Effective coverage cascade of the maternal healthcare continuum in Cameroon: An analysis of inequalities from service contact to quality-adjusted coverage

doi:10.21203/rs.3.rs-4990359/v1

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Effective coverage cascade of the maternal healthcare continuum in Cameroon: An analysis of inequalities from service contact to quality-adjusted coverage

https://doi.org/10.21203/rs.3.rs-4990359/v1

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Background: The progressive increase in maternal health (MH) coverage observed in Cameroon over the past decades has not been accompanied by a satisfactory reduction in maternal mortality. In this study, we analysed inequalities in service contact, intervention care and quality-adjusted coverage of maternal healthcare continuum in order to assess the capacity of the health system to provide comprehensive and equitable care.

Methods: We combined data from the 2018 Cameroon Demographic and Health Survey and the 2015 Emergency Obstetric and Neonatal Care Evaluation Survey of Health Facilities to estimate the quality-adjusted coverage of antenatal care and intra- and postpartum care. Inequalities were assessed using absolute and relative inequality measures.

Results: Contact coverage for antenatal care services was 86.3% (95% CI: 85.4-87.1), but only 14.4% (95% CI: 13.7-15.1) of women had the opportunity to receive comprehensive antenatal care of optimal quality. Similarly, contact coverage for intra- and postpartum care was 68.5% (95% CI: 67.3-69.6), but only 31.4% (95% CI: 19.6-26.4) of women had the opportunity to receive optimal quality intra- and postpartum care. The missed opportunity for health benefits was greater for intra- and postpartum care than for antenatal care (20.0% vs. 10.9%). There are large inequalities in the quality-adjusted coverage of the continuum of maternal healthcare by region, place of residence, religion, level of education and wealth quintile index. Relative inequalities are accentuated when the quality of MH services is taken into account. Although all categories of women are affected by the loss of opportunities for health benefits, it is the most advantaged socioeconomic groups who are most affected.

Conclusion: Our results highlight the importance of using effective coverage indicators specific to maternal health care, and of combining measures of relative and absolute inequalities to establish priorities and propose recommendations to policy-makers adapted to the context of low- and middle-income countries.

Statistical Epidemiology

Inequalities

quality-adjusted coverage

maternal health

continuum of care

quality of care

opportunity for health gains

The post-2015 Sustainable Development Goals (SDGs) aim to reduce the global maternal mortality ratio (MMR) to less than 70 per 100,000 live births by 2030. Much has been done to achieve universal maternal health (MH) coverage in sub-Saharan Africa (SSA), but maternal mortality remains a major public health problem in the region, which recorded almost 70% of maternal deaths that occurred globally in 2020 (1, 2). All the major causes of maternal death in SSA, including obstetric haemorrhage, hypertensive disorders during pregnancy, non-obstetric complications and pregnancy-related infections (3), can be prevented or managed with quality care during pregnancy, childbirth, and the immediate postpartum period (1). Maternal deaths in the region reflect inequalities in access to quality health services (1), and achieving universal coverage of quality maternal health care is of paramount importance. The barriers to achieving universal health coverage in SSA are multiple, interdependent and complex (4). Studies have shown that the use of MH services varies by geographical location, with lower levels of coverage in rural compared to urban areas (5–10). Certain cultural beliefs and attitudes can also restrict the use of MH services (11–13). In addition, poor socioeconomic conditions are a major barrier to access to health services, posing a challenge to countries in achieving the Sustainable Development Goals (14). The social and structural barriers that limit access to quality health services should therefore be identified in order to define policies to address them (15).

The gradual increase in maternal health care coverage observed in several SSA countries in recent decades has not been accompanied by a satisfactory reduction in maternal mortality (16, 17). This has raised questions about the quality of care provided in these countries, and has stimulated the expansion of the use of effective coverage indicators to take quality into account when assessing coverage of care interventions. Given the multiple calculation methods employed, the WHO/UNICEF Effective Coverage Think Tank proposed a cascade of seven steps, adapted from Amouzou et al. (18), to facilitate consensus on the definition and measurement of effective coverage for maternal, newborn, child and adolescent health and nutrition (19). In contrast to intervention coverage indicators, effective coverage indicators take into account the readiness and quality of health services, thereby accounting for the impact of service use on health improvement (18–21). Quality-adjusted coverage is the fifth step in the effective coverage cascade. At this stage, the care intervention is adjusted in accordance with the facility's level of readiness and the quality of care being provided (19).

In Cameroon, there has been a gradual improvement in the coverage of antenatal care, skilled birth attendance and postnatal care over the past three decades. However, the MMR has remained high, with periods of increase, ranging from 511 maternal deaths per 100,000 live births over the period 1991–1998 to 689, over the period 1998–2004, to 782 per 100,000 live births during the period 2004–2011, before declining to 406 between 2011 and 2018 (22). In response to this problem, the country's Health Sector Strategy 2020–2030 (HSS) identifies refocused antenatal care (ANC) and skilled attendance at birth as the main pillars for reducing maternal mortality. This is based on improved financial and cultural accessibility, the availability of services and care, capacity building of service providers and community mobilisation on reproductive, maternal, newborn and child health issues (23). In the HSS, the monitoring of MH interventions is based on service contact indicators. For instance, the HSS sets a target of 95% coverage of births attended by skilled personnel by 2027, without an explicit assessment of the quality of care provided.

