Levels and variations in the quality of facility-based antenatal care in Kenya: evidence from the 2010 service provision assessment.

Quality of care is emerging as an important concern for low- and middle-income countries working to expand and improve coverage. However, there is limited systematic, large-scale empirical guidance to inform policy design. Our study operationalized indicators for six dimensions of quality of care that are captured in currently available, standardized Service Provision Assessments. We implemented these measures to assess the levels and heterogeneity of antenatal care in Kenya. Using our indicator mix, we find that performance is low overall and that there is substantial variation across provinces, management authority and facility type. Overall, facilities performed highest in the dimensions of efficiency and acceptability/patient-centeredness, and lowest on effectiveness and accessibility. Public facilities generally performed worse or similarly to private or faith-based facilities. We illustrate how these data and methods can provide readily-available, low-cost decision support for policy.

Key Messages

Quality of care is emerging as an important policy concern but there is little systematic evidence.

This study operationalizes indicators for six dimensions of quality of antenatal care on the 2010 Kenya SPA.

Performance is low overall and varies across facilities, with important implications for policy design.

Introduction

As many low- and middle-income countries (LMIC) continue to make significant improvements in expanding access to health care services, policy-makers are increasingly cognizant of the need to improve the quality of these services. A growing body of evidence suggests that quality shortfalls are substantial even for basic health care services (Das and Gertler 2007; Das ; Jha ), prompting calls to measure and address these gaps. On a global level, the quality of health care services is featured in the Sustainable Development Goals, the successors of the Millennium Development Goals (LCSDSN 2015). Many countries are actively exploring how to ensure or raise the quality of care, with policies ranging from supporting access to private sector providers that may offer higher quality to providing financial rewards to providers for achieving quality targets.

However, there is currently little systematic documentation of the levels and variations in quality of care that could support the design and deployment of effective policies (Scott and Jha 2014). One practical constraint to analysing the quality of care is the shortage of reliable facility data in developing LMIC ( Glassman and Ezeh 2014).

In this article, we illustrate how data from large-scale facility surveys currently available for 14 countries can be used to gauge the quality of antenatal care (ANC) services and provide basic decision support to policy makers. We focus on ANC services which have relatively less complex standards of care than other aspects of health care services. We derived 14 indicators in six dimensions from existing quality of care frameworks and applied these measures to the 2010 Kenya SPA to examine their empirical distributions across geography, facility type and management authority.

We discuss how such analyses can be used to inform the design of quality improvement policies, such as Kenya’s nascent results-based financing (RBF) initiative and the country’s recent drive to provide free maternal care in public facilities. RBF schemes—programmes that pay for pre-defined, measurable outputs as opposed to inputs related to service delivery—have grown in scale and scope across LMICs over the last decade. With support from donors, countries have begun adapting RBF programmes to improve quality of care in addition to increasing quantity of care. Meanwhile, demand-side interventions that could substantially increase utilization could lead to deterioration in the quality of care.

This article illustrates how existing facility surveys can be used to conduct low-cost quality of health care analyses. Such analyses can help identify potential areas of concern that could be addressed in the design of policies like Kenya’s free maternal care programme, or that could be monitored more closely during implementation. For informing programmes like RBF, baseline quality of care analyses can help determine which issues to target and how to design incentive structures to encourage overall improvements and address heterogeneities across facilities. Throughout, we describe the potential and challenges of using surveys like the SPA to evaluate the quality of ANC services.

Data and methods

To adequately capture the complexities of quality of ANC services, we reviewed quality frameworks proposed by Donabedian (1988), the World Health Organization (2006), the Organisation for Economic Co-operation and Development (OECD, Kelley and Hurst 2006) and the Institute of Medicine (2001). We selected the six dimensions of quality of care proposed by the WHO as the operative conceptual framework for this study. These dimensions align with the other frameworks and are the most appropriate for quality measurement in the developing country context. WHO provides the following definitions for the six dimensions: (1) effectiveness: ‘delivering health care that is adherent to an evidence base and results in improved health outcomes for individuals and communities, based on need’; (2) efficiency: ‘delivering health care in a manner which maximizes resource use and avoids waste’; (3) accessibility: ‘delivering health care that is timely, geographically reasonable, and provided in a setting where skills and resources are appropriate to medical need’; (4) acceptability/patient-centeredness: ‘delivering health care which takes into account the preferences and aspirations of individual service users and the cultures of their communities’; (5) equity: ‘delivering health care which does not vary in quality because of personal characteristics such as gender, race, ethnicity, geographical location or socioeconomic status’; and (6) safety: ‘delivering health care which minimizes risks and harm to service users’.

