Mozambique, located on the southeast coast of Africa, is divided into 11 provinces, including 1 provincial city (Multimedia Appendix 1, Figure S1A). According to data announced by the General Population and Housing Census 2017 in Mozambique [23], the estimated total population was 30.07 million, with the majority living in rural areas and generally sparsely and unevenly distributed (Multimedia Appendix 1, Figure S1B). Nearly 80% of people living in poverty live in rural areas distant from basic public facilities [24]. The country’s overall economy remains extremely underdeveloped (gross domestic product <US $1.00/person/day); the highest average consumption level in Maputo City is 6.9 times higher than the lowest, in Zambezia province (Multimedia Appendix 1, Figure S1C) [25].

According to a report from the Mozambique Ministry of Health, the country’s maternal mortality rate was as high as 451 deaths per 100,000 live births, and the infant mortality rate was 67.4 deaths per 1000 live births in 2020 [26]. Factors such as long travel distances from residential locations to health care facilities, lack of available transportation, and poorly constructed transportation networks contribute to significant service delays for women of reproductive age accessing CMPHS [15,27].

Data on 2 perspectives were needed for identifying indicators reflective of the delivery of the 3 service packages in Mozambique for calculating spatial accessibility. Following the existing literature and considering both representativeness and implementability, 2 categories of data were used for indicator identification and 3 categories of data were used to facilitate the analysis of spatial accessibility, including supply side data (health care facility data), demand side data (distribution data for women of reproductive age), and environmental data (for calculating geospatial barriers) [28-30]. The data sources are shown in detail in Table 1.

A detailed flowchart describing the 2-step procedure is shown in Figure 1.

ArcGIS (version 10.5; Esri, Inc) was used to calculate spatial access to CMPHS in this study. A geodatabase incorporating supply side data, demand side data, and administrative boundary data was first generated [43].

The nearest-neighbor method (NMM) was used to calculate the shortest travel time necessary to access ANC, ID, and PNC [44]. For each grid of women of reproductive age, only the nearest health care facility was considered [41]. The travel mode of walking at a speed of 4 km/h for spatial accessibility assessment and a threshold of 2 hours were used as criteria for timely access (Multimedia Appendix 1, Table S3) [5]. Additionally, the travel time along the straight-line distance was used to compare the travel time criterion [18,45]. The shortest travel time needed to reach ANC, ID, and PNC was calculated separately, and the average travel time was calculated for each province or provincial capital.

An overlap analysis was applied to identify multilevel health care access zones. On the basis of identifying areas able to access ANC, ID, and PNC in a timely manner (ie, within a 2 h threshold), 3 layers representing timely access to ANC, ID, and PNC, respectively, were overlapped to generate 8 types of multilevel health care access zones. Then, percentages for area and women of reproductive age covered within each type of multilevel health care access zone were calculated.

The study was approved by the Swiss Confederation, represented by the Swiss Federal Department of Foreign Affairs, acting through the Swiss Agency for Development and Cooperation Global Cooperation Department and Southern Medical University of the People’s Republic of China (81067392). Core data used in this study were derived from the National Survey on Infrastructure, Equipment, Human Resources and Health Services 2018, which was collected between February and April 2018 through a cross-sectional survey of health care facilities. The survey included a census of all health facilities in the country and collected data through interviews with key informants at health care facilities. The core standard Service Availability and Readiness Assessment (SARA) questionnaire of the WHO was used and adapted to the Mozambique context [37,38]. The SARA questionnaire and linked protocols were granted approval by Mozambique’s Ministry of Health. More information about the primary data collection procedures is available in the Mozambique SARA 2018 report [38]. Permission to use the data was obtained from Mozambique’s Ministry of Health by coauthors from Mozambique. Other data, including population distribution and administrative boundaries, were extracted from publicly available data sets. No human participants or animals were involved in the study.

The identified indicators for the 3 service packages and the percentage of health care facilities capable of providing each service (indicators) are shown in Table 2. Among all health care facilities (N=1542), 784 were able to provide ANC, 365 were able to provide ID, and 299 were able to provide PNC.

Most health care facilities within the study area managed to cover all services listed in each individual service package, with some indicators having fairly good penetration. The provision of intermittent preventive treatment in pregnancy for malaria, a component of the ANC service package, was offered in 1408 (91.31%) health care facilities. Conversely, some indicators showed suboptimal coverage; for example, emergency obstetric care was available in only 504 (32.68%) health care facilities and injectable antibiotics intended for neonatal sepsis were available in just 766 (49.68%) health care facilities (Table 2).

