Frequent refresher training on newborn resuscitation and potential impact on perinatal outcome over time in a rural Tanzanian hospital: An observational study

Objectives Globally, perinatal mortality remains high, especially in sub-Saharan countries, mainly because of inadequate obstetric and newborn care. Helping Babies Breathe (HBB) resuscitation training as part of a continuous quality improvement (CQI) programme may improve outcomes. The aim of this study was to describe observed changes in perinatal survival during a 6-year period, while adjusting for relevant perinatal risk factors. Setting Delivery rooms and operating theatre in a rural referral hospital in northern-central Tanzania providing comprehensive obstetric and basic newborn care 24 hours a day. The hospital serves approximately 2 million people comprising low social-economic status. Participants All newborns (n=31 122) born in the hospital from February 2010 through January 2017; 4893 were born in the 1-year baseline period (February 2010 through January 2011), 26 229 in the following CQI period. Interventions The HBB CQI project, including frequent HBB training, was implemented from February 2011. This is a quality assessment analysis of prospectively collected observational data including patient, process and outcome measures of every delivery. Logistic regression modelling was used to construct risk-adjusted variable life adjusted display (VLAD) and cumulative sum (CUSUM) plots to monitor changes in perinatal survival (primary outcome). Results During the 6-year CQI period, the unadjusted number of extra lives saved according to the VLAD plot was 150 despite more women admitted with pregnancy and labour complications and more caesarean deliveries. After adjusting for these risk factors, the risk-adjusted VLAD plot indicated that an estimated 250 extra lives were saved. The risk-adjusted CUSUM plot confirmed a persistent and steady increase in perinatal survival. Conclusions The risk-adjusted statistical process control methods indicate significant improvement in perinatal survival after initiation of the HBB CQI project with continuous focus on newborn resuscitation training during the period, despite a concomitant increase in high-risk deliveries. Risk-adjusted VLAD and CUSUM are useful methods to quantify, illustrate and demonstrate persistent changes in outcome over time. This is a retrospective analysis of data from a prospective observational study conducted at HLH, a rural referral hospital located in northern-central Tanzania from February 2010 through January 2017. The baseline ePMR was 2.7% (n=133 deaths). The catchment area for HLH is approximately 2 million people comprising predominantly low social economic status.20 HLH provides comprehensive obstetric and basic newborn care 24 hours a day, 7 days a week. The labour ward has six delivery rooms with one delivery bed each, and one operating theatre where caesarean sections (CS) take place. Data were collected from all delivery rooms and the operating theatre. Figure 1 presents different interventions and events during the study period. Overview of different interventions and events during the study period. CQI, continuous quality improvement; HBB, Helping Babies Breathe; RCT, randomised controlled trial. Data collection for the National HBB study started in February 2010. HBB consisted of practical training on basic newborn care and resuscitation.10–12 15 One full-day HBB training was conducted in April 2010, facilitated by master trainers from the Tanzanian Ministry of Health. However, not all relevant staff were trained and no CQI efforts were introduced after the training. Evaluation 7 months after this 1-day HBB training revealed no changes in clinical management,21 leading to initiation of the HBB CQI programme in 2011. Due to lack of improvement in clinical management in the delivery room,21 a programme encouraging frequent brief onsite simulation HBB trainings among the midwives was implemented in February 2011 (HBB CQI). Five local midwives were trained to become HBB trainers, with the responsibility to facilitate ongoing frequent HBB trainings in the labour ward.12 Short (5–10 min) mandatory HBB simulation-based training sessions were conducted on a weekly basis over the following 6 years. In early 2013, the Safer Births project was initiated, which included instalment of newborn resuscitation monitors (Laerdal Global Health, Stavanger, Norway). The newborn heart rate was displayed on the monitor as a continuous feedback to the provider. In 2016, a newborn ventilation trainer system (Laerdal Global Health) for low-dose high-frequency onsite practice was introduced. The training involved the use of a novel newborn manikin (NeoNatalie Advanced Prototype, Laerdal Global Health) which could be adjusted to simulate four different common resuscitation scenarios. These were based on real data from more than 1000 live resuscitations observed and recorded by the newborn resuscitation monitor at HLH. The training system was easily operated using a tablet providing an immediate feedback to the provider after a training session, with specific tips to improve. The training system facilitated both individual skills and scenario team training. As part of Safer Births, two randomised controlled studies comparing different devices for fetal heart rate monitoring, involving low-risk deliveries, were conducted at HLH.22 23 The first study, from March 2013 to August 2015, compared a wind-up handheld Doppler (FreePlay) and the Pinard fetoscope (commonly used in this setting).22 The second study, from February 2016 to January 2017, compared a new strap-on continuous fetal heart rate monitor named Moyo (Laerdal Global Health) and the Pinard fetoscope for intermittent monitoring.23 Moyo is a robust low-cost device developed for low-resource settings, reported to improve midwifery care.24 No significant changes in ePMR outcomes were reported in the two randomised controlled studies at HLH.22 23 Between October 2014 and June 2016, a randomised controlled study comparing the standard newborn resuscitator (Laerdal Medical) with a new upright resuscitator (Laerdal Global Health) for ventilation of non-breathing newborns was conducted at HLH.25 This study included additional training on bag mask ventilation skills. No