Indicators

We identified ANC quality indicators that can be constructed at the facility level through a review of peer-reviewed and grey literature, as well as ANC-specific indicators used commonly in quality checklists of RBF programmes in LMICs; in some cases these overlapped. We included the latter because indicators in RBF programmes are clearly of interest to policy makers and have been operationalized for LMICs. Together, these indicators were mapped into six quality of care dimensions using the WHO dimension definitions and to the SPA survey. We selected candidate indicators in terms of reproducibility from SPA data and to ensure RBF indicators were represented. The mapping and the final list of 14 indicators are presented in Supplementary Appendix S1, which also describes how some indicators were adapted to the Kenyan context and data.

Data

We calculated the individual indicators using the 2010 Kenya SPA, a facility-based cross-sectional survey. The SPA is a standardized survey supported by the Demographic and Health Surveys Programme of USAID and routinely administered by ICF International and in-country partners.

The 2010 Kenyan SPA consists of several survey instruments that are administered concurrently and generally on the same day: a facility audit, health care worker interviews and client observations and exit interviews for ANC, family planning and sick child visits (for comprehensive documentation and questionnaires see NCAPD 2011). The facility audit entails an interview with the in-charge of the facility or the senior-most staff member; direct verification of the presence of certain commodities, equipment and amenities; and verification of their use. The health worker interview asks about specific training received and services offered by the health worker, as well as workers’ opinions on the work conditions at the facility. The client–provider observations are based on observation protocols specific to ANC, family planning services and sick child care. The exit interviews capture clients immediately after a consultation and verify the services received and client opinions regarding those services. The exit interviews are also specific to ANC, family planning and sick child services.

Data from the Kenyan SPA are representative at the provincial, facility type and management authority levels (NCAPD 2011). Seven hundred and three facilities or 11% of all Kenyan facilities were randomly selected, with 695 successfully surveyed. Voluntary counselling and testing (VCT) centres, maternity and hospital facilities were oversampled. Health workers were sampled to cover a range of services provided; for observations and exit interviews clients were systematically sampled. We selected the Kenyan SPA as the data are recent and there is large geographic variation in maternal mortality rates. Figure 1 shows the distribution of public, private for profit and faith-based facilities across Kenya. The SPA was implemented between January and May 2010.

Figure 1.

Distribution of ANC facilities in SPA and analytic sample.

In this study, we included facilities if they reported providing ANC services and also completed the ANC portion of the facility audit, had non-missing data and were not an HIV VCT facility. We excluded VCTs since they do not routinely provide ANC services. We excluded NGO/private not-for-profit facilities from the final analytic sample due to small sample sizes. Because many facilities have missing data for one or more indicator, the analysis sample consists of 144 out of 545 non-VCT and non-NGO/private not-for-profit facilities with ANC services with a completed questionnaire (26%). Table 1 shows the basic characteristics of the facilities in the analytic sample. Compared with other eligible facilities that had missing data, the analytical sample has relatively more district or sub-district hospitals and more public facilities (see Table 1). In addition to data from the facility audit, we used client observations (n = 654) and exit interviews (n = 638) for facilities in the analytic sample with median counts of five per facility for both.

Table 1.

Characteristics of ANC facilities in the analytic sample and excluded ANC facilitiesa

	Facilities in the analytic sample N (%)	Excluded ANC facilities N (%)	Total ANC facilities N (%)
Facility type	144	401	545
National/Provincial Hospitals	6 (4)	3 (1)	9 (2)
District/Sub-district/Other Hospitals	92 (64)	134 (33)	226 (41)
Health Centres/Clinics	30 (21)	113 (28)	143 (26)
Dispensaries/Maternities	16 (11)	151 (38)	167 (31)
Management authority	144	401	545
Public	113 (78)	209 (52)	322 (59)
Private for Profit	9 (6)	130 (32)	139 (26)
Mission/Faith-Based	22 (15)	62 (15)	84 (15)
Province	144	401	545
Nairobi	7 (5)	43 (11)	50 (9)
Central	20 (14)	57 (14)	77 (14)
Coast	14 (10)	53 (13)	67 (12)
Eastern	19 (13)	55 (14)	74 (14)
Northeastern	9 (6)	33 (8)	42 (8)
Nyanza	16 (11)	61 (15)	77 (14)
Rift Valley	26 (18)	52 (13)	78 (14)
Western	33 (23)	47 (12)	80 (15)