As defined by the World Health Organization (WHO), the quality of care is the extent to which health services for individuals and populations increase the likelihood of achieving desired health outcomes and are consistent with available evidence-based professional knowledge (24). Furthermore, the 2016 WHO Guidelines on the Care of Pregnant Women recommend a series of health system interventions designed to enhance the quality of care and increase service utilisation, including the continuity of care provided by skilled health personnel throughout the antenatal, intrapartum and postnatal periods (25). It is therefore evident that the effective coverage of MH services must take into account the continuum of interventions in order to determine the extent to which pregnant women and mothers receive comprehensive, quality health services during antenatal, intrapartum and postnatal care, with the aim of preventing the majority of maternal deaths (1, 26, 27). However, despite the laudable efforts of countries to achieve universal health coverage, it is imperative to examine whether inequalities are not exacerbated when considering access to comprehensive, quality maternal healthcare. The most common approach for examining inequalities in maternal healthcare coverage has been to consider them either from the perspective of access to care interventions separately (13, 15, 28–30) or by combining reproductive, maternal, newborn and child health (MNCH) interventions (31–34). The first approach does not consider the continuity and content of care needed by women during the antenatal, intrapartum and postnatal periods when assessing coverage of interventions. With regard to the second approach, the composite coverage indicator that was developed provides a global overview of maternal, newborn and child health and is therefore unable to provide specific information on the expected benefits in terms of MH. For example, the administration of tetanus toxoid vaccination during antenatal care is intended to protect the newborn and cannot be linked to a maternal health benefit. In this study, we consider the readiness of health facilities and the quality of services across the maternal healthcare continuum to assess missed opportunities for health gains and the resulting geographic, cultural and socioeconomic inequalities. The objective of this study is to provide recommendations concerning the development of health programmes and policies aimed at reducing equity gaps in access to quality health services, in accordance with the goal of reducing the MMR in Cameroon and countries with similar contexts.

Data sources

In order to develop a cascade of effective coverage and estimate the quality-adjusted coverage of the maternal healthcare continuum in Cameroon, two data sources providing information on MH service use and the availability and/or quality of MH services provided by health facilities were identified. It is recommended that the reference period for MH service utilisation data should overlap with the data collection period for data on service availability or quality. This approach enables the linkage of MH service utilisation to the medical context in which services are provided. The 2018 Cameroon Demographic and Health Survey (CDHS-2018) and the 2015 Cameroon Emergency Obstetric and Neonatal Care Needs Rapid Assessment Survey (EONCS-2015) were identified as meeting the aforementioned criteria. Indeed, the data collection for the CDHS-2018 was conducted between 16 June 2018 and 19 January 2019. The reference period for maternal health information was the five-year period preceding the CDHS-2018 (22). In the case of the EONCS-2015, the data collection period was from 7 to 27 December 2015. The reference period for the information covered the three months prior to EONCS-2015 (35).

Both surveys were designed to be representative at the national level, as well as at the level of place of residence and the 12 study regions, including the 10 administrative regions and the two capital cities of the country (Yaounde and Douala). The main objective of the CDHS-2018 was to provide updated estimates of basic demographic and health indicators, including maternal health coverage, and in particular coverage of antenatal care, delivery care and postnatal care. The objective of the EONCS-2015 was to assess the capacity of health facilities to provide quality emergency obstetric and newborn care services. The assessment of health facilities was conducted with a focus on structural characteristics, including the availability of equipment, infrastructure, drugs and human resources. In addition, the assessment considered the availability of emergency obstetric and neonatal care signals, including the administration of parenteral antibiotics, uterotonics, parenteral anticonvulsants, manual removal of the placenta, assisted vaginal delivery, caesarean section, blood transfusion and neonatal resuscitation. Furthermore, the assessment encompassed other essential services, such as refocused ANC, active management of the third stage of labour, postnatal care, family planning and so forth (35).

Study population

The study population consisted of women of reproductive age (15–49 years) who were residents of Cameroon at the time of the survey and who had had a live birth in the 5 years prior to the survey.

Measuring quality-adjusted coverage

The estimation of quality-adjusted coverage was achieved through the combination of service utilisation data derived from the CDHS-2018 with data obtained from EONCS-2015, a health facility assessment of service delivery capacity. The care interventions received were adjusted based on the probability of receiving appropriate quality care, which was estimated from the EONCS-2015 data. Table 1 illustrates the variables from the EONCS-2015 data that were employed to account for the capacity of health facilities to provide quality maternal healthcare.

The calculation of coverage indicators was conducted separately for antenatal care and intra- and postpartum care, as components of the cascade of effective coverage of the maternal healthcare continuum. In the cascade, intrapartum and postpartum care are considered to form a single continuum of care, given that contact with intrapartum services leads to immediate postpartum care. Figure 1 depicts the cascade, which has been adapted from the work of Amouzou et al. (18) and Marsh et al.(19). This adaptation takes into account maternal healthcare interventions that have been recommended by the WHO as a strategy to enhance the quality of care and increase service utilisation (25, 36, 37). The statistical analyses were conducted with consideration of the CDHS-2018 sampling weights. Firstly, three indicators of antenatal care coverage were calculated. The target population (p) was defined as all women who had given birth within the previous five years. Based on the target population (p) of all women who had a live birth in the last five years, three estimates were calculated: (a) the percentage of women who had at least one antenatal care visit from a skilled health personnel in a health facility during their last pregnancy; (b) the percentage of women who, during their last pregnancy, had at least four antenatal care visits by skilled health personnel in a health facility and who received at least seven antenatal care contents (weight measurement, height measurement, blood pressure measurement, urine sample collection, blood sample collection, intermittent preventive treatment for malaria, iron supplementation); and (c) the proportion of women who received at least four antenatal care visits from qualified medical professionals at a healthcare facility during their most recent pregnancy and who received at least seven antenatal care items, adjusted according to the probability of receiving care of an appropriate standard. The indicator (a) is service contact coverage for antenatal care, (b) is intervention coverage for antenatal care continuum, and (c) is quality-adjusted coverage for antenatal care continuum. In a manner consistent with the previous approach, three indicators of intra- and postpartum care coverage were estimated. In consideration of the target population (p) of all women who have had a live birth in the last five years, we estimated (d) the percentage of those who were assisted at delivery by a skilled attendant in a health facility; (e) the percentage of those who were assisted at delivery by a skilled attendant in a health facility and who received a postpartum visit within 48 hours of delivery; and (f) the percentage of those who were assisted at delivery by a skilled attendant in a health facility and who received a postpartum visit within 48 hours of delivery, adjusted for the probability of receiving care of adequate quality. The indicator (d) is service contact coverage for intra- and postpartum care, (e) is intervention coverage for intra- and postpartum care continuum, and (f) is quality-adjusted coverage for intra- and postpartum care continuum.

In this study, the indicators of the third and fourth stages of the cascade proposed by Amouzou et al. (18) and Marsh et al. (19) were merged into the intervention continuum coverage (c) or (e). Moreover, due to the unavailability of data from health facilities, the indicators for the final two stages of the cascade, namely adherence to provider instructions coverage (g) and achievement of the desired health outcome coverage (h), are not included in the analysis.

