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Objectives Modelling and assessing the loss of geographical accessibility is key to support disaster response and rehabilitation of the healthcare system. The aim of this study was therefore to estimate postdisaster travel times to functional health facilities and analyse losses in accessibility coverage after Cyclones Idai and Kenneth in Mozambique in 2019. Setting We modelled travel time of vulnerable population to the nearest functional health facility in two cyclone-affected regions in Mozambique. Modelling was done using AccessMod V.5.6.30, where roads, rivers, lakes, flood extent, topography and land cover datasets were overlaid with health facility coordinates and high-resolution population data to obtain accessibility coverage estimates under different travel scenarios. Outcome measures Travel time to functional health facilities and accessibility coverage estimates were used to identify spatial differences between predisaster and postdisaster geographical accessibility. Results We found that accessibility coverage decreased in the cyclone-affected districts, as a result of reduced travel speeds, barriers to movement, road constraints and non-functional health facilities. In Idai-affected districts, accessibility coverage decreased from 78.8% to 52.5%, implying that 136 941 children under 5 years of age were no longer able to reach the nearest facility within 2 hours travel time. In Kenneth-affected districts, accessibility coverage decreased from 82.2% to 71.5%, corresponding to 14 330 children under 5 years of age having to travel >2 hours to reach the nearest facility. Damage to transport networks and reduced travel speeds resulted in the most substantial accessibility coverage losses in both Idai-affected and Kenneth-affected districts. Conclusions Postdisaster accessibility modelling can increase our understanding of spatial differences in geographical access to care in the direct aftermath of a disaster and can inform targeting and prioritisation of limited resources. Our results reflect opportunities for integrating accessibility modelling in early disaster response, and to inform discussions on health system recovery, mitigation and preparedness.
In this study, accessibility is measured as the travel time to health facilities and accessibility coverage (ie, coverage) is defined as the estimated number or percentage of people covered or located within a travel time catchment area.21 To model accessibility to health facilities, we consider topography, road networks, constraints to movement (eg, rivers, lakes and flood extent), target population distribution and the locations of functional health facilities. We accessed and prepared multiple data layers (table 1) assembled in the aftermath of Cyclones Idai and Kenneth, between April and September 2019. A total of three scenarios were prepared, representing (1) pre-Idai and pre-Kenneth (before March 2019), (2) post-Idai (up to 1 week postcyclone) and (3) post-Kenneth (up to 1 week postcyclone) situations. We modelled population travel time to the nearest health facility and accessibility coverage for two cyclone-affected regions. Overview of data layers and data sources *Source date represents the imagery acquisition date for the flood extents and the release date for all other data. CIESIN, Centre for International Earth Science Information Network; DNGRH, National Directorate for Water Resource Management; GDACS, Global Disaster Alert and Coordination System; HDX, Humanitarian Data Exchange; INE, National Institute for Statistics Mozambique; INGC, National Institute for Disaster Management Mozambique; LOG-WFP, Logistics Cluster World Food Programme; UN-OCHA ROSEA, United Nations Office for the Coordination of Humanitarian Affairs Southern and Eastern Africa; SIS-MA, Ministry of Health Mozambique; SRTM, Shuttle Radar Topography Mission; UNOSAT, United Nations Operational Satellite Applications Programme. The projection, resolution and alignment of geospatial data were processed using Quantum Geographical Information System (V.3.4)22 and, to a limited extent, R (V.3.5.2).23 As indicated in table 1, most data layers were retrieved from open data platforms. All rasters and shapefiles were saved in the projection system of Mozambique, that is, UTM-37S (EPSG:32 737). The data preparation process is briefly described in this section and is fully detailed in online supplemental file 1. bmjopen-2020-039138supp001.pdf Elevation data were obtained from the Shuttle Radar Topography Mission in tiles at a resolution of 30 m and mosaiced to cover the whole country.24 Slopes were derived from it and were accounted for when modelling walking movements. Land cover data were downloaded for the whole African continent at 100 m resolution from Copernicus Global Land Service25 and were clipped to the extent of Mozambique. As analyses were carried out at 30 m resolution, the land cover raster was resampled at a resolution of 30 m, using nearest neighbour interpolation. The precyclone road network dataset was retrieved from Open Street Map (OSM) through the Geonode Platform of the National Institute for Disaster Management Mozambique, and linked to the road damage information as indicated by the Logistics Cluster of the World Food Programme (LOG-WFP).26 27 Historical postcyclone status of roads and road segments were manually digitised from PDF maps provided by LOG-WFP. The maps were cross-referenced with the OSM road network layer, to include postcyclone road damage status, that is, (1) open, (2) restricted and (3) closed. Road damages as a consequence of Cyclones Idai and Kenneth were taken from maps dated 19 March and 3 May 2019, respectively (table 1).26 27 Information on road type and damage were combined in order to obtain unique road type-damage combinations (online supplemental file 2). Information on rivers and lake layout were obtained as shapefiles from the National Directorate for Water Resource Management. Only primary rivers and lakes were considered as barriers to movement, under the informed assumption that smaller rivers and streams were passable by the population. This assumption was checked for several instances against background satellite imagery. Flood extents for Idai (on 19 March 2019) and Kenneth (on 2 May 2019) were sourced as shapefiles from Sentinel-1 and Copernicus EMSR354, respectively.28 29 The flood extents were visually inspected and found to be largest on those two dates, and thus represent the biggest constraints for healthcare access. All flooded areas were