Dataset Information

Validating the Global Surgery Geographical Accessibility Indicator: Differences in Modeled Versus Patient-Reported Travel Times.

ABSTRACT: BACKGROUND:Since long travel times to reach health facilities are associated with worse outcomes, geographic accessibility is one of the six core global surgery indicators; this corresponds to the second of the "Three Delays Framework," namely "delay in reaching a health facility." Most attempts to estimate this indicator have been based on geographical information systems (GIS) algorithms. The aim of our study was to compare GIS derived estimates to self-reported travel times for patients traveling to a district hospital in rural Rwanda for emergency obstetric care. METHODS:Our study includes 664 women who traveled to undergo a Cesarean delivery in Kirehe, Rwanda. We compared self-reported travel time from home to the hospital (excluding waiting time) with GIS estimated travel times, which were computed using the World Health Organization tool AccessMod, using linear regression. RESULTS:The majority of patients used multiple modes of transportation (walking?=?48.5%, public transport?=?74.2%, private transport?=?2.9%, and ambulance 70.6%). Self-reported times were longer than GIS estimates by a factor of 1.49 (95% CI 1.40-1.57). Concordance was higher when the GIS model took into account that all patients in Rwanda are referred via their health center (??=?1.12; 95% CI 1.05-1.18). CONCLUSIONS:To our knowledge, in this largest to date GIS validation study for geographical access to healthcare in low- and middle-income countries, a standard GIS model was found to significantly underestimate real travel time, which likely is in part because it does not model the actual route patients are travelling. Therefore, previous studies of 2-h access to surgery will need to be interpreted with caution, and future studies should take local travelling conditions into account.

SUBMITTER: Rudolfson N

PROVIDER: S-EPMC7266844 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Validating the Global Surgery Geographical Accessibility Indicator: Differences in Modeled Versus Patient-Reported Travel Times.

Rudolfson Niclas N Gruendl Magdalena M Nkurunziza Theoneste T Kateera Frederick F Sonderman Kristin K Nihiwacu Edison E Ramadhan Bahati B Riviello Robert R Hedt-Gauthier Bethany B

World journal of surgery 20200701 7

<h4>Background</h4>Since long travel times to reach health facilities are associated with worse outcomes, geographic accessibility is one of the six core global surgery indicators; this corresponds to the second of the "Three Delays Framework," namely "delay in reaching a health facility." Most attempts to estimate this indicator have been based on geographical information systems (GIS) algorithms. The aim of our study was to compare GIS derived estimates to self-reported travel times for patien ...[more]

PMID: 32274536

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Project description:IntroductionLimited geographical access to quality Emergency Obstetric and Newborn Care (EmONC) is a major driver of high maternal mortality. Geographic access to EmONC facilities is identified by the global community as a critical issue for reducing maternal mortality and is proposed as a global indicator by the Ending Preventable Maternal Mortality (EPMM) initiative. Geographic accessibility models can provide insight into the population that lacks adequate access and on the optimal distribution of facilities and resources. Travel scenarios (i.e., modes and speed of transport) used to compute geographical access to healthcare are a key input to these models and should approximate reality as much as possible. This study explores strategies to optimize and harmonize knowledge elicitation practices for developing travel scenarios.MethodsKnowledge elicitation practices for travel scenario workshops (TSW) were studied in 14 African and South-Asian countries where the United Nations Population Fund supported ministries of health and governments in strengthening networks of EmONC facilities. This was done through a mixed methods evaluation study following a transdisciplinary approach, applying the four phases of the Interactive Learning and Action methodology: exploration, in-depth, integration, and prioritization and action planning. Data was collected in November 2020-June 2021 and involved scoping activities, stakeholder identification, semi-structured interviews (N = 9), an evaluation survey (N = 31), and two co-creating focus group discussions (N = 8).ResultsEstimating realistic travel speeds and limited time for the workshop were considered as the largest barriers. The identified opportunities were inclusively prioritized, whereby preparation; a favorable composition of attendees; validation practices; and evaluation were anticipated to be the most promising improvement strategies, explaining their central place on the co-developed initial standard operating procedure (SOP) for future TSWs. Mostly extensive preparation-both on the side of the organization and the attendees-was anticipated to address nearly all of the identified TSW challenges.ConclusionThis study showed that the different identified stakeholders had contradicting, complementing and overlapping ideas about strategies to optimize and harmonize TSWs. Yet, an initial SOP was inclusively developed, emphasizing practices for before, during and after each TSW. This SOP is not only relevant in the context of the UNFPA EmONC development approach, but also for monitoring the newly launched EPMM indicator and even in the broader field of geographic accessibility modeling.