The quality-adjusted coverage (QC) for the antenatal care continuum (c) and for the intra- and postpartum care continuum (f) was calculated as follows (38):

$$\:QC={Q}_{i,j}^{s,w}*{U}_{i,j}^{s,w}$$

maternal healthcare was grouped into two types, antenatal care and intra- and postpartum care: s = 1.2.

We considered 12 analysis regions, distinguishing between the country's two capitals cities (Yaounde and Douala) and the country's 10 administrative regions of the country: i = 1,2...,12.

Health facilities were classified into four categories: higher level public facilities (public hospitals), lower level public facilities (public health centres), higher level private facilities (private hospitals) and lower level private facilities (private health centres): j = 1,2,3,4.

$\:{U}_{i,j}^{s,w}$ is the intervention coverage for the antenatal care continuum (b) or for the intra- and postpartum care continuum (e) for woman w who received type s care in health facility category j in region i.

$\:{Q}_{i,j}^{s,w}\:$ is the probability score of receiving adequate quality care for woman k who received type s care in health facility category j in region i. The probability scores of receiving adequate quality care were estimated using the following formula:

$$\:{Q}_{i,j}=\sum\:_{k=1}^{{n}_{i,j}}\frac{{q}_{i,j,k}}{{n}_{i,j}}$$

$\:{Q}_{i,j}$ is the probability score of receiving maternal healthcare of adequate quality in a health facility of category j in region i.

$\:{\text{n}}_{i,j}\:$ is the number of health facilities of category j in region i.

$\:{q}_{i,j,k}$ is the probability score of receiving maternal healthcare of adequate quality in health facility k, category j, region i, obtained by:

$$\:{q}_{s}=\sum\:_{i=1}^{{n}_{s}}\frac{{v}_{s,i}}{{n}_{s}}*100$$

$\:{v}_{s,i}\:$ is the binary variable indicating the availability of service i for type s care in the health facility.

$\:{n}_{s}\:$ is the total number of services evaluated for type s care.

Table 1

variables employed in the calculation of the probability of receiving adequate quality antenatal, intra- and postpartum care
Antenatal care	Intra- and postpartum care
Antenatal care services	Obstetrical care services 7 days a week
Refocused antenatal consultation	Postnatal care 7 days a week
	Cesarean section services
	Provision of family planning services
Diagnostic equipment (scales, thermometer, stethoscope, blood pressure monitor, ultrasound scanner)	Diagnostic equipment (scales, thermometer, stethoscope, blood pressure monitor, ultrasound scanner)
Infrastructures (permanent source of electricity, water and means of communication), rooms (antenatal consultation, labor, delivery, postnatal care, operating room, laboratory)	Infrastructures (permanent source of electricity, water and means of communication), rooms (labor, delivery, delivery suite, operating room, laboratory)
Medical supplies and equipment (beds for obstetrics department, sterilizer)	Medical supplies and equipment (beds for the obstetrics department, sterilizer, delivery boxes, Caesarean section boxes)
Drugs and commodities (Gentallin + C1:D25e/gentamycin, Metronidazole, Amoxicillin, Chlorhexidine, Corticosteroid, Magnesium sulfate, Diazepam, Synthocinone/oxytocin, Misoprostol, Ergometrine, Quinine salts, ACT, ARVs)	Drugs and commodities (Gentallin + C1:D25e/gentamycin, Metronidazole, Amoxicillin, Chlorhexidine, Corticosteroid, Magnesium sulfate, Diazepam, Synthocinone/oxytocin, Misoprostol, Ergometrine, Quinine salts, ACT, ARVs)
Healthcare personnel (General Practitioner/Biologist, Obstetrician/Gynecologist, Surgeon, Midwife/Nurse/Caregiver, Laboratory Technician/Laboratory Engineer)	Healthcare personnel (General Practitioner/Biologist, Obstetrician/Gynecologist, Surgeon, Midwife/Nurse/Caregiver, Laboratory Technician/Laboratory Engineer)
	General anaesthesia, Spinal anaesthesia
	Prophylactic uterotonics to prevent postpartum hemorrhage
	Active management of the third stage of labor

Inequality analysis

In order to assess the geographical, cultural and socioeconomic inequalities in MH, we considered the following five dimensions of disparity: region, place of residence, religion, level of education and quintile of women's economic well-being. In addition, we considered the capital cities (Yaounde and Douala) as a distinct category of the region variable and place of residence, in order to take account of the specific characteristics of these two metropolises in terms of the availability of infrastructure, equipment and healthcare personnel.

The amplitude of inequalities was measured by indicators that consider the estimates of the various intermediate subgroups situated between the most favoured and least favoured categories. For the non-ordered subgroup dimensions of region, place of residence and religion, absolute inequality and relative inequality were assessed using the mean absolute deviation from the weighted mean difference from mean (MADw) and relative inequality by the mean absolute deviation from the weighted index of disparity (IDISw), respectively. Considering the weight $\:{p}_{i}\:$of each subgroup, the estimated $\:{y}_{i}$ for each subgroup, the number of $\:\text{n}$ subgroups and the overall mean $\:\mu\:,\:$of MADw and IDISw are calculated as follows:

MADw$\:=\:\frac{{\sum\:}_{\text{i}\:}{p}_{i}\left|{\text{y}}_{\text{i}}\:-\:{\mu\:}\right|}{\text{n}}$ (4) IDISw$\:=\:\frac{{\sum\:}_{\text{i}}{p}_{i}\left|{\text{y}}_{\text{i}}\:-\:{\mu\:}\right|/\text{n}}{{\mu\:}}\text{*}100$ (5)

MADw and IDISw have positive magnitudes, with 0 indicating no inequality and higher values indicating higher levels of inequality (39).

Concerning the ordered subgroup dimensions (level of education and wealth quintile index), absolute and relative inequality were assessed using the slope index of inequality (SII) and relative index of inequality (RII), respectively. If there is no inequality, SII takes the value 0. Higher RII values indicate higher levels of inequality. Positive values indicate a concentration of the indicator among favoured subgroups, and negative values indicate a concentration of the indicator among the disadvantaged. RII only takes positive values. If there is no inequality, the RII is 1. The further the RII is from 1, the greater the level of inequality. Values greater than 1 indicate a concentration of the indicator among the advantaged, and values less than 1 indicate a concentration of the indicator among the disadvantaged (39).

