Improving accessibility to radiotherapy services in Cali, Colombia: cross-sectional equity analyses using open data and big data travel times from 2020.

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. METHODOLOGY: Using 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 OUTCOMES: We 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. RESULTS: Traffic 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.

Dynamic measurements of geographical accessibility considering traffic congestion using open data: a cross-sectional assessment for haemodialysis services in Cali, Colombia.

Background: Many cities with traffic congestion lack accessibility assessments accounting for traffic congestion and equity considerations but have disaggregated georeferenced municipal-level open data on health services, populations, and travel times big data. We convened a multistakeholder intersectoral collaborative group that developed a digital, web-based platform integrating open and big data to derive dynamic spatial-temporal accessibility measurements (DSTAM) for haemodialysis services. We worked with stakeholders and data scientists and considered people's places of residence, service locations, and travel time to the service with the shortest travel time. Additionally, we predicted the impacts of strategically introducing haemodialysis services where they optimise accessibility. Methods: Cross-sectional analyses of DSTAM, accounting for traffic congestion, were conducted using a web-based platform. This platform integrated traffic analysis zones, public census and health services datasets, and Google Distance Matrix API travel-time data. Predictive and prescriptive analytics identified optimal locations for new haemodialysis services and estimated improvements. Primary outcomes included the percentage of residents within a 20-min car drive of a haemodialysis service during peak and free-flow traffic congestion. Secondary outcomes focused on optimal locations to maximise accessibility with new services and potential improvements. Findings were disaggregated by sociodemographic characteristics, providing an equity perspective. The study in Cali, Colombia, used geographic and disaggregated sociodemographic data from the adjusted 2018 Colombian census. Predicted travel times were obtained for two weeks in 2020. Findings: There were substantial traffic variations. Congestion reduced accessibility, especially among marginalised groups. For 6-12 July, free-flow and peak-traffic accessibility rates were 95.2% and 45.0%, respectively. For 23-29 November, free-flow and peak traffic accessibility rates were 89.1% and 69.7%. The locations where new services would optimise accessibility had slight variation and would notably enhance accessibility and health equity. Interpretation: Establishing haemodialysis services in targeted areas has significant potential benefits. By increasing accessibility, it would enhance urban health and equity. Funding: No external or institutional funding was received.

Dynamic accessibility by car to tertiary care emergency services in Cali, Colombia, in 2020: cross-sectional equity analyses using travel time big data from a Google API.

OBJECTIVES: To test a new approach to characterise accessibility to tertiary care emergency health services in urban Cali and assess the links between accessibility and sociodemographic factors relevant to health equity. DESIGN: The impact of traffic congestion on accessibility to tertiary care emergency departments was studied with an equity perspective, using a web-based digital platform that integrated publicly available digital data, including sociodemographic characteristics of the population and places of residence with travel times. SETTING AND PARTICIPANTS: Cali, Colombia (population 2.258 million in 2020) using geographic and sociodemographic data. The study used predicted travel times downloaded for a week in July 2020 and a week in November 2020. PRIMARY AND SECONDARY OUTCOMES: The share of the population within a 15 min journey by car from the place of residence to the tertiary care emergency department with the shortest journey (ie, 15 min accessibility rate (15mAR)) at peak-traffic congestion hours. Sociodemographic characteristics were disaggregated for equity analyses. A time-series bivariate analysis explored accessibility rates versus housing stratification. RESULTS: Traffic congestion sharply reduces accessibility to tertiary emergency care (eg, 15mAR was 36.8% during peak-traffic hours vs 84.4% during free-flow hours for the week of 6-12 July 2020). Traffic congestion sharply reduces accessibility to tertiary emergency care. The greatest impact fell on specific ethnic groups, people with less educational attainment and those living in low-income households or on the periphery of Cali (15mAR: 8.1% peak traffic vs 51% free-flow traffic). These populations face longer average travel times to health services than the average population. CONCLUSIONS: These findings suggest that health services and land use planning should prioritise travel times over travel distance and integrate them into urban planning. Existing technology and data can reveal inequities by integrating sociodemographic data with accurate travel times to health services estimates, providing the basis for valuable indicators.