significant changes in ePMR were reported during the study period.25 In 2014–2017, HLH took part in a premature multicentre study led by the Ministry of Health, including premature newborns less than 34 weeks’ gestation.26 A bundle-of-care approach (ie, antenatal corticosteroids, maternal and newborn antibiotics, immediate HBB intervention and avoidance of hypothermia) was introduced, but no significant change in newborn mortality was reported at HLH.26 During the reference study period (2011–2017), a high turnover of midwives was noted, particularly in relation to new government employment opportunities every midyear (experienced HLH staff leaving to be employed in other government-owned health facilities) and towards the end of each year (HLH recruited new midwives who had completed midwifery training at Haydom School of Nursing to fill the gaps). An ambulance fee was introduced in July 2013 and a delivery fee in January 2014. Trained research assistants have observed every delivery in the labour ward, working 2–3 in each shift covering 24 hours a day, 7 days a week, using a structured data collection form. The observations started in July 2009, 6 months before the National HBB study.11 In this period the staff became familiar to the observers (minimising the Hawthorne effect) and the research assistants were intensively trained in live observations and accurate data collection and reporting. Data collection for this study took place from February 2010 through January 2017. Information collected included pregnancy complication, labour process and outcome, newborn information and birth attendant information. Additionally, to facilitate electronic physiological data collection, newborn resuscitation monitors (Laerdal Global Health), connected to a dry electrode ECG sensor for rapid heart rate detection and a self-inflating bag mask for newborn ventilation, were installed in every delivery room, including the operating theatre where CS took place from March 2013. Data were collected prospectively during this study period, and there was a data quality control system to ensure the validity. This study was undertaken in a rural setting, comprising a poor population with a high illiteracy rate and little infrastructure. In such settings, involvement of patients and public is particularly difficult and demanding. However, several individual projects during the study period, like the randomised studies, actively involved the patients. Furthermore, our results are continuously shared through community meetings and community leaders’ meetings. The published paper will be located in the hospital library where the community has access. This study was approved by the National Institute for Medical Research (NIMR) and the Ministry of Health in Tanzania (the HBB CQI programme Ref NIMR/HQ/R.8a/Vol IX/1247 and the Safer Births project Ref NIMR/HQ/R8a/Vol IX/1434), and by the Regional Committee for Medical and Health Research Ethics, Western Norway (Ref 2009/302 and Ref number 2013/110/REK). All relevant healthcare providers were informed about the different HBB CQI and Safer Births quality assessment studies and gave oral consent. Patients were also informed about ongoing studies. Oral consents were obtained for participation in the randomised controlled studies. For the quality assessment studies, patient consents were not obtained as approved by the ethical committees. Basic count data are presented as numbers and percentages and continuous data as means and SDs. The aim of this study was to monitor and document changes in perinatal survival over time while adjusting for relevant risk factors within the cohort. Therefore, perinatal characteristics and risk factors not related to clinical management were included as explanatory variables in a logistic regression modelling. These risk factors were: birth weight, gestational age, fetal heart rate status, pregnancy complication, fetal presentation of the newborn, multiple birth, source of admission, maternal infection, delivery mode, pre-eclampsia, uterine rupture, cord prolapse and bleeding before labour. First, univariable logistic regression models with each of the listed potential risk factors for perinatal mortality as explanatory variable and perinatal survival as response were fitted. Those risk factors with a p value <0.2 in the univariable model were included in the multivariable modelling. Then a stepwise model selection procedure was run, and finally, removed variables were reintroduced one by one and kept if found significant. Goodness of fit was verified by the Hosmer-Lemeshow test. The regression model was fitted based on the data in the baseline period. For the data after the baseline period, we constructed a risk-adjusted VLAD plot,27 presenting the CUSUM of expected outcome for each newborn if the baseline situation had persisted, minus the observed outcome. The expected outcome is the probability of death according to the logistic regression model. The observed outcome is numbered 0 for survival and 1 for death. The VLAD plot can then be interpreted as the cumulative excess number of survivors over time, compared with the baseline rate taking into account risk factors. For comparison we also made a VLAD plot without the risk adjustment. Moreover, as a formal statistical monitoring procedure with a signal limit to detect persistent changes, a risk-adjusted CUSUM based on the same logistic regression model as the VLAD plot was constructed.28–30 The CUSUM was constructed to quickly detect an improvement of 0.5 percentage points in the ePMR from the baseline level. The signal limit of this CUSUM was calculated such that with no change in the true survival probability there would on average be one false alarm every 100 months (ie, if there is no change in the true survival probability the CUSUM would remain close to zero and only go above the signal limit on average once per 100 months). The calculations of the CUSUM were done using methods implemented in the R package spcadjust.31 For comparison we also made a CUSUM plot without the risk adjustment. Since the aim of this study was to document the impact of improved management on early perinatal (ie, fresh stillbirths and 24 hours’ newborn deaths) survival, macerated stillbirths were not included in the regression model and the SPC analyses.