The rates of variation of absolute (RVA) and relative (RVR) inequalities were finally calculated in order to assess the relative variation in coverage from a level of the cascade in comparison to the previous level.

Sociodemographic characteristics of the study population

The present study included 6,446 women aged 15–49 years who had had at least one live birth in the last five years. The average age of these women was 28.2 years. Overall, more than half of the women (52.9%) lived in rural areas, 14.5% in capital cities (Yaounde or Douala) and 32.9% in other urban areas. Catholic, Protestant and Muslim women accounted for 36.1%, 27.3% and 24.8%, respectively. Other Christians (8.3%), animists (1.5%) and those who were not religious (2.0%) accounted for smaller proportions. Two out of five women (41.0%) had attained secondary education, almost one in three (31.3%) had primary education, one in five (21.9%) had no education at all, and 5.8% had attained tertiary education. In terms of economic well-being, 16.2% and 16.9% of women were in the highest and lowest quintiles, respectively, and 23.9% were in the middle quintile (Table 2).

Table 2

Sociodemographic characteristics of the study population
Sociodemographic characteristics		Number	%
Residence	Capital cities	936	14.5
	Urban Aure	2103	32.6
	Rural	3407	52.9
Religion	Catholic	2329	36.1
	Protestant	1762	27.3
	Other Christian	535	8.3
	Muslim	1597	24.8
	Animist	94	1.5
	No religion	129	2.0
Education	No education	1414	21.9
	Primary	2019	31.3
	Secondary	2642	41.0
	Superior	371	5.8
Wealth Quintile Index	Lowest	1088	16.9
	Second	1466	22.7
	Middle	1542	23.9
	Fourth	1306	20.3
	Highest	1044	16.2
Average age (years)		6 446	28.2

Cascade of effective coverage for maternal healthcare continuum

Contact coverage for antenatal care services was 86.3% (95% CI: 85.4–87.1), indicating that 13.7% of pregnant women missing the opportunity to contact the health system. Only one in four women (25.3%; 95% CI: 24.2–26.5) benefited from the continuum of antenatal care, in terms of both contact and content. Three out of five women (86.3%-25.3%) had lost the opportunity to receive comprehensive antenatal care following their initial contact with health services. Finally, only 14.4% (95% CI: 13.7–15.1) of women had the opportunity to receive comprehensive antenatal care of optimal quality, while 10.9% (25.3%-14.4%) missed this opportunity due to the inability of certain health facilities to provide quality services (Fig. 2).

Contact coverage of intra- and postpartum care services was found to be lower than that of antenatal care services, with a contact coverage of 68.5% (95% CI: 67.3–69.6), representing a gap of 31.5%. One in two women (51.4%; 95% CI: 50.2–52.6) had received all of the intra- and postpartum care interventions. Of these, 31.4% (95% CI: 19.6–26.4) were likely to benefit from optimal care, while 20.0% (31.4%-51.4%) missed this opportunity (Fig. 3). Overall, the loss of opportunity to receive quality care was greater for intra- and postpartum care than for antenatal care (20.0% versus of 10.9%).

Inequalities in antenatal care coverage

Table 3 shows the service contact coverage, the intervention coverage and the quality-adjusted coverage for the antenatal care continuum by region, place of residence, religion, level of education and wealth quintile index. Additionally, it presents the absolute and relative inequality indices and their rates of variation. The contact coverage exhibited considerable variation across regions, with rates ranging from 70.0% (95% CI: 64.7–75.0) in the Adamawa region to 99.1% (95% CI: 95.0-100) in the South-West region. Contact coverage was 97.0% (95% CI: 96.0-97.9) in capital cities, 93.0% (95% CI: 91.8–94.1) in other urban areas, and 78% (95% CI: 76.9–79.7) in rural areas. The absolute and relative inequality indices demonstrated the presence of geographical inequalities, particularly between regions (MADw: 0.8; IDISw: 0.9), as well as an inequality according to place of residence, wherein capital cities exhibited a greater advantage over other urban areas and rural areas (MADw: 2.7; IDISw: 3.1). Furthermore, women who identified as Catholic had higher contact coverage for antenatal care (91.7%, 95% CI: 90.6–92.8) compared to women of other religious affiliations. The lowest contact coverage was observed among Animist women (72.9%, 95% CI: 54.0-71.1). The MADw and IDISw were 0.8 and 0.9, respectively, indicating the presence of inequalities in antenatal care contact coverage between women of different religious convictions.

The proportion of women with no education who had contact with antenatal care services was 66.6% (95% CI: 64.3–68.8). This figure increased markedly to 99.3% (95% CI: 97.9–99.8) among women with higher education. Similarly, contact coverage increased with the wealth quintile index, from 66.7% (95% CI: 64.2–69.2) among women in the lowest quintile to 98.3% (95% CI: 97.4–99.0) among those in the highest quintile. The estimates indicated that relative inequalities were in favour of the high socioeconomic groups (RII > 1 for level of education and wealth quintile index), with low absolute inequalities (SII = 0.5 for level of education and SII = 0.4 for wealth quintile index).

A significant discrepancy was observed between intervention coverage for the antenatal care continuum and service contact coverage across all regions. The lowest intervention coverage was recorded in the Nord region at 6.3% (95% CI: 4.6–8.3), while the highest was observed in Yaounde at 45.6% (95% CI: 41.6–49.7). Following a variation of 222.2%, relative inequality by region was markedly more pronounced for intervention coverage for the antenatal care continuum than for service contact (IDISw = 2.9). Nevertheless, the absolute inequality by region exhibited a slight decline (RVR=-12.5%). With regard to place of residence, intervention coverage for the antenatal care continuum exhibited considerable variation, from 42.7% (95% CI: 39.8–45.5) in capital cities to 16.9% (95% CI: 15.5–18.3) in rural areas. This was accompanied by a relative inequality index that was higher than the index for service contact (IDISw: 9.7 vs. 3.1) and an absolute inequality index that fell slightly (RVR=-11.1%). On the other hand, there was a notable decline in intervention coverage for the antenatal care continuum, from 33.6% (95% CI: 31.7–35.6) among Catholic women to 6.2% (95% CI: 0.20–13.8) among those of Animist faith. Between the stages of contact and intervention, the absolute inequality increased by 37.5%, and the relative inequality increased by 388.9%. This indicates that women from the most disadvantaged socio-cultural groups, particularly those who belong to the Animist and Muslim communities were less likely to benefit from the various antenatal care interventions.