Project description:In this study, we evaluated and forecasted the cumulative opportunities for residents to access radiotherapy services in Cali, Colombia, while accounting for traffic congestion, using a new people-centred methodology with an equity focus. Furthermore, we identified 1-2 optimal locations where new services would maximise accessibility. We utilised open data and publicly available big data. Cali is one of South America's cities most impacted by traffic congestion.MethodologyUsing a people-centred approach, we tested a web-based digital platform developed through an iterative participatory design. The platform integrates open data, including the location of radiotherapy services, the disaggregated sociodemographic microdata for the population and places of residence, and big data for travel times from Google Distance Matrix API. We used genetic algorithms to identify optimal locations for new services. We predicted accessibility cumulative opportunities (ACO) for traffic ranging from peak congestion to free-flow conditions with hourly assessments for 6-12 July 2020 and 23-29 November 2020. The interactive digital platform is openly available.Primary and secondary outcomesWe present descriptive statistics and population distribution heatmaps based on 20-min accessibility cumulative opportunities (ACO) isochrones for car journeys. There is no set national or international standard for these travel time thresholds. Most key informants found the 20-min threshold reasonable. These isochrones connect the population-weighted centroid of the traffic analysis zone at the place of residence to the corresponding zone of the radiotherapy service with the shortest travel time under varying traffic conditions ranging from free-flow to peak-traffic congestion levels. Additionally, we conducted a time-series bivariate analysis to assess geographical accessibility based on economic stratum. We identify 1-2 optimal locations where new services would maximize the 20-min ACO during peak-traffic congestion.ResultsTraffic congestion significantly diminished accessibility to radiotherapy services, particularly affecting vulnerable populations. For instance, urban 20-min ACO by car dropped from 91% of Cali's urban population within a 20-min journey to the service during free-flow traffic to 31% during peak traffic for the week of 6-12 July 2020. Percentages represent the population within a 20-min journey by car from their residence to a radiotherapy service. Specific ethnic groups, individuals with lower educational attainment, and residents on the outskirts of Cali experienced disproportionate effects, with accessibility decreasing to 11% during peak traffic compared to 81% during free-flow traffic for low-income households. We predict that strategically adding sufficient services in 1-2 locations in eastern Cali would notably enhance accessibility and reduce inequities. The recommended locations for new services remained consistent in both of our measurements.These findings underscore the significance of prioritising equity and comprehensive care in healthcare accessibility. They also offer a practical approach to optimising service locations to mitigate disparities. Expanding this approach to encompass other transportation modes, services, and cities, or updating measurements, is feasible and affordable. The new approach and data are particularly relevant for planning authorities and urban development actors.

