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Introduction Sierra Leone, one of the countries with the highest maternal and perinatal mortality in the world, launched its first National Emergency Medical Service (NEMS) in 2018. We carried out a countrywide assessment to analyse NEMS operational times for obstetric emergencies in respect the access to timely essential surgery within 2 hours. Moreover, we evaluated the relationship between operational times and maternal and perinatal mortality. Methods We collected prehospital data of 6387 obstetric emergencies referrals from primary health units to hospital facilities between June 2019 and May 2020 and we estimated the proportion of referrals with a prehospital time (PT) within 2 hours. The association between PT and mortality was investigated using Poisson regression models for binary data. Results At the national level, the proportion of emergency obstetric referrals with a PT within 2 hours was 58.5% (95% CI 56.9% to 60.1%) during the rainy season and 61.4% (95% CI 59.5% to 63.2%) during the dry season. Results were substantially different between districts, with the capital city of Freetown reporting more than 90% of referrals within the benchmark and some rural districts less than 40%. Risk of maternal death at 60, 120 and 180 min of PT was 1.8%, 3.8% and 4.3%, respectively. Corresponding figures for perinatal mortality were 16%, 18% and 25%. Conclusion NEMS operational times for obstetric emergencies in Sierra Leone vary greatly and referral transports in rural areas struggle to reach essential surgery within 2 hours. Maternal and perinatal risk of death increased concurrently with operational times, even beyond the 2-hour target, therefore, any reduction of the time to reach the hospital, may translate into improved patient outcomes.
This was a retrospective study analysing NEMS operational times in response to obstetric emergencies recorded countrywide between 1 June 2019 and 31 May 2020, thus including both the rainy (June to November) and the dry (December to May) season. The study facilities consisted of 1368 PHUs and 33 referral hospitals, including government district hospitals, faith-based clinics, and health centres managed by non-governmental organisations (figure 1). PHUs provide different levels of care, which can be described as ‘level one PHUs’ providing basic ante-natal and post-natal care and assistance to uncomplicated deliveries, and ‘level two PHUs’ offering basic emergency obstetric and neonatal care (BEmONC) services, which included the administration of antibiotics, oxytocics and anticonvulsants, manual removal of placenta and retained products of delivery, assisted vaginal delivery and basic neonatal resuscitation.16 Comprehensive emergency obstetric care (CEmOC) services, including all the BEmOC functions plus caesarean section and blood transfusion, were provided only at the district hospital level. Healthcare professionals in the PHUs were responsible for activating NEMS after providing primary assessment and care to pregnant women. The emergency requests received via phone from the PHUs were evaluated and managed by trained nurses at the NEMS OC. Subsequent phases entailed the dispatch of ambulance teams, composed of trained paramedic and an ambulance driver, and contact with the proposed referral facility.14 At the district level, ambulance to population ratio ranged from 0.8 to 1.8 ambulances per 100 000 inhabitants and ambulance distribution in the different districts was based on population density and the dimension of the geographical area covered.14 Treatment provided in the ambulances entailed oxygen delivery, administration of rectal misoprostol for prevention of postpartum haemorrhage, fluid resuscitation, assistance to labour and delivery, and basic life support manoeuvres. Ambulance personnel underwent a series of ad hoc basic training courses that included the management of medical, trauma, obstetrics, gynaecology and paediatric emergencies and basic life support and resuscitation manoeuvres without the support of automated external defibrillator.14 Distribution of district hospitals, peripheral health units (PHUs) and National Emergency Medical Service ambulances in Sierra Leone. While currently managed by the Ministry of Health and Sanitation and financed though governmental budget, during the study period the NEMS has been managed and coordinated by a government-backed joint venture comprising Doctors with Africa (CUAMM, Padua, Italy), the Regional Government of Veneto (Italy) and the Research Center in Emergency and Disaster Medicine (Università del Piemonte Orientale, Italy) and financed by the World Bank.14 Although in July 2017 the new administrative division of Sierra Leone increased the number of districts from 14 to 16, the NEMS design and implementation was based on the initial district subdivision. Moreover, in this study the two districts of Western Area Urban and Western Area Rural, which included the densely inhabited capital city of Freetown and its surroundings, were analysed together as ‘Western Area’. We retrieved prehospital data from the OC software, an in-house developed software for call-taking, triage, aided dispatching, mission monitoring and data collection. The OC software also recorded data on operational times received from the 81 ambulance units dispatched on the ground, as paramedics were required to contact the OC by cell phone at the following time points: (1) when leaving ambulance station, (2) when arriving at the PHU, (3) when departing from the PHU, (4) when arriving at hospital, (5) when departing from hospital and (6) when arriving at ambulance station. We used this information to calculate the prehospital time (PT), defined as the time elapsed between the receipt of the emergency call from the PHU and the arrival at the hospital facility. In addition, we defined other time variables of interest, which included dispatch time (DT), response time (RT), time on scene (ToS) and TT, described in figure 2. Additional data extracted by OC software included age of the patient, mission priority, mission complaint. We included in the analysis 6387 obstetric emergencies classified as ‘Red’ triage codes, clinically defined as ‘immediately life threatening’, while we excluded ‘Yellow’ triage codes, clinically defined as ‘not life-threatening but still serious’. Evaluation and triage of obstetric cases was performed by the OC operators through codes and scripted questions adapted from the Medical Priority Dispatch system,17 available on request from the authors. Data on the population of Sierra Leone and its districts were extracted from the 2015 Sierra Leone Census, as reported on the Sierra Leone Statistics website.18 Prehospital operational times of the National Emergency Medical Service (NEMS) in Sierra Leone dispatch time: time between the receipt of the emergency call and the dispatch of NEMS ambulance. Response time: time between the receipt of the emergency call and the arrival at the peripheral health unit (PHU). Time on scene: time between the arrival at the PHU and departure from the PHU. Travel time: time between departure from the PHU and the arrival at the hospital facility. Prehospital time: total time elapsed between the receipt of the emergency call and the arrival at the hospital facility. We adopted the 10th Revision of the International Classification of Diseases (ICD-10) to define maternal mortality as ‘deaths from any cause related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy’. According to ICD-10, the perinatal period included the ‘time frame that begins before birth and ends 28 days following the delivery.’ To evaluate the association between PT and maternal and perinatal mortality, we retrieved data from the national referral coordinators’ database, storing details on all incoming referrals collected at each hospital facility including in-hospital patient outcomes, a piece of information that was only available from January 2020 onwards. For this reason, we used a unique mission code to merge the above-mentioned database with a subset of data from the OC software, corresponding to 1717 referrals of obstetric emergencies recorded from 1 January 2020to 31 of May 2020. Among these 1717 obstetric emergencies, 1606 missions included maternal conditions also affecting the neonate, while the remaining 111 missions referred to conditions limited to the mother (eg, postpartum haemorrhage). All emergency obstetric referrals were classified according to the type of emergency recorded by the OC and based on patient assessment at the PHUs level. For each district, we used median and IQRs to display the operational times and we estimated the proportion of missions that had a PT within 2 hours. The association between PT and mortality was investigated using modified Poisson regression models for binary data with robust estimate of the variance.19 Natural cubic splines were incorporated into the models to assess the shape of the association and allow for possible non-linear effects. The optimal degree of smoothing was chosen using a model selection procedure proposed by Royston and Sauerbrei.20 21 All the analyses were performed using Stata V.15 (StataCorp. 2017. Stata Statistical Software: Release 15. StataCorp). Patients and the public were not involved in the design of this study and in the dissemination plans of our research.
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