Furthermore, the coverage of antenatal care interventions increased with both education level and wealth quintile index. The coverage ranged from 11.7% (95% CI: 9.9–13.7) among women with no education to 45.3% (95% CI: 40.3–50.4) among women with higher education. Similarly, the coverage ranged from 8.3% (CI: 6.6–10.3%) among women in the lowest quintile to 41.8% (95% CI: 38.9–44.7) among those in the highest quintile. With regard to these two socioeconomic dimensions, the absolute inequality indices remained constant (RVR = 0), whereas the relative inequality indices increased considerably in relation to the service contact indices (RII = 5.7; RVR = 200.0 for level of education and RII = 6.3; RVR = 270.6 for wealth quintile index).

The quality-adjusted coverage for the antenatal care continuum was markedly limited in the North (2.7%; 95% CI: 1.7-3.0) and Far North (5.7%; 95% CI: 4.8–6.6) regions, whereas it was comparatively greater in the capital cities of Yaounde (32.9%; 95% CI: 29.8–35.9) and Douala (31.5%; 95% CI: 28.3–34.7). The quality-adjusted coverage for the antenatal care continuum in the capital cities was substantially higher than that observed in other regions, such as the South-West, North-West and West. Despite these regions having similar levels of contact coverage with antenatal care services to those observed in the capital cities, the quality-adjusted coverage for the antenatal care continuum was considerably lower. Furthermore, the quality-adjusted coverage for the antenatal care continuum increased by twofold when moving from one level of urbanisation to the next. In rural areas, only 7.6% (95% CI: 6.9–8.3%) of women received comprehensive, quality antenatal care. In other urban areas, the percentage was 15.5% (95% CI: 14.2–16.7), while in capital cities it was 32.2% (95% CI: 30.0-34.4). The quality-adjusted coverage for the antenatal care continuum among Animist women was found to be exceedingly low, at 1.7% (95% CI: 0.1–3.4). Furthermore, only 7.0% (95% CI: 6.1–7.8) of Muslim women had received comprehensive, quality antenatal care. The quality-adjusted coverage for the antenatal care continuum exhibited considerable variation, with estimates ranging from 4.1% (95% CI: 3.4–4.8) among women with no education to 33.1% (95% CI: 19.7–22.2) among those with higher education. Similarly, the coverage was significantly disparate across socioeconomic groups, with estimates ranging from 2.7% (95% CI: 2.1–3.4) among those in the lowest quintile to 30.2% (95% CI: 28.0-32.4) among those in the highest quintile.

For all the dimensions of inequality examined, the consideration of quality of care had accentuated relative inequalities compared to what was observed for coverage of the antenatal care continuum, as the RVR ranged from 20.7 to 122.2%. The wealth quintile index showed the largest increase in inequality (122.2%), followed by level of education (112.3%) and place of residence (55.7%). On the other hand, the absolute inequality index values tended to decrease between the intervention coverage and the quality-adjusted coverage for the continuum of antenatal care, as the RVR varied from − 28.6% to -8.3%.

Inequalities in intra- and postpartum care coverage

Table 4 presents service contact coverage, intervention coverage and quality-adjusted coverage for the continuum of intra- and postpartum care by region, place of residence, religion, education level and wealth quintile index, absolute and relative inequality indices and their rate of variation. Service contact coverage for intra- and postpartum care ranged from 36.9% (95% CI: 34.2–9.7) in the Far North region to 99.1% (95% CI: 95.0-100) in the Southwest region. There were absolute and relative inequalities between regions (MADw = 1.6; IDISw = 2.3). Service contact coverage decreased from 93.1% (95% CI: 91.6–94.5) in capital cities to 50.2% (95% CI: 48.5–51.9) in rural areas, with significant inequalities (MADw = 6.1; IDISw = 8.9). There were also disparities according to religion (MADw = 1.7; IDISw = 2.5), with women of animist faith having the lowest contact coverage (22.0%; 95% CI: 15.2–30.0) while those of Catholic faith had the highest coverage (79.5%; 95% CI: 77.9–81.1).

The woman's level of education and the wealth quintile index were also factors contributing to inequalities in contact with intra- and postpartum care services. Women with higher education (SII = 0.8; RII = 5.5) and those in the highest quintile were the most advantaged (SII = 0.8; RII = 4.7), with contact coverage above 95%.

As with service contact, quality-adjusted coverage for intra- and postpartum care continuum was highest in the South‒West region (84.5%; 95% CI: 76.4–90.7) and lowest in the Far-North region (31.1%; 95% CI: 28.5–33.8). In terms of contact with services, absolute inequality between regions decreased (RVR=-18.8%), while relative inequality increased (RVR = 13.0%). Capital cities had the highest coverage (66.5%; 95% CI: 63.8–69.0) compared to all other urban areas (63.9%; 95% CI: 61.8–66.0) and rural areas (38.3%; 95% CI: 36.7–40.0). The absolute and relative inequality indices for intervention coverage for intra- and postpartum care continuum decreased compared to the gaps observed for service contact (RVR=-29.9%; RVR=-3.4%). The same variations were observed for religion, level of education and wealth quintile index.

The quality-adjusted coverage for intra- and postpartum care continuum was highest in the South‒West region (59.8%; 95% CI: 54.4–65.3), followed by Douala (50.2%; 95% CI: 47.2–53.3) and Yaounde (48.9%; 95% CI: 46.0-51.8), and lowest in the North (12.9%; 95% CI: 11.5–14.2) and Far-North (15.2%; 95% CI: 13.8–16.5) regions. This indicator varied from 10.5% (95% CI: 9.5–11.5) among women with no education to 56.6% (95% CI: 53.3–59.9) among those with higher education and from 8.9% (95% CI: 7.9–9.9) among women in the lowest quintile to 53.9% (95% CI: 51.9–55.9) among those in the highest quintile. For all dimensions, there was an increase in relative inequality and a decrease in absolute inequality from the intervention stage to the quality-adjusted stage. The largest increases in relative inequality were observed for education (RVR = 57.7) and the wealth quintile index (RVR = 57.9).

