Background: Travel time to care is known to influence uptake of health services. Generally, pregnant women who take longer to transit to health facilities are the least likely to deliver in facilities. It is not clear if modelled access predicts fairly the vulnerability in women seeking maternal care across different spatial settings. Objectives: This cross-sectional analysis aimed to (i) compare travel times to care as modelled in a GIS environment with self-reported travel times by women seeking maternal care in Community Level Interventions for Pre-eclampsia: Mozambique, India and Pakistan; and (ii) investigate the assumption that women would seek care at the closest health facility. Methods: Women were interviewed to obtain estimated travel times to health facilities (R). Travel time to the closest facility was also modelled (P) (closest facility tool (ArcGIS)) and time to facility where care was sought estimated (A) (route network layer finder (ArcGIS)). Bland–Altman analysis compared spatial variation in differences between modelled and self-reported travel times. Variations between travel times to the nearest facility (P) with modelled travel times to the actual facilities accessed (A) were analysed. Log-transformed data comparison graphs for medians, with box plots superimposed distributions were used. Results: Modelled geographical access (P) is generally lower than self-reported access (R), but there is a geography to this relationship. In India and Pakistan, potential access (P) compared fairly with self-reported travel times (R) [P (H₀: Mean difference = 0)] < .001, limits of agreement: [− 273.81; 56.40] and [− 264.10; 94.25] respectively. In Mozambique, mean differences between the two measures of access were significantly different from 0 [P (H₀: Mean difference = 0) = 0.31, limits of agreement: [− 187.26; 199.96]]. Conclusion: Modelling access successfully predict potential vulnerability in populations. Differences between modelled (P) and self-reported travel times (R) are partially a result of women not seeking care at their closest facilities. Modelling access should not be viewed through a geographically static lens. Modelling assumptions are likely modified by spatio-temporal and/or socio-cultural settings. Geographical stratification of access reveals disproportionate variations in differences emphasizing the varied nature of assumptions across spatial settings. Trial registration ClinicalTrials.gov, NCT01911494. Registered 30 July 2013, https://clinicaltrials.gov/ct2/show/NCT01911494

Geographic proximity to health facilities is a known determinant of access to maternal care. Methods of quantifying geographical access to care have largely ignored the impact of precipitation and flooding. Further, travel has largely been imagined as unimodal where one transport mode is used for entire journeys to seek care. This study proposes a new approach for modeling potential spatio-temporal access by evaluating the impact of precipitation and floods on access to maternal health services using multiple transport modes, in southern Mozambique. A facility assessment was used to classify 56 health centres. GPS coordinates of the health facilities were acquired from the Ministry of Health while roads were digitized and classified from high-resolution satellite images. Data on the geographic distribution of populations of women of reproductive age, pregnancies and births within the preceding 12 months, and transport options available to pregnant women were collected from a household census. Daily precipitation and flood data were used to model the impact of severe weather on access for a 17-month timeline. Travel times to the nearest health facilities were calculated using the closest facility tool in ArcGIS software. Forty-six and 87 percent of pregnant women lived within a 1-h of the nearest primary care centre using walking or public transport modes respectively. The populations within these catchments dropped by 9 and 5% respectively at the peak of the wet season. For journeys that would have commenced with walking to primary facilities, 64% of women lived within 2 h of life-saving care, while for those that began journeys with public transport, the same 2-hour catchment would have contained 95% of the women population. The population of women within two hours of life-saving care dropped by 9% for secondary facilities and 18% for tertiary facilities during the wet season. Seasonal variation in access to maternal care should not be imagined through a dichotomous and static lens of wet and dry seasons, as access continually fluctuates in both. This new approach for modelling spatio-temporal access allows for the GIS output to be utilized not only for health services planning, but also to aid near real time community-level delivery of maternal health services.

Geographic information systems (GIS) are increasingly recognized tools in maternal health. To evaluate the use of GIS in maternal health and to identify knowledge gaps and opportunities. Keywords broadly related to maternal health and GIS were used to search for academic articles and gray literature. Reviewed articles focused on maternal health, with GIS used as part of the methods. Peer reviewed articles (n=40) and gray literature sources (n=30) were reviewed. Two main themes emerged: modeling access to maternal services and identifying risks associated with maternal outcomes. Knowledge gaps included a need to rethink spatial access to maternal care in low- and middle-income settings, and a need for more explicit use of GIS to account for the geographical variation in the effect of risk factors on adverse maternal outcomes. Limited evidence existed to suggest that use of GIS had influenced maternal health policy. Instead, application of GIS to maternal health was largely influenced by policy priorities in global maternal health. Investigation of the role of GIS in contributing to future policy directions is warranted, particularly for elucidating determinants of global maternal health.

