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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.
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 Lagos State, located in the southwestern part of Nigeria, has various geographical terrains (including land and water) and settlement types (including a central metropolis, 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 estimate. 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 pregnant women at facilities.15 In Lagos, the most typical mode of transport is by road. However, in many parts of the state, the road infrastructure 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 concentration of highly skilled health personnel and equipment and sometimes ‘free’ or reduced hospital cost.20 In emergencies, 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 hospitals.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 were extracted from patient records over 6 months by the in-country research team, all of whom were qualified medical doctors, including consultant obstetricians, resident doctors and medical officers who had clinical experience working in the obstetrical units of the hospitals 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. In each hospital, we identified and included all pregnant women who presented with an obstetrical emergency, 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. 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 application. 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 untraceable (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 afternoon (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 alternatives 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 Classification 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 Following data cleaning and validation, we calculated cause‐specific case fatality rates and conducted descriptive 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 complications 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 and considered differences observed as statistically significant 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). Patients and the public were not involved in the design, conduct, reporting or dissemination of this research.
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