Table 3

Inequalities in service contact coverage, intervention coverage and quality-adjusted coverage for antenatal care continuum
		Contact (%)	95%CI	Interventions (%)	95%CI	Adjusted- quality (%)	95%CI
Cameroon		86.3	(85.4–87.1)	25.3	(24.2–26.5)	14.4	(13.7–15.1)
Region	Adamawa	70.0	(64.7–75.0)	20.7	(15.6–26.7)	8.0	(5.6-10.36)
	Centre	88.2	(85.7–90.4)	27.5	(24.2–31.0)	14.3	(12.4–16.2)
	Douala	97.6	(96.1–98.7)	39.8	(35.9–43.8)	31.5	(28.3–34.7)
	East	82.0	(78.6–85.1)	28.3	(24.3–32.6)	13.9	(11.5–16.4)
	Far-North	77.2	(74.7–79.5)	15.2	(12.9–17.7)	5.7	( 4.8–6.6 )
	Littoral	95.0	(91.0-97.6)	29.9	(23.5–37.1)	19.9	(15.6–24.1)
	North	70.6	(67.6–73.5)	6.3	( 4.6–8.3 )	2.3	( 1.7–3.0 )
	North‒West	98.5	(96.8–99.5)	19.0	(15.2–23.2)	12.3	( 9.7–14.9)
	West	98.3	(97.0-99.1)	26.6	(23.4–29.9)	17.0	(14.8–19.2)
	South	93.8	(88.8–97.0)	35.6	(27.9–43.8)	16.1	(13.1–19.0)
	South‒West	99.1	(95.0 -100)	17.8	(11.0-26.3)	15.0	( 9.0 -20.9)
	Yaounde	96.5	(94.8–97.8)	45.6	(41.6–49.7)	32.9	(29.8–35.9)
	MADw\|RVA	0.8\|NA*		0.7\|-12.5		0.5\|-28.6
	IDISw \|RVR	0.9\|NA		2.9\|222.2		3.5\|20.7
Residence	Capital cities	97.0	(96.0-97.9)	42.7	(39.8–45.5)	32.2	(30.0-34.4)
	Other urban	93.0	(91.8–94.1)	26.4	(24.4–28.5)	15.5	(14.2–16.7)
	Rural	78.3	(76.9–79.7)	16.9	(15.5–18.3)	7.6	( 6.9–8.3 )
	MADw\|RVA	2.7\|NA		2.4\|-11.1		2.2\|-8.3
	IDISw \|RVR	3.1\|NA		9.7\|212.9		15.1\|55.7
Religion	Catholic	91.7	(90.6–92.8)	33.6	(31.7–35.6)	20.8	(19.5–22.1)
	Protestant	88.0	(86.3–89.5)	22.5	(20.4–24.8)	13.6	(12.2–14.9)
	Other Christian	88.3	(85.2–90.9)	28.9	(24.7–33.3)	18.4	(15.5–21.3)
	Muslim	79.9	(78.0-81.7)	16.0	(14.2–17.9)	7.0	( 6.1–7.8 )
	Animist	62.9	(54.0-71.1)	6.2	(0.20–13.8)	1.7	( 0.1–3.4 )
	No Religion	72.9	(64.3–80.3)	22.2	(14.1–32.2)	11.2	( 6.6–15.9)
	MADw\|RVA	0.8\|NA		1.1\|37.5		0.8\|-27.3
	IDISw \|RVR	0.9\|NA		4.4\|388.9		5.7\|29.5
Education	No education	66.6	(64.3–68.8)	11.7	( 9.9–13.7)	4.1	( 3.4–4.8 )
	Primary	87.9	(86.4–89.3)	20.6	(18.7–22.6)	11.4	(10.2–12.5)
	Secondary	96.3	(95.5–97.0)	32.1	(30.3–34.0)	20.9	(19.7–22.2)
	Superior	99.3	(97.9–99.8)	45.3	(40.3–50.4)	33.1	(19.7–22.2)
	SII\|RVA	0.5\|NA	( 0.4–0.5 )	0.5\|0.0	( 0.4–06 )	0.4\|-20.0	( 0.3–0.5 )
	RII\|RVR	1.9\|NA	( 1.9–2.0 )	5.7\|200.0	( 5.2–6.3 )	12.1\|112.3	(7.1–17.0)
Wealth Quintile Index	Lowest	66.7	(64.2–69.2)	8.3	( 6.6–10.3)	2.7	( 2.1–3.4)
	Second	82.4	(80.3–84.3)	17.4	(15.3–19.7)	7.7	( 6.7–8.7 )
	Middle	89.1	(87.3–90.7)	21.0	(18.7–23.3)	11.5	(10.1–12.8)
	Fourth	96.6	(95.5–97.5)	34.5	(31.9–37.2)	23.3	(21.5–25.2)
	Highest	98.3	(97.4–99.0)	41.8	(38.9–44.7)	30.2	(28.0-32.4)
	SII\|RVA	0.4\|NA	( 0.3–0.5 )	0.4\|0.0	( 0.3–0.5 )	0.3\|-25.0	( 0.2–0.4 )
	RII\|RVR	1.7\|NA	( 1.6–1.8 )	6.3\|270.6	( 4.1–8.4 )	14.0\|122.2	(7.9–20.2)
*NA: Not applicable

