Seasonal variation in geographical access to maternal health services in regions of southern Mozambique

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

This study was conducted as part of the feasibility study in preparation for the Community Level Interventions in Pre-eclampsia Trial (CLIP, {“type”:”clinical-trial”,”attrs”:{“text”:”NCT01911494″,”term_id”:”NCT01911494″}}NCT01911494) in Mozambique [28]. CLIP is an ongoing community-based cluster randomized control trial, aimed at reducing all cause maternal and perinatal mortality and morbidity in the intervention areas. Ethics approval for CLIP was obtained from the CISM Institutional Review Board (CIBS—CISM), the Mozambique National Committee for Bioethics (CNBS) and the UBC Review Board, while approval for the mapping component was also acquired from the Simon Fraser University research ethics board. GPS points representing the households where all women of reproductive age in the study area lived were captured from the CLIP baseline census, and were used to determine the spatial population distribution. Characteristics of women of reproductive age within each household were also recorded and include pregnancies within the 12 months prior to the baseline census, completed pregnancies, pregnancy outcomes. These data were used to determine where populations that required access to maternal health services lived, as well as total number of pregnancies and women of reproductive age in different communities. To account for the impact of adverse weather events we sought to use empirical records of precipitation and floods (Fig. 2). Daily raster records of precipitation within the study area from March 2013 to October 2014 were acquired from the Famine Early Warning Systems Network [29]. These precipitation data are generated on a daily basis using satellite imagery and ground rain gauges [30, 31]. This specific timeline was chosen to coincide with the timeline of the CLIP baseline census. Daily flood extent raster data for the same period was acquired from the Global Flood Observatory [32]. These are also generated on a daily basis from the MODIS satellite [33]. All the required precipitation and flood data were available except for 25 days of flood data and 4 days of precipitation data. These datasets were combined as a first step for creating an impedance surface used to estimate the effect of precipitation and floods on reducing access to health centres. Impedance surfaces are a grid based geographical representation of the ease of traversing through space, with high speed features such as highways, taking less time to traverse when compared to lower speed features such as footpaths for the same unit distance [34]. We assumed that flooded areas were not navigable by any means of transport, while road segments that had precipitation above 1 mm, based on the rainy day threshold [35], would have had reduced maximum travel speeds, as expressed in Table 1. Sample precipitation and flooding data Impact of precipitation on speed limits A new geographical dataset of average weekly rainfall was created from the daily precipitation data. This shift in temporal scale was to account for the impact of precipitation beyond the day it occurred. The 1 mm rainy day threshold (calculated as a weekly average) was used to determine whether a week was to be classified as a rainy week. An initial road network dataset was provided by the Mozambique National Cartography and Remote Sensing Centre CENACARTA. These data constituted mainly of highways and a few major paved roads and were therefore highly inadequate for the community-level analysis that was intended for this study. We also considered open street map data [36] for the study areas but found it to be inadequate for modeling spatial access at the intended scale due to many missing roads at the community level. Data gaps were filled through a process of manual digitization of the missing roads from a high resolution Bing Maps satellite image service [37]. These roads were classified into highways, major paved roads, major unpaved roads, minor paved roads, minor unpaved roads and trails. A separate verification process was done by staff at CENACARTA and two other independent reviewers to identify and correct instances of misclassification, missing roads and other geometric errors. Each road segment was assigned a value for travel time based on the length of the road segment and the speed limit. The speed limit was dependant on the road type, whether there was precipitation above the 1 mm weekly threshold, and if the road segment had been classified as being flooded at the time, with precipitation inducing a 20 and 30% reduction in the speed limit on paved roads and unpaved roads respectively. The estimates for the impact of precipitation on speed limits were derived from previous studies [14, 38–41] and are summarised in Table 1. The GPS coordinates of all public health facilities (Fig. 1) in the country were acquired from the Ministry of Health in Mozambique and research partners at Manhiça Research Centre. These facilities were classified into primary health centres (PHCs), secondary health facilities (SHF) and tertiary health facilities (THF). Data from a 2014 assessment of public health facilities that was conducted as part of the feasibility study for the CLIP trial were used to alter that classification of some of the facilities acquired from the Ministry, because the CLIP facility assessment had more recent results. None of the facilities outside the study area were reclassified due to a lack of recent data. Data on transport options available at each facility was also acquired from the CLIP facility assessment and used as the basis for deciding the most likely mode of transport that women would use to navigate through the facility referral chain. Three modes of transports were considered for this project; walking, driving or using public transport. Both walking and driving were treated as single transport modes. However, for public transport we modelled travel assuming that women