Project description:BackgroundAccess to major services, often located in urban centres, is key to the realisation of numerous Sustainable Development Goals (SDGs). In Kenya, there are no up-to-date and localised estimates of spatial access to urban centres. We estimate the travel time to urban centres and identify marginalised populations for prioritisation and targeting.MethodsUrban centres were mapped from the 2019 Kenya population census and combined with spatial databases of road networks, elevation, land use and travel barriers within a cost-friction algorithm to compute travel time. Seven travel scenarios were considered: i) walking only (least optimistic), ii) bicycle only, iii) motorcycle only, iv) vehicle only (most optimistic), v) walking followed by motorcycle transport, vi) walking followed by vehicle transport, and vii) walking followed by motorcycle and then vehicle transport (most pragmatic). Mean travel time, and proportion of the population within 1-hour and 2-hours of the urban centres were summarized at sub-national units (counties) used for devolved planning. Inequities were explored and correlations between the proportion of the population within 1-hour of an urban centre and ten SDG indicators were computed.ResultsA total of 307 urban centres were digitised. Nationally, the mean travel time was 4.5-hours for the walking-only scenario, 1.0-hours for the vehicle only (most optimistic) scenario and 1.5-hours for the walking-motorcycle-vehicle (most pragmatic) scenario. Forty-five per cent (21.3 million people) and 87% (41.6 million people) of Kenya's population resided within 1-hour of the nearest urban centre for the least optimistic and most pragmatic scenarios respectively. Over 3.2 million people were considered marginalised or living outside the 2-hour threshold in the pragmatic scenario, 16.0 million Kenyans for walking only, and 2.2 million for the most optimistic scenario. County-level spatial access was highly heterogeneous ranging between 8%-100% and 32%-100% of people within the 1-hour threshold for the least and most optimistic scenarios, respectively. Counties in northern and eastern parts of Kenya were generally most marginalised. The correlation coefficients for nine SDG indicators ranged between 0.45 to 0.78 and were statistically significant.ConclusionTravel time to urban centres in Kenya is heterogeneous. Therefore, marginalised populations should be prioritised during resource allocation and policies should be formulated to enhance equitable access to public services and opportunities in urban areas.

Project description:BackgroundTaking patient centeredness into account is important in healthcare. The European Cancer Consumer Quality Index (ECCQI) is a validated tool for international benchmarking of patient experiences and satisfaction. This study aimed to further validate the ECCQI in larger and more uniform groups of high volume tumours such as breast and prostate cancer. A second objective was the verification of the influence of cultural factors of the country to determine its possible use in international benchmarking.MethodsData from two survey studies in eight European countries were combined. Socio-demographic correlations were analysed with Kruskall-Wallis and Mann-Whitney tests. Cronbach's alpha was calculated to validate internal consistency. Influences of masculinity (MAS), power distance (PD) and uncertainty avoidance (UA) were determined by linear regression analysis in a general model and subgroup models.ResultsA total of 1322 surveys were included in the analysis (1093 breast- and 348 prostate cancer patients). Cronbach's alpha was good (α ≥ 0.7) or acceptable (0.5 ≤ α ≤ 0.7) in 8 out of 9 questionnaire categories, except in the category 'Safety' (α = 0.305). Overall ECCQI scores ranged from 22.1 to 25.1 between countries on a 1-35 scale (categories had a 1-4 scale). In certain subcategories such as 'Organisation' (range 2.2 vs 3.0) and 'Supervision & Support' (range 3.0 vs 3.8) a large difference was observed between countries. Differences in 'Overall opinion' were however small: mean scores of 3.7 vs 3.9, whereas median scores were all the maximum of 4.0. Power distance was positively associated with higher patient satisfaction scores whereas Uncertainty avoidance was negatively associated with these scores. Masculinity was only associated with patient satisfaction scores in lower educated patients. We found the highest impact of culture on overall scores in Hungary and Portugal and the lowest in Romania.ConclusionsThe ECCQI shows high internal consistency in all categories except 'Safety'. Especially in separate categories and overall ECCQI scores the questionnaire showed discriminative value. This study showed a positive correlation of power distance and a negative correlation for uncertainty avoidance in some countries. When using the ECCQI for international benchmarking these two dimensions of culture should be taken into account.

Dataset Information

Validating the Global Surgery Geographical Accessibility Indicator: Differences in Modeled Versus Patient-Reported Travel Times.

Publications

Validating the Global Surgery Geographical Accessibility Indicator: Differences in Modeled Versus Patient-Reported Travel Times.

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