Table 4

Inequalities in contact coverage, intervention continuum coverage and quality-adjusted coverage for intra- and postpartum care continuum
		Contact (%)	95%CI	Interventions (%)	95%CI	Adjusted- quality (%)	95%CI
Cameroon		68.5	(67.3–69.6)	51.4	(50.2–52.6)	31.4	(19.6–26.4)
Region	Adamawa	52.5	(46.9–58.1)	44.1	(38.5–49.7)	23.0	(19.6–26.4)
	Centre	74.1	(70.9–77.2)	51.4	(47.8–55.0)	28.1	(25.9–30.2)
	Douala	94.9	(92.9–96.5)	66.3	(62.5–70.0)	50.2	(47.2–53.3)
	East	59.5	(55.3–63.6)	37.0	(33.0-41.1)	21.0	(18.3–23.7)
	Far-North	36.9	(34.2–39.7)	31.1	(28.5–33.8)	15.2	(13.8–16.5)
	Littoral	95.5	(91.6–97.9)	59.3	(52.1–66.2)	40.7	(36.0-45.4)
	North	39.5	(36.4–42.7)	28.2	(25.3–3.11)	12.9	(11.5–14.2)
	North‒West	91.2	(88.0-93.8)	76.0	(71.6–80.1)	46.9	(43.8–50.0)
	West	93.8	(91.9–95.5)	76.4	(73.2–79.4)	48.6	(46.3–50.8)
	South	84.4	(77.8–89.6)	72.5	(64.9–79.3)	35.7	(32.5–38.8)
	South‒West	99.1	(95.0- 100)	84.5	(76.4–90.7)	59.8	(54.4–65.3)
	Yaounde	91.4	(89.0-93.5)	66.6	(62.9–70.2)	48.9	(46.0-51.8)
	MADw\|RVA	1.6\|NA*		1.3\|-18.8		1.1\|-19.1
	IDISw \|RVR	2.3\|NA		2.6\|13.0		3.4\|32.4
Residence	Capital cities	93.1	(91.6–94.5)	66.5	(63.8–69.0)	49.6	(47.5–51.7)
	Other urban	83.8	(82.1–85.4)	63.9	(61.8–66.0)	39.6	(38.2–41.1)
	Rural	50.2	(48.5–51.9)	38.3	(36.7–40.0)	20.4	(19.5–21.4)
	MADw\|RVA	6.1\|NA		4.4\|-29.9		3.7\|-15.9
	IDISw \|RVR	8.9\|NA		8.6\|-3.4		11.8\|37.2
Religion	Catholic	79.5	(77.9–81.1)	58.3	(56.3–60.3)	37.7	(36.3–39.1)
	Protestant	73.0	(70.8–75.2)	56.0	(53.5–58.4)	34.4	(32.7–36.0)
	Other Christian	73.5	(69.5–77.3)	48.8	(44.4–53.1)	32.3	(29.1–35.4)
	Muslim	52.8	(50.5–55.1)	42.1	(39.9–44.4)	22.3	(21.0-23.7)
	Animist	22.0	(15.2–30.0)	22.0	(15.2–30.0)	11.7	(7.4–16.0)
	No Religion	61.2	(52.3–69.7)	40.3	(31.8–49.3)	24.8	(19.2–30.4)
	MADw\|RVA	1.7\|NA		1.2\|-29.4		1.0\|-16.7
	IDISw \|RVR	2.5\|NA		2.2\|-12.0		3.1\|40.9
Education	No education	30.9	(28.7–33.1)	22.7	(20.7–24.7)	10.5	(9.5–11.5)
	Primary	69.4	(67.3–71.4)	53.0	(50.8–55.2)	30.6	(29.2–32.0)
	Secondary	88.5	(87.2–89.7)	65.4	(63.5–67.2)	42.3	(41.0-43.7)
	Superior	98.0	(96.2–99.1)	77.6	(73.2–81.6)	56.6	(53.3–59.9)
	SII\|RVA	0.8\|NA		0.7\|-12 .5	( 0.5–0.9)	0.6\|-14.3	( 0.5–0.7 )
	RII\|RVR	5.5\|NA		4.2\|-23.6	( 2.1–6.3 )	6.5\|57.7	(2.4–10.6)
Wealth Quintile Index	Lowest	27.9	(25.6–30.4)	20.0	(17.9–22.3)	8.9	( 7.9–9.9 )
	Second	57.6	(55.0-60.1)	44.8	(42.2–47.4)	24.1	(22.6–25.6)
	Middle	75.3	(73.0-77.6)	57.2	(54.5–59.8)	33.3	(31.5–35.0)
	Fourth	89.6	(87.8–91.2)	63.2	(60.6–65.8)	41.3	(39.5–43.2)
	Highest	95.8	(94.5–96.8)	74.8	(72.3–77.3)	53.9	(51.9–55.9)
	SII\|RVA	0.8\|NA		0.6\|-25.0	( 0.5–0.7 )	0.5\|-16.7	( 0.4–0.6 )
	RII\|RVR	4.7\|NA		3.8\|-19.1	( 1.9–5.7 )	6.0\|57.9	( 2.7–9.4 )
*NA: Not applicable

The present analysis of inequalities in service contact, intervention care and quality-adjusted coverage of maternal healthcare continuum in Cameroon was based on a combination of data from a population survey and a health facility survey. There was a significant loss of opportunity for health benefits for women who should benefit from quality interventions throughout pregnancy, childbirth and the postpartum period. There are inequalities in quality-adjusted coverage of MH according to region, place of residence, religion, level of education and wealth quintile index. The development of a cascade of effective coverage for MH highlights the challenges facing the health system, and the analysis of inequalities enables us to define priorities for improving effective coverage in the context of scarce resources.

In the cascade, quality-adjusted coverage was very low compared to service contact coverage. Other studies have also shown that quality-adjusted coverage can be low even when service contact coverage is high (16–18). In addition, the greatest loss for antenatal care was observed at the intervention coverage stage, while the greatest loss for intra- and postpartum care was observed at the contact stage. This highlights the weak capacity of the health system to ensure that pregnant women continue with maternal healthcare after initial service contact. Another study that combined maternal, newborn and child health care found that, overall, the greatest loss of effective coverage occurred at the service contact stage (15). It also found that service contact was lower for intra- and postpartum care than for antenatal care. A recent study of 15 countries in sub-Saharan Africa reported similar findings (28). Furthermore, the overall analysis of the maternal healthcare cascade in the present study showed that there was more loss of quality gain in intra- and postpartum care than in antenatal care. These results suggest that the health system is less prepared to provide high-quality intra- and postpartum care than antenatal care.