aPercentages may not total to 100 due to rounding

Analysis

We first examined the individual indicators as well as dimension-specific indices constructed as equally weighted averages of the respective indicators. All indicators and indices were constructed at the facility level. The indicators were constructed to range from 0 to 1, with higher values representing better quality. We calculated medians and interquartile ranges (IQR) across all facilities and by facility type (national and provincial hospitals; district, sub-district and other hospitals; health centres and clinics; dispensaries and maternities), management authority (public, private for profit, faith-based), and geography (eight provinces). For the comparisons across facilities, we focus on medians rather than means, as indicators and indices were either non-normally distributed or categorical.

To facilitate overall inter-dimension comparisons, we calculated coefficients of variation, defined as the ratio of the dimension standard deviation to its mean. Finally, we calculated an overall quality of care score for each facility by equally weighting and averaging five dimensions (all but equity) calculated on the facility level.

We address the sixth dimension—equity—separately from the five other dimensions and outside of the overall facility score due to data limitations. The SPA provides data on individual clients only in the observation and exit interviews, and information on client characteristics is limited. We therefore compared median scores for patients with low and high education, defined as no or primary schooling, and secondary school or above, respectively. We used three indicators which are collected in patient observations and which are linked to corresponding patient exit interviews, where education is recorded: ANC physical examination service score, patient satisfaction recorded after the ANC consultation, and whether the visit was conducted by qualified ANC provider. Because the number of observations/exit interviews at each facility is relatively small, we did not construct facility-level measures for education.

Limitations

The limitations of our study highlight practical challenges in using the SPA data to analyse quality of care. First, we focused on ANC, which has limited impact on mortality and may not be reflective of other aspects of quality maternal and neonatal health care. Similarly, our set of 14 indicators for quality ANC, though selected based on our review of quality frameworks and the ANC literature, may not adequately cover all aspects of quality. In particular, the selected indicators are mostly structure- and process-oriented as the SPA is not designed to capture outcome indicators. Further, our ability to measure the dimension of equity is impacted by the limited demographic data available in the SPA: age and education level derived from ANC exit interview data. We analysed equity at the exit interview level but analysed the remaining five quality dimensions at the facility level.

Low ANC observation/exit interview counts for many facilities may impact the stability of indicators derived from observations. SPA data collection procedures set a maximum limit of five observations and associated exit interviews per provider of ANC services, and 15 for any given facility (NCAPD 2011). In practice, the obtained range for ANC observations from each facility varied from 0 to 10. Where possible, we constructed indicators from the facility audit to avoid the stability issue; for some indicators this was not feasible, e.g. for patient satisfaction levels.

Design choices in the SPA may affect survey responses in several ways. For example, data based on exit interviews can be subject to ‘courtesy bias’, limiting variation in scores and introducing an upward bias. Observed visits and subsequent exit interviews may also induce the Hawthorne effect if providers know clients will be surveyed after the consultation and, as a result, lead to inflated client exit interview responses ( Glick 2009).

We included 144 of 545 possible ANC facilities from the original SPA sample once data were restricted to eligible facilities with data for all component indicators. Data for the indicators of evidence-based maternal care, physical exam score, infection prevention score and whether a visit was conducted by a qualified ANC provider were missing somewhat more frequently than data for other indicators (see Supplementary Appendix Table S1.1). To address this issue, we performed a sensitivity analysis excluding the indicator for evidence-based maternal care and found qualitatively similar results for the unaffected dimensions, i.e. the dimensions effectiveness, efficiency and acceptability/patient-centeredness. We also addressed concerns about the analytic sample size by comparing results for component and composite indices, where we allowed the sample size to vary across quality dimensions, compared with the overall sample restricted within and across dimensions. These results, as well as coefficient of variation results, were qualitatively similar for the analytic sample compared with results of the sensitivity analysis.

We conducted two assessments of the missing data and the potential implications (see Supplementary Appendix Tables S3.1 and S3.2). First, we examined facility characteristics as predictors of missing data in multivariate regressions for all eligible facilities (n = 545) across each dimension and the overall quality score. The dependent variable was a binary indicator of whether the facility does or does not have data, and the independent variables were facility characteristics (region, managing authority, facility type and whether facility has: regular management meetings, back-up generator with fuel, catchment estimation data available, any first ANC visit observed). In general, we found that facilities with routine management meetings were consistently less likely to have missing data across dimensions, and that private-for-profit-managed facilities were more likely to have missing data compared with publicly managed, except for the accessibility dimension. Second, for each quality dimension, we used t-tests to compare mean values of the subset of facilities with quality measures for all domains vs the larger set of facilities with complete data for the specific dimension tested but missing data for alone or more of the other dimensions. We found no differences in quality between facilities with complete and incomplete data at the dimension level.

