Study design and data collection
This secondary data analysis draws on two Emergency Obstetric and Newborn Care (EmONC) assessments from 2008 and 2016 (17,18). Both were national cross-sectional censuses of public and private health facilities.
The data collection instruments for 2008 were adapted from a set of standard modules previously used in many countries (19). The 2016 assessment used the same core tools administered in 2008 that underwent a global revision with local adaptation in 2016. The six modules relevant to this study were: M1 - basic infrastructure; M2 - human resources; M3 - inventory of drugs, equipment and supplies; M4 - summary service statistics; M5 - performance of signal functions; and M8 - case review for CDs. All facilities received modules 1-5; only facilities that performed CDs received M8. The latter was modified in 2016 to include information not in 2008 such as a woman’s characteristics needed to classify her into one of the Robson 10 groups, type of anesthesia and professional cadre who performed the operation.
The first assessment was launched October 1, 2008 and completed by January 15, 2009; the 2016 survey commenced in mid-May and was completed by mid-December 2016. In 2008, a private company conducted the assessment and prepared the databases (20) and in 2016, this responsibility shifted to the Ethiopian Public Health Institute. Details about the data collector training and survey execution can be found in the final reports (17, 18).
Study population and setting
If the Ethiopian Food, Medicine and Health Control Authority approved a facility as a site to deliver routine and/or operative childbirth services, it was eligible for the assessment. Between 2008 and 2016 the Ministry of Health led a massive infrastructure expansion, adding about 3000 health centers and 200 hospitals (21,22). In 2008, 751 health facilities with childbirth services were visited, including 111 hospitals. In 2016, 3,804 were visited, 316 of which were hospitals. This paper focuses on hospitals designated to provide comprehensive emergency obstetric care.
Although we call both assessments a census, in 2008 15 facilities were not visited because they did not appear on the master list of licensed facilities, 12 of which were in the capital of Addis Ababa. In 2016, 11 facilities were not visited due to civil unrest but few if any of these service sites were hospitals. Finally, two hospitals refused to participate in the 2016 assessment.
In each hospital, a subset of women who delivered by cesarean had their records reviewed. In 2008 data collectors identified three women per hospital and in 2016 only two cases per hospital were selected because of the increase in the number of hospitals. The selection criteria remained the same: 1) cases occurred in the previous 12 months, and 2) they were the last women who had had a cesarean but were no longer under postoperative care, regardless of survival.
Processes and comparisons
This paper features three units of analysis: aggregated hospital service statistics, hospitals and individual women who delivered by cesarean. Aggregated service statistics (M4) covered 12 consecutive months prior to the assessment (July 2007–June 2008 and January-December 2015). Service data included the number of deliveries by mode of delivery; these data were used to estimate CD rates. Modules 1, 2, 3 and 5 provided information to assess hospital readiness to perform CD while the CD chart review (M8) was the information source for service quality and record-keeping at the individual level.
Variables and index creation
For both population-based and institutional CD rates, the numerator was the sum of all CDs performed at each hospital. The denominator for the population-based rate was the number of expected births in each region, calculated from population figures from the Planning and Programming Department of the Federal Ministry of Health and the crude birth rate established by the 2016 Demographic and Health Survey (13,23). The denominator for the institutional CD rate was the sum of all births at each hospital that had provided cesarean services in the three months prior to the assessment.
To assess health system readiness to provide CD services, we created a binary summary score (yes or no), based on an algorithm defined by the availability of at least one health professional able to perform the operation and another to provide anesthesia, plus readiness items that had to be functioning and included EITHER an anesthesia machine + (halothane or ketamine) OR regional anesthesia (lignocaine/ lidocaine 4% or bupivacaine) AND an oxygen cylinder with manometer and flowmeter (low flow) tubes and connectors, an operating table and a functioning adjustable light (20). Although not included in the algorithm, interruptions in water and electricity in the operation theaters were assessed.
Quality of clinical management was measured by use of a partograph, administration of prophylactic antibiotics and uterotonics, time interval from decision to incision, type of anesthesia, clinician who performed the CD and maternal and newborn outcomes.
A final analysis of clinical management was based on the Robson 10-group classification scheme, designed to determine institutional cesarean rates for clinically relevant and mutually exclusive groups (24). The classification system depends on six characteristics of women that are easily captured: parity (nulliparous/multiparous), number of fetuses (singleton/multiple), onset of labor (spontaneous or induced/CD before labor started), previous CD (yes/no), fetal lie (cephalic/ transverse/breech) and gestational age (<37 weeks/ >37 weeks). Because the 10 groups (see Figure 1) tend to have different cesarean rates, the classification scheme is used to inform where changes in clinical management should be made. One of the goals of its usage is to reduce cesareans among nulliparous women (groups 1 and 2), who are known to be vulnerable to unnecessary CDs, and who are often the biggest group (25). Overuse of CD among these women sets up a domino effect that contributes to repeated cesareans.
Our aim in using the Robson classification was to determine the distribution of cases according to the 10 groups to show their relative size, if and how the group sizes varied across managing authority, and the extent to which the group sizes aligned with other studies.
Our key stratifying variable – hospital managing authority – was defined with three categories: public or government, private for-profit and private not-for-profit management by non-governmental organizations and/or religious missions.
To produce descriptive statistics (frequencies, percentages, means and medians) we used SPSS version 24. Since our data sources were censuses and not random samples, nor did they represent some theoretical population, we performed no statistical tests.
This paper utilizes secondary data; permission to use the data was granted by the Ethiopian Public Health Institute and the Family Health Division at the Federal Ministry of Health.