There were 1542 health care facilities in total across the study area, among which were 1490 primary-level health care facilities (urban and rural health centers or community health posts), 43 secondary-level health care facilities (rural, district, and general hospitals), and 9 tertiary-level health care facilities (central and provincial hospitals or specialized and military hospitals). Among the primary-level facilities, 51.48% (767/1490), 21.34% (318/1490), and 18.46% (275/1490) were able to provide ANC, ID, and PNC, respectively. For the secondary facilities, the respective percentages were 37% (16/43), 91% (39/43), and 23% (17/43). For the tertiary-level health care facilities, the respective percentages were 11% (1/9), 89% (8/9), and 78% (7/9).

Figure 2 shows the spatial distribution of the 3 levels of health care facilities able to deliver the 3 service packages. The spatial distribution of different levels of health care facilities able to provide ANC, ID, and PNC, respectively, is shown in Multimedia Appendix 1, Figure S2.

In Mozambique, the median shortest travel times needed to access ANC, ID, and PNC were 2.38 (IQR 1.38-3.89) hours, 3.69 (IQR 1.87-5.28) hours, and 4.16 (IQR 2.48-6.67) hours, respectively. The shortest travel times required to reach the nearest health care facilities offering these services varied significantly across different regions. For ANC services, the shortest travel time ranged from 0.46 (IQR 0.26-0.69) hours in Maputo City to 4.95 (IQR 3.03-7.21) hours in Manica province. For ID services, it ranged from 0.74 (IQR 0.47-1.04) hours in Maputo City to 18.20 (IQR 11.67-24.65) hours in Niassa province. For PNC services, the shortest travel time ranged from 1.34 (IQR 0.83-1.85) hours in Maputo City to 10.76 (IQR 7.53-13.66) hours in Inhambane province. Except for Manica province, the shortest travel time needed to access ID was greater than for ANC in all other provinces. The shortest travel time spent to access ID was found to be distinctly longer than that for PNC in 3 provinces, namely Gaza, Maputo, and Niassa (Table 3).

The results for coverage of different services in Figure 3 shows that women of reproductive age living in Maputo City were able to obtain timely ANC, ID, and PNC. Conversely, multiple provinces with lower population density were recognized as underserved areas, including the provinces of Niassa, Cabo Delgado, Gaza, and Inhambane, especially for the provision of ID and PNC.

For women of reproductive age in Mozambique, more than 21% (living in about 2.69% of Mozambique’s area) lived in type 1 areas (able to obtain timely CMPHS including ANC, ID, and PNC); more than 51% (living in 83.25% of Mozambique’s area) lived in type 8 areas (not able to obtain timely access to any service packages of CMPHS); only 27.5% (living in about 14.07% of Mozambique’s area) lived in type 2-7 areas (able to access 1 or 2 types of services of CMPHS). The second highest percentage of women of reproductive age fell within type 5 areas, indicating that approximately 10% (living in about 7.42% of Mozambique’s area) were able to obtain ANC services in a timely manner. The third highest percentage of women of reproductive age fell within type 2 areas, meaning that more than 9% (living in about 2.19% of Mozambique’s area) were able to reach ANC and PNC in a timely manner (Table 4).

As can be seen in Figure 4 and Multimedia Appendix 1, Figure S3, the coverage of CMPHS was low in Mozambique, especially in the provinces of Inhambane, Niassa, and Gaza. Maputo City was the only region that was able to provide timely CMPHS for women of reproductive age.

Our study proposed a 2-step procedure to assess the spatial accessibility of CMPHS and verified its applicability through a case study in Mozambique. The results first showed spatial access to different types of CMPHS and disparities among different service packages. By using an overlay analysis, this procedure could identify 7 types of underserved areas and the detailed spatial distribution of existing well-served areas. The results from this procedure have evidence-based implications to inform decision-making processes related to health resource allocation, particularly in low-resource settings confronted with similar challenges. This can ultimately aid in improving maternal and newborn health outcomes. The necessity of enhancing spatial accessibility as a critical solution to improve coverage of CMPHS is also highlighted. In the specific case of Mozambique, only 21% of women of reproductive age could access CMPHS in a timely manner, and these women were predominately clustered in the most-developed region, Maputo City. This highlights that spatial access to CMPHS remains a significant challenge and that improvements in the coverage of ID and PNC are urgently needed.