Our analytic sample may be subject to selection bias for several reasons. First, although the SPA’s sampling frame is the official Master Facility List it may not adequately represent all facilities in Kenya, e.g. smaller private providers. Second, as noted, there is non-random missing data across facilities, which determines which facilities are included in the analytic sample.

We constructed certain dimensions from overlapping indicators, primarily because several composite indicators included iron and/or folate tablets. Further, we constructed the overall quality of care score from dimensions comprised of several overlapping indicators. This overlap could introduce correlations among the affected dimensions and may give slightly more weight to the relevant indicators in the overall score. Finally, given we did not utilize regression adjustment in our analyses, some of the observed variation, e.g. across facility types, may be related to other observable as well as unobservable factors.

Results

Overall performance on quality of care indicators and dimensions

Table 2 shows the 14 quality indicators as components of five quality of care dimensions measured on the facility level (excluding equity, which we discuss below).

Table 2.

Mean, median, and IQR of facility scores for each quality of care indicator and dimensions, restricted across dimensionsa

	Mean	Median	25th percentile	75th percentile
Effectiveness (coefficient of variation CV = 0.36)	0.67	0.55	0.50	0.94
ANC key services score	0.92	1.00	0.88	1.00
ANC physical exam score	0.42	0.23	0.00	1.00
Efficiency (CV = 0.20)	0.84	0.92	0.79	0.96
Pre-ANC consultation services score	0.86	1.00	0.80	1.00
Counselling on postpartum family planning offered	0.81	1.00	1.00	1.00
ANC service readiness score	0.85	0.88	0.79	0.94
Accessibility (CV = 0.30)	0.71	0.68	0.68	0.93
Number of days per month ANC services are provided	0.69	0.71	0.71	0.71
Folate availability	0.88	1.00	1.00	1.00
Available and functional equipment/supplies score	0.78	1.00	1.00	1.00
Availability of evidence-based maternal care	0.51	1.00	0.00	1.00
Acceptability/patient-centeredness (CV = 0.20)	0.88	1.00	0.67	1.00
Availability of ANC visual education material	0.75	1.00	0.50	1.00
Adequate privacy during ANC consultation	0.97	1.00	1.00	1.00
Average patient satisfaction post-ANC consultation	0.91	1.00	1.00	1.00
Safety (CV = 0.18)	0.78	0.75	0.75	0.88
Infection protection score	0.58	0.50	0.50	0.75
Visit conducted by qualified ANC provider	0.98	1.00	1.00	1.00
Overall quality of care scoreb(CV = 0.13)	0.78	0.77	0.71	0.84

aN = 144. Indicators comprising each dimension are weighted equally. All dimensions and indicators range from 0 (lowest quality) to 1 (highest quality). The analytic sample is restricted across indicators comprising the dimensions of effectiveness, efficiency, accessibility, acceptability/patient-centeredness and safety

bOverall quality of care score is an average of five dimensions: effectiveness, efficiency, accessibility, acceptability/patient-centeredness and safety ranging from 0 (lowest quality) to 1 (highest quality)

Overall and relative to the maximum score of 1.00, facilities performed well on most indicators. The two lowest performing indicators were: ANC physical exam score (median score of 0.23) and infection prevention score (0.50). Because we first constructed scores at the facility level, and these scores may be continuous, the figures in Table 2 should not be interpreted as the share of facilities meeting a certain standard but rather as the score of the median facility. For instance, the median score of 0.71 for the indicator ‘number of days per month ANC services provided’ implies that the median facility in our sample offered these services 71% of days in a 4-week (28 days) month.

Quality of care dimensions varied considerably in terms of median performance. Facilities performed highest in the areas of acceptability/patient-centeredness (median score of 1.00) and efficiency (0.92). Conversely, performance was lowest for the effectiveness and accessibility dimensions, with respective median scores of 0.55 and 0.68. Safety had a middling performance (0.75). There was substantial variation across indicators within a dimension, e.g. the poor performance for the effectiveness dimension is primarily driven by a lack of adequate ANC physical exam services. The coefficients of variation for dimensions indicate that dispersion was lowest for safety (0.18) and highest for the effectiveness dimension (0.36).

