Many pregnant women living in the central cities and remote towns typically travelled to CEmOC facilities around them. We identified four hotspots from which pregnant women travelled >60 minutes to facilities. Mean travel time and distance to reach tertiary referral hospitals were significantly higher than the secondary facilities. Our findings suggest that actions taken to address gaps need to be contextualized. Our approach provides a useful guide for stakeholders seeking to comprehensively explore geographical inequities in CEmOC access within urban/peri-urban LMIC settings.
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Background: Travel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel. Methods: We extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women's journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min. Results: The median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50-52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min. Conclusions: Existing modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate 'closer-to-reality' estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.