treated under two scenarios: 1) as being impassable, under the assumption that people avoid traversing flood water to prevent further injury, 2) as being passable by foot at an average walking speed of 1.5 km/hour. In the first scenario, health facilities located on flood extents were always treated as inaccessible since they are located on barriers. While cyclones impact entire populations, the burden disproportionately affects children and women.30 It is estimated that for Cyclones Idai and Kenneth >50% of the affected population were children, and with flood waters rising above 6 m, their movements to safety and healthcare were particularly limited.5 Moreover, children under 5 years of age represent the age group used as benchmark for child survival targets in both the Millennium Development Goals and the Sustainable Development Goals.31 In this context and through the collaborative work with UNICEF, this analysis aimed at informing the impact of the disasters on the burden for specific child health services that target children under 5 years of age (eg, immunisation). High-resolution population density estimates for children under 5 years of age were obtained from the Facebook Connectivity Lab and Center for International Earth Science Information Network (CIESIN)32 with a 30 m resolution. Although several gridded populations datasets are available, the Facebook CIESIN dataset was assumed to have the most realistic reallocation of population to settlements.33 In addition, other frequently used high-resolution gridded population datasets, such as WorldPop,34 use distances from roads and villages as covariates, and this can produce collinearity when used in conjunction with accessibility models. Population density was used to run zonal statistics on the cyclone-affected districts. In this step, the total population per district is summed and the estimated absolute number of children under 5 years of age that are able to reach a facility in a predefined travel time catchment are calculated. Additionally, geographic coordinates of all villages (ie, communities) in Idai-affected districts were obtained from UNICEF Mozambique, which had gathered this information through a community mapping initiative conducted by health officials, 6–8 months before Cyclone Idai made landfall. These community locations were used to extract precyclone and postcyclone travel time for each community to the nearest functional health centre. Unfortunately, geographic coordinates of villages in Kenneth-affected districts were not available at the time of study. The geographic coordinates of all health facilities were sourced from the health management information system, Ministry of Health in Mozambique.35 Data cleaning was undertaken in cases where the geographic coordinates for health facilities were located outside the international border of Mozambique or for coordinates falling on barriers to movement (online supplemental file 1). Information on damaged health facilities was provided in tabular format by WHO. The health system in Mozambique comprises four levels: the primary level consists of urban and rural health centres, the secondary level consists of general, rural and district hospitals, the tertiary level comprises provincial capital hospitals and quaternary facilities comprise the central and specialised hospitals.36 Health facilities of all levels were included in the model. Districts that were most affected by Cyclones Idai and Kenneth (‘cyclone-affected districts’, thereafter) were identified in close collaboration with UNICEF and humanitarian responders. All statistics presented below were calculated for these identified districts, with 26 such districts in the Idai-affected region and 11 districts in the Kenneth-affected region (figure 1A, B). Storm trajectories of both cyclones and road damages in both districts are also presented (figure 1C-F). Cyclone-affected districts, cyclone trajectory and road damages. (A) Idai-affected districts. (B) Kenneth-affected districts. (C) Idai cyclone trajectory*. (D) Kenneth cyclone trajectory*. (E) Road damages in Idai-affected districts. (F) Road damages in Kenneth-affected districts. *Cyclone paths as reported on Global Disaster Alert and Coordination System. To model travel times and accessibility coverages, we used AccessMod 5 (V.5.6.30), in particular the ‘accessibility’ and ‘zonal statistics’ modules.21 37 AccessMod models geographical accessibility using terrain-based least-cost path distance calculation. This open-source software has been successfully applied in many different settings, among which accessibility and referral assessments of health facility networks, optimisation modelling of health programmes in obstetric and neonatal care (EmONC),38 primary health care,39 emergency care,40 referral times41 and treatment of fever cases.42 Using the ‘merge land cover’ module in AccessMod, we overlaid the roads, rivers, lakes, flood extent and land cover datasets to obtain a single 30 m resolution raster dataset, to which different travel scenarios were applied. The travel scenarios (presented in online supplemental file 2) were derived using local information as model inputs on precyclone and postcyclone travel speeds and travel modes. Both scenarios were developed in close collaboration with UNICEF Mozambique, with focus on geographical accessibility to functional health facilities for the target population of children under 5 years of age. Postcyclone travel speeds were adjusted for wet weather conditions as heavy rains persisted in the direct aftermath of both cyclones. During the postcyclone situation, restricted and closed roads that were not inundated were assumed to be unpassable by any vehicle, but they were perceived to be accessible by foot. All land cover classes outside of the road network and the barriers were considered as passable. We assumed a functional bridge where a road segment crossed a river. To account for uncertainty of the assumed travel speeds, we also considered both precyclone and postcyclone motorised travel speeds with a 20% slower and 20% faster speed, as adapted from Ouma et al.40 Accessibility coverage of the network of health centres was calculated at the 2 hour maximum travel time limit. This limit was deemed appropriate to capture the extent of effective access, and is often used in health accessibility studies, notably in maternal health.38 There was no patient or public involvement in this study. Health facility functionality status was shared in tabular format by WHO. All other geospatial data were publicly available.