Dynamic geographical accessibility assessments to improve health equity: protocol for a test case in Cali, Colombia.

This protocol proposes an approach to assessing the place of residence as a spatial determinant of health in cities where traffic congestion might impact health services accessibility. The study provides dynamic travel times presenting data in ways that help shape decisions and spur action by diverse stakeholders and sectors.  Equity assessments in geographical accessibility to health services typically rely on static metrics, such as distance or average travel times. This new approach uses dynamic spatial accessibility measures providing travel times from the place of residence to the health service with the shortest journey time. It will show the interplay between traffic congestion, accessibility, and health equity and should be used to inform urban and health services monitoring and planning. Available digitised data enable efficient and accurate accessibility measurements for urban areas using publicly available sources and provide disaggregated sociodemographic information and an equity perspective. Test cases are done for urgent and frequent care (i.e., repeated ambulatory care). Situational analyses will be done with cross-sectional urban assessments; estimated potential improvements will be made for one or two new services, and findings will inform recommendations and future studies. This study will use visualisations and descriptive statistics to allow non-specialized stakeholders to understand the effects of accessibility on populations and health equity. This includes "time-to-destination" metrics or the proportion of the people that can reach a service by car within a given travel time threshold from the place of residence. The study is part of the AMORE Collaborative Project, in which a diverse group of stakeholders seeks to address equity for accessibility to essential health services, including health service users and providers, authorities, and community members, including academia.

Efectos del entrenamiento en marcos relacionales de condicionalidad sobre el responder relacional deíctico / Effects of Training in Conditional Relational Framing on the Deictic Relational Responding

Resumen Desde la Teoría de los Marcos Relacionales, la toma de perspectiva se concibe como un patrón de responder relacional arbitrariamente aplicable bajo control de claves contextuales deícticas personales, espaciales y temporales. Además, se asume necesaria la capacidad para responder relacionalmente bajo el control de claves contextuales condicionales. Sin embargo, no se ha reportado evidencia empírica que permita identificar la relación existente entre los marcos de condicionalidad y la precisión en el responder relacional deíctico. El objetivo del estudio fue analizar si el entrenamiento en marco de condicionalidad podría facilitar el desempeño sobre el responder deíctico. Participaron cuatro niños con desarrollo normalizado que fueron divididos en dos condiciones experimentales (C1 y C2). Los participantes de ambas condiciones se expusieron a un protocolo de evaluación y entrenamiento en marcos deícticos. A su vez, solo los participantes de la C2 se expusieron a una fase de evaluación y entrenamiento en marcos de condicionalidad previo al entrenamiento en marcos deícticos. Los resultados mostraron que todos los participantes exhibieron mejoras en sus ejecuciones post-test en las relaciones deícticas y que los participantes de C2 obtuvieron mejores resultados con respecto a su pre-test en los niveles de mayor complejidad deíctica tras el entrenamiento en marcos condicionales. Se discute la necesidad de protocolos de toma de perspectiva más naturalistas que posibiliten indagar en cómo se desarrollan los términos relacionales y se abstrae la función de las claves contextuales que permiten interpretar, atribuir y predecir el comportamiento de otro.

Abstract According to Relational Frame Theory, perspective taking is conceived as an arbitrarily applicable relational pattern of relational responding under the control of personal, spatial, and temporal deictic contextual cues. Furthermore, the ability to respond relationally under the control of conditional contextual cues is assumed to be necessary. However, no empirical evidence has been reported to identify the relationship between conditionality frames and accuracy in deictic relational responding. The study aimed to analyze whether training in conditionality frames could facilitate performance on deictic responding. Four children with normalized development participated and were divided into two experimental conditions (C1 and C2). Participants in both conditions were exposed to an evaluation protocol and training on deictic frames. In turn, only C2 participants were exposed to an evaluation and training phase in conditionality frames before the training in deictic frames. Results showed that all the participants exhibited improvements in their post-test performances on deictic relations and that the C2 participants performed better with respect to their pre-test on the levels of greater deictic complexity after training in conditional frames. We discuss the need for more naturalistic perspective-taking protocols that allow us to investigate how relational terms are developed and abstract the role of contextual cues that allow us to interpret, attribute, and predict another's behavior.