Variation across and within provinces

Quality of care varied substantially across provinces (Figure 2; for means and IQR see Supplementary Appendix Table S2.1). Six out of eight provinces performed relatively poorly or only moderately well in terms of effectiveness, with Central and Nairobi being exceptions. Almost all were high performers in the efficiency dimension (median scores above 0.8), with the limited sample for Nairobi scoring lowest (0.81). Five provinces scored 0.68 or 0.73 on accessibility and the remainder of provinces performed better overall in this dimension (scores larger than 0.79). Most provinces were moderate to high performers in terms of acceptability/patient-centeredness, except Northeastern which performed worse than all other provinces (median score 0.67). Six provinces scored 0.75 in the safety dimension; Nairobi and Central were higher with median safety scores of 0.88. We also found that provinces differed in their relative performance across quality of care dimensions. For instance, Central performed well overall, whereas Northeastern performed well in accessibility and efficiency, but poorly or only moderately well in other dimensions.

Figure 2.

Scores for quality dimensions by province, facility type and management authority. Median and 25th and 75th percentiles.

Variation across and within facility types

Figure 2 also shows scores across four facility types: national/provincial hospitals; district/sub-district/other hospitals; health centres/clinics; and dispensaries/maternities. All facility types consistently performed well in terms of efficiency (median scores above 0.90). Most performed poorly in terms of effectiveness (median scores of 0.75 or lower) with the exception of national/provincial hospitals (median score 0.83). Facility types varied in their performance for the other dimensions. For instance, district/sub-district/other hospitals and dispensaries/maternities performed comparatively poorly on effectiveness.

Performance also varied within facility types. Within district hospitals and lower-level facilities, effectiveness had the lowest scores; the highest scoring dimensions included efficiency and acceptability/patient-centeredness. Within national and provincial hospitals, the efficiency dimension had the highest median score; these facilities performed moderately well across all other dimensions.

Variation across and within management authorities

Finally, Figure 2 depicts quality dimensions grouped by three management authority types in Kenya. Public facilities performed worse than or about the same as private for profit or faith-based facilities. They performed poorly in the accessibility dimension, relative to other management types. Faith-based facilities performed better or about the same as other facilities. The dimensions of highest consistent performance across management authorities were acceptability/patient-centeredness and efficiency. Within management authorities, facilities run by faith-based organizations performed consistently well in terms of efficiency, accessibility, and acceptability/patient-centeredness and moderately well on effectiveness and safety. Inter-dimension variation was greatest for public facilities.

Variation by education level (equity dimension)

To approximate the equity dimension of the WHO framework, we calculated median scores by low/high education level for three indicators that are available in the ANC observation/patient exit interview data, where patients’ education is also recorded. We found that overall median scores for the two groups were similar for all three measures calculated on the ANC client level: ANC physical examination service score (0.75), patient satisfaction post-ANC consultation (1.00) and whether the visit was conducted by qualified ANC provider (1.00; detailed results not shown).

Discussion

Quality of health care is quickly emerging as a major concern in many LMIC, particularly as efforts to expand access to care are gaining traction. In this article, we constructed quality of care indicators from Kenyan facility data to explore the level and heterogeneity in ANC quality. Our findings indicate low overall performance (on our specific set of measures) in effectiveness, and comparatively high performance on the efficiency and acceptability/patient-centeredness dimensions. However, we also found substantial variation across Kenyan provinces, facility type and management authority, with public facilities generally underperforming relative to faith-based and private for profit facilities.

A possible explanation for the finding of good performance in the equity dimension is that the available indicators already performed well, so that there is little scope for variations. For instance, almost all patients reported being seen by an adequately trained provider. These findings from the SPA are supported by the 2008–09 household Demographic and Health Survey (KNBS, ICF Macro 2010), which suggests that the proportion of women ages 15–49 receiving ANC from a skilled provider differs little by education level. However, almost one-quarter of women with no education did not receive ANC services for the most recent birth, compared with only 3% of women with secondary education or better. One explanation could be that low- and high-education households have different access to care but, once in the facility, receive comparable care from providers (as measured in the SPA). Thus, this finding also highlights the sensitivity of the results to the choice and availability of indicators.