Women in the country's poorest regions, particularly in the North and Far North (40) and in rural areas, were among the most disadvantaged. Moreover, regions with satisfactory service contact coverage (over 98% for antenatal care) could end up with very limited quality-adjusted coverage, especially compared to the capital cities (Yaounde and Douala), which are more likely to have the best staff, infrastructure and other health inputs. Overall, animist women, those with no education and those in the lowest quintile had very limited coverage. In addition to improving cultural and financial accessibility for these categories of women, it is of paramount importance to improve the quality of services in the most impoverished regions and rural areas. These areas must be provided with the necessary infrastructure, equipment, essential medicines and human resources for MH services. Furthermore, it is imperative that public health decision-makers prioritise the provision of quality antenatal care services that facilitate continuity of care (16). This will also have a positive effect on improving contact for intra- and postpartum care.

Our results indicate that inequalities in quality-adjusted coverage for maternal healthcare in Cameroon are particularly exacerbated by socioeconomic conditions, notably education level and wealth quintile index. For antenatal care, relative inequalities tended to increase as one moved from a step of the cascade to the next. For intra- and postpartum care, relative inequalities tended to decrease when moving from service contact coverage to intervention coverage, except between regions. For both antenatal care and intra- and postpartum care, when moving from intervention coverage to quality-adjusted coverage, relative inequalities increased, while absolute inequalities decreased. This indicates that there was a decrease in access to quality healthcare when moving from the intervention stage to the quality of intervention stage and an increase in disparities in percentage terms. In other words, although the absolute difference between levels of access to quality care decreased, the relative proportion of this disparity to the average increased. This is the result of a greater reduction in access for the most advantaged groups compared to the least advantaged groups, accentuating the relative percentage gap despite a reduction in the absolute gap. As a result, it was the most advantaged socioeconomic group that suffered the greatest loss of health gain opportunities due to the quality of services offered by the health system. The quality of health services in Cameroon probably reflects the shortage and unequal distribution of health personnel (41) as well as the anarchic proliferation of unlicensed private health facilities outside government control (23).

Limitations

One of the limitations of estimating effective coverage for MH services in Cameroon is that there are few sources of maternal healthcare data on health facilities. We used the most recent data from a population survey and a survey of health facilities in Cameroon. Aggregation of health facility data was carried out at the health facility category level (public hospitals, public health centres, private hospitals and private health centres) in each region. This leads to a reduction in the variability of health facility data at the regional level and a decrease in the accuracy to correctly represent the phenomenon it is intended to capture, unlike data from individual health facilities. Nevertheless, the categorisation of health facilities whose data was combined with individual women's information in the analytical framework provides valuable insights that can inform decision-making processes.

Another limitation is the lack of direct observational data on clinical care provided by health professionals at the time of the survey. The quality of care was assessed on the basis of the availability of services and the services provided in the three months prior to the survey of health facilities, without observational evidence of compliance with care guidelines by health professionals. However, in the absence of data on direct observation of clinical procedures, it is unlikely that there are significant differences in the quality of care.

Finally, it is important to note that, as with any retrospective survey, the CDHS-2018 may be subject to recall bias on the part of participants, which could lead to analyses and decision-making based on inaccurate data. Indeed, it requires a significant investment of effort for the women surveyed to recall events related to their pregnancy over a five-year period.

In the maternal healthcare cascade, relative inequalities are accentuated when the quality of services is taken into account. Although the loss of opportunity for health benefits concerns all categories of women, the most advantaged socioeconomic groups are the most affected. The findings of this study emphasise the necessity of utilising indicators of effective coverage that are specific to maternal healthcare. Furthermore, the combination of measures of relative and absolute inequality is crucial for the establishment of priorities and the formulation of concrete recommendations for health programme managers and policy-makers in low- and middle-income countries.

95% CI

95% Confidence interval

ACT

Artemisinin-based combination therapy

ANC

antenatal care

ARV

antiretroviral treatment for HIV-infected patients

CDHS

Cameroon Demographic and Health Survey

EONCS

Cameroon Emergency Obstetric and Neonatal Care Needs Rapid Assessment Survey

HSS

Health Sector Strategy

IDISw

weighted index of disparity

MADw

weighted mean difference from the mean

maternal health

MMR

maternal mortality ratio

RII

relative index of inequality

RVA

Rate of variation of absolute inequality

RVR

Rate of variation of relative inequality

SDG

Sustainable Development Goal

SII

slope index of inequality

SSA

Sub-Saharan African

Ethics approval and consent to participate

Ethical approval was not required for this study because it was based on publicly available data.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Author details

¹ Institut National de la Statistique (INS), Rue 3025, Quartier du Lac, P.O Box 134, Yaounde, Cameroon

² African Population and Health Research Center (APHRC), APHRC Campus, 2nd Floor, Manga Close off Kirawa Road, P.O. Box 10787-00100, Nairobi, Kenya

³Universidade Federal de Pelotas (UFPEL), Center for Epidemiological Research, International Center for Equity in Health, Pelotas, Brazil

Funding

This work was funded by the Countdown to 2030’s BMGF, grant number INV-042414. The study design, data analysis, manuscript drafting, and submission were independent of any funding influence.

Author contributions

MS designed the study, conducted the analysis and drafted the manuscript. AJDB contributed to the conceptualisation of the equalities analysis. ADD, EMS and ABS supervised the work and contributed to the analysis. All authors reviewed and contributed to the manuscript.

Acknowledgements

We acknowledge the training provided by the African Population and Health Research Center (APHRC) through the Countdown 2030 Fellowship Program, which prompted this study. We would also like to acknowledge DHS Program and the National Institute of Statistics (Cameroon) for freely providing the data sets for these analyses.

Data availability

All data used in this analysis are publicly available upon request from the DHS Program (http://www.dhsprogram.com) and the National Institute of Statistics (https://ins-cameroun.cm/).

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The authors declare no competing interests.

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Effective coverage cascade of the maternal healthcare continuum in Cameroon: An analysis of inequalities from service contact to quality-adjusted coverage

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Abstract

Figures

Introduction

Methods

Data sources

Study population

Measuring quality-adjusted coverage

Inequality analysis

Results

Sociodemographic characteristics of the study population

Cascade of effective coverage for maternal healthcare continuum

Inequalities in antenatal care coverage

Inequalities in intra- and postpartum care coverage

Discussion

Limitations

Conclusion

Abbreviations

Declarations

Author details

Funding

Author contributions

Acknowledgements

Data availability

References

Additional Declarations

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