Quality of maternity care and its determinants along the continuum in Kenya: A structural equation modeling analysis

Background: Improving access to delivery services does not guarantee access to quality obstetric care and better survival, and therefore, concerns for quality of maternal and newborn care in low-and middle-income countries have been raised. Our study explored characteristics associated with the quality of initial assessment, intrapartum, and immediate postpartum and newborn care, and further assessed the relationships along the continuum of care. Methods: The 2010 Service Provision Assessment data of Kenya for 627 routine deliveries of women aged 15-49 were used. Quality of care measures were assessed using recently validated quality of care measures during initial assessment, intrapartum, and postpartum periods. Data were analyzed with negative binomial regression and structural equation modeling technique. Results: The negative binomial regression results identified a number of determinants of quality, such as the level of health facilities, managing authority, presence of delivery fee, central electricity supply and clinical guideline for maternal and neonatal care. Our structural equation modeling (SEM) further demonstrated that facility characteristics were important determinants of quality for initial assessment and postpartum care, while characteristics at the provider level became more important in shaping the quality of intrapartum care. Furthermore we also noted that quality of initial assessment had a positive association with quality of intrapartum care (β = 0.71, p < 0.001), which in turn was positively associated with the quality of newborn and immediate postpartum care (β = 1.29, p = 0.004). Conclusions: A continued focus on quality of care along the continuum of maternity care is important not only to mothers but also their newborns. Policymakers should therefore ensure that required resources, as well as adequate supervision and emphasis on the quality of obstetric care, are available. The 2010 SPA dataset of Kenya was used to explore our objectives. The SPA is a nationally representative health facility cross-sectional survey, and its complete details can be found elsewhere [31]. The survey data included availability of all necessary items in the facility for providers to offer quality delivery services as well as the providers’ information. Random sampling was used to select health facilities from the Master Facility List (MFL) of 6,192 operational public and private health facilities (see distribution of facilities across the country in Fig 1). The sampling strategy was designed to allow for representation of all levels of health care system and different managing authorities. A total of 703 facilities were sampled, representing roughly 11% of all health facilities in Kenya. In these facilities, health professionals who attended 627 routine delivery clients aged 15–49 were assessed for adherence to several signal functions and tracer items of delivery care that included intrapartum, newborn and immediate postpartum care. The signal functions used in our study are grounded on validated signal functions in sub-Saharan Africa (SSA) [17]. However, there were varying extent of missing data patterns in different indicators of the initial assessment and examination stage (221), intrapartum care (224), and newborn and immediate postpartum care (111). Moreover, the determinants of quality of care variables also had missing values as indicated herein: level of health facility (25), age (39), years of experience (39), and received incentives (39). After excluding the missing variable in any of the indicator used in our analyses, only 290 observations had complete data. Handling of missing data is described in the statistical analysis subsection. The validated and recommended signal functions and tracers of quality of intrapartum, newborn and postpartum care–suggested by experts in SSA [17] and listed in Table 1 –included 7 items during initial assessment and examination stage (i.e. asked if the woman experienced any danger sign, performed a general examination, took the temperature, took the blood pressure, took the pulse, washed hands before examination, and wore sterile gloves for vaginal examination), 7 items at the time of intrapartum care (i.e. explained all procedures, prepared uterotonic drug for active management of third stage labor, used partograph during labor, prepared newborn resuscitation equipment, correctly administered uterotonics, assessed integrity of placenta/membranes, and assessed for perineal/vaginal lacerations), and 6 items for newborn and immediate postpartum care (i.e. immediately dried the baby with towel, assessed newborn resuscitation effort and placed on mother’s abdomen skin-to-skin, tied or clamped the cord but not immediately after birth, took the mother’s vital signs 15 minutes after birth, palpated uterus 15 minutes after delivery, and assisted the mother to initiate breastfeeding within 1 hour). All the quality of care indicators were dichotomized. These quality measures reflect the minimum standards of obstetric care, irrespective