would walk to the nearest major road to access transport [25], and then drive from that point on. Therefore, for the public transport option we used the same speed limits for travelling through minor roads and trails as we did for walking, but changed the speed limits to be the same as driving for major roads and highways (see Table 1). The likely scenarios of travel from the home to PHC and subsequent levels of the health care system were determined from the CLIP facility assessment and baseline census. During the facility assessment, information was recorded pertaining to the transport options available at each facility for patient referrals. Data on the personal transport options, as well as transport plans in the event of pregnancy related emergencies were also recorded for every household included in the baseline census and used to decide on the most likely characteristics of the women’s journeys to access maternal care. The process for modeling spatio-temporal variation in access to care is illustrated in Fig. 3. Access to care was modelled from the central location of the populated regions within all the neighbourhoods, instead of the commonly used centre of the actual neighborhood boundaries, which would include uninhabited regions including forests and agricultural zones. The model was developed to estimate travel times from these population centres of 417 neighbourhoods to the nearest health facility accounting for multiple modes of transport, and how this changed overtime. We assumed that most of the population would navigate through the referral chain in a hierarchical sequence; i.e. from home to PHC to SHF then THF. Most higher-level facilities will have lower level ones on premise (e.g. SHFs will normally have PHCs), thereby adopting multiple classifications. This design of the health care system therefore somewhat accounts for instances where people may bypass lower levels of the health care system by going directly to facilities that are closest to them. Modeling process for calculating potential spatial access to maternal care services The closest facility tool in the ArcGIS software was used to calculate the quickest route between neighborhoods and facilities based on the predefined speed limits along the road network dataset [42] for each of the 87 weeks of the study. Speed limits depended on the impedance values imposed on the road by the road type, precipitation and floods as illustrated in Table 1. The service area tool [43] was used to create cartographically generalized visualizations illustrating the change in spatial access throughout the study at a macro scale. Once the quickest travel times to the different level facilities for all travel scenarios were calculated for each week in the study timeline, the data was organized into 4 quartiles, for each week, indicating the travel times at the 25th, 50th, 75th and 100th percentiles for all neighborhoods. This exploratory process was done to highlight the disparities that existed in travel times. We also compared the travel times on the best day in the dry season and the worst day in the wet season to evaluate the extreme impact of precipitation and flooding on access to maternal care for women of reproductive age in general, and for those whom were likely to have been pregnant during these times. Given that it is impossible for women that were registered as having been pregnant during the study to have been pregnant for the entire timeline, we estimated the number of pregnancies at any given time assuming equal likelihood of being pregnant throughout the timeline. The 1- and 2-h travel time thresholds were used for primary care facilities and all other higher level facilities (SHF and THF) respectively, to differentiate women’s access to basic maternal and antenatal care from life-saving care delivered through BEmOC and CEmOC facilities at SHF and THF respectively. Communities that would have been totally isolated from health care services because of flooding were also identified. Isolation was when the total travel time to the nearest facility was greater than 99,999,999 min, which was the total time assigned for travelling through a flooded road segment that would have essentially been impassable using motorized transport or on foot. Approximately 85% of the roads in the study area are unpaved, with 75% of these being classified as minor unplanned roads [37]. The road infrastructure is thus typically hard to traverse in the wet season and not usable when flooded (Fig. 4). As the network algorithm used in this study identified the optimum routes with the quickest possible time for traversing from a community to a health facility, instances where travel times were greater than 99,999,999 indicated that no alternate route existed, meaning the road infrastructure could not be used. Illustration of the effect of precipitation on hampering transport on minor unpaved roads—Photo taken by field team during a field visit to Calanga, in Maputo province According to the baseline census, most women of reproductive age in the study area were likely to either walk or use public transport to travel to the nearest primary health centre (Table 2). This is since 70% of all households indicated not having private transport, and almost 72% of household indicated that pregnant women would nonetheless have access to transport funds when needed. Transport options available based on the CLIP baseline census and facility assessment According to the facility assessment, most women were likely be driven from primary care facilities to higher level facilities. Women were either driven by ambulance from secondary to tertiary facilities, or by private cars from primary health centres to secondary facilities that are pre-arranged with car owners in the community (Table 2). These findings led to a 6 scenario spatio-temporal model of access to care, that depicts the common modes of transport from the community to PHCs and through the facility referral network, including;

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• Dima Health