Mozambique has one of the highest rates of maternal mortality in sub-Saharan Africa. The main influences on maternal health encompass social, economic, political, environmental and cultural determinants of health. To effectively address maternal mortality in the post-2015 agenda, interventions need to consider the determinants of health so that their delivery is not limited to the health sector. The objective of this exploratory qualitative study was to identify key community groups’ perspectives on the perceived determinants of maternal health in rural areas of southern Mozambique. Eleven focus group discussions were conducted with women of reproductive age, pregnant women, matrons, male partners, community leaders and health workers. Participants were recruited using sampling techniques of convenience and snow balling. Focus groups had an average of nine participants each. The heads of 12 administrative posts were also interviewed to understand the local context. Data were coded and analysed thematically using NVivo software. A broad range of political, economic, socio-cultural and environmental determinants of maternal health were identified by community representatives. It was perceived that the civil war has resulted in local unemployment and poverty that had a number of downstream effects including lack of funds for accessing medical care and transport, and influence on socio-cultural determinants, particularly gender relations that disadvantaged women. Socio-cultural determinants included intimate partner violence toward women, and strained relationships with in-laws and co-spouses. Social relationships were complex as there were both negative and positive impacts on maternal health. Environmental determinants included natural disasters and poor access to roads and transport exacerbated by the wet season and subsequent flooding. In rural southern Mozambique, community perceptions of the determinants of maternal health included political, economic, socio-cultural and environmental factors. These determinants were closely linked with one another and highlight the importance of including the local history, context, culture and geography in the design of maternal health programs.

Google Maps Directions Application Programming Interface (the API) and AccessMod tools are increasingly being used to estimate travel time to healthcare. However, no formal comparison of estimates from the tools has been conducted. We modelled and compared median travel time (MTT) to comprehensive emergency obstetric care (CEmOC) using both tools in three Nigerian conurbations (Kano, Port-Harcourt, and Lagos). We compiled spatial layers of CEmOC healthcare facilities, road network, elevation, and land cover and used a least-cost path algorithm within AccessMod to estimate MTT to the nearest CEmOC facility. Comparable MTT estimates were extracted using the API for peak and non-peak travel scenarios. We investigated the relationship between MTT estimates generated by both tools at raster celllevel (0.6 km resolution). We also aggregated the raster cell estimates to generate administratively relevant ward-level MTT. We compared ward-level estimates and identified wards within the same conurbation falling into different 15-minute incremental categories (<15/15-30/30-45/45-60/+60). Of the 189, 101 and 375 wards, 72.0%, 72.3% and 90.1% were categorised in the same 15- minute category in Kano, Port-Harcourt, and Lagos, respectively. Concordance decreased in wards with longer MTT. AccessMod MTT were longer than the API’s in areas with ≥45min. At the raster cell-level, MTT had a strong positive correlation (≥0.8) in all conurbations. Adjusted R2 from a linear model (0.624-0.723) was high, increasing marginally in a piecewise linear model (0.677-0.807). In conclusion, at <45-minutes, ward-level estimates from the API and AccessMod are marginally different, however, at longer travel times substantial differences exist, which are amenable to conversion factors.

Objectives: To identify and measure the place-specific determinants that are associated with adverse maternal and perinatal outcomes in the southern region of Mozambique.Design: Retrospective cohort study. Choice of variables informed by literature and Delphi consensus.Setting: Study conducted during the baseline phase of a community level intervention for pre-eclampsia that was led by community health workers.Participants: A household census identified 50 493 households that were home to 80 483 women of reproductive age (age 12–49 years). Of these women, 14 617 had been pregnant in the 12 months prior to the census, of which 9172 (61.6%) had completed their pregnancies.Primary and secondary outcome measures: A combined fetal, maternal and neonatal outcome was calculated for all women with completed pregnancies.Results: A total of six variables were statistically significant (p≤0.05) in explaining the combined outcome. These included: geographic isolation, flood proneness, access to an improved latrine, average age of reproductive age woman, family support and fertility rates. The performance of the ordinary least squares model was an adjusted R²=0.69. Three of the variables (isolation, latrine score and family support) showed significant geographic variability in their effect on rates of adverse outcome. Accounting for this modest non-stationary effect through geographically weighted regression increased the adjusted R² to 0.71.Conclusions: The community exploration was successful in identifying context-specific determinants of maternal health. The results highlight the need for designing targeted interventions that address the place-specific social determinants of maternal health in the study area. The geographic process of identifying and measuring these determinants, therefore, has implications for multisectoral collaboration.Trial registration number: NCT01911494.