of the type of health facilities where the delivery service is performed. a, Private hospitals include non-governmental organization, private-not-for-profit, private-for-profit, mission and faith-based hospitals b, General linear regression was used to analyze the difference in the continuous indicators while χ2 was used for the categorical variables; %wt, weighted using complex survey method (Taylor series linearization) to adjust for the sampling design; AMTSL, Active management of third stage labor; M (SD), Mean (standard deviation) The determinants of quality of care were categorized into three groups: facility, provider, and region. The facility indicators include: the level of health facility, including primary care level (district/sub-district hospitals and health centers), secondary and tertiary care levels (provincial and national hospitals), and private hospitals (non-governmental organizations, private-not-for-profit, private-for-profit, and mission and faith-based hospitals); managing authority (government versus non-government), which could be different than its ownership since some private hospitals were managed and supported by the government, and vice versa; delivery capacity (as indicated by the number of delivery couches); number of deliveries in the past 12 months; whether a delivery fee was administered; availability of piped water; and, availability of central electric supply. The government operated hospitals included most of the health centers, sub-district hospitals, district hospitals, provincial hospitals, and the national hospitals. Provider characteristics variables include six indicators: age, gender, years of experience, qualification (divided into three categories i.e. specialist/BSN nurse, registered nurse/midwife, and enrolled nurse/midwife), whether obstetrics and gynecologist (OB/GYN) are available for night duty, and whether providers received financial or non-financial incentives (i.e. categorized as no incentives, non-financial incentives only, and both financial/non-financial incentives). Furthermore, region characteristics included eight provinces (i.e. Central, Coast, Eastern, Nairobi, Northeastern, Nyanza, Rift valley, and Western) and 96 districts to account for any remaining influences from locality-specific factors. We explored characteristics associated with quality of maternity care in three phases–descriptive statistics, negative binomial regression analyses, and finally, structural equation modeling (SEM). For the descriptive statistics, we presented the average performance on quality indicators across the maternity care continuum at different levels of health facility for observations with complete information (n = 290). Chi-squared and general linear regression were used to test the differences across different types of facilities for categorical and continuous variables, respectively. A p-value of less than .05 was considered statistically significant. In the second phase of exploring the determinants of quality at each of the three stages of obstetric care, we combined the dichotomized quality measures into an additive quality indicator, reflecting the count of signal functions offered, respectively for the three stages of care. Negative binomial regression was employed to explore the potential provider and facility determinants. Observations with missing data were also excluded at this phase of our analysis. All regression analyses were weighted using complex survey method (Taylor series linearization) to adjust for the sampling design (see S1 File for programming codes in Stata). Our results were presented as crude and adjusted incidence rate ratios (IRRs). The statistical software for the second phase of the analysis was Stata 13.1 [32]. In the third and final phase of our analysis, we used SEM technique to determine, in addition to determinants of quality, the interrelationship among quality of care in different phases along the care continuum. SEM assessed the latent variables, for example, quality of intrapartum care, using observables measures, such as the seven dichotomized intrapartum care indicators, and it allowed us to examine the determinants of quality and the influences of the quality in an earlier phase on a later phase, all at the same time (see S1 File for SIMPLIS syntax). All of the data (n = 627) were used at this stage after imputing missing values. Under the SEM, we were able to better leverage information in the dataset in the presence of missing data. A modern method for imputation accounting for Missing at Random (MAR) and Missing Completely at Random (MCAR) assumptions, i.e. the Full Information Maximum Likelihood (FIML) method, was employed in LISREL 8.80 to estimate the relationships along the continuum of care for quality maternity care [33]. One benefit of FIML method which employs the Expectation Maximization (EM) imputation technique is the reliable estimation procedure even up to 50% of missing data [29,34,35]. The complex relationships among quality of care for different phases of care, as well as their determinants were presented along with the parameter estimates of the structural model using a path diagram.