Large disparities among regions in accessing CMPHS were also a critical issue. Inequities in CMPHS exist at the national, provincial, and district levels and always present fundamental barriers to progress, particularly among the most disadvantaged population groups [46]. This has been validated by our findings that the provinces of both Niassa and Cabo Delgado, which have lower population densities, tended to be more underserved, especially in accessing ID and PNC service packages. Our study also found that Maputo City was the only administrative unit that was capable of providing effective access to all the 3 service packages. We assume this is mainly because Maputo City is the capital of Maputo and the economic and social development center of Mozambique, so its development status far exceeds all the other areas [47]. In addition, the frequent occurrence of natural disasters, which greatly hinders health care service delivery, further adds to health inequities between the southern and northern regions in Mozambique, leading to lower rates of both ANC and ID visits in the southern region, corresponding to 74 additional maternal deaths and 726 additional deaths of children under the age of 1 month by the end of 2021 [48].

In the context of Mozambique’s poor accessibility and low equity in health care, the implementation of a decentralized allocation strategy to address the inequity issue has been recognized as an effective measure to promote both accessibility and coverage of CMPHS, which can take China’s successful experiences as an example [49,50]. China has made remarkable achievements and passed milestones in achieving SDGs through implementing a decentralized allocation strategy to improve accessibility and equality of health care services. China clearly indicated that women and children were target groups in the cooperation for global health in the Belt and Road Initiative in 2017 and implemented 100 foreign assistance programs for women and children’s health in low-income countries, so decentralized allocation strategies from China can help low-income countries improve their own CMPHS [51]. For example, strategies for the promotion of primary health care, along with the optimization of a hierarchical health care delivery system intended for maternal and child health care, were implemented. According to these strategies, integrated service packages delivered via the collaborative efforts of tertiary hospitals and primary maternal health care facilities proved to be an effective solution to strengthen the capacity of local health care facilities, as well as their corresponding referral systems, which resulted in increased in-hospital births and significantly reduced maternal mortality [50,52,53]. Furthermore, community-based interventions with components to engage or mobilize communities have been increasingly implemented to improve maternal and newborn health outcomes in low-resource settings [54-56]. For example, training programs should be provided to cultivate highly skilled health care professionals among local health care facilities [55,57].

Few studies have assessed the spatial accessibility of health care resources in Mozambique. A previous study reported that 67.3% of Mozambique’s population lived outside a 1-hour travel distance from primary health care centers [58]. However, spatial barriers to accessing health care services are gaining increasing attention. According to the Statistical Yearbook 2018 of Mozambique, the average travel distance needed to access a health care facility for people living in remote areas was 12.3 km [34]. The 2018-2019 Mozambique Humanitarian Response Plan reported that about 50% of the population lived more than 20 km away from the nearest health care facility [59]. Given the national focus on maternal and perinatal health, studies assessing spatial access to CMPHS have been carried out in the context of low-resource settings [14,17,60]. Previous findings suggest that 49.8% of women of reproductive age in Mozambique lived outside a 2-hour travel area from the nearest hospital for emergency services [61]. In southern Mozambique, 46% of pregnant women were able to access the nearest primary health care facility within 1 hour by walking, while 64% of women living in the region could receive life-saving service delivery within 2 hours [46]. While most existing literature has only assessed spatial access to single services, our results are consistent with previous findings that spatial access to maternal and perinatal health care services is poor in Mozambique. Moreover, our study further considered 3 separate service packages, representing important stages in the continuity of maternal and perinatal health care services, revealing spatial access to CMPHS in a straightforward manner.

Our findings provide important evidence on the development of tailored, country-specific policies and interventions to address the geographical disparities of CMPHS in low-resource settings, which can support the development of resource allocation strategies based on the priority of CMPHS delivery. Using Mozambique as a case study of a low-resource setting, we recommend that public health policy makers and decision-makers draw from successful lessons such as decentralized allocation strategies. Through a comprehensive understanding of the geographical barriers to access to CMPHS, these strategies can quickly and efficiently improve maternal and neonatal health outcomes and contribute to the achievement of SDG3 and UHC.

This study does have several limitations. First, we set a scenario of women of reproductive age walking to health care facilities to reflect the most basic requirement of the vast number of vulnerable people in Mozambique. Other travel modes were not considered due to data limitations and the complexity of factors influencing the choice of travel mode. Specific travel thresholds for different levels of health care facilities were also not set due to a lack of reference data. Second, we used the spatial distribution of women of reproductive age as demand-side data instead of pregnancies due to data availability. Third, the integration of indicators representing the 3 service packages failed to take into account the type of health care facilities providing each service. Consequently, the continuum of services in different health care facilities was not further analyzed.

Using Mozambique as a case study, this research used a 2-step procedure to assess the accessibility of CMPHS and proposes targeted policy suggestions for improving health outcomes. Further studies could be conducted, especially in low-resource settings, with reference to local context, and thus inform resource allocation–related decision-making procedures. In turn, this could potentially contribute to the improvement of global maternal and neonatal health, the realization of SDG3, and the attainment of UHC.