Lessons from using existing facility surveys to measure the quality of ANC care

Our study illustrates the promises and challenges of operationalizing quality of care frameworks on standardized facility surveys, such as the SPA. On the one hand, these data are readily available (and more SPAs are planned) and can facilitate quality assessments and inform the design and scale-up of health policies. They can also serve as diagnostic tools and provide baseline measures against which to measure progress. On the other hand, we had to exclude or modify some accepted facility-based quality metrics in order to operationalize SPA data, and there was substantial missing data. This latter challenge suggests caution in interpreting or extrapolating our specific findings to all of Kenya. We also found variation across indicators within a particular dimension, indicating that the choice (and availability) of quality indicators matters for quality assessments. Similarly, the SPA does not cover several issues that are known to be important for quality, such as provider effort (Das and Gertler 2007; Das ). Overall, our study therefore also suggests that existing assessment tools may benefit from harmonization and a redesign to rationalize and optimize tracking of meaningful measures that map to existing quality of care frameworks (Lee ). This approach is endorsed in the Roadmap for the Measurement and Accountability for Health Summit held in June 2015 (USAID, World Bank, World Health Organization 2015). A harmonized instrument may also allow for more frequent and high-quality data collection, and could help track quality of care over time.

Implications for designing RBF programmes

The observed variations in quality of care have implications for designing interventions to improve quality, such as RBF which has emerged as a popular approach for increasing provider performance, especially for primary care. Kenya piloted an RBF scheme in Samburu County in 2011 with support from the World Bank, and is expanding to public facilities across 20 northern, rural counties, with the intent to explore eventual integration of private-side facilities including faith-based facilities (World Bank 2013).

In the design of RBF programmes, there are a number of central decisions for consideration which are related to the payout function; for example, what indicators to include and how to reward the rewarded indicators. Specific choices include whether to pay for exceeding thresholds or pay on a linear schedule, and whether to pay directly for quality or scale quantity payments by broader measures of quality (Basinga ; Sherry ).

Our methods and findings can help inform these decisions. First, programmes should address the quality as well as the quantity of care, as some dimensions of quality are consistently low. Second, the degree of inter-facility variation can provide guidance for determining the relative financial incentives, e.g. rewarding more generously those dimensions and indicators that perform very poorly (to encourage attention) or very highly (to defray potentially high marginal costs of further improvements). Third, baseline variations across facilities imply that it is challenging to set a threshold that simultaneously incentivizes high and low performers. A suitable payout function could involve graduated payments or only pay for improvements above facilities’ baseline performance. Fourth, although variation across provinces could be accommodated by a regionally differentiated RBF, there are substantial variations within each geographic area which also need to be addressed. Finally, our findings indicate scope for interventions to complement the RBF programme. We captured basic systemic quality problems—such as number of days ANC services are offered—which may be costly to rectify and for which RBF incentives may be too small to nudge providers into action. Similarly, the consistently low performance in the effectiveness dimension could be addressed in a larger, non-RBF effort.

Implications for demand-side interventions

Our findings can also contribute to designing demand-side interventions and tracking their effects on service quality. Kenya introduced free maternal care in public facilities in 2013 amid concerns that these facilities may find it challenging to adequately respond to the expected increase in demand (Bourbonnais 2014; Chuma and Maina 2014). Institutional delivery rates in Kenya have already increased significantly from 42.6% in Kenya Demographic and Health Survey 2008-09 to 61.2% in Kenya Demographic and Health Survey 2014, but there is a scope for further growth. In other settings, the combination of rapidly introduced demand-side interventions alongside stagnant supply-side conditions has led to decreases in quality (Chaturvedi ). Our analysis of facility data collected prior to the start of this initiative indicates potential challenges and could be used to identify ‘hotspot’ areas such as effectiveness, which may need particular attention. Further, we identified groups of facilities which may struggle to maintain or increase quality—a particular concern for public facilities which are likely to experience the largest increase in demand and which already perform comparatively poorly on most quality dimensions.

Conclusion

Our findings suggest that policies need to address and account for heterogeneity in quality of ANC. In Kenya, the good performance of some facilities (for at least some of their patients) indicates scope for improvement in this context: raising all facilities to the level of best performance should be feasible and would lead to significant overall gains in quality. There is some evidence that changes in payment modalities could facilitate such gains (Das and Gertler 2007; Basinga ), possibly in tandem with other interventions such as targeted training or investments in facility improvements. In general, there is a need for more systematic and harmonized data on the quality of care in LMIC.

Supplementary data

Supplementary data are available at HEAPOL online